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JMIR Preprints
A preprint server for pre-publication/pre-peer-review preprints as well as ahead-of-print (accepted) manuscripts
Background: As the integration of artificial intelligence (AI)-enabled tools expands within clinical practice, understanding contributors to trust and adoption is critical for successful implementation. While transparency mechanisms such as confidence scores and explanatory text are often proposed to promote trust in AI applications, the role of clinician’s baseline attitudes toward AI as a determinant of trust has not been well characterized. Objective: To examine the relationship between baseline attitudes toward AI and clinician trust in a simulated AI-enabled diagnostic assistance application, and to assess whether transparency mechanisms modify this relationship. Methods: In a randomized experiment, clinicians (including students and trainees) completed the Attitude Toward Artificial Intelligence (ATAI) scale. They were then presented with 6 case vignettes, followed by exposure to AI assistance in one of three application conditions: control (no transparency), numeric confidence score, or narrative explanation with citation. Participants provided updated diagnoses after assistance across all scenarios. After completion, trust was measured using the Trust and Acceptance of Artificial Intelligence Technology (TrAAIT) scale, including both a total score and application-specific core score. Associations between attitudes and trust were assessed using correlation and linear regression analysis, with transparency condition included as a covariate. Results: The study enrolled 220 participants, excluding 3 with missing trust data. Among analyzed participants, baseline attitudes toward AI were heterogeneous and only slightly positive on average. Attitudes were moderately associated with overall trust in AI (Pearson r = 0.45, P<.001) and remained significantly associated when trust was restricted to application-specific domains (Pearson r = 0.33, P<.001). In multivariable linear regression, higher ATAI scores were independently associated with application-specific trust (β = 0.13 (CI 0.08-0.18)per 1-point increase in ATAI, P< .001). Transparency condition was not independently associated with trust and did not meaningfully modify the relationship between attitudes and trust. Attitudes toward AI were only modestly correlated with self-reported technology adoption orientation. Conclusions: Baseline attitudes toward AI represent a meaningful antecedent to clinician trust in AI-enabled diagnostic tools, and this association persists at the application level. Transparency mechanisms alone were insufficient to overcome the influence of pre-existing attitudes. These findings suggest that efforts to promote trust and adoption of clinical AI may benefit from addressing clinician attitudes directly, rather than relying solely on interface-level transparency features. Clinical Trial: N/A, This is not a clinical trial.
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Background: As the integration of artificial intelligence (AI)-enabled tools expands within clinical practice, understanding contributors to trust and adoption is critical for successful implementatio...
Background: As the integration of artificial intelligence (AI)-enabled tools expands within clinical practice, understanding contributors to trust and adoption is critical for successful implementation. While transparency mechanisms such as confidence scores and explanatory text are often proposed to promote trust in AI applications, the role of clinician’s baseline attitudes toward AI as a determinant of trust has not been well characterized. Objective: To examine the relationship between baseline attitudes toward AI and clinician trust in a simulated AI-enabled diagnostic assistance application, and to assess whether transparency mechanisms modify this relationship. Methods: In a randomized experiment, clinicians (including students and trainees) completed the Attitude Toward Artificial Intelligence (ATAI) scale. They were then presented with 6 case vignettes, followed by exposure to AI assistance in one of three application conditions: control (no transparency), numeric confidence score, or narrative explanation with citation. Participants provided updated diagnoses after assistance across all scenarios. After completion, trust was measured using the Trust and Acceptance of Artificial Intelligence Technology (TrAAIT) scale, including both a total score and application-specific core score. Associations between attitudes and trust were assessed using correlation and linear regression analysis, with transparency condition included as a covariate. Results: The study enrolled 220 participants, excluding 3 with missing trust data. Among analyzed participants, baseline attitudes toward AI were heterogeneous and only slightly positive on average. Attitudes were moderately associated with overall trust in AI (Pearson r = 0.45, P<.001) and remained significantly associated when trust was restricted to application-specific domains (Pearson r = 0.33, P<.001). In multivariable linear regression, higher ATAI scores were independently associated with application-specific trust (β = 0.13 (CI 0.08-0.18)per 1-point increase in ATAI, P< .001). Transparency condition was not independently associated with trust and did not meaningfully modify the relationship between attitudes and trust. Attitudes toward AI were only modestly correlated with self-reported technology adoption orientation. Conclusions: Baseline attitudes toward AI represent a meaningful antecedent to clinician trust in AI-enabled diagnostic tools, and this association persists at the application level. Transparency mechanisms alone were insufficient to overcome the influence of pre-existing attitudes. These findings suggest that efforts to promote trust and adoption of clinical AI may benefit from addressing clinician attitudes directly, rather than relying solely on interface-level transparency features. Clinical Trial: N/A, This is not a clinical trial.
The rapid expansion of artificial intelligence in health care, driven by advanced data and analytics tools, has been accompanied by growing calls for collaboration among technology developers, health...
The rapid expansion of artificial intelligence in health care, driven by advanced data and analytics tools, has been accompanied by growing calls for collaboration among technology developers, health systems, and communities. Existing iterations of these relationships often performative or legacy fixtures that fail to address persistent underlying asymmetries in power, access, authority, and value. Current data-driven innovations also often reproduce legacy extractive practices across divested populations while providing whole-of-life clinical data from entire communities that remain excluded from the insights and benefits of these tools.
The Extractive-Collaborative Technology Continuum draws on principles of ethical leadership, community-engaged research, and health equity to map how relationships between developers and communities must evolve from extractive models to collaborative learning systems. This transition toward a more ethical posture of change leadership and the implementation of innovation necessitates a shift toward a culture of shared governance and collaborative stewardship with a commitment to shared and mutual benefit.
The ideal state for collaboration considers and appropriately integrates local knowledge, enacts and delivers reciprocal benefits, and links incentives to the maturity of these practices. Moving from a paradigm of data as property to that of data as a promise ensures that innovation serves communities as the fullest expression of the science of health and the art of caring. The legitimacy of change management and implementation of innovation in care environments depends on the integrity of the relationships that develop and sustain trust and collaboration to the benefit of all.
Background: Online medical consultation (OMC) services have gained considerable attention as integral components of telemedicine. Recently, artificial intelligence (AI) has been increasingly integrate...
Background: Online medical consultation (OMC) services have gained considerable attention as integral components of telemedicine. Recently, artificial intelligence (AI) has been increasingly integrated into OMC platforms, facilitating enhanced consultation efficiency and clinical decision-making capabilities. Despite the availability and potential benefits of AI-driven OMC services, the public acceptance and willingness to pay (WTP) for AI-driven OMC services remain low. Objective: This study aimed to explore public perception of the AI-driven OMC services and to further evaluate the WTP for the service. Methods: We conducted semi-structured qualitative interviews with patients, caregivers, and healthcare professionals to explore factors influencing public acceptance and WTP for AI-driven OMC services. The study was informed by the theories of perceived risk and perceived benefit, which guided the development of the interview guide. All interviews were audio-recorded and transcribed verbatim. Data were analyzed using NVivo 15 with deductive thematic analysis guided by these theories, and coding was independently conducted and cross-checked by two researchers to ensure credibility and consistency. Results: Thematic analysis of 20 in-depth interviews identified 2 main themes and 11 subthemes. Perceived risks and perceived benefits emerged as two key perspectives influencing participants’ acceptance and WTP. Psychological, privacy, social, functional, health, and financial risks reduced acceptance, whereas convenience, diversity, reliability, efficiency, and educational benefits promoted it. Reported WTP ranged from $0 to $30, with service-experienced participants generally reporting higher values than service-naive participants. Conclusions: This study identified the facilitators and barriers influencing public acceptance and WTP for AI-driven OMC services with theoretical constructs. Our findings offer valuable insights for the development and refinement of AI-driven OMC services, enabling more targeted pricing strategies and tailored services that can address the preferences and concerns of the public.
Background: AI is increasingly being explored as a tool to enhance efficiency, access, and diagnostic accuracy in mental health care. However, the perspectives on the use of AI from clinicians, who ar...
Background: AI is increasingly being explored as a tool to enhance efficiency, access, and diagnostic accuracy in mental health care. However, the perspectives on the use of AI from clinicians, who are central to the delivery and oversight of care, remain underexamined. Objective: This study aimed to explore clinicians' perceptions of AI in clinical care, including perceived benefits, risks, barriers to implementation, and training needs. Methods: A cross-sectional mixed-methods survey was distributed from August to November 2024 to mental health professionals in the US. Quantitative data were analyzed using descriptive statistics, while open-ended responses were analyzed thematically to identify key insights. Results: Respondents (n=62) were mostly not currently using AI in practice. The most frequently endorsed benefits included reductions in clinical workload, improved efficiency, and enhanced data analysis. However, a majority expressed discomfort using AI in patient care, and concerns were raised about inaccurate outputs, algorithmic bias, privacy, and weakened therapeutic rapport. Barriers to adoption included clinician resistance, lack of validation, and challenges with technical integration. Most respondents believed that specialized training in AI ethics and applications is important for clinicians. Qualitative findings reinforced concerns about dehumanization, cultural insensitivity, ethical accountability, and insufficient technological literacy. Conclusions: Mental health professionals view AI as a potentially useful adjunct, but not a replacement, in care. Ethical concerns, limited trust, and a strong emphasis on the human dimensions of therapy suggest that implementation must proceed with caution. Clinician-informed strategies, ethical frameworks, and targeted training are essential to support the responsible and effective integration of AI into mental health practice.
Background: Digital health interventions (DHIs) are identified as a potential means of improving dietary health at scale. However, engagement and retention rates are often low, reducing their impact a...
Background: Digital health interventions (DHIs) are identified as a potential means of improving dietary health at scale. However, engagement and retention rates are often low, reducing their impact and effectiveness. In dietary DHIs, a range of application (app) features have been implemented to improve engagement. However, it remains unclear what the key, early predictors of engagement and retention are at scale, in real-world deployment. This is particularly important given that retention is often overestimated in controlled research studies, and most attrition occurs soon after initial engagement. Objective: This study aimed to assess predictors of 7- and 14-day retention with a free-to-use, personalized, dietary DHI, using large-scale, real-world data from a commercial launch. It also aimed to assess predictors of day-to-day retention across the first 14-days of use, given that factors affecting initial engagement may differ from those affecting ongoing use. Methods: Users of a free-to-use dietary DHI that analysed food characteristics for dietary health based on food photographs and packaging barcodes were given unique identifiers (n = 60,987). Demographic information, use of app features, and app usage patterns were included in logistic regression models to assess predictors of retention 7- and 14-days after enrolment in the app. Multilevel modelling was used to assess predictors of engagement on each of the first 14-days of app enrolment. Results: Specific app-related activities and user referral sources consistently predicted a greater likelihood of retention at both 7- and 14-days. While photographing and scanning food were key features, the strongest predictor of engagement was the subsequent logging of consumption, which yielded higher predictive value than the capture events alone, and enabling app notifications to allow engagement reminders. User referral sources were also among the strongest predictors of retention: Users who reported hearing about the app via related podcasts or via “friends and family” had higher odds of retention at 7- and 14 days. Those who heard about the app via television or radio were less likely to engage with the app at 7- and 14-days. Conclusions: This study suggests that immediate interaction with dietary DHI activities linked to real-life behaviors (e.g., logging food eaten) increases the likelihood of continued engagement. It also suggests that engagement with personal contacts who may also be engaged in app-related activities can enhance retention, as can engagement with related media such as podcasts. However, some sub-groups of users may engage with free-to-use dietary DHIs more casually, with potentially less committed motivation to continue. In these situations, greater initial personalization for groups identified as at risk of attrition could enhance engagement.
Background: China has the world’s largest diabetic population, with suboptimal glycemic control and fragmented care posing severe challenges to primary health care (PHC) institutions. Barriers inclu...
Background: China has the world’s largest diabetic population, with suboptimal glycemic control and fragmented care posing severe challenges to primary health care (PHC) institutions. Barriers including uneven practitioner competence, clinical inertia, and disjointed clinical-public health data limit effective diabetes management, while clinical decision support systems (CDSS) show potential for improving chronic disease care but lack real-world evidence for comprehensive integration in Chinese PHC. Objective: To evaluate the effectiveness of a Clinical Decision Support System (CDSS) intervention for patients with poorly controlled type 2 diabetes in primary care settings and to establish a standardized model for intelligent diabetes management. Methods: A retrospective cohort study was conducted among diabetic patients with baseline glycated hemoglobin (HbA1c) ≥7.0 mmol/L who visited 16 community health service centers in Kunshan from January to September 2024. Patients were divided into a CDSS intervention group and a control group (routine diagnosis and treatment). 1:1 nearest neighbor propensity score matching (PSM) was used to balance baseline characteristics. After a 1-year follow-up, the glycemic achievement rates, changes in metabolic indicators and hospitalization incidence were compared. Multivariate regression and subgroup analyses were performed to determine the effect of the CDSS intervention and the subgroup heterogeneity. Results: A total of 27,494 patients were included, resulting in 6,136 matched pairs after PSM (all standardized mean differences <0.1). The average reduction in HbA1c in the intervention group was 0.90±1.67 mmol/L, significantly greater than the 0.20±1.97 mmol/L reduction in the control group (difference=-0.70 mmol/L, 95%CI: -0.76~-0.63, P<0.001). The glycemic control rate was significantly higher in the intervention group (63.9% vs 47.4%, difference=17.0 percentage points, 95%CI: 15.0~18.0, P<0.001). The intervention group also had better metabolic indicators and lower hospitalization rate. Multivariate regression identified CDSS as a significant predictor of HbA1c improvement (regression coefficient=-0.68, OR=2.40, both P<0.001), with greater benefits observed in older patients, those with longer disease duration, baseline HbA1c 8~9 mmol/L and existing complications. Conclusions: CDSS intervention can significantly improve glycemic control, optimize metabolic indicators and reduce hospitalization risk in primary care diabetic patients. which is suitable for grassroots management and can be used as a promotable and replicable standardized plan.
Background: Virtual Communities of Practice (vCoPs) provide collaborative spaces where healthcare professionals and patients can exchange knowledge, experiences, and support. Gamification—the applic...
Background: Virtual Communities of Practice (vCoPs) provide collaborative spaces where healthcare professionals and patients can exchange knowledge, experiences, and support. Gamification—the application of game design elements in non-game contexts—is increasingly being integrated into digital health platforms to improve engagement. However, the effectiveness of gamification within healthcare-focused virtual communities of practice (vCoPs) remains unclear. Objective: To evaluate how gamification has been implemented within healthcare vCoPs. Methods: A systematic search of eight databases (Medline, CINAHL, Embase, Scopus, Web of Science, PsycInfo, ERIC, Global Health) identified studies published from 2000 onwards. Eligible studies reported empirical evaluations of healthcare-related vCoPs incorporating at least one gamification feature. Titles, abstracts, and full texts were screened independently by four reviewers . Data extraction and risk-of-bias assessment followed PRISMA 2020 guidance . Due to heterogeneity in study designs and outcomes, results were synthesised narratively. Results: Of 890 records identified, 269 duplicates were moved, and 621 unique studies were screened and four studies met inclusion criteria. Designs included one quasi-experimental study, two mixed-methods studies, and one cluster randomised controlled trial. Gamification elements across studies included badges, levels, points, challenge-based tasks, and interactive simulations. Educational and professional-development vCoPs reported improvements in engagement, participation, and self-reported learning. The patient-facing intervention demonstrated improved adherence to the Mediterranean diet but no changes in activation, mental health, quality of life, physical activity, or medication adherence. Conclusions: Gamification within healthcare vCoPs shows potential to enhance engagement and perceived value, particularly in educational and professional settings. Evidence for broader behaviour change or clinical impact remains limited. Further rigorous trials with standardised gamification components and longer follow-up are required to assess effectiveness and scalability. Clinical Trial: This review was registered on PROSPERO, an international prospective register of systematic review (February 2025, reference CRD420251242967).
Background: Acute ischemic stroke (AIS) treatment selection requires rapid, guideline-concordant integration of clinical, imaging, and laboratory data, including therapeutic windows, contraindications...
Background: Acute ischemic stroke (AIS) treatment selection requires rapid, guideline-concordant integration of clinical, imaging, and laboratory data, including therapeutic windows, contraindications, stroke severity, and imaging eligibility. This process is complex, expertise-dependent, and vulnerable to safety-critical errors. Objective: To develop and validate a structured multi-agent large language model (LLM) framework for real-world AIS decision support, and to assess whether it can improve guideline adherence, safety auditability, and physician decision-making, particularly among junior physicians and non-specialists. Methods: We developed a multi-agent LLM workflow that imposed structured outputs and guideline-based reasoning to generate treatment recommendations (intravenous thrombolysis, endovascular thrombectomy, standard medical therapy, or non-AIS/non-stroke) and TOAST subtypes. The framework was evaluated using multicenter retrospective real-world cases, prospectively collected clinical cases, and literature-derived challenging cases. Performance was assessed against clinical reference standards. Safety was assessed by omission and hallucination event rates and clinician-rated usefulness (5-point Likert scale). In a prospective physician study, paired physician-by-case decisions with and without LLM output were analyzed using a binomial generalized linear mixed-effects model with crossed random intercepts for physician and case. Results: Framework augmentation improved treatment recommendation accuracy across representative Baichuan, Qwen, DeepSeek, and GPT models. In Group A, accuracy increased from 0.546 to 0.687, 0.574 to 0.697, 0.687 to 0.847, and 0.737 to 0.851, respectively. Similar improvements were observed in Group B (0.595 to 0.684, 0.587 to 0.671, 0.671 to 0.813, and 0.698 to 0.798) and Group C (0.507 to 0.667, 0.618 to 0.674, 0.646 to 0.729, and 0.597 to 0.750). Compared with the standalone model, the augmented framework also showed higher safety scores (4.36 vs 4.02), lower hallucination rates (3.1% vs 4.7%), and lower omission rates (10.3% vs 16.6%). In the prospective physician study, treatment decision accuracy increased from 73.1% to 88.6% with LLM support, with greater gains among junior physicians and non-specialists. Conclusions: A structured multi-agent framework improved LLM performance in AIS treatment recommendation and TOAST classification, while providing safer, more auditable decision support. It was also associated with higher physician decision accuracy, with larger gains among less-experienced physicians, suggesting potential to reduce expertise-related disparities in stroke care. Prospective multicenter studies are needed to assess effects on workflow and clinical outcomes. Clinical Trial: Chinese Clinical Trial Registry ChiCTR2400092800; registered November 22, 2024.
Background: Over the past decade, the rise of social media has profoundly transformed the ways health information is disseminated and discussed. Social media platforms such as Twitter/X have become ke...
Background: Over the past decade, the rise of social media has profoundly transformed the ways health information is disseminated and discussed. Social media platforms such as Twitter/X have become key spaces where users can share their experiences and opinions regarding traditional, complementary, and alternative medicines (TCAM). However, trends on social media, particularly during the COVID-19 pandemic, remain poorly documented. Objective: The study aimed to characterize temporal trends in TCAM-related discourse on X (formerly Twitter) across English, Spanish, and French between 2015 and 2024. Methods: We conducted a retrospective observational analysis of annual mention counts in public X posts in English, Spanish, and French that contained 39 main TCAM-related terms over the 2015-2024 period. Descriptive analyses summarized cumulative mentions. Inferential trend analyses were restricted to the 2015-2019 period and used term-wise ordinary least squares regression. Trends were retained if R² ≥ 0.70 and an exact permutation test on the slope yielded P≤.05 (120 permutations, exhaustive given 5 observations). Uncertainty around 2015-2019 relative changes was quantified using 95% bootstrap percentile confidence intervals. Results: Cumulative mentions of the 39 TCAM-related terms totalled 191,994,848 over 2015-2024. The most frequently cited terms were “yoga” (79,618,183), “meditation” (55,560,372), and “mindfulness” (18,915,377), together accounting for 80.3% of all mentions. Cumulative mentions were predominantly in English (88.6%) compared to French (8.9%) and Spanish (2.5%). For example, “yoga” was used 71,203,218 times in English, 7,370,196 in Spanish, and 1,044,769 in French. Annual trajectories showed no order-of-magnitude surges among the top 10 terms; the largest year-to-year increase from 2019 to 2020 was observed for “ayurveda” (+186%). Trend analyses over 2015-2019 identified 14 significant terms in English (13 decreasing; 1 increasing +229% “intermittent fasting”), 8 in Spanish (3 increasing), and 11 in French (5 increasing). The terms “reiki” and “reflexology” decreased in all three languages. Conclusions: Between 2015 and 2024, the text-based analyses related to TCAM on X (formerly Twitter) were highly concentrated in a small set of body-mind practice labels and in English compared to Spanish and French. No peak was observed across the overall TCAM corpus during the COVID-19 pandemic, although isolated terms such as “ayurveda” showed marked short-term increases. The results indicate a relative stability of TCAM-related needs and concerns. These findings reflect mention frequency only and cannot be interpreted as indicators of professional practice or actual consumer use.
Background: Cognitive frailty can cause decline in activities of daily living and quality of life in older adults, increasing the risk of malnutrition, hospitalization, depression, disability, dementi...
Background: Cognitive frailty can cause decline in activities of daily living and quality of life in older adults, increasing the risk of malnutrition, hospitalization, depression, disability, dementia, and death. Therefore, early intervention to reverse the cognitive and physical functions of older adults with cognitive frailty has become important and urgent for improved quality of life. At present, only few relevant studies exist, theoretical support is lacking, and quality of intervention is inconsistent. Objective: To examine the effect of a Motor-Cognitive Dual-Task intervention based on the Health Belief Model on functional improvement in older adults with cognitive frailty. Methods: In this randomized controlled trial, we recruited 90 older adults diagnosed with cognitive frailty from a community in China from January 2024 to October 2024. The participants were randomly divided into two groups: the experimental group received an 8-week Motor-Cognitive Dual-Task based on the Health Belief Model, and the control group received a single exercise intervention. Questionnaires were used to assess the patients' functional status before the intervention, post-intervention, and at the 6-month follow-up. Results: After 8 weeks of Motor-Cognitive Dual-Task intervention based on the Health Belief Model, frailty (t=2.750, P=.007), cognitive function (t=-2.577, P=.012), balance function (t=-2.866, P=.005), and self-care ability (t=-2.822, P=.006) were significantly improved among older adults with cognitive frailty. These beneficial effects were sustained at the 6-month follow-up. Further, after 6 months of follow-up, the outcome indicators improved in the intervention group, and the incidence of falls was lower than that in the control group (χ²=4.444, P=.035). However, no significant between-group differences were observed in several domains of quality of life throughout the intervention and follow-up periods. Conclusions: The Motor-Cognitive Dual-Task intervention based on the Health Belief Model significantly improved cognitive function, balance function, and self-care ability and reduced the incidence of falls in older adults with cognitive frailty. The study findings suggest that a comprehensive Motor-Cognitive Dual-Task intervention can be an effective rehabilitation strategy for older adults with cognitive frailty.
Background: Digital platforms are primary sources of sexual health information for young adults, yet they are widely perceived as unreliable. This tension between distrust and reliance remains underth...
Background: Digital platforms are primary sources of sexual health information for young adults, yet they are widely perceived as unreliable. This tension between distrust and reliance remains undertheorized. Objective: To examine how Chinese college students use online sources for sexual health information under conditions of uncertainty, and to explore how they navigate the tension between distrust and reliance. Methods: We conducted in-depth semi-structured interviews with 22 Chinese college students and analysed the data using a thematic approach informed by an interpretive qualitative framework. Results: Participants reported frequent reliance on online sexual health information despite widespread concerns about its reliability. We identify a gap between informational relevance and practical usability, in which information often matches users’ queries but fails to support actionable decisions. To navigate this gap, participants construct what we term functional trust—the capacity to use information with sufficient confidence to act, even without certainty about its accuracy. This process is achieved through adaptive strategies, including cross-platform verification, experiential validation, and platform-specific evaluation. However, constructing functional trust incurs significant cognitive and emotional costs, including confusion, information overload, and search abandonment. Continued reliance on uncertain information is further shaped by structural conditions, particularly the absence of accessible offline sources and the use of precautionary decision-making under perceived risk. Conclusions: The use of online sexual health information does not depend on trust in the conventional sense, but on the construction of functional trust under conditions of persistent uncertainty. Addressing challenges in digital sexual health communication requires not only improving information quality but also enhancing its usability and reducing the cognitive burden of navigation within structurally constrained environments.
Background: Rapid research responses to emerging infectious disease (EID) outbreaks depend not only on how quickly studies are launched, but also on whether their data can be combined, compared, and r...
Background: Rapid research responses to emerging infectious disease (EID) outbreaks depend not only on how quickly studies are launched, but also on whether their data can be combined, compared, and reused across studies. Health data standards, including shared vocabularies, terminology, and information models, are the structural prerequisite for interoperable, findable, accessible, interoperable, and reusable (FAIR) data. Despite European Commission (EC) investments exceeding €130 million across the EID cohort and clinical trial consortia coordinated through the Cohort Coordination Board (CCB) and Trial Coordination Board (TCB), little empirical evidence exists on the extent to which these consortia adopt standards, the barriers they face, or what funders could do to improve implementation. Objective: To characterise health data standards adoption across EC-funded EID consortia, identify the barriers that prevent uptake, and generate evidence-based recommendations for funders to strengthen standards implementation for the rapid reuse of interoperable participant-level data in epidemic detection and response. Methods: We conducted a cross-sectional online survey May 2023-Feb 2024, developed through a literature review and stakeholder consultation, with CCB and TCB-affiliated EC-funded EID consortia. Research networks and consortia outside these boards could participate if forwarded the survey. We collected information on consortium characteristics, standards use, barriers to adoption, awareness of EC-supported standardisation initiatives, and recommendations for improving uptake. Responses were analysed descriptively; open-text responses were categorised thematically. Results: Thirty-three responses, representing 15 consortia or research networks spanning over 40 countries were collected. Most responses came from cohort consortia. Adoption of data standards was limited. The most frequently used standards were ICD codes (n=10) and the Systematised Nomenclature of Medicine Clinical Terms (n=9); 7 respondents reported not using standards. The main barriers were insufficient experience applying standards (n=17), lack of budget (n=12), uncertainty about which standard to use (n=12), uncertainty about which standards related studies used (n=9), and inadequate tools (n=9). Awareness of EC initiatives designed to support standards adoption was strikingly low, suggesting that EC investment in standards support is not reaching its intended audience. Respondents recommended dedicated budgets, clearer guidance on preferred standards by data type, better communication of the benefits of standards adoption, stronger tooling, and funder mandates. Conclusions: Health data standards are underused across European EID consortia, representing a preventable bottleneck for pandemic preparedness despite substantial public investment. European funders can address this through the following actions recommended by major EC-funded EID Consortia: mandating dedicated standards budgets at the grant submission stage, issuing formal guidance on preferred standards by data type, investing in open-source tooling that delivers value to data generators, requiring machine-actionable data management plans, and establishing a public registry of standards adopted by funded consortia. Strengthening coordinated standards adoption is a necessary and achievable step toward the FAIR, interoperable research data infrastructure that effective pandemic response demands. Clinical Trial: Not applicable.
Background: Virtual reality (VR) platforms have demonstrated small-to-moderate effect sizes for motor, balance, and dual-task outcomes across neurological conditions, yet fewer than one-third of physi...
Background: Virtual reality (VR) platforms have demonstrated small-to-moderate effect sizes for motor, balance, and dual-task outcomes across neurological conditions, yet fewer than one-third of physical therapists regularly integrate VR into practice, citing limited space, inadequate training, and uncertainty about clinical utility. The Instrumented Multitask Assessment System (IMAS) is a compact VR platform combining a 360° omnidirectional walking surface, an immersive headset, and integrated biophysical sensors for real-time adaptive cognitive-motor dual-task training. Preliminary work demonstrated feasibility for dual-task assessment in military service members with mild traumatic brain injury, but clinician perspectives on acceptability and implementation for routine neurorehabilitation remain unexplored. Objective: This study explored practicing neurorehabilitation clinicians’ perceptions of the feasibility, acceptability, and implementation requirements of the IMAS platform. Guided by the Theoretical Framework of Acceptability (TFA), we sought to understand how clinicians perceive the coherence, burden, and potential effectiveness of the IMAS to inform iterative design modifications for subsequent patient-focused pilot testing. Methods: A qualitative descriptive design using semi-structured interviews was employed. Eighteen licensed neurorehabilitation clinicians (physical therapists, occupational therapists, and speech-language pathologists) with a minimum of 2 years’ experience treating adults with neurological disorders were recruited via purposive sampling across diverse US clinical settings. Interviews were conducted via videoconference. Data were analyzed using reflexive thematic analysis with 2 investigators independently coding transcripts and resolving discrepancies through structured consensus meetings. Reporting followed the Standards for Reporting Qualitative Research guidelines. Results: Participants were predominantly female (11/18, 61%), physical therapists (12/18, 67%), held professional doctorates (12/18, 67%), and worked in outpatient settings (11/18, 61%), with a mean of 11.11 (SD 9.05) years of neurorehabilitation experience. Saturation was reached by the sixteenth interview. Analysis yielded four themes: (1) System Design and Customization Needs, reflecting emphasis on principles of neuroplasticity and environmental customization as essential features; (2) Implementation Challenges and Barriers, encompassing learning curves, safety concerns, resource constraints, workflow considerations, and technical limitations as burden dimensions; (3) Perceived Benefits and Advantages, including enhanced therapeutic outcomes, patient safety, engagement through gamification, and immediate feedback; and (4) Training and Support Requirements, highlighting comprehensive hands-on training and tiered technical support as requisites for clinician self-efficacy. Conclusions: Clinicians perceived the IMAS as coherent with established neurorehabilitation principles and therapeutically promising; however, substantial barriers related to cost, space, workflow disruption, and technical reliability were identified as limiting factors for adoption. The TFA revealed that high coherence and perceived effectiveness alone cannot overcome high burden when organizational capacity, financial resources, and technical support remain inadequate. Successful implementation requires coordinated efforts to address safety, reduce costs, validate effectiveness through patient outcome studies, and embed comprehensive training within clinical settings.
Background: The Registry of Stroke Care Quality (RES-Q) is healthcare quality improvement platform used globally. RES-Q collects structured quality-of-care data for stroke patients, requiring clinicia...
Background: The Registry of Stroke Care Quality (RES-Q) is healthcare quality improvement platform used globally. RES-Q collects structured quality-of-care data for stroke patients, requiring clinicians to manually extract information from electronic health records or documents such as discharge summaries. This process is essential but time-consuming, particularly given the variability, length, and semi-structured nature of clinical reports. Objective: To develop and evaluate a multilingual Evidence-Based Question-Answering framework that identifies supporting text spans in clinical reports of stroke patients and proposes answer suggestions for structured clinical forms, with the goal of reducing clinician workload while preserving full human oversight. Methods: We conduct a multilingual study using 1,596 pseudonymized stroke discharge summaries in six languages, annotated with question-evidence-answer triplets. Encoder-based language models are used to extract evidence spans from the reports, while generative language models are used to predict normalized form answers based on the extracted evidences. We compare multiple training strategies: models trained on reports in a single target language, models trained jointly on reports in different languages, and models trained on original reports combined with cross-lingual data augmentations. We evaluate performance on Evidence Extraction, Answer Prediction, and end-to-end Evidence-Based Question Answering across the six languages. Results: The presented Evidence-Based Question-Answering system achieves 89% end-to-end accuracy in form filling across six languages (77% for patient-specific questions and 95% for default or unverifiable items). Evidence Extraction is the primary bottleneck, reaching 85% F1 and 79% Exact Match, whereas Answer Prediction based on extracted evidences is more stable, achieving 95% accuracy. The performance varies by question type, and cross-lingual training generally reduces Evidence Extraction performance but has little effect on Answer Prediction. Model performance is influenced more by reporting practices and dataset characteristics than by language itself. Conclusions: Evidence-Based Question Answering over multilingual stroke discharge summaries enables human-in-the-loop validation and effective answer prediction with moderate computational resources. Evidence Extraction is the main bottleneck, while Answer Prediction is robust across languages and model sizes. The approach supports structured data collection, though generalization to new languages requires target-language training data.
Background: Reproducibility is a cornerstone of scientific validity, yet many biomedical studies lack sufficient transparency for independent verification. Recent advances in Large Language Models (LL...
Background: Reproducibility is a cornerstone of scientific validity, yet many biomedical studies lack sufficient transparency for independent verification. Recent advances in Large Language Models (LLMs) enable the development of autonomous agent systems capable of performing complex research tasks, offering new opportunities to assess and enhance reproducibility at scale. Objective: To evaluate the ability of LLM-based autonomous agents to reproduce key findings from published Alzheimer’s disease studies using a shared, publicly available dataset. Methods: We used the National Alzheimer’s Coordinating Center Uniform Data Set “Quick Access” dataset. Five eligible studies were identified through citation-based screening and predefined inclusion criteria. We developed a multi-agent system using GPT-4o (Autogen framework), simulating a research team to generate and execute code based on study abstracts, methods, and selected data dictionary variables. Reproducibility was evaluated at the assertion level using extracted abstract findings, with agreement defined by numerical tolerance or directional consistency. We additionally assessed statistical method alignment and overall workflow coherence. Results: A total of 35 findings were extracted across 5 studies. LLM agents reproduced a mean of 53.2% of findings, with 3/5 studies achieving majority replication. Agreement was higher for directionality and significance than for numerical estimates. Exact statistical method alignment occurred in 1/5 studies; 8/15 comparisons were partially aligned, mainly for standard methods. Domain-specific methods were often omitted or simplified. Reproduction required iterative correction (mean 35.6 steps), with code errors in 47.2% of runs but resolved autonomously. Failures were primarily due to incomplete reporting and incorrect implementation Conclusions: LLM-based autonomous agents demonstrate moderate capability in reproducing published biomedical findings, particularly for studies with clear, well-specified methods. However, reproducibility is limited by incomplete reporting, challenges in implementing domain-specific methods, and breakdowns in multi-step workflow fidelity. These findings suggest that LLM agents may serve as scalable tools for preliminary reproducibility assessment, while emphasizing the need for improved methodological transparency and validation frameworks in biomedical research.
Background: Patient-facing digital health tools such as mobile health (mHealth) apps, wearables, and digital therapeutics have expanded rapidly and show promise for improving chronic disease managemen...
Background: Patient-facing digital health tools such as mobile health (mHealth) apps, wearables, and digital therapeutics have expanded rapidly and show promise for improving chronic disease management. Despite increasing evidence of effectiveness, health systems and payers continue to face challenges integrating these tools into routine care. Objective: This study examined the decision-making processes of health system and payer leaders regarding the adoption and sustainability of patient-facing digital health tools within their organizations. Methods: We conducted semi structured interviews with nine senior leaders from a large Midwestern academic health system and affiliated payer organizations, including a provider owned health plan and a state Medicaid program. Interviews explored digital health adoption decisions, perceived value and fit, barriers, and sustainability considerations, focusing on adoption of an evidence-based mHealth intervention for alcohol use disorder as a use case. Transcripts were analyzed using thematic analysis with inductive and deductive coding. Results: Four decision making mechanisms shaped adoption and sustainability decisions: prioritization under organizational constraint, risk mitigation, operational fit, and value determination. These mechanisms describe how leaders navigate limited organizational capacity, reduce uncertainty and protect against clinical, financial, and operational risks, assess whether tools can integrate within existing clinical and technical systems, and determine whether anticipated and measurable benefits justify adoption and continued organizational support. Conclusions: Adoption and sustainability of patient-facing digital health tools are shaped by dynamic organizational decision-making processes that often remain invisible to patients, clinicians, researchers, and developers. Making these processes visible may help better align digital health tools with the realities of the healthcare system to support implementation.
Background: The growing integration of Personalized Risk Prediction (PRP) and Artificial Intelligence (AI) substantially re-shapes diagnostic and therapeutic decision-making in health care. At the sam...
Background: The growing integration of Personalized Risk Prediction (PRP) and Artificial Intelligence (AI) substantially re-shapes diagnostic and therapeutic decision-making in health care. At the same time, its responsible adoption depends not only on technical performance, but also on patients’ perspectives and acceptance. Objective: This study systematically examined patients’ perspectives across several European countries and explored how patients’ technology-related attitudes relate to their evaluations of personalized and AI-supported ap-proaches in cardiac care. As part of the PROFID (Prevention of Sudden Cardiac Death After Myocardial Infarc-tion by Defibrillator Implantation) project, its focus is on the ethical use of PRP and AI in the clinical context of decision-making regarding sudden cardiac death (SCD) prevention and implantable cardioverter-defibrillator (ICD) implantation. Methods: The study used a cross-sectional survey design with a standardized questionnaire including multimedia con-tent. The target population comprised adults aged 18 years or older living in six European countries who met at least one of the following (self-reported) clinical criteria: heart failure, myocardial infarction (MI), cardiac arrest, or current ICD implantation. An exploratory factor analysis (EFA) was used to identify and evaluate internally consistent scales, and subsequent regression analyses examined associations between these scales and technological openness, sociodemographic characteristics, and patients’ views on PRP and AI in cardiac care. Results: The sample consisted of 470 participants from Germany (n=210), the Netherlands (n=86), the United Kingdom (n=145), and three other European countries (n=29; Austria, Belgium, and Spain). Overall, 51.9% (244/470) of respondents were male and 48.1% (226/470) were female. The mean age of the sample was 61.12 (SD 12.62) years.
The EFA showed six clearly interpretable factors: (1) Perceived benefits and support of PRP models in medical decision-making (MDM); (2) Perceived benefits and support of AI in MDM; (3) Transparency expectations in algorithmic decision-making; (4) Support for delegating decisions to algorithms; (5) Self-reported AI literacy and (6) Preference for shared decision-making (SDM). The regression analysis showed the relations of technologi-cal readiness, self-reported AI literacy, support for delegation of decisions to algorithms, transparency expecta-tions in algorithmic decision-making, preferences for SDM, and educational attainment to predict
patients’ perceived benefits and support of PRP or AI in MDM. Conclusions: The findings support existing assumptions while also highlighting additional aspects that should be considered if high-level technologies are used in decision-making processes related to ICD implantation. PRP and AI were generally perceived as useful tools to support decision-making regarding ICD indication, provided that trans-parency is ensured and patients remain actively involved in the decision-making process. Mandatory use and full delegation to decision-making directly by Al were broadly rejected. Generally, men showed more positive perceptions of the use of AI in MDM than women. The attributed acceptance of delegation to PRP models was significantly higher than AI.
Background: Chronic pain is a prevalent and complex condition requiring long-term, multidimensional management. Digital therapeutics (DTx) have emerged as a promising nonpharmacological intervention;...
Background: Chronic pain is a prevalent and complex condition requiring long-term, multidimensional management. Digital therapeutics (DTx) have emerged as a promising nonpharmacological intervention; however, evidence regarding their effectiveness remains inconsistent due to heterogeneity in intervention types and study designs. Objective: This study aimed to systematically review and meta-analyze the effectiveness of digital therapeutics in reducing pain among patients with chronic pain. Methods: A systematic review and meta-analysis were conducted following PRISMA 2020 guidelines. Electronic databases, including PubMed, Embase, CINAHL, and the Cochrane Library, were searched from inception to December 10, 2025. Randomized controlled trials evaluating DTx interventions in adults with chronic pain were included. The primary outcome was pain intensity, and secondary outcomes included physical function and psychological outcomes (quality of life, anxiety, depression, and pain catastrophizing). Effect sizes were calculated as standardized mean differences using a random-effects model, and risk of bias was assessed using the Cochrane Risk of Bias tool. Results: A total of 7 studies were included in the meta-analysis. Digital therapeutics demonstrated a statistically significant reduction in pain intensity (SMD = -0.87, 95% CI: -1.70 to -0.03, p = 0.04); however, heterogeneity was substantial (I² = 97%). No significant effects were observed for physical function (SMD = 0.62, 95% CI: -1.57 to 2.80, p = 0.58) or overall psychological outcomes (SMD = -0.92, 95% CI: -2.01 to 0.17, p = 0.10). Among psychological outcomes, quality of life showed a trend toward improvement (SMD = 0.28, p = 0.07), whereas anxiety, depression, and pain catastrophizing showed no significant effects and substantial heterogeneity. Conclusions: Digital therapeutics may contribute to reductions in pain intensity in patients with chronic pain; however, the effects on physical function and psychological outcomes remain inconsistent. The high level of heterogeneity suggests that the effectiveness of DTx varies considerably depending on intervention characteristics and study design. Further high-quality and standardized trials are needed to establish the clinical effectiveness of DTx. Clinical Trial: PROSPERO CRD 420261355510; https://www.crd.york.ac.uk/PROSPERO/view/CRD420261355510
Background: Suboptimal glycemic control remains a major public health challenge for patients with type 2 diabetes and prediabetes. Remote glucose monitoring offers scalable support for self-management...
Background: Suboptimal glycemic control remains a major public health challenge for patients with type 2 diabetes and prediabetes. Remote glucose monitoring offers scalable support for self-management, but evidence on its real-world effectiveness and the causal impact of varying engagement levels is limited. Objective: To estimate the effect of patient engagement measured through glucose monitoring frequency on hemoglobin A1c (HbA1c). Methods: We analyzed 1,479 adults with type 2 diabetes or prediabetes enrolled in the iHealth Unified Care program, integrating Bluetooth glucose meters, a mobile app, lifestyle coaching, and primary care coordination. Engagement during the first six months was defined as the weekly frequency of glucose monitoring. The causal effect of monitoring frequency on HbA1c was estimated using marginal structural models with inverse probability weighting to address time-varying confounding. Results: At 6 months, HbA1c decreased by 0.53 (SD 1.46) percentage points (p < 0.001). We observed a dose-response relationship across engagement tiers: the highest-engagement group (16.99 measurements/week) achieved a 1.00 percentage point HbA1c reduction versus 0.34 in the lowest tier. In weighted models, each additional weekly measurement was associated with a 0.03 percentage point greater HbA1c reduction (p < 0.01). Findings were consistent in sensitivity analyses at 3 and 12 months. Conclusions: Engagement with a digitally enabled, primary care-integrated remote glucose monitoring program significantly improved glycemic outcomes across all engagement levels. Higher monitoring frequency produced greater HbA1c reductions, underscoring the importance of fostering sustained patient engagement to optimize diabetes management. Clinical Trial: Not Applicable
Background: Preterm birth (PTB) remains a major global health challenge and a leading cause of neonatal morbidity and mortality. Increasing evidence suggests that gut microbiota–derived metabolites...
Background: Preterm birth (PTB) remains a major global health challenge and a leading cause of neonatal morbidity and mortality. Increasing evidence suggests that gut microbiota–derived metabolites play a crucial role in maternal–fetal health. Over the past two decades, research in this field has evolved from taxonomic descriptions toward functional and metabolic mechanisms, yet a systematic understanding of this transition remains limited. Objective: This study aimed to systematically characterize the global research landscape on gut microbiota and preterm birth from 2006 to 2025, with a focus on identifying the shift from taxonomic associations to metabolite-centered functional mechanisms and highlighting emerging translational directions. Methods: A bibliometric analysis was conducted using data retrieved from Web of Science Core Collection, Scopus, and PubMed. Publications between 2006 and 2025 were analyzed using the bibliometrix package in R. Analytical approaches included annual publication trend analysis, country and collaboration analysis, keyword co-occurrence networks, thematic evolution analysis, and burst keyword detection. Cross-database validation was performed to ensure the robustness of emerging research themes. Results: A total of 479 core publications were identified, showing a non-linear growth trend with a marked increase after 2018. Early studies primarily focused on taxonomic descriptions of microbial composition, whereas more recent research emphasized functional and metabolic pathways. Keyword and thematic analyses revealed that microbiota-derived metabolites, immune regulation, and probiotic interventions have become central research themes. Cross-database validation confirmed consistent trends, highlighting a transition toward metabolite-centered mechanisms in the field. Conclusions: Research on gut microbiota and preterm birth has gradually shifted from descriptive taxonomic approaches to function-oriented and metabolite-driven perspectives. Microbiome-derived metabolites, such as short-chain fatty acids and tryptophan metabolites, emerge as potential mediators linking microbial activity to host outcomes. These findings provide a conceptual foundation for developing digital biomarkers and data-driven risk prediction models in maternal–fetal health, supporting future integration with mHealth platforms for early monitoring and intervention. Clinical Trial: Not applicable
Background: Artificial intelligence (AI) and the expanding scope of advanced practice providers (APPs) are rapidly transforming healthcare delivery. These concurrent trends have major implications for...
Background: Artificial intelligence (AI) and the expanding scope of advanced practice providers (APPs) are rapidly transforming healthcare delivery. These concurrent trends have major implications for the medical workforce and clinical education. Objective: This study evaluates U.S. medical students' familiarity, perceived impact, and preparedness regarding AI integration and APP scope expansion in medical practice and identifies which trend they believe will more significantly affect their future career prospects. Methods: A cross-sectional online survey with five sections, including Demographics, Familiarity with AI and APPs, Opinions on AI, Opinions on APPs, and Final Reflections, was distributed to medical students enrolled in accredited U.S. institutions. The survey included 5-point Likert-scale, multiple-choice, and open-ended questions. Quantitative data were analyzed using descriptive statistics, and qualitative responses were examined through thematic analysis. Results: A total of 105 valid responses were collected from 43 U.S. medical schools. Students reported greater familiarity with AI than APP scope expansion (71.4% vs. 57.7% rating ≥3; McNemar's test, P=.032). Key AI benefits included administrative efficiency, diagnostic accuracy, and disease prevention; drawbacks included overreliance, algorithmic bias, and error risk. For APPs, benefits included reduced workload and improved access; concerns centered on inconsistent training and reduced oversight. Students rated AI's impact more positively than APP's (W = 531.0, P<.001): 52.5% viewed AI as enhancing their roles, while APP perceptions were more divided (44.2% positive, 35.6% negative). Radiology (80.0%) and pathology (61.9%) were identified as the specialists most affected by AI; family medicine (70.5%) and internal medicine (55.2%) by APP expansion. Preparedness was low for both, with 70.5% and 47.6% rating themselves 1–2 out of 5 for AI and APPs, respectively (W = 414.0, P<.001). Students were nearly evenly split on career threats: 45.6% identified APPs, 39.8% AI, and 14.6% both equally (χ² = 16.85, P<.001). Qualitative responses emphasized the need for AI education, interprofessional training, and policy literacy. Conclusions: This study found that medical students recognize AI and APP expansion as major forces reshaping healthcare, but feel largely unprepared for their impact. The findings highlight the need for curricular reforms that incorporate AI literacy, provide hands-on learning opportunities, and offer guidance on interprofessional collaboration, education on scope-of-practice regulations, and advocacy skills to better equip future physicians for evolving clinical environments.
Background: Aging is commonly associated with declines in physical performance, while physical activity interventions have been shown to benefit older adults. Traditional face‑to‑face intervention...
Background: Aging is commonly associated with declines in physical performance, while physical activity interventions have been shown to benefit older adults. Traditional face‑to‑face interventions often limit participation due to mobility, scheduling, and accessibility challenges. mHealth offers a promising alternative; however, older adults may still face difficulties related to digital literacy, motivation, and perceived ease of use. Involving family members in mHealth‑based physical activity interventions may help enhance engagement and ultimately improve physical performance among community‑dwelling older adults. Objective: This study aimed to examine both the effects of mHealth-based PA interventions involving family members on the physical performance of community-dwelling older adults and the role of family members in these interventions. Methods: This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and the Synthesis Without Meta-analysis (SWiM) guidelines. A comprehensive search was conducted across Web of Science, CINAHL, Scopus, PsycINFO, PubMed, and Embase from database inception to 30 June 2025. The inclusion criteria focused on involving family support for mHealth-based physical activity interventions targeting community-dwelling older adults aged 60 years or above. Studies involving caregivers without a clearly defined family role were excluded. A narrative synthesis was used to analyze data relevant to the review aims. Results: Seven studies (n = 285) met the inclusion criteria. None of the identified studies explicitly required a family member to participate in the interventions, and only two studies specifically recruited family members. Adherence was assessed using various strategies, with reported rates ranging from 15% to 100%. Most interventions used mHealth technologies for communication and delivery of educational materials. Across studies, family members most commonly served as supervisors, technology-use supporters, and safety monitors. Physical performance outcomes were measured through both physical function and physical activity. Six studies assessed physical function using indicators such as mobility, strength, endurance, and balance; however, the effectiveness of interventions in improving physical function remained inconclusive. Two studies evaluated physical activity levels and reported positive effects. Conclusions: This review provides insights into the roles of family members in mHealth‑based PA interventions for community‑dwelling older adults. Involving family members may enhance physical performance, particularly in physical activity. Closer relationships between family members and older participants were associated with higher adherence. However, the combined effect of family support and mHealth technology on improving overall physical performance remains unclear. Clinical Trial: The protocol for this review was registered with the PROSPERO international prospective register of systematic reviews (registration number CRD42024575953).
Background: Transgender men and transmasculine people who have sex with men (TMSM) are at elevated risk for HIV acquisition, have unmet HIV prevention needs, and have low uptake of antiretroviral pre-...
Background: Transgender men and transmasculine people who have sex with men (TMSM) are at elevated risk for HIV acquisition, have unmet HIV prevention needs, and have low uptake of antiretroviral pre-exposure prophylaxis (PrEP). To our knowledge, there are no published efficacious behavioral interventions to decrease HIV risk specifically for TMSM; this includes peer-delivered strategies that demonstrate high acceptability in this population. Objective: This paper describes the development, refinement, and optimization of theory-informed, peer-delivered digital interventions—which were feasible to implement and highly acceptable among the participant population—designed to increase PrEP uptake and adherence among adult transmasculine people who have sex with individuals assigned male at birth within the context of a full-scale, randomized factorial trial. Methods: Between March 2023-March 2025, we used an iterative community-engaged approach that included: (1) community and key stakeholder input, (2) theory- and evidence-informed manualized content with iterative refinement, (3) interventionist training and preparation, and (4) process evaluation and fidelity monitoring. Two theoretical frameworks and one theory of behavior change guided content development, structure, format and evaluation: The Healthcare Accessibility Framework, the Gender Affirmation Framework, and the Information, Motivation, and Behavioral Skills Model of behavior change. Results: A 4-member Community Advisory Board of transmasculine individuals partnered with the research team to co-design interventions and study procedures. Additional input was obtained through one-on-one key stakeholder consultations with nine topic experts, clinicians, and community experts at partner organizations. Iterative refinement incorporated evidence synthesis, manual drafting, mock sessions, and structured feedback loops resulting in two refined virtually delivered interventions: (1) PrEP4T, a 6-session one-on-one peer navigation intervention (60-90 minutes/session) emphasizing goal setting, harm reduction, and supportive referrals; and (2) LS4TM, a 6-session peer-facilitated group-based behavioral intervention (2 hours/session), focusing on sexual health knowledge, gender affirmation, communication, and social support. A digital standard of care (SOC) resource guide of curated, gender-affirming sexual health, HIV prevention, and community resources was also developed. Interventionist training included approximately 15 hours of knowledge/skill building (asynchronous and live didactic sessions) and at least 12 hours of applied practice (mock sessions with structured feedback). Process evaluation and fidelity relied on participant- and interventionist-completed case reporting forms, reflecting community prioritization of interventionist and participant comfort during sessions. Key lessons learned included the importance of flexible, manualized intervention content structures that support fidelity while allowing personalized adaptation; using gender-affirming language through mirroring and dual phrasing; centering “voice” and “choice” in HIV prevention decision-making; incorporating behavior change scaffolding (e.g., goal setting, elicit-provide-elicit techniques); offering hands-on peer navigation and curated SOC resources to address structural access barriers; and leveraging digital tools to enhance engagement, shared learning, and community connection. Conclusions: Co-designing with transmasculine communities through an iterative, community-engaged development and refinement processes was essential for producing culturally responsive, theoretically-grounded, and gender-affirming HIV prevention interventions. Our findings can inform future peer-delivered and digital HIV prevention strategies tailored to the needs of specific populations, grounded in community partnerships and lived experience. Clinical Trial: ClinicalTrials.gov NCT06182280; https://clinicaltrials.gov/ct2/show/NCT06182280
Background: Person-centered, collaborative and preventive care involving primary care providers and community care providers is considered essential in response to increasingly complex care needs. The...
Background: Person-centered, collaborative and preventive care involving primary care providers and community care providers is considered essential in response to increasingly complex care needs. The implementation of such care, summarised as ‘intersectoral collaboration for health,’ is hindered by limited knowledge of other disciplines, fragmented communication, and unclear expectations. Although education plays an important role in equipping care providers with the necessary knowledge, skills, and values required for intersectoral collaboration for health, intersectoral approaches in education for primary care providers and community care providers remain scarce. Informal forms of education such as serious gaming hold promise to not only improve knowledge and skills, but also stimulate interaction and collaboration. Serious gaming could thus provide a suitable educational format to support intersectoral collaboration for health by simultaneously educating and connecting primary care providers and community care providers. Objective: This study describes the systematic and iterative development of Game2Connect, a serious game designed to support primary care providers and community care providers in implementing intersectoral collaboration for health through the framework of Positive Health. Methods: Game2Connect was developed using a combination of Intervention Mapping and Developmental Evaluation. First, a needs assessment was conducted through interviews with six care recipients and eighteen primary care providers and community care providers. Then, behavioural outcomes for the serious game were formulated. Next, a game concept was drafted in collaboration with 25 representatives from educational institutions, health organisations and serious game developers, and a prototype was created. Finally, the prototype was iteratively refined through nine pilot sessions with 57 primary care providers and community care providers and 22 other professionals based on observations and user experiences. Results: The final version of Game2Connect consists of two facilitated serious gaming sessions that incorporate video cases, theoretical questions, practical challenges, peer feedback, and goal setting in a team-based board game. Iterative refinements resulted in improvements to game design (e.g. a tailored game board and game cards with scannable QR-codes so that questions can be continuously adapted), content (e.g. clearer and more diverse cases and questions), and user experience (e.g. a simplified goal setting assignment). Participants across pilot sessions reported positive experiences, describing the game as engaging, relevant, and insightful. Interactions between disciplines and subsequent insights into each others’ perspectives were identified as key strengths in support of intersectoral collaboration for health. Conclusions: This study shows how the combination of Intervention Mapping and Developmental Evaluation contributed to the development of education that is both scientifically sound and contextually fitting. Furthermore, it highlights the potential for serious gaming as an engaging and interactive educational approach for care professionals. Future research is needed to evaluate the impact of Game2Connect on behaviour and collaboration in practice, and to explore its implementation in more diverse settings.
Background: Artificial intelligence is increasingly deployed in healthcare workflows, and small open-source language models are gaining attention as viable tools for low-resource settings where cloud...
Background: Artificial intelligence is increasingly deployed in healthcare workflows, and small open-source language models are gaining attention as viable tools for low-resource settings where cloud infrastructure is unavailable. Despite their growing accessibility, the reliability of these models, particularly the stability of their outputs under different phrasings of the same clinical question, remains poorly understood. Objective: This study systematically evaluates prompt sensitivity and answer consistency in small open-source language models on clinical question answering benchmarks, with implications for low-resource healthcare deployment. Methods: Five open-source language models spanning distinct architectural and training paradigms (Phi-3 Mini, Llama 3.2, Gemma 2, Mistral 7B, and Meditron-7B) were evaluated across three clinical question answering datasets (MedQA, MedMCQA, PubMedQA) using five controlled prompt style variations, yielding 15,000 total inference calls conducted locally on consumer CPU hardware without fine-tuning. Consistency scores, accuracy, and instruction-following failure rates were measured and interpreted in the context of each model's training and architectural design. Results: Consistency and accuracy were largely independent across models and datasets. Gemma 2 achieved the highest consistency scores (0.845 to 0.888) but the lowest accuracy (33.0 to 43.5%), producing perfectly consistent yet incorrect answers on 77 of 200 MedQA questions (38.5%), a failure mode termed reliable incorrectness. Llama 3.2 demonstrated moderate consistency (0.774 to 0.807) alongside the highest accuracy (49.0 to 65.0%). Roleplay prompts consistently reduced accuracy across all models and datasets, with Phi-3 Mini showing the largest decline of 21.5 percentage points on MedQA. Instruction-following failure rates varied by model and were not determined by parameter count, with Phi-3 Mini exhibiting the highest UNKNOWN rate at 10.5% on MedQA. Meditron-7B, a domain-pretrained model without instruction tuning, exhibited near-complete instruction-following failure on PubMedQA (99.0% UNKNOWN rate), demonstrating that domain knowledge alone is insufficient for structured clinical question answering. Conclusions: High consistency does not imply correctness in small clinical language models; models can be reliably incorrect, representing a potentially dangerous failure mode in clinical decision support. Roleplay prompt styles may reduce reliability in healthcare AI applications. Among the models evaluated, Llama 3.2 demonstrated the strongest balance of accuracy and reliability for low-resource deployment. These findings highlight the necessity of multidimensional evaluation frameworks that assess consistency, accuracy, and instruction adherence jointly for safe clinical AI deployment.
Background: The aging population has heightened the public health concern posed by emotion-driven health rumors (such as fear and hope). Older adults are particularly vulnerable to such rumors, due to...
Background: The aging population has heightened the public health concern posed by emotion-driven health rumors (such as fear and hope). Older adults are particularly vulnerable to such rumors, due to factors including limited knowledge of e-health literacy (eHL) services and increased sensitivity to health issues. Objective: In this study, we looked at how physical health status and psychological health status differently affect Chinese older adults' willingness to share dread rumors or wish rumors. We also explored whether eHL plays a moderating role in these relationships. Methods: The study used a cross-sectional survey and recruited participants aged 65 and older. The sample was evenly distributed by gender, consisting primarily of individuals with secondary educational attainment, and most participants had no medical background. Results: Results revealed that the willingness to share dread rumors exhibited a significant negative association with physical and psychological health status; an association strengthened by higher levels of eHL. For wish rumors, a significant positive association was identified between health status and the willingness to share rumors; this association was likewise amplified by higher eHL. Conclusions: These findings suggest that physical and psychological health status both influence rumor-sharing willingness in a consistent direction. Meanwhile, eHL acts as a more complex moderator—its effect depends on the type of emotional response that a rumor triggers. These insights add to health communication theory and also point toward more targeted eHL interventions.
Background: Bone invasion in Oral Squamous Cell Carcinoma (OSCC) is a significant prognostic variable whose association with other important histopathological variables like Depth of Invasion (DOI) an...
Background: Bone invasion in Oral Squamous Cell Carcinoma (OSCC) is a significant prognostic variable whose association with other important histopathological variables like Depth of Invasion (DOI) and Worst Pattern of Invasion (WPOI) has not been carefully examined. DOI is implemented in exsisting staging systems, and WPOI is acknowledged to predict tumor aggressiveness. Limited literature has been conducted to determine their collaborative relationship with bone involvement. Understanding these correlations will enhance histopathological assessment, which leads to better clinical decisions. Objective: This study will help to assess the presence of bone invasion in OSCC patients. It will also help in determining DOI and WPOI. Further, a comparative study will be carried out between the occurrence and degree of bone invasion with DOI and WPOI in OSCC patients. Methods: Histopathologically confirmed 80 cases of OSCC, which have been through surgical resection, will be included in this observational study. DOI will be evaluated based on hematoxylin and eosin-stained sections using standardized guidelines. WPOI will be measured in types 1-5 according to the set criteria and will be graded accordingly. Bone invasion will be evaluated histopathologically following decalcification, which will be categorized as either present or absent. Statistical tests will include chi-square analysis and logistic regression to establish the relationship between DOI, WPOI and bone invasion. Results: It is expected that the increased values of DOI and improved grades of WPOI (Types 4 and 5) will have a strong correlation with bone invasion. The cases exhibiting infiltrative patterns of invasion and a deeper depth of the tumor are likely to show more aggressive biological behavior and more chance of bone infiltration. These findings are expected to support the use of DOI, WPOI and bone invasion as predictive markers in OSCC. By March 6, 2026 data of 50 patients are collected, and the remaining 30 patient’s data will be collected during the study. Conclusions: It is anticipated that the study will emphasize the importance of DOI and WPOI evaluation and assessment of bone invasion to increase accuracy and prognosis in OSCC cases. Establishing significant correlations between these parameters can lead to better histopathological assessment and improved treatment planning, leading to better patient outcomes. Clinical Trial: not required
Background: Sarcopenia affects 10%–27% of older adults and is associated with increased morbidity and functional declines. Existing screening tools rely on static assessments and fail to capture con...
Background: Sarcopenia affects 10%–27% of older adults and is associated with increased morbidity and functional declines. Existing screening tools rely on static assessments and fail to capture continuous daily functional behaviors. In this study, we developed an integrated multimodal framework combining wearable-derived physical activity data with clinical features for sarcopenia screening. Objective: This study aimed to develop and evaluate a multimodal deep learning framework integrating wearable-derived physical activity data and clinical features to enable scalable and real-world sarcopenia screening. Methods: One hundred community-dwelling adults aged ≥65 years (50 with sarcopenia, diagnosed according to the AWGS 2019 criteria, and 50 controls without sarcopenia) were recruited in northern Taiwan. The Sarcopenia Dynamic-Static Fusion (SarcFuse) framework integrated 7-day accelerometer-derived physical activity signals with cross-sectional clinical features (demographics, anthropometric measures, body composition, and handgrip strength) using a query-based cross-attention multimodal fusion architecture. Data were split 70/30 for training/testing with five-fold cross-validation. Performance was evaluated using the accuracy, sensitivity, specificity, F1-score, and area under the receiver operating characteristic curve (AUROC), with 95% confidence intervals (CIs). Results: This study included 50 participants with sarcopenia and 50 normal controls, with mean ages of 72.94 ± 4.52 years and 70.74 ± 3.38 years, respectively. Compared with controls, the sarcopenia group showed lower BMI (21.48 vs 23.17 kg/m²), lower upper-limb muscle percentage (10.60% vs 11.30%), higher upper-limb fat percentage (15.18% vs 14.39%), higher trunk muscle percentage (53.71% vs 52.76%), lower trunk fat percentage (50.44% vs 51.92%), and reduced appendicular skeletal muscle mass (ASM) (12.78 vs 15.60 kg) and skeletal muscle mass index (SMI) (5.26 vs 6.22 kg/m²) (all P<.01). For baseline comparison, maximal handgrip strength and SARC-F combined with calf circumference questionnaire (SARC-CalF) achieved AUROCs of 88.89% and 68.06%, respectively. The proposed SarcFuse framework achieved the best overall performance, with an AUROC of 96.06% (95% CI: 94.18%–97.67%), accuracy of 93.20% (95% CI: 91.20%–94.94%), F1-score of 91.31% (95% CI: 88.69%–93.68%), and recall of 89.33% (95% CI: 85.71%–92.72%), with the lower bounds of key performance metrics exceeding those of baseline screening tools. Interpretability analyses further identified clinically meaningful composite indices, including the AWGS 2019 risk index, overall muscle mass index, and fat-muscle balance, as key contributors to model predictions. Conclusions: This study demonstrates the feasibility of a multimodal screening framework for sarcopenia that integrates clinical and wearable-derived data. The proposed framework may support scalable risk stratification and early identification in community and clinical settings, rather than serving as a stand-alone diagnostic tool.
Further multicenter external validation and prospective evaluation are required to confirm generalizability and clinical utility. Clinical Trial: This study was approved by the Institutional Review Board of Taipei Medical University (No. N202204103).
Background: Patients with chronic kidney disease (CKD) on dialysis face complex self-management challenges that generate persistent information needs traditional healthcare fails to address. Retrieval...
Background: Patients with chronic kidney disease (CKD) on dialysis face complex self-management challenges that generate persistent information needs traditional healthcare fails to address. Retrieval-augmented generation (RAG) improves large language model accuracy, but whether personalized RAG outperforms general RAG in reducing these needs remains unclear. Objective: This study assessed whether personalized constraint RAG (C-RAG) more effectively reduces information needs among dialysis patients compared to open-domain general RAG (O-RAG) and standard care, and identified predictors of chatbot engagement. Methods: A two-phase sequential mixed-methods study was conducted. Phase 1 evaluated four large language models (Claude 3.5 Sonnet, Gemini 1.5 Flash, CLOVA X, ChatGPT-4o) using 60 patient-generated questions assessed by 15 clinical experts. Phase 2 was a three-arm parallel pilot randomized controlled trial (October–December 2025) across three tertiary hospitals in Seoul, South Korea. Two chatbot variants were developed using GPT-4o: C-RAG, incorporating patient-specific clinical parameters for personalized responses, and O-RAG, using the same knowledge base without personalization. Participants were randomized 1:1:1 to C-RAG, O-RAG, or standard care. The primary outcome was change in information needs across medication, dietary, and diagnostic domains (5-point Likert scales, baseline and 4 weeks), analyzed using ANCOVA. Semi-structured interviews with nine participants provided mechanistic insights. Results: Phase 1 identified ChatGPT-4o as the optimal model, achieving the highest clinical accuracy (mean 3.60), safety (mean 3.67), and readability (Flesch-Kincaid Grade Level 9.8 vs 13.2–14.5 for other models). In Phase 2, 45 participants were enrolled; 42 completed the study (42/45, 93% retention; 14 per group). ANCOVA revealed significant group differences in medication (F2,38=4.574, P=.017) and dietary information needs (F2,38=4.232, P=.022). Bonferroni-corrected pairwise comparisons showed C-RAG had significantly lower information needs than O-RAG for medication (observed means 2.93 vs 3.86; Cohen d=-0.86, P=.017) and dietary domains (observed means 2.93 vs 3.86; Cohen d=-0.86, P=.018). No significant differences were found between either RAG group and the control group. Secondary outcomes showed no significant group differences. Dietary management was the predominant query topic (205/691, 29.3%). Participants dialyzed for 1–3 years showed higher engagement (9/10, 90% vs 5/18, 27.8%; Fisher exact P=.006). Qualitative analysis identified five themes centered on disease-specific access, dietary management as the primary need, and dialysis duration as a moderator of utility. Conclusions: Personalized RAG significantly outperforms general RAG with large effect sizes (Cohen d=-0.86) substantially exceeding digital health benchmarks. Medium-to-large effects against standard care (Cohen d=-0.54 to -0.64) support a fully powered confirmatory trial, with an estimated 50–55 participants per group needed to evaluate long-term clinical outcomes. Clinical Trial: KCT0011656; https://cris.nih.go.kr/cris/search/detailSearch.do?seq=32626
Background: Unplanned extubation (UEX) in the intensive care unit (ICU) is a serious adverse event that threatens patient safety. Current prevention strategies rely primarily on nurse surveillance and...
Background: Unplanned extubation (UEX) in the intensive care unit (ICU) is a serious adverse event that threatens patient safety. Current prevention strategies rely primarily on nurse surveillance and physical restraints—both of which have inherent limitations, including the inability to provide continuous monitoring and potential conflicts with patient-centered care principles. Computer vision technology may offer a noncontact monitoring approach to support nursing practice. Objective: This study aimed to develop and preliminarily evaluate a computer vision–based monitoring prototype designed to assist ICU nurses in preventing UEX by detecting patient hand proximity to preset high-risk zones, with particular emphasis on a guard mode that detects protective gear removal. Methods: Following a design science research approach, we built a monitoring system using the open-source MediaPipe library. The system comprised four components: (1) patient tracking with a three-level detection mechanism, (2) hand detection and risk assessment with tiered alerting, (3) dynamic risk zone management, and (4) a PyQt5-based user interface. A guard mode was implemented to specifically monitor for protective gear removal—a critical feature supporting stepwise physical restraint reduction. Laboratory simulations were conducted under various occlusion scenarios. Five ICU nurses participated in usability assessments using a 5-point Likert scale. Performance metrics included tracking accuracy, hand detection coverage, risk judgment accuracy, false alarm rate, and alert response time. Results: Across 120 simulated test scenarios, target localization accuracy reached 92.3%, and dynamic risk zone adaptation accuracy was 90.0%. Hand recognition coverage was 95.8% across 800 frames. Two-level risk judgment accuracy was 93.3%, with the false alarm rate reduced to 2.8%. The average alert response time was 0.4 seconds. In guard mode, the system successfully distinguished between gloved hands (protective gear) and bare hands, triggering dedicated alerts upon detection of gear removal. Nurses rated the system’s usability at 4.2 out of 5. Figures are provided to illustrate the system interface and the visual differences in hand monitoring between normal and guard modes. Conclusions: This study describes the development and preliminary evaluation of a computer vision–based system for UEX prevention. Our findings suggest the system is technically feasible for supporting nursing surveillance. The guard mode, in particular, offers a novel approach to supporting gradual restraint reduction—a key priority in patient-centered care. Further clinical validation is needed to assess its effectiveness in practice settings. Clinical Trial: None
Background: Taiwan has the world’s highest prevalence of hemodialysis (HD), with arteriovenous fistula (AVF) stenosis representing a critical complication affecting dialysis adequacy and patient out...
Background: Taiwan has the world’s highest prevalence of hemodialysis (HD), with arteriovenous fistula (AVF) stenosis representing a critical complication affecting dialysis adequacy and patient outcomes. Traditional AVF monitoring relies on clinical expertise and subjective assessment, potentially delaying stenosis detection. Objective: This study aimed to evaluate electronic stethoscope monitoring for early AVF stenosis detection compared to traditional nursing assessment, and identify risk factors associated with AVF stenosis in HD patients. Methods: We conducted a 3-month prospective study at National Taiwan University Hospital Yunlin Branch Hemodialysis Center from March to June 2023. Thirty adult HD patients with AVF (>3 months dialysis vintage) were enrolled. AVF sounds were recorded 2-6 times weekly at two locations: 3cm proximal to the anastomosis and the arterial needle insertion site. Sound signals were converted to decibel (dB) measurements using short-time Fourier transform. Clinical and demographic data were collected including monthly albumin levels, blood pressure (BP), dialysis parameters, and medical history. Chi-square tests, t-tests, and logistic regression analysis identified stenosis risk factors. Results: Among 30 patients generating 1,462 AVF sound recordings, abnormal AVF sound was defined as <-28.59 dB using three-standard-deviation analysis. One patient developed clinically apparent stenosis during the study period. Retrospective analysis revealed electronic stethoscope detected stenosis one month before clinical diagnosis. Logistic regression identified significant risk factors for stenosis (percutaneous transluminal angioplasties (PTA) within 3 years): diabetes mellitus (OR=18.67, p=0.005), hypertension (p=0.019), body mass index ≤25 (p=0.006), higher mean systolic BP (OR=1.82 per 10mmHg, p=0.018), FX dialyzer type (OR=5.25, p=0.048), and greater number of prior PTAs (OR=2.47, p=0.003). Conclusions: Electronic stethoscope monitoring demonstrated potential for earlier AVF stenosis detection compared to traditional clinical assessment. The identified risk factors—including a novel association with FX dialyzer use—can guide targeted surveillance protocols. Implementing objective acoustic monitoring may improve timely intervention and AVF longevity in high-risk HD patients. Clinical Trial: The study protocol was approved by the Institutional Review Board of NTUH (IRB No. 202302099RINA) and registered at ClinicalTrials.gov (NCT07436559).
Background: With the rise of digital health, mental health applications (MHapps) have become increasingly popular to support self-management of depression, anxiety, and stress. Star-ratings are often...
Background: With the rise of digital health, mental health applications (MHapps) have become increasingly popular to support self-management of depression, anxiety, and stress. Star-ratings are often used as indicators of app quality. However, they do not fully capture if the MHapp reflects real-life user experiences. Instead, examining the patterns of engagement and app usage can highlight whether high ratings align with diverse user needs. Objective: This study analyzes the user experience of top-rated depression and anxiety apps and assesses user perceptions with apps that have higher star ratings based on user reviews. Methods: To identify relevant mobile applications, we used a systematic search strategy that mimicked a user’s journey and identified the five most highly rated MHapps with approximately 750 user reviews. Using a hybrid coding technique informed by prior literature, we analyzed reviews to identify emerging themes that explained user experiences. Results: User experiences reflected a complex interplay between perceived benefits and frustration. While users valued conveniences and accessibility to mental health resources, they raised significant concerns and criticism focused on interface design, limited features, functionality, and technical support. Additionally, pricing structure, subscription gatekeeping and privacy concerns followed initial use satisfaction among users. Conclusions: App usability elicited paradoxical responses and showed multiple overlapping themes showcasing features that may support some users, can hinder others. This study highlights the complexity of user perceptions influencing experience with self-management MHapps. It emphasizes that star ratings are not sufficient to reflect if a MHapp meets user needs, and continued improvement in app design is needed to meet the diverse needs of users for better engagement and satisfaction. Clinical Trial: NA
Background: Simulation-based education is increasingly used in medical training to safely develop clinical skills. However, the perceived relevance of a simulation (“utility value”) to a learner...
Background: Simulation-based education is increasingly used in medical training to safely develop clinical skills. However, the perceived relevance of a simulation (“utility value”) to a learner’s future work may influence its effectiveness. Objective: This study evaluated a digital radiology simulation module for medical technical assistant (MTA) trainees in Germany, examining learning gains and the moderating role of utility value. Methods: Fifty German MTA-trainees (specializing in radiology or laboratory medicine) completed a pretest and posttest requiring them to arrange 12 steps of a contrast-injection procedure in correct order. All participants then engaged in an interactive computer simulation of the procedure. After the simulation, trainees rated their motivation, self-efficacy, and perceived utility value. Results: Trainees showed significant improvement in procedural sequencing from pretest to posttest (p < .001), indicating effective learning. Radiology-specialized trainees reported higher utility value for the training than lab-focused trainees (p < .001), though no group differences emerged in motivation or self-efficacy. Importantly, perceived utility value moderated learning gains: trainees who saw the simulation as more useful achieved greater improvements. Conclusions: The findings support expectancy-value theory in a simulation context. Incorporating personal relevance and aligning simulations with learners’ specializations may enhance training outcomes. The study highlights theoretical implications for motivation (utility value’s role) and practical implications for designing targeted simulations, while acknowledging limitations such as sample size and domain specificity.
Background: The rise of online data collection in psychological and health research has been accompanied by an increase in fraudulent participation, threatening data quality and research integrity....
Background: The rise of online data collection in psychological and health research has been accompanied by an increase in fraudulent participation, threatening data quality and research integrity.
Method: We conducted semi-structured interviews and an open-ended questionnaire with six research teams across the UK who encountered fraudulent participants while conducting online studies across diverse populations (children aged 10+, adolescents, mid- older-aged adults aged 55 – 75, and general adult populations). Studies employed multiple methodologies, including online surveys, ecological momentary assessment (EMA), qualitative online interviews, and online diaries. Teams were selected to represent a range of methodological contexts; findings are intended as an exploratory contribution to awareness and discussion rather than a comprehensive account of fraud experiences across online research.
Results: Reported fraudulent participation rates ranged from 1% to 89%, with social media recruitment platforms (particularly Facebook and X/Twitter) being most vulnerable. Analysis revealed that the manifestation of fraud varied by study design and population, with compensation-offering studies experiencing higher rates. Standard detection methods employed by studies included IP address inspection, address verification, demographic consistency checks, and interviewer intuition for qualitative studies. Teams reported significant impacts on research resources, study timelines (including a 2-month study pause in one case), and the decision-making process in ethics applications. In addition, teams also developed preventive strategies through experience.
Conclusion: This paper presents an exploratory, experience-based framework intended to raise awareness of online research fraud and support researchers in thinking through context-appropriate detection and prevention strategies. Findings suggest that one-size-fits-all solutions are inadequate and that approaches need to be tailored to study design, recruitment methods, and resource constraints. We recommend that fraud mitigation planning be considered a standard requirement in research funding applications.
Background: Informal caregivers of individuals with Alzheimer disease and related dementias (ADRD) face significant psychosocial and physiological burdens. While digital health interventions based on...
Background: Informal caregivers of individuals with Alzheimer disease and related dementias (ADRD) face significant psychosocial and physiological burdens. While digital health interventions based on lifestyle medicine offer scalable support, the structural integration of these technologies across specific clinical pillars remains unclear. Objective: The objective of this scoping review was to map the digital health landscape for ADRD caregivers (2016–2025) to evaluate the distribution of technological modalities across 6 lifestyle medicine pillars and identify critical informatics gaps. Methods: A total of 131 eligible US-based studies were identified from PubMed, CINAHL, and Web of Science. A hybrid extraction methodology combined manual full-text contextual validation with quantitative text mining. Structural relationships among clinical pillars, digital modalities, and research methodologies were mapped and quantified using the Jaccard coefficient via KH Coder relational network analysis. Results: The field is steadily transitioning toward active, data-driven interventions, supported by a robust cluster of randomized controlled trials (n=41). Network analysis revealed a highly concentrated “Psychosocial Core," heavily reliant on legacy web/online and telehealth platforms to address stress management (n=102) and social connection (n=35). Conversely, a severe "Biobehavioral Gap" exists. Physiological pillars such as nutrition (n=2) remain in an "Informatics Desert" with zero connectivity to advanced modalities (eg, artificial intelligence [AI] and sensing, virtual reality [VR] and games). While total studies addressing physical activity (n=11) and sleep (n=12) remain low, a nascent sensing frontier shows early promise, with emerging AI applications beginning to target objective biometric monitoring. Conclusions: While digital psychosocial support for ADRD caregivers has been extensively researched, holistic physiological care remains technologically underserved. To evolve from fragmented support to unified digital therapeutics, next-generation platforms must purposefully integrate AI and wearable sensing to bridge these biobehavioral informatics deserts, creating cohesive, multi-pillar care ecosystems. Clinical Trial: The review protocol was prospectively registered with the Open Science Framework on March 31, 2026 (https://osf.io/ksvp4)
Background: Overdose and suicide deaths due to non-prescribed fentanyl have increased significantly. Fortunately, there are treatments available that could reduce the risk of death. However, for healt...
Background: Overdose and suicide deaths due to non-prescribed fentanyl have increased significantly. Fortunately, there are treatments available that could reduce the risk of death. However, for healthcare systems to implement programs addressing the needs of patients who use non-prescribed fentanyl, they must first be able to identify them. International Classification of Diseases (ICD) codes are not a good option for identifying patients who use non-prescribed fentanyl for two reasons. First, opioid use disorder (OUD) diagnoses are not always coded. Second, when they are coded, ICD codes for OUD do not specify whether fentanyl is being used rather than other, less lethal opioids. Objective: To develop natural language processing (NLP) approaches to identifying current non-prescribed fentanyl use in electronic health record (EHR) documentation. Methods: This retrospective cross-sectional study included Veterans Health Administration (VHA) patients seen in the VHA between 4/5/23 and 12/23/24. A term list was developed to identify fentanyl-related mentions in clinical text, and 250-character snippets surrounding identified mentions were extracted. Veterans (n=3,878) were randomly sampled from five predefined groups based on the presence of one of four terms (fentanyl, fent, blues, tranq) in their EHR documentation. Physician annotators classified snippets into “non-prescribed fentanyl use,” “prescribed fentanyl use,” or “other.” Labeled data was used to train, validate, and compare the ability of penalized logistic regression, Bio-Clinical BERT, LLaMA 3 8B, and Mistral 7B to classify snippets. Model performance was evaluated using weighted average precision, recall, and F1 scores. Results: Among 7,389 snippets, 9.6% were “non-prescribed fentanyl use,” 40.3% were “prescribed fentanyl use,” and 50% were “other.” LLaMA 3 8B achieved the highest weighted average F1 score (0.954) outperformed penalized logistic regression with a small statistically significant difference, while differences between Mistral 7B and LLaMA 3 8B were not statistically significant. Top discriminative terms included “consumption,” “purchased,” and “streets” for non prescribed fentanyl use; “dressing,” “patch(es),” and “1500” for prescribed; and “laced,” “testing,” and “fears” for other fentanyl use. Conclusions: NLP can accurately identify non-prescribed fentanyl use in EHR documentation. This approach may support risk prediction and targeting of interventions to patients exposed to non-prescribed fentanyl.
Background: Digital technology in health and social care can improve the well-being of people with long-term health conditions, but prior research has identified factors that hinder its adoption, part...
Background: Digital technology in health and social care can improve the well-being of people with long-term health conditions, but prior research has identified factors that hinder its adoption, particularly accessibility issues and challenges to its integration into everyday life. It is therefore important to fully understand both the facilitators and hindrances to the adoption of such technology. Objective: To explore the facilitators and hindrances to the adoption of digital technology in the everyday lives of adults with long-term health conditions. Methods: This scoping review systematically mapped relevant research following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). Thematic analysis was used to critically analyze the identified articles. Results: Forty-six articles were selected that examined 5,018 adults aged 18 and over. Six themes were identified: personal characteristics and preconditions; perceived usefulness in everyday life; design and technical functionality; support and guidance; human interaction; and integrity and trustworthiness. Conclusions: The findings are discussed in relation to the key constructs of the Unified Theory of Acceptance and Use of Technology (UTAUT): performance expectancy, effort expectancy, social influence, and facilitating conditions. Digital technologies support the daily lives of adults with long-term health conditions, but several challenges remain, including functionality that is not adapted to specific diseases or ages and a perceived lack of human interaction. Thus, digital technology is not a one-size-fits-all solution but should be an adaptable tool that accommodates individual preferences and contexts and that complements in-person human interactions.
Background: Healthcare organizations increasingly rely on complex digital systems, but software onboarding often depends on manuals and classroom-based training that do not fit well with fast-paced cl...
Background: Healthcare organizations increasingly rely on complex digital systems, but software onboarding often depends on manuals and classroom-based training that do not fit well with fast-paced clinical workflows. Interactive in-app guidance may better support learning during real work, although healthcare-specific evidence is still limited. Objective: To synthesize evidence on effective onboarding mechanisms for healthcare software and to explore how interactive in-app guidance compares with traditional onboarding in terms of perceived learning support, cognitive burden, and adoption-related outcomes. Methods: The study used a sequential design with two components:
(1) a systematic literature review following Kitchenham’s procedures; and
(2) a mixed-methods survey administered via Qualtrics to healthcare professionals (n = 44), complemented by a small screened subsample of IT professionals with healthcare DAP implementation experience (n = 5).
Quantitative data were analysed descriptively, and qualitative responses were examined through thematic analysis to explain and contextualize the observed patterns. Results: The findings from both the literature review and the survey showed a consistent pattern: workflow-embedded onboarding approaches, including hands-on practice, stepwise contextual guidance, and searchable in-app support, were perceived to reduce learning friction and cognitive effort while improving confidence. Among healthcare respondents, 61% reported greater willingness to use the software after onboarding. Continued use was mainly associated with remembering how to use features, interface usability, workflow efficiency, and perceived impact on patient care. IT respondents highlighted implementation constraints related to integration, analytics, and compliance, but also perceived reductions in support burden. Conclusions: Interactive, context-sensitive onboarding appears to be a practical strategy to support healthcare software adoption, especially because it aligns learning with real workflows. The findings support the use of workflow-embedded guidance to improve usability in context and user confidence during onboarding, while also indicating the need for stronger healthcare-specific, outcome-based evaluations of DAP-enabled approaches.
Background: Sepsis is a major global cause of morbidity and mortality, and early, accurate severity assessment is essential to improve outcomes among older adults. Traditional diagnostic tools often f...
Background: Sepsis is a major global cause of morbidity and mortality, and early, accurate severity assessment is essential to improve outcomes among older adults. Traditional diagnostic tools often fail to capture the syndrome’s complexity, while machine learning (ML) can exploit routinely collected clinical and laboratory data for more precise classification. Objective: This study aims to develop and validate an explainable machine learning pipeline for early detection and severity stratification of sepsis in geriatric emergency care using routinely available admission laboratory and clinical data. Methods: The analysis was conducted on a large Italian hospital dataset including patients with and without sepsis and six ML algorithms using nested cross-validation were compared. Recursive Feature Elimination was applied for feature selection, and model interpretability was examined through SHAP values. Results: Ensemble methods, particularly XGBoost and Random Forest, showed the best performance in distinguishing sepsis from non-sepsis cases and provided solid severity stratification using only 10–20 features with an accuracy of 76.7%. Key predictors involved hepatic, renal, and immune markers, with Random Forest uniquely identifying absolute neutrophil count and total bilirubin as strong indicators of severe sepsis Conclusions: These findings highlight the potential of tree-based ML models for clinically interpretable, real-time sepsis risk stratification. Clinical Trial: Data for this study were collected as part of the ReportAGE project at IRCCS INRCA (Ancona, Italy), approved by the IRCCS INRCA Ethics Committee (reference CEINRCA-20008) and registered on ClinicalTrials.gov (reference NCT04348396).
Background: The COVID-19 pandemic revealed substantial disparities in health outcomes across U.S. communities. High-resolution mobility data offer new opportunities for real-time surveillance of behav...
Background: The COVID-19 pandemic revealed substantial disparities in health outcomes across U.S. communities. High-resolution mobility data offer new opportunities for real-time surveillance of behavioral responses, yet less is known about how precautionary behavior varied within cities and how these differences were associated with subsequent COVID-19 hospitalization risk. Objective: To assess how community-level risk shapes heterogeneity in precautionary mobility behavior within cities and whether these differences are associated with subsequent COVID-19 hospitalization risk. Methods: We analyzed anonymized mobile-device data from 824 ZIP codes in the 20 most populous counties in Texas and COVID-19 hospitalization records from 620 ZIP codes (February–July 2020). We constructed a ZIP code–level measure of precautionary mobility reduction (PMR) and linked it to a composite community risk score derived from CDC social vulnerability indicators. Generalized additive mixed models estimated associations among community risk, mobility patterns, and weekly hospitalization rates, adjusting for epidemic timing, population size, and spatial clustering. Results: Mobility declined following statewide stay-at-home orders but varied substantially across ZIP codes. On April 2, 2020, PMR was negatively associated with community risk (slope −0.16; 95% CI −0.19 to −0.13; p<.001). At peak divergence, high-risk ZIP codes reduced mobility by 12–13 percentage points less than low-risk ZIP codes (95% CI 11–15). Greater PMR was associated with lower subsequent hospitalization risk (IRR=0.41; 95% CI=0.31–0.53), whereas higher community risk was associated with increased hospitalization risk (IRR=3.03; 95% CI=2.57–3.58). The protective association of mobility reduction was attenuated in higher-risk communities (interaction IRR=2.79; 95% CI=2.08–3.73). Conclusions: Mobility data can support real-time surveillance of behavioral responses during epidemics. Within-city differences in mobility reduction were associated with subsequent hospitalization risk, but their protective effect was weaker in higher-risk communities. Integrating digital mobility data with community risk metrics may improve targeted and equitable public health responses.
Background: Large language models (LLMs) can generate fluent summaries of longitudinal medical records, but in high-stakes clinical settings, verification burden remains a barrier to trust. Existing p...
Background: Large language models (LLMs) can generate fluent summaries of longitudinal medical records, but in high-stakes clinical settings, verification burden remains a barrier to trust. Existing provenance mechanisms, such as document-level citations and section references, often require manual search within long, fragmented notes, limiting their usefulness during time-constrained workflows for clinicians. Objective: To design and evaluate a sentence-level provenance interface (“click-to-inspect”) that enables rapid verification of AI-generated longitudinal medical record summaries at the level of individual statements. Methods: We developed and tested a web-based interface in which every sentence in an AI-generated longitudinal patient summary is clickable and linked to a semantically matched source sentence in the originating clinical note. Clicking a sentence opens the source note in a side-by-side view, scrolls to the matched passage, and highlights it in context. Formative usability testing was conducted with 46 clinician interactions using synthetic longitudinal patient charts. Participants included medical students, residents, and attending physicians across multiple specialties including internal medicine, dermatology, radiology, plastic surgery, anesthesiology, interventional radiology, obstetrics-gynecology, and family medicine. Usability was assessed using the System Usability Scale (SUS) and Net Promoter Score (NPS), alongside qualitative feedback. Results: Clinicians reported high usability (mean SUS score 86.25, SD 7.77; 95% CI 83.96–88.54) and a positive overall experience (NPS 35; 22/46 promoters, 18/46 passives, 6/46 detractors). Participants described rapid access to supporting evidence as critical for trust calibration during first-pass chart review. Qualitative feedback identified friction in traditional citation-based interfaces and supported sentence-level inspectability as a low-friction verification mechanism. Conclusions: Sentence-level provenance transforms AI-generated summaries from static narratives into interactive verification tools. An approach that enables rapid, selective inspection of individual claims during longitudinal chart review, may reduce verification burden and support calibrated reliance in high-risk clinical contexts. Clinical Trial: NA
Background: During the COVID-19 pandemic, rapid identification and reporting of laboratory-confirmed cases were essential for effective disease surveillance and response. However, the surge in testing...
Background: During the COVID-19 pandemic, rapid identification and reporting of laboratory-confirmed cases were essential for effective disease surveillance and response. However, the surge in testing, particularly from non-traditional settings, led many facilities to rely on fax-based reporting, creating delays and data quality challenges for public health agencies. In response, the Arkansas Department of Health developed a web-based reporting portal to streamline submissions and reduce reporting burden; the impact of this transition on data timeliness and completeness remains understudied. Objective: This study aimed to assess the impact of transitioning to a rapidly developed electronic lab reporting portal on the timeliness and completeness of COVID-19 lab report data. Methods: We used the Arkansas Department of Health (ADH) lab reporting data (2021-2023) to assess changes in timeliness and completeness of COVID-19 lab report data for the testing facilities that transitioned from faxing to the portal from pre- to post-implementation. For timeliness, we measured the time between lab draw and ADH data receipt; for completeness, we calculated missingness of seven information fields. Results: We found reductions in the time to data receipt for positive (5.0 vs. 13.1 days; p<0.0001) and negative (6.9 vs. 15.1 days; p<0.0001) COVID-19 test results. Additionally, we found improvements in completeness after portal implementation for positive (88.3% vs. 71.6%; p<0.0001) and negative (90.7% vs. 61.5%; p<0.0001) results. Conclusions: The portal implementation by ADH improved the quality of COVID-19 lab data, thus, strengthening the disease surveillance efforts during the pandemic and future research studies.
Background: Antibiotic consumption is a key driver of antimicrobial resistant (AMR) infections, and the majority of usage is outside of hospital settings in low- and middle-income countries (LMICs), b...
Background: Antibiotic consumption is a key driver of antimicrobial resistant (AMR) infections, and the majority of usage is outside of hospital settings in low- and middle-income countries (LMICs), but we know remarkably little about usage patterns and drivers. The lack of reliable data poses challenges for establishing targets, understanding AMR burden and designing and evaluating interventions. Objective: This study aimed to design and develop an AI-based chatbot for the collection of community antibiotic usage data in Thailand. Methods: The study employed a proof-of-concept design, developing CHATSCi-AMI with GPT-4o-mini on the LINE platform. Internal testing involved 35 MORU staff, using a chatbot pipeline (Dify.ai, ChatBean, REDCap) for demographic, medication, and knowledge data collection. Web scraping with BeautifulSoup and AI summarisation structured data, with a simulated dataset (50 users, 12 weeks) analysed for misuse patterns (e.g., 64.4% inappropriate use). Results: CHATSCi-AMI successfully integrated with LINE, achieving stable functionality in testing, with rich menus and image recognition for antibiotic identification. The simulated dataset revealed 64.4% inappropriate antibiotic use (95% CI: 50.2–78.6%, p<0.01), highlighting misuse trends. User feedback (n=35) indicated high satisfaction (80% rated usability ≥4/5), though technical errors occurred in 5% of interactions. Conclusions: CHATSCi-AMI demonstrates feasibility for scalable AMR surveillance in LMICs, offering a novel approach with longitudinal data collection and AI structuring. Future work post-OxTREC approval should focus on real-world data and enhanced privacy measures to support public health policy and individual monitoring.
Background: The proliferation of AI-driven voice chatbots necessitates updated methods for usability assessment. While the Speech User Interface Service Quality (SUISQ) questionnaire is a comprehensiv...
Background: The proliferation of AI-driven voice chatbots necessitates updated methods for usability assessment. While the Speech User Interface Service Quality (SUISQ) questionnaire is a comprehensive tool, its original structure may not capture the nuances of modern speech bots. Objective: This pilot study details an exploratory evaluation of the SUISQ and the subsequent development of a reduced version of SUISQ for Voice Interfaces (SUISQ-RVI). Methods: Using a dataset (N=48) from a simulated speech bot interaction, we conducted descriptive and exploratory data analyses on the full SUISQ and its previously reduced versions (SUISQ-R and SUISQ-MR). Results: Preliminary results indicate that the original four-factor structure may not be optimal for conversational AI contexts. Guided by our exploratory factor analysis, we reduced the original 25-item instrument to a 17-item scale demonstrating a five-factor structure. In this pilot sample, the SUISQ-RVI outperformed previous reduced versions by explaining more variance with better model fit and reliability. Conclusions: These initial findings suggest the SUISQ-RVI is a promising, streamlined tool for evaluating modern voice interfaces, though further confirmatory validation with larger cohorts is warranted. Clinical Trial: NOT APPLICABLE
Background: Although concern about low-value care (LVC) practices has grown in recent years, interventions relying solely on informational or educational strategies have not proven effective in reduci...
Background: Although concern about low-value care (LVC) practices has grown in recent years, interventions relying solely on informational or educational strategies have not proven effective in reducing them. This suggests a need to involve professionals and/or patients in collaborative decision-making processes and change strategy design. This study builds on this premise using the Fogg Behavior Model, which posits that a behavior can only occur when motivation, ability, and a prompt converge at the same time. Objective: he aim of this study is to develop a guide to designing, implementing and evaluating interventions that reduce LVC practices. We present a specific case study involving the deprescribing of benzodiazepines in primary care and use it as an example of the process to be followed to reduce other practices of this kind. Methods: This study was conducted in two primary care centers in Catalonia, Spain. A total of 31 professionals (physicians and nurses) took part in focus groups employing three techniques from the Fogg Behavior Model: Swarm of Behaviors, Focus Mapping, and Golden Behaviors. Through these techniques, we worked with participants to compile a set of actions for implementation. These actions were tailored to the conditions and capacities of their health centers and were assessed by the participants as feasible and effective in reducing benzodiazepine prescribing. Results: Based on this practical experience, we developed our ten-step BeProGuide, which outlines a series of tasks that we recommend completing in any project aimed at reducing LVC practices. This is presented as a detailed checklist to support informed decision-making. Conclusions: Our research operationalizes the Fogg Behavior Model by setting out a concrete, replicable procedure for reducing LVC clinical practices. In doing so, it transforms this conceptual framework into an actionable methodological tool, BeProGuide, which takes the form of a step-by-step guide and detailed checklist. This guide is not only applicable in health and medicine, but can be used in other fields such as education, work and organizations, and environmental protection.
Background: The relationship between female physical performance and menstrual cycle (MC) phases is widely discussed in applied sports science and clinical kinesiology. However, current evidence remai...
Background: The relationship between female physical performance and menstrual cycle (MC) phases is widely discussed in applied sports science and clinical kinesiology. However, current evidence remains inconsistent, particularly regarding muscle strength and biomechanical risk factors for injury. Objective: This study aims to examine the relationship between MC phases and lower limb injury risk factors, with a particular focus on the hamstrings-to-quadriceps strength ratio (H/Q ratio). Methods: Following an initial session, participants will monitor their MC over 3 months using a mobile application, urinary ovulation tests, and self-reported symptoms, while also recording perceived readiness for physical activity. Each participant will then complete two laboratory assessments: the early follicular phase (days 1–3 of menstrual bleeding) and the peri-ovulatory phase. Testing will include isokinetic strength assessment, evaluation of muscle–tendon mechanical properties, and body composition analysis. Results: Recruitment will begin in June 2026. A total of 32 participants will be enrolled in two waves (June–July 2026 and June–July 2027). Preliminary results are expected by 06/2028. Conclusions: This study may improve understanding of MC–related changes in neuromuscular function and support individualized, non-invasive approaches to training and injury prevention in women. Associating objectively estimated MC phases with a mobile application, urinary ovulation tests and subjective perceptions of performance will provide insights into the agreement between perceived and physiological indicators of performance readiness. Clinical Trial: NCT07462286
Background: Asymptomatic bacteriuria (ASB) has been associated with preterm birth and pyelonephritis, and until recently, screening for ASB was recommended for all women in first trimester of pregnanc...
Background: Asymptomatic bacteriuria (ASB) has been associated with preterm birth and pyelonephritis, and until recently, screening for ASB was recommended for all women in first trimester of pregnancy. Recent divergence between NICE guidelines (which do not recommend routine screening) and NHS initiatives such as the Saving Babies’ Lives Care Bundle (which recommends screening in high-risk women) may have led to inconsistencies in UK clinical practice. Objective: To describe the protocol for a national multicentre study evaluating variation in ASB screening practices across UK maternity units and examining associated maternal and neonatal outcomes. Methods: The TU(L)IPS study is a national, multicentre observational study conducted through the UK Audit and Research Collaborative in Obstetrics and Gynaecology (UKARCOG) network. The study comprises two components: (1) a structured survey of UK maternity units to characterise local ASB screening guidelines and practices, and (2) a retrospective cohort study using routinely collected clinical data to assess adherence to local and national guidance and to describe maternal and neonatal outcomes. Participating sites contribute data via a standardised REDCap-based data collection platform using predefined variables and harmonised outcome definitions. Analyses will be descriptive, including frequencies and proportions, with comparisons stratified by screening status, urine culture result categories, and preterm birth risk groups. Results: The study was launched nationally in January 2025. Recruitment closed with 28 NHS Trusts participating across the United Kingdom. Stage 1 data collection was completed in December 2025. Stage 2 data collection is scheduled to conclude in March 2026, with the database lock planned for March 2026. Data cleaning and analysis will commence following the database lock. The anticipated study completion date is May 2026. Conclusions: This study will provide a comprehensive national evaluation of variation in ASB screening practices and associated outcomes in pregnancy. By using a standardised, protocolised approach across multiple sites, the TU(L)IPS study aims to generate contemporary evidence to inform clinical practice and national policy.
Background: Renal fibrosis is the final common pathway of chronic kidney disease progression and a critical histological predictor of renal function decline and allograft failure. Routine clinical mon...
Background: Renal fibrosis is the final common pathway of chronic kidney disease progression and a critical histological predictor of renal function decline and allograft failure. Routine clinical monitoring with estimated glomerular filtration rate (eGFR) and albuminuria is insensitive to the development of fibrosis. Renal biopsy is invasive, which limits repeated assessment and suffers from sampling bias. Consequently, non-invasive imaging biomarkers that can accurately quantify fibrosis, monitor disease progression, and predict outcomes are highly desirable.
Multiparametric magnetic resonance imaging (mpMRI) offers extensive characterization of renal structural and functional properties. Previous work has indicated that MRI measures are associated with fibrosis development and with declining renal function. However, there remains a sparsity of longitudinal data and comprehensive validation of MRI measures against histology and measured glomerular filtration rate (mGFR). Objective: Our ongoing longitudinal study aims to validate mpMRI against reference standard kidney biopsy in kidney transplant recipients (KTR) and to compare the time-dependent trajectories of imaging and functional markers across living kidney donors (LKD), KTR and healthy control (HC) cohorts to assess their prognostic value for mGFR decline. Methods: Participants: 32 living kidney donors (LKD), 32 KTR, and 32 healthy controls (HC).
Inclusion criteria: LKD and KTR who have been approved for transplant. HC must show no evidence of renal disease and have normal blood pressure.
Exclusion criteria: Contraindications to MRI or severe claustrophobia.
Time points: Baseline investigations prior to transplant surgery, with follow-up assessments at 3-, 12-, and 24-months post-transplant.
Data collection:
mpMRI is performed at 3T. The MRI protocol includes structural T1-weighted and T2-weighted imaging, T1 mapping, T2 mapping, T2* mapping, DWI, pseudo-continuous ASL, non-contrast-enhanced angiography, and phase-contrast measurement of renal artery flow.
Glomerular filtration rate will be measured by [99mTc]Tc-DTPA clearance.
A baseline allograft biopsy is performed in all KTR during the transplantation surgery. Subsequent protocol biopsies are planned at 3, 12, and 24 months in KTR. Extent of fibrosis is quantified using quantitative stereology.
Primary outcome:
Longitudinal association between quantitative MRI measures and histologically determined renal fibrosis.
Secondary outcomes:
Longitudinal divergence of MRI and functional markers across cohorts;
diagnostic performance for fibrosis; and predictive value for mGFR decline in KTR and LKD.
Linear mixed models will be used to study longitudinal associations. Receiver operating characteristic curve analysis will assess diagnostic performance. Results: Recruitment for the MPRENAL study commenced in November 2024. As of March 2026, enrolment is ongoing with 42 participants recruited. Full data analysis and results are projected for December 2029. Conclusions: Successful completion of this study is expected to provide robust histological validation of mpMRI and establish its utility as a non-invasive tool for monitoring renal health. Clinical Trial: ClinicalTrials.gov NCT06210555;
https://clinicaltrials.gov/ct2/show/NCT06210555
Background: Socially assistive robots are increasingly being explored in health care, but evidence from real pediatric outpatient settings remains limited. In particular, little is known about how con...
Background: Socially assistive robots are increasingly being explored in health care, but evidence from real pediatric outpatient settings remains limited. In particular, little is known about how contextual support, human facilitation, and organizational conditions shape everyday human-robot interaction and practical acceptance in hospital environments Objective: This study aimed to examine how visitor engagement with a social robot differed across 3 implementation conditions in a pediatric outpatient hospital, how engagement differed between children and adults, and which socio-technical factors staff identified as enabling or constraining implementation. Methods: This convergent mixed methods pilot study was conducted at Tallinn Children’s Hospital. The quantitative strand consisted of structured behavioral observation across 3 field conditions, baseline, poster support, and staff facilitation, in which 675 visitors were recorded. The qualitative strand consisted of 4 small staff-group interviews with 12 health care professionals focused on use opportunities, barriers, and implementation needs. Quantitative data were analyzed descriptively, and qualitative data were analyzed using qualitative content analysis with deductive and inductive coding. Results: Observable engagement increased across the 3 conditions, from 3.1% in the baseline phase to 22.6% with poster support and 38.1% with staff facilitation. Children engaged more often than adults across all phases. Staff reported that acceptance depended less on novelty alone than on role clarity, visible usefulness, multilingual guidance, workflow fit, troubleshooting support, and clear organizational ownership. The robot was perceived as most useful for wayfinding, reducing uncertainty, engaging children during waiting, and supporting a calmer outpatient atmosphere. Conclusions: The findings support a socio-technical view of robot acceptance in pediatric health care. Meaningful uptake emerged through the interaction of robot affordances, local mediation, and organizational embedding rather than through technology alone. Public-facing, narrowly scoped functions, such as wayfinding, waiting-time support, parent guidance, and simple procedural information, appear to be the most feasible early use cases for social robots in pediatric hospitals.
Background: Children with inattentive attention-deficit/hyperactivity disorder (ADHD) often present with impairments in executive functions and fine motor skills in addition to core inattentive sympto...
Background: Children with inattentive attention-deficit/hyperactivity disorder (ADHD) often present with impairments in executive functions and fine motor skills in addition to core inattentive symptoms. However, the effects of structured remote fine motor training on these outcomes remain unclear. Objective: To examine the effects of a 12-week telerehabilitation-based fine motor training program on inattention symptoms, executive functions, and fine motor performance in children with inattentive ADHD. Methods: This assessor-blinded randomized controlled trial investigated a 12-week remote fine motor training program delivered via Tencent Meeting in children aged 6-10 years with inattentive ADHD. Sixty-six children were randomly assigned to either the intervention group (n=33) or a wait-list control group (n=33). The intervention was conducted 3 times per week, 60 minutes per session, for 12 weeks. Assessments were performed at baseline, immediately postintervention, and at 3-month follow-up. The outcomes were inattention symptoms, executive functions and fine motor skills. Linear mixed models were used for the main analysis, and mediation analysis was performed to examine whether executive functions explained changes in inattention. Results: Compared with the wait-list control group, the intervention group showed significantly greater reductions in inattention symptoms at 12 weeks (MD= −3.85, 95% CI: −5.01 to −2.68) and 3-month follow-up (MD= −2.00, 95% CI: −3.17 to −0.83). For executive functions, significant between-group differences were observed in inhibitory control, immediate memory, and cognitive flexibility at both time points (P<0.05), while delayed memory was significant at 12 weeks only (MD= −3.03, 95% CI: 0.57 to 5.49) and showed no significant between-group difference at follow-up (MD= 1.62, 95% CI: −0.84 to 4.08). For fine motor outcomes, significant between-group differences were found in manual dexterity and hand-eye coordination at both 12 weeks and 3-month follow-up (P<0.05), and in writing skills at 12 weeks (MD= −6.85, 95% CI: −13.38 to −0.32) but not at follow-up (MD= −2.18, 95% CI: −8.71 to 4.35). Subgroup analyses suggested age-related variation in treatment response, with younger children showing more evident gains in fine motor performance and older children showing more sustained improvements in inattention and selected executive function domains. Mediation analysis showed that inhibitory control partially mediated the effect of the intervention on inattention (indirect effect: β= −0.85, 95% CI: −1.85 to −0.08). Conclusions: A 12-week remote fine motor training program may be a feasible, safe, and effective nonpharmacological intervention for children with inattentive ADHD. The intervention improved inattention symptoms, executive functions, and fine motor performance, with inhibitory control emerging as a potential mechanism underlying symptom improvement. The subgroup findings further suggest that developmental stage may influence the pattern of response, which may help guide age-tailored intervention design in future practice. Clinical Trial: Chictr.org.cn ChiCTR2200065413; https://www.chictr.org.cn/showproj.html?proj=182412
Background: Incentivisation is increasingly used to maintain engagement and support behaviour change within communities of practice (CoPs), yet its effectiveness across chronic disease contexts remain...
Background: Incentivisation is increasingly used to maintain engagement and support behaviour change within communities of practice (CoPs), yet its effectiveness across chronic disease contexts remains uncertain. Objective: To examine how incentives are integrated into CoPs and related peer-support models, and to assess their impact on participant activation, engagement, and health-related outcomes. Methods: PubMed/MEDLINE, Embase, Scopus, and CENTRAL were searched from inception to June 2025 using predefined terms relating to CoPs, incentivisation, and patient-centred outcomes. Peer-reviewed empirical studies involving incentivised CoPs or analogous peer-support interventions for adults with chronic conditions were eligible. Four reviewers independently screened studies, extracted data, and assessed risk of bias in line with PRISMA 2020 guidance. Heterogeneity in design and outcomes required narrative synthesis. Results: From 667 records, four randomised controlled trials met inclusion criteria. Financial incentives produced the greatest short-term gains in physical activity, while non-financial approaches such as gamification, points, badges, and structured peer support yielded modest improvements in step count, treatment adherence, or diet quality. No consistent effects were observed for patient activation, self-efficacy, mental health, or quality of life. Engagement moderated effectiveness, although attrition was common. Conclusions: Incentivisation can enhance short-term behavioural outcomes within CoPs, but evidence for sustained psychosocial benefit is limited. Larger, longer-term studies are needed to clarify which incentive strategies deliver durable improvements in engagement and self-management. Clinical Trial: This review was registered on PROSPERO, an international prospective register of systematic review (January 2026, reference CRD420251244276).
Background: Standard echocardiography reports use complex terminology, limiting patient comprehension and exacerbating preconsultation anxiety. Large language models (LLMs) can transform technical dat...
Background: Standard echocardiography reports use complex terminology, limiting patient comprehension and exacerbating preconsultation anxiety. Large language models (LLMs) can transform technical data into patient-friendly narratives by incorporating longitudinal comparisons with prior examinations. Objective: To develop an LLM-based patient-friendly echocardiography reporting system and evaluate its professional safety, patient comprehension, and impact on short-term anxiety. Methods: This study consisted of two stages. In the retrospective development stage, 60 patients with baseline hospitalization and follow-up echocardiography reports were included. Clinical diagnosis, hospitalization records, and serial echocardiographic data were integrated as model inputs using DeepSeek-V3.2. Generated reports followed a standardized four-module structure. Report quality was independently evaluated by two clinicians and an external LLM (Kimi 2.5, Moonshot AI) across four domains: data accuracy, information completeness, appropriateness of interpretation, and reasonableness of recommendations. In the prospective clinical evaluation stage, 100 patients undergoing echocardiography and 85 family members were enrolled between January 2026 and March 2026. Participants received both conventional and LLM-generated patient-friendly reports. A 5-point Likert scale assessed helpfulness in understanding results, effectiveness in addressing concerns, helpfulness in improving disease-related knowledge, and anxiety relief. Anxiety was measured using the STAI-6 at three time points: after echocardiography (APost-ECHO), after conventional report release (APost-CR), and after reading the patient-friendly report (APost-PFR). Results: All 60 reports in the retrospective stage were successfully generated. Professional evaluation showed high overall quality scores from both clinicians and the external LLM, with no significant difference between evaluators (mean total scores 18.15 [SD 1.36] vs 18.28 [SD 1.26]; P>.05). One hallucination event was identified. In the prospective stage, all 100 patients received patient-friendly reports. Both patients and family members rated the reports highly, with mean scores above 4.3 across all domains and no significant between-group difference in total scores (17.61 [SD 1.60] vs 17.62 [SD 1.03]; P>.05). Subgroup analyses showed greater perceived benefit among older patients and outpatients, particularly in report comprehension and overall evaluation (both P<.001). Patients with chronic heart failure, reduced left ventricular ejection fraction (≤40%), and left ventricular enlargement (>55 mm) reported higher scores for addressing concerns (all P<.001). Anxiety scores increased significantly after conventional report release (APost-CR vs APost-ECHO) and decreased significantly after reading the patient-friendly report (APost-PFR vs APost-CR; both P<.001). Older patients (>60 years) and outpatients showed significantly higher anxiety change rates than their counterparts (CR1 and CR2: both P<.001). The reduction in anxiety was positively correlated with subjective anxiety relief ratings (r=0.531; P<.001). Conclusions: The LLM-based patient-friendly echocardiography reporting system with longitudinal comparison demonstrated good feasibility and promising preliminary clinical utility. While maintaining high professional quality, it improved patient understanding of echocardiographic findings and was associated with reduced short-term anxiety, particularly among older adults and outpatients.
Background: A core feature of borderline personality disorder (BPD) is impaired emotion regulation, which is associated with heightened stress reactivity. Self-management tools providing in-the-moment...
Background: A core feature of borderline personality disorder (BPD) is impaired emotion regulation, which is associated with heightened stress reactivity. Self-management tools providing in-the-moment support may benefit this population. Mobile health apps could offer such support, but few address stress management in BPD. The Stress Autism Mate (SAM) app, developed for adults with autism, supports stress management through daily stress monitoring, pattern recognition and personalized feedback. Objective: This study evaluated the SAM app in adults with BPD traits, examining changes in daily stress as measured within the app and changes in perceived stress, coping self-efficacy, and resilience. Methods: A single-case A-B design was used to examine daily stress trajectories over 30 days in 16 adults with BPD traits, of whom 12 met the compliance threshold for single-case analyses. Non-overlap of All Pairs and Tau-U were calculated to quantify individual-level change. Standardized questionnaires assessed perceived stress, coping self-efficacy, and resilience at baseline, after four weeks of treatment as usual, post-intervention, and at 4-week follow-up. Changes were analyzed using linear mixed-effects models. Results: Daily stress trajectories varied across participants; three showed trends toward decreased stress, four toward increased stress, and five remained stable. Two participants showed statistically significant changes (Tau-U = −0.35, P < .001; Tau-U = 0.49, P = .039). Linear mixed-effects models showed that perceived stress decreased significantly at post-intervention (b = −0.36, 95% CI −0.71 to −0.02; P = .04) and follow-up (b = −0.53, 95% CI −0.93 to −0.14; P = .009), compared to baseline. Coping self-efficacy did not change immediately post-intervention but improved significantly at follow-up (b = 0.94, 95% CI 0.22 to 1.66; P = .01). Total resilience scores showed no significant change post-intervention but improved at follow-up (b = 0.22, 95% CI 0.04 to 0.40; P = .02). The resilience subscale acceptance of self and life improved both post-intervention (b = 0.24, 95% CI 0.04 to 0.44; P = .02) and at follow-up (b = 0.37, 95% CI 0.14 to 0.60; P = .002), compared to baseline. Conclusions: This study provides preliminary evidence that the SAM app may be feasible and benefit adults with BPD traits by reducing perceived stress and improving coping self-efficacy and resilience. Individual responses varied, highlighting the need for personalized approaches. Larger controlled trials and qualitative studies are needed to evaluate effectiveness and explore user experiences.
Background: Gastrointestinal (GI) cancers significantly affect nutritional intake, while clinical measures offer limited insight into how patients navigate dietary decisions in their daily lives. Soci...
Background: Gastrointestinal (GI) cancers significantly affect nutritional intake, while clinical measures offer limited insight into how patients navigate dietary decisions in their daily lives. Social media platforms, particularly comment sections, create spaces for interactive exchange in which dietary norms are collectively tested and renegotiated. Despite growing interest in online health discourse, the interactive dynamics of comment sections within GI cancer online communities remain underexplored. Objective: This study aimed to characterise dietary-related online discussions among a Chinese GI cancer community on Xiaohongshu, identifying prominent topics, emotional expression patterns, and the discursive processes through which dietary norms are constructed and negotiated at the community level. Methods: A mixed-methods approach integrating latent Dirichlet allocation (LDA) topic modelling, sentiment analysis, and Fairclough's three-dimensional critical discourse analysis (CDA) framework. Comments were collected from posts on Xiaohongshu identified through keyword searches combining GI cancer types with diet-related terms from September to December 2025, yielding 1,865 valid comments from 370 posts for analysis. Results: Nine topics were identified, grouped into four thematic categories: acute-phase nutritional support interventions, basic food choices and dietary principles, dietary management of common symptoms, and special conditions and metabolic diets. Sentiment analysis revealed predominantly neutral expressions (66.6%), followed by positive (21.4%) and negative (12.0%) sentiments. CDA revealed that dietary decisions were guided by risk minimization and bodily feedback rather than nutritional optimization. Interactive negotiations shifted epistemic authority from medical-centred to community-centred models. Traditional beliefs about fawu coexisted with biomedical perspectives, reflecting ongoing discursive renegotiation of cultural dietary prohibitions. Commercial discourse permeated discussions, linking disease management to consumption practices. Conclusions: Dietary discourse within this online community reflects complex negotiations among medical knowledge, experiential wisdom, and traditional cultural beliefs. Effective nutritional interventions require attention to patients' risk perceptions, emotional regulation through neutral language, cultural frameworks surrounding dietary prohibitions, and the commercial information environment shaping dietary decisions. Oncology nurses should integrate authoritative recommendations with patients' experiential knowledge and develop culturally sensitive approaches to nutritional counselling. Clinical Trial: This study is a retrospective observational content analysis of publicly available user-generated content on the Xiaohongshu platform, using latent Dirichlet allocation (LDA) topic modeling and sentiment analysis methods. The research does not involve any clinical intervention, human subject recruitment, experimental manipulation, or prospective clinical trial. As it is not a clinical trial or interventional study, trial registration is not required.
Open Peer Review Period: Apr 4, 2026 - Mar 20, 2027
Background: The Democratic Republic of Congo (DRC) faces a severe mental health crisis, exacerbated by decades of conflict, poverty, and a fragile healthcare system. This challenge is directly relevan...
Background: The Democratic Republic of Congo (DRC) faces a severe mental health crisis, exacerbated by decades of conflict, poverty, and a fragile healthcare system. This challenge is directly relevant to achieving Sustainable Development Goal 3 (SDG 3): Good Health and Well-being, particularly Target 3.4, which calls for promoting mental health and well-being by 2030. Objective: The primary research question of this study examines the dual role of cultural belief systems in shaping mental well-being within the complex environment of the DRC, specifically investigating how these systems both contribute to and challenge mental well-being. Methods: This study employed a qualitative research design based on a systematic literature review and synthesis of existing research. Data were drawn from academic databases (PubMed, PsycINFO, Google Scholar), grey literature from NGOs (WHO, UNHCR), and peer-reviewed journals published between 2000 and 2024. A thematic analysis approach was used to analyze the data, involving familiarization, coding, and theme generation. Results: The findings reveal that cultural beliefs function as a double-edged sword. Positive contributions include traditional healing practices, strong community and family support networks, and religious faith (with approximately 80% of the population identifying as Christian), which provide crucial psychological resilience. Conversely, the widespread attribution of mental illness to supernatural causes fosters stigma and leads to harmful practices. Epidemiological data indicates high prevalence rates of mental health disorders in conflict-affected areas, with PTSD estimated between 17% and 50%, and depression rates ranging from 27.8% to 62% in Ebola-affected areas. The mental health system is severely under-resourced, with only 5% of the population having access to services and a workforce of 0.08 psychiatrists per 100,000 people. Conclusions: Cultural belief systems in the DRC have a profound and dual impact on mental well-being. Addressing the mental health crisis requires an integrated approach that leverages the protective aspects of culture while mitigating harmful practices. Developing culturally competent mental health services and fostering collaboration between traditional and modern healthcare providers are essential steps toward achieving SDG 3 in the DRC.
We investigated the relationships between academic stress, family relationships, and social media use disorder (SMUD) among middle school students through the ecological systems perspective. We used m...
We investigated the relationships between academic stress, family relationships, and social media use disorder (SMUD) among middle school students through the ecological systems perspective. We used mixed-methods sequential explanatory. In addition, structural equation modeling showed that academic stress had a significant direct impact on SMUD. Restrictive family relationships significantly mediated this relationship. The proposed model explained 42.6% of the variance in SMUD. Qualitative findings highlighted family patterns and coping and revealed how rigid parental control and limited emotional support reinforced restrictive family dynamics, which in turn further linked academic stress to SMUD. Present research extends Ecological Systems Theory to online behavior and establishes restrictive family relationships as key mediating mechanisms in SMUD development. These findings underscore the importance of family-based interventions that promote open communication and adaptive stress management strategies to mitigate the risk of SMUD.
Background: Foodborne diseases remain a significant public health concern globally, imposing substantial health and economic burdens. The respiratory virus pandemic that began in 2020 has altered heal...
Background: Foodborne diseases remain a significant public health concern globally, imposing substantial health and economic burdens. The respiratory virus pandemic that began in 2020 has altered healthcare-seeking behaviors, infection control practices, and the epidemiology of various infectious diseases, yet its specific impact on foodborne diseases remains underexplored. Objective: This study aimed to analyze the impact of the respiratory virus pandemic on the incidence and etiological profile of foodborne diseases by conducting an in-depth analysis of clinical and etiological data from patients attending a gastrointestinal clinic over a six-year period, and to provide evidence to inform prevention strategies in the post-pandemic era. Methods: We collected data from patients diagnosed with foodborne diseases at the Gastrointestinal Clinic of Peking University Third Hospital between January 2018 and December 2019 (pre-pandemic) and between January 2022 and December 2023 (post-pandemic). Etiological monitoring results were obtained from the Beijing Haidian District Center for Disease Control and Prevention. Cases were divided into two groups based on time period, and chi-squared tests were used for comparisons. Results: A total of 1,064 patients were included. From 2018 to 2019, there were 561 cases, with 205 etiologically positive cases (36.54%), including 22 Salmonella, 24 Vibrio parahaemolyticus, 66 Escherichia coli O157:H7, and 43 norovirus cases. From 2022 to 2023, there were 503 cases, with 116 etiologically positive cases (23.06%), including 13 Salmonella, 7 Vibrio parahaemolyticus, 45 Escherichia coli O157:H7, and 17 norovirus cases. The overall positivity rate decreased significantly post-pandemic (27.6% vs. 16.3%, P<0.05). Students and males aged 16–25 years were the most affected demographic. Escherichia coli O157:H7 remained the predominant pathogen in both periods. Conclusions: The respiratory virus pandemic significantly impacted the epidemiology of foodborne diseases, with a notable decline in pathogen-positive cases after the pandemic. Targeted infection prevention measures and health education should be prioritized for students and young adults in gastrointestinal clinic settings. These findings contribute formative evidence for adapting public health strategies in the post-pandemic context.
Background: Generative artificial intelligence (AI) tools became widely available to the public in November 2022. The extent to which these tools are being used by medical school applicants during the...
Background: Generative artificial intelligence (AI) tools became widely available to the public in November 2022. The extent to which these tools are being used by medical school applicants during the admissions process is unknown. Objective: We aimed to estimate the extent of generative AI use in cohorts of applicants spanning the rollout of these tools. Methods: We retrospectively analyzed 6,000 essays submitted to a U.S. medical school in 2021–2022 (baseline, before wide availability of AI) and in 2023–2024 (test year) to estimate the prevalence of AI use and its relation to other application data. We used GPTZero, a commercially available detection tool, to generate a metric (P_human) for the likelihood that each essay was human-generated, ranging from 0 (entirely AI) to 1 (entirely human). Results: Fully human-generated negative controls demonstrated a median P_human of 0.93, while AI-generated positive controls demonstrated a median P_human of 0.01. Personal Comments essays submitted in the 2023–2024 cycle had a median P_human of 0.77 (95% confidence interval 0.76–0.78), versus 0.83 (95% CI 0.82–0.85) during the 2021–2022 cycle. Approximately 12.3 and 2.7% of essays were evaluated as having P_human < 0.5 in the test and baseline year, respectively. Secondary essays demonstrated lower P_human than AMCAS Personal Comments essays, suggesting more AI use. In multivariate analysis, younger age and higher GPA were significantly associated with lower P_human. No differences were observed in gender, MCAT score, undergraduate major, or socioeconomic status. P_human was not predictive of admissions outcomes in uni- or multivariate analyses. Conclusions: An AI detection algorithm identified significantly increased use of generative AI in 2023–2024 medical school admission applications, as compared to the 2021–2022 baseline. AI use did not appear to confer an admissions advantage. While these results provide information about the applicant pool as a whole, AI detection is imperfect. We do not recommend deploying AI detection on individual applications in live admissions cycles. Clinical Trial: Trial Registration: None
Background: Artificial intelligence (AI) is increasingly integrated into medical education, offering new ways for students to acquire knowledge and support clinical reasoning. However, the extent, pat...
Background: Artificial intelligence (AI) is increasingly integrated into medical education, offering new ways for students to acquire knowledge and support clinical reasoning. However, the extent, patterns, and implications of AI use among medical students remain incompletely understood. Objective: This study aimed to quantify real-time artificial intelligence use among medical students, including the proportion of study time devoted to AI, and to examine how use varies by training stage and engagement style (active vs passive). Methods: This longitudinal study recruited medical students from two osteopathic medical schools to complete a baseline survey and seven digital diary entries over a 21-day period. The diary method was designed to capture real-time AI use and reduce recall bias. Data were analyzed using Stata 19. Multiple linear regression models examined associations between AI use (total minutes and percentage of study time) and key variables, including year in training and type of use (active vs passive), adjusting for age, gender, and campus. Results: A total of 71 students completed the baseline survey (mean age 26.6 years, SD 2.8; 54.9% male; 77.4% pre-clinical). On average, students reported using AI tools during 19.4% of their total study time. Clinical-phase students (MS3–MS4) used AI significantly more than pre-clinical students (MS1–MS2), with an adjusted increase of 19.0 percentage points (p=0.003). Students classified as active users spent significantly more total time using AI than passive users (p=0.002). Across groups, AI use was primarily passive, including simplifying complex concepts, answering practice questions, and generating summaries. Clinical-phase students were more likely to use AI for practice questions. Conclusions: Medical students are incorporating AI into a substantial proportion of their study time, with greater use among clinical trainees and those engaging actively with these tools. Despite this, most use remains passive. Given mixed evidence regarding the impact of AI on deep learning and potential risks related to uncritical acceptance of AI-generated content, these findings highlight the need for further research on learning outcomes. Medical schools may benefit from providing guidance on responsible AI use, including critical evaluation, verification of outputs, and integration into evidence-based study strategies. The digital diary methodology offers a novel and practical approach for capturing real-time AI use and may inform future educational research and intervention design.
Background: Large language model (LLM)–based conversational agents are increasingly used in healthcare to support multi-turn dialogue. In oncology, where patients and caregivers experience complex i...
Background: Large language model (LLM)–based conversational agents are increasingly used in healthcare to support multi-turn dialogue. In oncology, where patients and caregivers experience complex informational and emotional needs throughout the disease trajectory, conversational agents may support information provision, symptom consultation, and emotional assistance. However, research specifically examining multi-turn conversational agents designed for cancer patients and caregivers remains limited. Objective: This scoping review aimed to map the research landscape of LLM-based multi-turn conversational chatbots developed for cancer patients and caregivers, focusing on system design, intervention purposes, evaluation approaches, safety considerations, and transparency of LLM-related components. Methods: This scoping review followed the Joanna Briggs Institute methodology and PRISMA-ScR guidelines. Six databases (PubMed, Embase, Scopus, Web of Science, CINAHL, and PsycINFO) were searched for studies published between January 2022 and January 2026. Studies were included if they described LLM-based chatbots designed for cancer patients or caregivers that supported multi-turn conversational interaction. Two reviewers independently conducted the study selection and data extraction. Results: Eight studies met the inclusion criteria. Most studies focused on prototype development, with limited research evaluating clinical outcomes. ChatGPT-based models were the most commonly used LLMs, and retrieval-augmented generation techniques were applied in several studies. Chatbots were primarily designed for emotional support or information provision. Evaluation approaches varied widely, including response quality, psychological outcomes, and user experience. However, no studies evaluated interaction-level characteristics such as conversational continuity or context retention. Reporting on safety risks, mitigation strategies, and LLM-specific components such as prompt design and model parameters was often limited. Conclusions: Research on LLM-based multi-turn conversational chatbots for cancer patients and caregivers remains at an early stage, characterized by prototype-oriented development and heterogeneous evaluation approaches. Future studies should adopt standardized reporting frameworks and develop evaluation methods addressing interaction-level performance, safety, and clinical effectiveness.
Background: Emotion regulation is a transdiagnostic construct encompassing distinct strategies that can be used to encourage adaptive coping skills in support of youth (ages 10 to 24) mental health. E...
Background: Emotion regulation is a transdiagnostic construct encompassing distinct strategies that can be used to encourage adaptive coping skills in support of youth (ages 10 to 24) mental health. Emotion regulation is responsive to training and intervention, such as via video and app-based games. Previous literature shows that video and app games impact the psychological well-being of youth and may be an accessible way to help youth learn about and implement emotional regulation strategies in their lives. Objective: Previous reviews have not focused on video and app games that support emotion regulation and how they may be beneficial to youth with and without a mental health diagnosis. This scoping review sought to collect, review, and understand the scope of research on the effects of video and app games on emotion regulation outcomes in youth, and areas in need of further investigation. Methods: We conducted a scoping search of MEDLINE, Embase and APA PsycINFO from the beginning of the database up to and including December 2025. Inclusion criteria consisted of studies in English, had youth with a mean age between 10 and 24 years old, and evaluated an intervention or an intervention adjunct to help improve their emotion regulation skills using a video or app game. We extracted population characteristics, the video game or app, the effectiveness of the intervention, and emotional regulation strategies addressed. Results: Following the screening of 14,456 studies, we identified 35 studies for inclusion in the final review. Five recurrent outcomes emerged: overall emotion regulation, improving mood/mood repair, reducing stress/anxiety, reducing mental health disorder symptoms, and increasing understanding of one’s own emotions. Conclusions: While most games led to reported improvements in these areas, some studies reported areas of continued difficulty. The results highlight that video and app games could serve as beneficial, easy-to-access tools to support youth emotion regulation through various strategies, with areas for future research and digital health improvement.
Background: The increasing burden of chronic diseases such as hypertension and diabetes necessitates a shift from reactive to proactive preventive care. This transition is now feasible through the con...
Background: The increasing burden of chronic diseases such as hypertension and diabetes necessitates a shift from reactive to proactive preventive care. This transition is now feasible through the convergence of large-scale health data, machine learning (ML), and patient-centered policies, such as South Korea’s MyData initiative. Objective: The objective of this study was to develop and validate ML models using routine health-screening data to predict the onset of hypertension and diabetes, thereby providing an evidence-based foundation for personalized, data-driven prevention. Methods: We constructed a cohort using data from the Clinical Data Warehouse (CDW) of Seoul St. Mary’s Hospital. Two distinct datasets were analyzed: 21,589 individuals for essential hypertension prediction and 22,255 individuals for type 2 diabetes mellitus prediction. Five ML models were used to classify disease onset. The final models were selected based on a comprehensive evaluation of the area under the receiver operating characteristic curve (AUROC) and the F1 score. Finally, the importance of variables in the selected models was confirmed using Shapley Additive Explanation (SHAP) values. Results: Among the models tested, logistic regression was used to predict essential hypertension and type 2 diabetes mellitus. The models demonstrated high predictive performance, with an AUROC of 0.842 for hypertension and 0.954 for diabetes. SHAP analysis revealed that age was the most influential predictor of hypertension, whereas HbA1c was the most significant predictor of diabetes. Conclusions: We successfully developed prediction models for hypertension and diabetes that are applicable within MyData services. These models have the potential to empower individuals in data-driven self-management and to enhance personalized disease prevention.
Background: Background: Regulatory frameworks such as the Belmont Report, the Common Rule, and the Declaration of Helsinki require informed consent to ensure participants understand a study’s purpos...
Background: Background: Regulatory frameworks such as the Belmont Report, the Common Rule, and the Declaration of Helsinki require informed consent to ensure participants understand a study’s purpose and can make voluntary decisions about their involvement. Regulations including the General Data Protection Regulation (Regulation (EU) 2016/679) further emphasise that consent must be freely given and revocable without disadvantage. Although informed consent forms (ICFs) are intended to be clear and accessible, they have become increasingly lengthy and complex. Large language models (LLMs) offer potential to navigate and interpret this complexity and have shown promise in biomedical information extraction tasks. However, their susceptibility to hallucinations limits reliability in high stakes settings. Retrieval augmented generation (RAG) can mitigate such errors. Objective: This study evaluates the integration of LLMs with RAG for reviewing data reuse language in ICFs and their ability to interpret complex textual structures. Methods: Methods: Firstly, we processed 438 ICFs from different trials, including multi-countries, languages and versions of ICFs. Using expertly curated prompts, we extracted information about data reuse using GPT-4.1. Comparing the LLM-generated data reuse outputs with human expert ground truth, we evaluated accuracy and the time required to extract information for each ICF. To further validate the workflow, we evaluated an independent set of 488 ICFs spanning additional trials, languages, and regions. For this cohort, we assessed the correctness of LLM outputs along with the quality of supporting evidence provided by the model. Results: Results: Across 438 ICFs, the system achieved 81.6% accuracy, which increased to 90% in a subsequent evaluation of additional 488 ICFs after prompt optimisation. Using a RAG-based approach, the system accurately extracted data reuse information across multiple languages and identified nuanced international regulatory requirements. Conclusions: Conclusion: This approach has the potential to significantly alleviate administrative burdens by automating labour-intensive processes, while also generating insights that could inform the standardisation of ICF creation. Ultimately, these advancements may contribute to reduce the complexity of ICFs, thereby improving their readability and comprehensibility for participants. Clinical Trial: NA
Background: Over the past decade, the application of machine learning (ML) models to solving a wide range of healthcare challenges has grown rapidly. However, the translation and integration of these...
Background: Over the past decade, the application of machine learning (ML) models to solving a wide range of healthcare challenges has grown rapidly. However, the translation and integration of these models into widespread applications in real-world clinical settings remain limited due to persistent issues especially relating to bias. Objective: This systematic review of systematic reviews aims to identify the most common types of bias that impede the widespread applicability and generalizability of machine learning models in healthcare. Methods: Using a standardized search strategy relevant systematic reviews on the application of machine learning models in healthcare published between January 1, 2022, and June 1, 2025, were identified across three databases: Pubmed, Embase and Medline. Only studies that examined the application of machine learning, including its subfield of deep learning, and that employed standardized tools for risk of bias assessment were included. Additional inclusion criteria required that studies adhere to the PRISMA guidelines for systematic reviews and be published in English. Risk of bias was assessed using a tailored and modified version of the Assessment of Systematic Reviews tool (AMSTAR 2), while methodological quality was evaluated according to the Risk of Bias in Systematic Reviews (ROBIS) guidelines Results: In total, 729 abstracts were identified, of which 60 studies met the inclusion criteria. Twenty-seven reviews applied machine learning models to prognostic tasks, 20 to diagnostic tasks, and 6 addressed both diagnosic and prognostic applications. Additionally, three reviews focused on disease management and three on decision support, while one review examined the use of machine learning for surgical skill assessment. The risk of bias in the primary studies were appraised using PROBAST (n=33), QUADAS-2 (n=17), Cochrane Handbook for Systematic Review of Interventions (n=3), ROBINS-1 (n=2), Robvis (n=1), QUIPS (n=1), EPHPP (n=1), PROBAST&TRIPOD (n=1), QUADAS-2 & PROBAST (n=1). The most frequently identified sources of bias included lack of external validation, analytical bias (e.g., inadequate handling of missing data, insufficient sample size, lack of internal validation, and poor reporting of model calibration), participant selection bias, bias related to the reference standard or ground truth, feature selection bias, and issues related to model interpretability and algorithmic bias. Conclusions: Machine learning has the potential to improve healthcare. However, realizing this potential at scale requires addressing the aforementioned sources of bias and establishing standardized methodological guidelines for future studies. This is essential to improving the generalizability and acceptibility of ML models and to facilitating their safe integration into routine clinical practise.
Open Peer Review Period: Apr 2, 2026 - Mar 18, 2027
Background: Post-stroke seizures and post-stroke epilepsy are important complications that affect recovery and long-term quality of life after stroke. Predicting which patients are at higher risk rema...
Background: Post-stroke seizures and post-stroke epilepsy are important complications that affect recovery and long-term quality of life after stroke. Predicting which patients are at higher risk remains challenging, and recent studies have explored the use of artificial intelligence to improve prediction. Objective: This scoping review aimed to map how artificial intelligence and related computational methods have been applied to predict post-stroke seizures and epilepsy, to describe the role of neuroimaging in these models, and to assess representation of African settings in the existing literature. Methods: A systematic search identified studies that used statistical, machine learning, radiomics, or deep learning approaches to predict seizure-related outcomes after stroke. Traditional statistical models and clinical risk scores were the most commonly used methods. A smaller number of studies applied machine learning techniques, while radiomics and deep learning approaches were reported in only a few studies. Neuroimaging was mainly used to predict long-term epilepsy rather than early seizures. Most models were evaluated using internal validation only, with limited external validation. Results: Studies from African settings were underrepresented and primarily focused on describing seizure frequency and associated factors using regression analysis. No studies were identified that developed or validated artificial intelligence-based prediction models using African datasets. Conclusions: Overall, the findings show that while interest in AI-based prediction is growing, advanced methods remain limited, and major gaps exist in model validation and geographic representation. These findings highlight the need for context-specific, well-validated prediction models, particularly in African healthcare settings.
Background: Osteoporotic vertebral fractures are a leading cause of pain, disability, and reduced quality of life in the elderly population. Although vertebroplasty is widely used to relieve pain and...
Background: Osteoporotic vertebral fractures are a leading cause of pain, disability, and reduced quality of life in the elderly population. Although vertebroplasty is widely used to relieve pain and stabilize fractures, postoperative rehabilitation remains suboptimal, with limited functional recovery. Seated Baduanjin, a modified form of traditional Baduanjin exercise tailored for frail elderly patients, has been successfully applied in the rehabilitation of knee osteoarthritis, cervical spondylosis, and sarcopenia. However, its application in patients after vertebroplasty has not yet been systematically synthesized. Objective: This systematic review and meta-analysis aims to evaluate the efficacy and safety of seated Baduanjin exercise compared with usual care in terms of pain, physical function, and quality of life among elderly patients with osteoporotic vertebral fractures following vertebroplasty. Methods: This protocol has been registered in PROSPERO (CRD420261344980). We will search PubMed, Cochrane Library, EMBASE, Web of Science, China National Knowledge Infrastructure (CNKI), Wanfang Data, and VIP Database from inception to July 2026, with no language or date restrictions. Only randomized controlled trials evaluating seated Baduanjin in elderly patients (aged ≥60 years) after vertebroplasty will be included. Two reviewers will independently screen the literature, extract data, assess the risk of bias using the Cochrane RoB 2.0 tool, and evaluate the quality of evidence using the GRADE system. The primary outcome is pain intensity. Secondary outcomes include physical function, quality of life, and balance function. Meta-analysis will be performed using RevMan 5.4. Heterogeneity will be assessed using the I² statistic and Cochran’s Q test: a fixed-effects model will be used when I² < 50% and P ≥ 0.1; if I² ≥ 50% or P < 0.1, subgroup analyses will be conducted. If heterogeneity persists, sensitivity analyses or exploratory subgroup analyses will be performed. Should the heterogeneity remain unexplained, a random-effects model will be adopted, and the GRADE evidence level will be downgraded. Results: As of January 2026, the preliminary screening of titles and abstracts for 241 studies has been completed. Full-text screening is expected to be finalized by May 2026, and data analysis is planned for completion by August 2026. Approximately two-thirds of the relevant studies have been published since 2020. In terms of geographic distribution, the study samples are highly concentrated in Asia. The results will be comprehensively presented around the core outcomes: the primary outcome will be presented as changes in the Visual Analogue Scale (VAS). Secondary outcomes will be assessed using physical function measures such as the Oswestry Disability Index and quality of life scales such as the SF-36 and EQ-5D. The pooled effect sizes with 95% confidence intervals for the corresponding outcome measures will be reported. Additionally, the incidence of adverse events will be statistically analyzed. Conclusions: If the findings of this study confirm the efficacy and safety of seated Baduanjin exercise, it may provide a viable approach for non-pharmacological rehabilitation in elderly patients following vertebroplasty. However, some studies may have a risk of bias, such as insufficient standardization of intervention protocols and difficulty in implementing blinding. Due to variations in intervention protocols, outcome measures, and patient cultural backgrounds, substantial heterogeneity is anticipated. Moreover, the limited number of available original randomized controlled trials and the high geographic concentration of the study samples may restrict the generalizability of the conclusions. Future research should focus on optimizing intervention protocols and supplementing the evidence with high-quality, multicenter, large-sample randomized controlled trials to enhance the reliability of the findings.
PROSPERO registration number: CRD 420261344980 Clinical Trial: This systematic review protocol was developed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA-P) guidelines. It has been registered on the International Prospective Register of Systematic Reviews (PROSPERO) platform (CRD420261344980). Any methodological changes during the research process will be recorded and updated in the PROSPERO record.
Background: Chronic pain is a leading cause of disability and requires multidimensional assessment of pain intensity and functioning, yet electronic health records (EHRs) rarely capture these measures...
Background: Chronic pain is a leading cause of disability and requires multidimensional assessment of pain intensity and functioning, yet electronic health records (EHRs) rarely capture these measures systematically. By contrast, surveys collecting patient-reported outcomes can assess pain over multiple dimensions but remain resource-intensive and difficult to scale for continuous population-level monitoring. Objective: The objective of this study is to develop and validate a domain-informed natural language processing (NLP) framework to derive pain severity and functional interference outcomes from unstructured clinical narratives. Using a benchmark dataset of 3,726 Veterans with longitudinal survey measures, we aim to demonstrate that NLP-derived outcomes can serve as a reliable, scalable surrogate for resource-intensive patient-reported surveys. Methods: This study utilizes a retrospective cohort of 3,726 Veterans with chronic musculoskeletal pain initiating Complementary and Integrative Health (CIH) therapies across 18 Veterans Health Administration (VA) Whole Health Flagship sites (2021–2023). The dataset encompasses longitudinal patient-reported outcome surveys serving as the benchmark, linked with both structured and unstructured data from the VA EHR. To guide extraction and enable scalable processing, we developed a seed lexicon and annotation guidelines based on established psychometric instruments and input from subject matter experts (SMEs). Currently, SMEs are annotating clinical notes in iterative batches. Upon completion, a large language model (LLM) will annotate additional notes. These annotations will be used to fine-tune a lightweight language model capable of processing the entire cohort. The study employs a three-stage validation process, assessing: (1) documentation completeness; (2) inference accuracy, evaluating agreement between model outputs and SME annotations at both the LLM and fine-tuned model levels; and (3) concordance with patient-reported outcomes captured independently from the EHR. Results: To date, a cohort of 3,726 Veterans has been identified. The longitudinal survey data have been linked to clinical notes corresponding to the survey time period. The seed lexicon and annotation guidelines have been developed. Annotation is underway in iterative batches to drive the subsequent LLM adaptation and three-stage validation. Conclusions: This protocol outlines a framework for identifying severe pain interference from clinical narratives, addressing a critical gap in health care system surveillance. To our knowledge, this is the first study to validate clinical text-based pain outcome extraction against patient-reported outcomes in a nationwide longitudinal cohort. If successful, this approach will enable health care systems to continuously monitor pain-related functional interference and support more holistic, patient-centered pain management at scale.
Background: Internet-based cognitive behavioral therapy (iCBT) is an effective and scalable alternative to face-to-face psychotherapy, but its reach is constrained by the time therapists spend reviewi...
Background: Internet-based cognitive behavioral therapy (iCBT) is an effective and scalable alternative to face-to-face psychotherapy, but its reach is constrained by the time therapists spend reviewing patient input and manually drafting written responses. Studies suggest that large language models (LLMs) may be capable of generating high-quality therapeutic text, and may potentially be able to support therapists to deliver high quality care and increasing the number of patients each therapist can serve. The suitability of LLMs in an iCBT setting, however, remains insufficiently studied. Objective: This study aims to evaluate the quality of LLM-generated iCBT responses to patient messages by comparing them to the quality of responses produced by humans. Methods: In a pre-registered blinded clinician rating experiment, experienced clinicians assessed the quality of human-produced versus LLM-generated therapist responses within a simulated iCBT treatment for functional somatic disorder. Raters were exposed to a stimulus material consisting of five fictitious patient messages, each paired with one human and one LLM-generated response. Raters assessed message/response pairs on five quality dimensions (overall quality, helpfulness, empathy, professionalism, and protocol adherence) and were asked to indicate the source of the response (human/LLM). Analyses were primarily descriptive, supplemented by exploratory statistical tests and descriptive thematic content analysis of open-ended text fields. The full pre-registered study protocol is available at https://osf.io/yxncv/. Results: A total of 61 raters provided data while 54 were eligible and included for analysis. Human- and LLM-generated responses were rated similarly across quality dimensions on a 1-5 scale: overall quality (LLM: M = 4.00 vs human: M = 3.96, d = 0.06), helpfulness (LLM: M = 3.85 vs human: M = 3.93, d = 0.13), professionalism (LLM: M = 4.25 vs human: M = 4.11, d = 0.24), and protocol adherence (LLM: M = 4.13 vs human: M = 4.13, d = 0.03). LLM-generated responses, however, received higher scores on empathy (LLM: M = 4.31 vs human: M = 4.08, d=0.42). Raters correctly identified the source of human-generated responses (79%) more accurately than LLM-generated responses (63%). In all, 55% of raters responded to one or more open text fields. Qualitative analysis indicated that LLM-generated responses were perceived as polished but also generic and at times excessively empathetic. Conclusions: LLM-generated responses were judged to be of comparable quality to those written by human therapists, though qualitative feedback indicated they were at times generic and insufficiently challenging. These findings provide initial support for the feasibility of using LLMs as therapist-support tools in iCBT, but further research is needed to determine whether their integration yields tangible clinical and organizational benefits.
Background: Virtual reality (VR) interventions have been increasingly applied in health care, but their multifaceted impacts on physical, mental, and social health outcomes remain unclear. Existing sy...
Background: Virtual reality (VR) interventions have been increasingly applied in health care, but their multifaceted impacts on physical, mental, and social health outcomes remain unclear. Existing systematic reviews and meta-analyses have reported inconsistent findings, highlighting the need for an umbrella review to synthesize the highest level of evidence. Objective: This study employed an umbrella review approach to systematically integrate current evidence from systematic reviews and meta-analyses on the impact of virtual reality (VR) interventions on multidimensional health outcomes. It aims to comprehensively evaluate the effectiveness, strength of evidence, and limitations of VR interventions across different populations and outcomes, thereby providing a basis for clinical practice and future research. Methods: Strictly adhering to PRISMA-P guidelines, systematic searches were conducted in databases such as Embase, Medline, Cochrane Library, and Web of Science from their inception to August 2025. Systematic reviews and meta-analyses evaluating the impact of VR interventions on human health outcomes were included. Two researchers independently performed literature screening and data extraction, while the AMSTAR2 2 and GRADE tools were used to assess methodological quality and evidence quality, respectively. Results: Multiple meta-analyses were integrated. The findings revealed that the effects of VR interventions exhibited significant "population-outcome" specificity. VR interventions demonstrated positive effects in improving upper limb function in stroke patients, motor abilities in children with cerebral palsy, aerobic capacity in patients with cardiovascular diseases, exercise capacity in patients with chronic obstructive pulmonary disease, as well as cognitive function, musculoskeletal function, balance, and pain (e.g., knee osteoarthritis, fibromyalgia) across various populations (SMD/MD were statistically significant). Additionally, VR interventions enhanced the quality of life in some populations. However, VR did not show significant associations with improvements in activities of daily living in dementia patients, long-term (3-6 months) pain and functional improvements in patients with chronic low back pain, or certain balance functions in frail older adults. The evidence exhibited high heterogeneity, and the GRADE quality of evidence for most outcomes was rated as "low" or "very low." Conclusions: VR interventions hold broad application prospects in the health field, but their effects are not universal and must adhere to the principle of "precision adaptation." The current evidence system is limited by factors such as high heterogeneity and uneven methodological quality. Future research should focus on standardizing intervention protocols, validating long-term effects, and exploring underlying mechanisms to enhance evidence quality and promote scientific clinical translation.
Background: The ankle joint is central to postural adjustment and daily movement; age-related decline in ankle function directly affects balance and mobility. Accessible, repeatable ankle control meth...
Background: The ankle joint is central to postural adjustment and daily movement; age-related decline in ankle function directly affects balance and mobility. Accessible, repeatable ankle control methods remain scarce for older adults. Serious games offer a potential alternative for exercise and high-resolution movement monitoring. However, many systems emphasize overall task success or conventional objective outcomes, while detailed analysis of movement signals during dynamic gameplay remains limited. Objective: This study aimed to develop Balance Wood, a serious game for interactive digital analysis of ankle range of motion. We examined the system’s feasibility and measurement sensitivity in capturing behavioral adaptations in older and younger adults, establishing an interpretable, signal-based analysis framework. Methods: Balance Wood is a bilateral ankle-control system where two tilting foot devices generate a combined control angle for a bucket-control serious game. We included 40 participants (20 older and 20 younger adults) who completed three consecutive trials of varying difficulty. From the recorded tilt-angle signals, five main trial-level outcomes were derived for inferential analysis: typical forward amplitude, typical backward amplitude, forward holding proportion, backward holding proportion, and overspeed proportion. Effective proportions of forward and backward flexion-extension were calculated to measure movement patterns descriptively. Repeated-measures analyses of variance were performed separately for both age groups. Sphericity was assessed using the Mauchly test, applying Greenhouse-Geisser correction when required. To address outcome-level multiplicity for the five outcomes per group, a Bonferroni-corrected threshold of P<.01 was used, with effect sizes interpreted alongside uncorrected P values. Results: In older adults, repeated-measures analysis of variance revealed nominally significant trial effects (uncorrected P<.05) for typical forward amplitude (F_2,38=4.169, P=.023, η_p^2=.180) and typical backward amplitude (F_1.373,26.084=4.338, P=.036, η_p^2=.186; Greenhouse-Geisser corrected). Despite large effect sizes, neither survived the Bonferroni-corrected threshold of P<.01. Forward holding proportion (F_2,38=3.122, P=.056, η_p^2=.141) and backward holding proportion (F_2,38=2.563, P=.090, η_p^2=.119) did not reach nominal statistical significance. Overspeed proportion remained stable (F_2,38=0.416, P=.663, η_p^2=.021). Post hoc testing indicated the most evident pairwise change was an increase in typical backward amplitude from Trial 1 to Trial 2, demonstrating system sensitivity to task-induced adaptations. In younger adults, no inferential outcomes showed significant trial effects, suggesting a ceiling effect or greater movement reserve. Descriptively, older adults showed increasing typical amplitudes and a directionally asymmetric profile of effective flexion-extension, whereas younger adults exhibited numerically larger amplitudes and a more directionally balanced profile. Conclusions: In older adults, repeated-measures analysis of variance revealed nominally significant trial effects (uncorrected P<.05) for typical forward amplitude (F_2,38=4.169, P=.023, η_p^2=.180) and typical backward amplitude (F_1.373,26.084=4.338, P=.036, η_p^2=.186; Greenhouse-Geisser corrected). Despite large effect sizes, neither survived the Bonferroni-corrected threshold of P<.01. Forward holding proportion (F_2,38=3.122, P=.056, η_p^2=.141) and backward holding proportion (F_2,38=2.563, P=.090, η_p^2=.119) did not reach nominal statistical significance. Overspeed proportion remained stable (F_2,38=0.416, P=.663, η_p^2=.021). Post hoc testing indicated the most evident pairwise change was an increase in typical backward amplitude from Trial 1 to Trial 2, demonstrating system sensitivity to task-induced adaptations. In younger adults, no inferential outcomes showed significant trial effects, suggesting a ceiling effect or greater movement reserve. Descriptively, older adults showed increasing typical amplitudes and a directionally asymmetric profile of effective flexion-extension, whereas younger adults exhibited numerically larger amplitudes and a more directionally balanced profile. Clinical Trial: Findings demonstrate the feasibility and sensitivity of Balance Wood for digital analysis of ankle control. The system successfully captured behavioral adaptations and distinct movement strategies responding to changing task demands. The signal-based framework shows significant potential for continuous monitoring and detailed profiling of directional amplitude, near-limit holding behavior, and speed regulation during gameplay.
Background: Background: Alzheimer’s disease (AD) is increasingly recognized as a disorder of large-scale brain network disruption. Immersive virtual reality (VR) has emerged as a promising digital h...
Background: Background: Alzheimer’s disease (AD) is increasingly recognized as a disorder of large-scale brain network disruption. Immersive virtual reality (VR) has emerged as a promising digital health intervention for cognitive stimulation, but its neurophysiological effects on whole-brain network organizations remain poorly characterized. Objective: Objective: This study aimed to investigate whether immersive VR stimulation modulates functional brain network organization in individuals with AD using electroencephalography (EEG)–based graph-theoretical analysis. Methods: Methods: A total of 60 participants were enrolled, including 20 patients with AD, 20 individuals with mild cognitive impairment (MCI), and 20 healthy controls. EEG recordings were obtained during three experimental states: pre-VR resting, VR stimulation, and post-VR resting. Functional connectivity networks were constructed using phase locking value across multiple frequency bands. Graph-theoretical metrics, including global efficiency, modularity, and nodal strength, were calculated to characterize large-scale brain network topology and its state-dependent modulation. Results: Results: VR stimulation induced measurable reorganization of functional brain networks. In patients with AD, VR reduced abnormal hemispheric asymmetry, enhanced bilateral connectivity, and modulated the balance between network integration and segregation. The strongest effects were observed in the high-frequency band (32–70 Hz), where VR increased distributed long-range connectivity. These network-level changes reduced functional differences between AD and healthy controls and revealed distinct dynamic response patterns across clinical groups. Conclusions: Conclusions: Immersive VR dynamically modulates large-scale brain network organization in Alzheimer’s disease. These findings support the potential of VR as a scalable digital intervention for cognitive rehabilitation and demonstrate the utility of network-based EEG biomarkers for evaluating technology-enabled cognitive therapies.
Background: Perinatal mood and anxiety disorders occurring any time during the perinatal period affect 15–20% of birthing parents in the USA and are frequently underdiagnosed. Technology-based tools...
Background: Perinatal mood and anxiety disorders occurring any time during the perinatal period affect 15–20% of birthing parents in the USA and are frequently underdiagnosed. Technology-based tools may improve early detection of mental health issues, expand access to support, and reduce barriers to help-seeking behaviors. Objective: This study aimed to evaluate the feasibility, acceptability, and practicability of “Be Well, Mommy,” a mobile interactive booklet designed to support postpartum mental health, developed using a 10-step Social Marketing framework. Secondary objectives included assessing its potential to raise mental health awareness, normalizing postpartum emotional challenges, and facilitating help-seeking with patients, and healthcare provider perspectives to inform refinements. Insights from this feasibility trial will guide improvements and future large-scale implementation. Methods: Researchers conducted a qualitative feasibility project in Southeast Georgia with 15 first-time female parents and five healthcare providers. Participants were asked to use the booklet for two weeks and were followed by three focus groups with mothers and five in-depth provider interviews. Researchers analyzed data thematically using NVivo 15 and the APEASE criteria: Acceptability, Practicability, Perceived Effectiveness, Affordability, Side-effects, and Equity. Results: Findings revealed mixed acceptability. Birthing parents described the booklet as supportive, easy to navigate, visually appealing, and helpful in normalizing emotional challenges after childbirth. Experts, however, expressed concerns about limited clinical depth and difficulties in integrating it into practice. Practicability was generally high, though minor navigation and technical issues were identified. The free, mobile-friendly format enhanced affordability and equity. No major safety concerns emerged, although some participants found celebrity stories less relatable. Conclusions: Overall, “Be Well, Mommy” demonstrates preliminary feasibility as an accessible, awareness-raising tool that may support early recognition of postpartum mental health concerns and encourage help-seeking. Refinements, including expanded evidence-based content, improved navigation, and enhanced provider engagement, are recommended prior to large-scale implementation. Further longitudinal and quantitative research is needed to assess effectiveness and scalability. Clinical Trial: N/A-Pilot study
Background: Hospitalized children frequently experience pain and distress. Pain is a multidimensional experience involving both sensory and emotional components, necessitating multimodal management st...
Background: Hospitalized children frequently experience pain and distress. Pain is a multidimensional experience involving both sensory and emotional components, necessitating multimodal management strategies. Socially assistive robots (SARs) have shown promise as non-pharmacological interventions in pediatric care. However, the interaction mechanisms through which SARs influence pain and emotional responses, particularly positive emotion and real-time emotional dynamics during child–robot interaction, remain underexplored. Objective: This study, titled the HAPPY (Hospitalized Assistance for Pediatric Pain Yields) study, aimed to evaluate the association between a SAR-based intervention and postoperative pain in hospitalized children and to examine whether different levels of engagement are associated with changes in real-time emotional dynamics. Methods: A single-group pretest–posttest design was conducted with 37 hospitalized children (mean age 7.35, SD 2.06 years) following tonsillectomy or adenoidectomy. The intervention was structured into three sequential phases: Phase 1 (warm-up/limited engagement), Phase 2 (educational video/passive engagement), and Phase 3 (social interaction/active engagement). Pain was assessed using the Wong-Baker FACES pain scale and observed behavioral FLACC scales. Emotional response, as valence, was measured using an automated facial expression recognition system (FaceReader 10). Changes in pain were analyzed using Wilcoxon signed-rank tests, and differences in emotional valence across phases were examined using the Friedman test with post hoc pairwise comparisons. Results: Self-reported pain significantly decreased from a median of 6 (IQR 4–6) to 4 (IQR 2–4) (P<.001), and observer-rated behavioral pain decreased from a median of 3 (IQR 2–4) to 1 (IQR 1–2) (P<.001). Overall differences in emotional valence across phases did not reach statistical significance (P=.053; Kendall’s W=0.084). However, the V-shaped trajectory of emotional valence was observed, with the lowest values during the passive engagement phase 2 (mean –0.24, SD 0.20) and relatively higher values during the active engagement phase 3 (mean –0.15, SD 0.13). The exploratory post hoc analyses indicated a significant increase in emotional valence from Phase 2 to Phase 3 (adjusted P=.012). Conclusions: SAR-based interventions were associated with reductions in postoperative pain in hospitalized children. Although overall emotional differences across phases were not statistically significant, the observed pattern suggests that active engagement may be associated with more positive emotional responses compared to passive engagement. These findings highlight the potential importance of interaction quality in SAR interventions and provide insight into the processes underlying their clinical effects in pediatric care. Clinical Trial: No
Background: Online reviews of health care services represent a growing source of unsolicited, citizen-generated data that can complement traditional instruments for monitoring public perception of hea...
Background: Online reviews of health care services represent a growing source of unsolicited, citizen-generated data that can complement traditional instruments for monitoring public perception of health systems. However, longitudinal analyses examining how citizen perception evolved before, during, and after the COVID-19 pandemic remain scarce, and existing studies have rarely differentiated between levels of care. Objective: This study aimed to examine the longitudinal evolution of public perception of a regional public health system over a ten-year period, with particular attention to differences between primary care and hospital services, and to assess whether the COVID-19 pandemic produced a temporary disruption or a more persistent structural shift in citizen evaluation of health care. Methods: A retrospective longitudinal observational study was conducted using 47,589 online reviews of 812 public health care facilities in Andalusia, Spain, collected from Google Maps and covering the period 2016–2025. Reviews were classified as positive or negative based on star ratings, validated against manual annotation using Cohen's kappa. The proportion of negative reviews was analyzed across three periods: pre-pandemic (2016–2019), pandemic (2020–2021), and post-pandemic (2022–2025). Structural breaks were identified using change-point detection analysis. Logistic regression models with robust standard errors clustered at the facility level were used to quantify differences in negative sentiment across levels of care and over time. Results: The proportion of negative reviews increased from 38.7% in the pre-pandemic period to 73.7% during the pandemic, remaining elevated at 66.5% in the post-pandemic period. Change-point detection identified March 2020 as a major structural break. The pandemic had markedly different effects across levels of care: negative reviews in primary care rose from 34.8% to 81.9% during the pandemic, remaining at 75.7% post-pandemic, whereas hospital care showed a more moderate increase from 43.8% to 55.6%, remaining stable thereafter. Logistic regression models confirmed that the trajectory of negative perception in primary care diverged significantly from hospital care during and after the pandemic, with interaction terms indicating substantially higher odds of negative reviews in primary care during the pandemic (OR = 5.28, 95% CI 3.95–7.07) and post-pandemic periods (OR = 3.66, 95% CI 2.66–5.03). Conclusions: The findings indicate that the COVID-19 pandemic was associated with a persistent structural shift in public perception of health care services rather than a temporary fluctuation, and that this shift was disproportionately concentrated in primary care. The sustained deterioration in citizen perception of primary care observed years after the acute crisis suggests that post-pandemic recovery strategies should explicitly address the post-crisis phase and prioritize the relational and communicative dimensions of primary care alongside structural capacity. Large-scale digital trace data offer a scalable and continuous complement to traditional patient satisfaction instruments for monitoring health system legitimacy over time.
As AI agents become increasingly capable of autonomous action in health care, a prerequisite remains underaddressed: the persistent, structured memory that makes such action contextually meaningful. C...
As AI agents become increasingly capable of autonomous action in health care, a prerequisite remains underaddressed: the persistent, structured memory that makes such action contextually meaningful. Clinicians face cognitive overload not from any single task but from the erosion of decision context over time. Existing tools—personal knowledge management frameworks, LLM built-in memory, and autonomous agents—each address parts of this problem but leave gaps in auditability, portability, or contextual persistence. This Viewpoint argues that memory should precede action: before AI agents can act meaningfully, they need persistent, human-controlled context. We describe externalized living memory—a structured knowledge base that both human and AI can read and write—as it emerged from the first author's practice as a cardiovascular radiologist and division chief. The approach is organized as a layered architecture with a routing table for scalable context loading and a governance hierarchy for sustainable maintenance. We illustrate the approach through clinical vignettes, compare it with existing solutions, and discuss limitations including the small-team evidence base and maintenance costs. An open-source implementation with templates and setup instructions accompanies this paper.
Background: The dissemination of accurate, timely pharmaceutical product information is foundational to patient safety. Electronic labeling (e-labeling) has emerged as promising digital health approac...
Background: The dissemination of accurate, timely pharmaceutical product information is foundational to patient safety. Electronic labeling (e-labeling) has emerged as promising digital health approach that enables real-time updates, enhanced accessibility, and integration within digital health information ecosystems. Despite growing global adaptation, regulatory frameworks remain fragmented across jurisdictions, creating disparities in health information equity and posing challenges to patient safety in interconnected healthcare environments. Objective: This study aimed to systematically identify and compare national pharmaceutical e-labeling policies across major regulatory jurisdictions, assess regulatory readiness and policy maturity, characterize implementation strategies, identify critical governance gaps, and provide evidence-based recommendations for a harmonized global framework advancing digital health equity. Methods: We systematically searched and reviewed publicly available pharmaceutical e-labeling policy documents from International Coalition of Medicines Regulatory Authorities (ICMRA) member countries covering January 2012 to January 2024. Two independent reviewers screened and classified documents as “legal” (binding regulations) or “non-legal” (guidance) based on regulatory authority. A two-step analysis was employed: a hybrid thematic coding approach combining deductive coding based on three predetermined domains (scope and implementation, operational infrastructure, advanced digital health considerations) with inductive coding to generate nine themes from the data, followed by a comparative analysis of domain coverage, regulatory level, and regional distribution. Inter-rater reliability was assessed using Cohen's kappa coefficient. Policy maturity was classified across five levels based on regulatory authority and scope. Results: Among 22 ICMRA member countries, only 11 (50.0%) had accessible e-labeling policies, yielding 34 documents: 5 legal (14.7%), 29 non-legal (85.3%). All 11 countries addressed foundational implementation, scope and operational infrastructure, but only 9 (81.8%) covered advanced considerations including accessibility, interoperability, with electronic health records, or personalized information. Legally binding framework for basic implementation existed in merely 4 countries (36.4%) and only 2 countries (18.2%) had binding provisions for advanced features. The most sophisticated digital health capabilities, interoperability with clinical systems and personalized patient information, were addressed in only 3 countries (27.3%), through non-legal guidance. A sequential policy progression trend was observed, with nations prioritizing operational infrastructure before advancing to more complex considerations. Dual independent coding achieved excellent reliability (Cohen's kappa ≥0.95 for all domains). Conclusions: Global pharmaceutical e-labeling policy development remains substantially fragmented with critical gaps in legal binding frameworks, technical standards, and patient-centered digital health integration. Only one-third of major regulatory markets possess legally binding implementation frameworks, while advanced interoperability and personalization capabilities remains largely unexplored. These findings highlight the urgent need for international harmonization through inter-agency collaboration, including adoption of binding minimum standards, machine-readable format mandates, and interoperability protocols aligned with global health informatics standards.
Background: Telemedicine offers potential benefits for patients with chronic neuro-orthopedic conditions, yet its integration into routine care remains limited. Objective: This study evaluated stakeho...
Background: Telemedicine offers potential benefits for patients with chronic neuro-orthopedic conditions, yet its integration into routine care remains limited. Objective: This study evaluated stakeholder perspectives on implementing complementary telemedicine consultations at our institution. Methods: This study followed a three-round Delphi design. The first round consisted of iterative questionnaire development in collaboration with two neuro-orthopedic specialists. The second and third rounds involved cross-sectional surveys distributed to patients and parents, healthcare professionals, and administrative staff. Participants completed items assessing demand, feasibility, preferred telemedicine settings, and perceived advantages and disadvantages. The first survey was conducted between August and November 2024, and the second between January and February 2025. Results: A total of 139 participants responded to the first survey, of whom 78 also replied to the second. High feasibility was reported, with most participants having access to necessary technology (78.6%) and an undisturbed environment (80.3%). The most frequently cited disadvantage across all groups was the inability to perform a physical examination. Potential advantages included faster management of acute concerns, shorter waiting times, improved interprofessional collaboration, and fewer last-minute appointment cancellations. The discussion of results was the preferred telemedicine setting in both rounds for patients/parents. Stakeholders also expressed strong support for multiprofessional consultations, particularly involving physiotherapists and orthopedic technicians. Conclusions: There is clear demand for multiprofessional telemedicine consultations in neuro-orthopedics. Based on stakeholder consensus, result-discussion appointments represent the most suitable starting point for implementation, with potential to improve accessibility, reduce travel burden, and support more flexible workflow organization. Clinical Trial: Since this is not a RCT we did not register this study.
Background: Online medical consultation (OMC) optimizes health system performance by enhancing service efficiency and improving healthcare accessibility. It is primarily used in areas such as disease-...
Background: Online medical consultation (OMC) optimizes health system performance by enhancing service efficiency and improving healthcare accessibility. It is primarily used in areas such as disease-related advisory services and post-diagnosis care management, although its widespread adoption has yet to be achieved. Objective: This study aims to analyze inpatients’ preferences, relative importance, and willingness to pay (WTP) for OMC attributes. Methods: This study employed a discrete choice experiment (DCE) to examine inpatients’ preferences for 7 attributes of OMC. Data were collected through face-to-face surveys conducted between June and November 2023 and were analyzed using mixed logit model. Based on the results of the mixed logit model, the relative importance was derived by calculating the difference between the utility values of the highest and lowest levels of each attribute as a percentage of the total utility range; the willingness to pay (WTP) was estimated by computing the ratio of the coefficients of non-monetary attributes to the coefficient of the monetary attribute. Results: At a significance level of 0.05, inpatients were significantly more likely to choose doctors with higher professional titles, higher-level hospitals (particularly provincial Class A tertiary hospitals), shorter waiting times, higher patient satisfaction, more user-friendly processes, stronger perceived privacy and security (medium levels were not a significant factor), and lower costs. Inpatients also exhibited a higher WTP, with the exception of moderate levels of privacy and security. Attributes were ranked in descending order of importance as follows: hospital level (34.35%), patient satisfaction (19.14%), doctor’s professional title (16.87%), platform usability (10.08%), fee (9.04%), privacy and security (5.42%), and waiting time (5.1%). Conclusions: Hospitals should focus on developing OMC platforms that are user-friendly, inclusive, and equipped with transparent evaluation systems. The government needs to clearly define the functional scope of OMC according to different hospital tiers and support its implementation by increasing investment, implementing dynamic pricing, exploring gradual inclusion in medical insurance reimbursement, and strengthening data security protections.
Background: Cancer-related fatigue (CRF) affects 60–90% of cancer patients yet remains underreported. Heart rate variability (HRV) via wearable devices offers passive symptom monitoring. Prior work...
Background: Cancer-related fatigue (CRF) affects 60–90% of cancer patients yet remains underreported. Heart rate variability (HRV) via wearable devices offers passive symptom monitoring. Prior work showed strong LF/HF–fatigue correlation (ρ = 0.86) in lung cancer, but generalizability to multi-cancer populations is unknown. Objective: This study aimed to evaluate whether three complementary wearable HRV-derived metrics—night LF/HF mean (autonomic level), LF/HF SD (autonomic instability), and the sleep-phase disorder ratio (episodic dysregulation)—can screen for multiple cancer symptoms in hospitalized patients with mixed cancer types. Methods: Forty-six hospitalized cancer patients (lung 28%, colorectal 22%, breast 13%, other 37%) wore PPG-based smart wristbands continuously while completing the Brief Fatigue Inventory (BFI), Edmonton Symptom Assessment System (ESAS), and Pittsburgh Sleep Quality Index (PSQI). Three HRV-derived metrics were examined: night LF/HF mean (autonomic level), LF/HF standard deviation (SD; autonomic instability), and the sleep-phase LF/HF disorder ratio (DR; episodic dysregulation). Results: The disorder ratio yielded nearly double the significant correlations of conventional HRV (28.6% vs. 15.1%). For breathlessness ≥ 4, night LF/HF achieved AUC = 0.868 (100% sensitivity). In combined models controlling for mean LF/HF, LF/HF SD was the only metric to independently predict BFI Total (P=.048) and ESAS Fatigue (P=.049). Night LF/HF and DR predicted somatic symptoms; LF/HF SD uniquely predicted fatigue. Conclusions: The complementary three-metric framework—level, instability, and episodic dysregulation—captures distinct symptom pathways and extends the LF/HF–fatigue relationship to heterogeneous cancer populations. Clinical Trial: N/A
Background: The rapid expansion of digital technologies alongside a growing aging population has reshaped contemporary society. While digital tools offer new opportunities for social connectedness, ol...
Background: The rapid expansion of digital technologies alongside a growing aging population has reshaped contemporary society. While digital tools offer new opportunities for social connectedness, older adults often encounter barriers to technology adoption and must adapt to increasingly digital environments. Although prior research has examined factors influencing older adults’ use of technology, limited attention has been given to the role of religious beliefs and spiritual resources. This gap is notable given the potential of spirituality to shape individuals’ attitudes, practices, and sense of agency in engaging with technology. Objective: This study aims to examine how spiritual resources influence technology-mediated communication among elderly residents in religious care settings in Hong Kong. Methods: This study employed a qualitative research design using Interpretative Phenomenological Analysis (IPA). In-depth interviews were conducted with 18 elderly residents, whose average age was 85.75 years (SD = 11.52). IPA was used to explore how individuals with varying levels of spiritual resources make sense of technology use, communicate about it, and integrate it into their daily routines. Results: The findings indicate that spiritual frameworks provide interpretive resources that shape how older adults make meaning of technology use. These frameworks also facilitate hybrid communication practices that combine traditional and digital approaches, and influence individuals’ sense of agency in making technology-related decisions, even within institutional constraints. Participants with higher levels of spiritual resources demonstrated richer communication vocabularies, developed innovative spiritual-digital networks, and exercised greater boundary management compared to those with fewer spiritual resources. Overall, spirituality enabled residents to integrate technology into their existing worldviews while partially mitigating structural limitations. Conclusions: The study highlights the important role of spiritual frameworks in shaping technology-mediated communication among older adults. It underscores the value of incorporating spiritual perspectives into research and practice aimed at understanding and supporting technology use in aging populations.
Background: Breast cancer is the most commonly diagnosed cancer worldwide and the leading cause of cancer death among women in low- and middle-income countries (LMICs) (Sung et al., 2021). Survival di...
Background: Breast cancer is the most commonly diagnosed cancer worldwide and the leading cause of cancer death among women in low- and middle-income countries (LMICs) (Sung et al., 2021). Survival disparities between high-income and LMICs are largely attributable to late-stage presentation and failure to receive timely, definitive surgery (World Health Organization [WHO], 2023). Health-system barriers—including shortages of trained surgical teams, weak referral pathways, affordability constraints, and limited theatre availability—prevent many eligible patients from accessing mastectomy or breast-conserving surgery. While a range of health policy and system-level interventions have been implemented, the evidence base remains fragmented and unsynthesised. Objective: To identify and synthesise evidence on health policy and health-system interventions implemented and evaluated in LMICs to improve access to definitive breast cancer surgery, and to assess their effects on: (1) receipt/coverage of indicated surgery; (2) timeliness of surgery; and (3) surgical service availability/capacity, including implementation and equity considerations where reported Methods: This is a pre-registered systematic review protocol (PROSPERO CRD420261333028). We will search CENTRAL, Embase, PubMed, Scopus, LILACS, Web of Science, and grey literature sources from January 2000 to March 2026 with no language restrictions. Evaluative quantitative, qualitative, and mixed-methods studies of implemented interventions will be included. Risk of bias will be assessed using RoB 2 (Sterne et al., 2019), ROBINS-I (Sterne et al., 2016), CASP, and MMAT 2018. Synthesis will follow a structured narrative approach using the WHO Health System Building Blocks framework, with meta-analysis where feasible. Certainty of evidence will be rated using a GRADE-informed approach. Results: This section will be completed upon review completion (target: 15 June 2026). Results will be presented in accordance with PRISMA 2020 guidelines and will include: a PRISMA flow diagram documenting records identified, screened, and included; a summary of included study characteristics; risk of bias assessments across all included studies; and a structured narrative synthesis of intervention effects, organised by the WHO Health System Building Blocks framework. Where sufficient homogeneity permits (≥3 comparable studies), meta-analytic results will be reported with pooled effect estimates, confidence intervals, and heterogeneity statistics (I², Cochran's Q). GRADE evidence profiles will be presented for primary outcomes. Conclusions: This systematic review will provide the first comprehensive synthesis of health policy and health-system interventions targeting access to definitive breast cancer surgery in LMICs. By mapping the evidence base across all three primary outcome domains — receipt of surgery, timeliness, and service availability — and situating findings within the WHO Health System Building Blocks framework, the review is designed to produce actionable intelligence for policymakers, health system planners, and international funders.
Findings will be disseminated through peer-reviewed publication in English and will be aligned with the WHO Global Breast Cancer Initiative implementation framework. The review addresses a critical accountability gap: as international investment in LMIC breast cancer surgical programmes grows, there is an urgent need for rigorous evidence on what works, for whom, and under what contextual conditions. Results are expected to directly inform programme design, resource allocation decisions, and future research priorities in global cancer surgery. Clinical Trial: PROSPERO (CRD420261333028)
Background: Post-acute intervention is pivotal to preventing functional disability among stroke patients. Nurse-led rehabilitation intervention can be an effective way to improve functional ability an...
Background: Post-acute intervention is pivotal to preventing functional disability among stroke patients. Nurse-led rehabilitation intervention can be an effective way to improve functional ability and reintegration into society of post-stroke patients. Objective: This study aims to evaluate the effectiveness of a structured nurse-led multidisciplinary post-acute rehabilitation program to improve the self-care functional disability among stroke patients. Methods: This is a parallel (1:1), open-label, prospective randomized controlled trial that has been conducted at the National Institute of Neurosciences and Hospital (NINS&H), Dhaka, Bangladesh. We include participants who are 18 years old and above, both males and females, regardless of stroke type or time, modified Rankin Scale (mRS) 2-4 with disability on upper and/or lower limb(s), physician advice for rehabilitation, require assistive devices for activities of daily living (ADL), possess a smartphone, and are willing to provide consent and participate in the study. We exclude individuals who are involved in other clinical trials, those planning to undertake institutional rehabilitation services, and those with communication difficulties (speech impairments). The intervention group receives rehabilitative education, assistive devices, and teleservices for biweekly follow-up after hospital discharge. The control group receives usual discharge education and advice on routine follow-up at discharge. The primary outcome measures functional independence after 6 months of rehabilitation. Secondary outcomes include the evaluation of 1) rehabilitation adherence, 2) motor function, 3) self-efficacy, 4) emotional status, and 5) activity participation of post-stroke disabled patients after 6 months of rehabilitation. Researchers evaluate patients at baseline, at midline after 3 months, and at endline after 6 months of intervention. Results: The patient's enrolment started in February 2026, and follow-up will be completed in October 2026. A total of 166 patients will be recruited in the intervention (n = 83) and control (n = 83) groups. This study was approved by the institutional review board of NINS&H, Dhaka, Bangladesh, on January 18, 2026. Conclusions: The results can contribute to a scalable, culturally appropriate nurse-led rehabilitation intervention to ensure smooth post-stroke daily living activities with disabilities. Clinical Trial: ClinicalTrials.gov NCT07384650; https://clinicaltrials.gov/search?id= NCT07384650
Background: Pterygium and cataract frequently co-exist in populations with high ultraviolet light exposure, particularly in rural India. Both conditions impair visual acuity and reduce quality of life...
Background: Pterygium and cataract frequently co-exist in populations with high ultraviolet light exposure, particularly in rural India. Both conditions impair visual acuity and reduce quality of life. Simultaneous small incision cataract surgery (SICS) combined with pterygium excision offers the advantages of a single surgical session, faster rehabilitation, and lower cost compared with sequential procedures. However, SICS employs a superior scleral tunnel incision of 6.5 to 7 mm, which itself induces against-the-rule surgically induced astigmatism. The net astigmatic outcome of simultaneous SICS and pterygium excision has not been prospectively characterised in a resource-limited setting. Objective: This protocol describes a prospective, single-arm interventional study designed to quantify the change in magnitude and axis of corneal astigmatism from baseline to Day 30 following a protocol-defined simultaneous surgical intervention consisting of manual SICS with posterior chamber intraocular lens implantation followed by primary nasal pterygium excision using the bare sclera technique in a single operative sitting. Methods: This is a prospective, single-arm interventional study with within-participant comparison conducted at the Department of Ophthalmology, Acharya Vinoba Bhave Rural Hospital, DMIHER (Deemed University), Wardha, India. All enrolled patients (n=100, aged ≥35 years with visually significant cataract and grade 1–2 nasal primary pterygium) receive the standardised surgical intervention: manual SICS with posterior chamber IOL implantation followed by primary nasal pterygium excision using the bare sclera technique in a single operative sitting. Sample size (n=100) was calculated using McNemar’s test for paired proportions, assuming 9.45% preoperative and 31.49% postoperative prevalence of clinically significant astigmatism (>1.0 D, a threshold representing astigmatism that commonly warrants spectacle correction), with α=0.05 and 95% power (minimum n=88), adjusted for 10–15% attrition.
The primary outcome is change in keratometric corneal astigmatism (diopters and axis) from baseline to Day 30. Secondary outcomes include Alpins vector analysis of surgically induced astigmatism, correlation of pterygium size with preoperative astigmatism, change in uncorrected and best-corrected visual acuity, refractive surprise rate, pterygium recurrence, patient satisfaction (pilot-tested questionnaire), and complications. Follow-up assessments occur at Day 1, Day 15, and Day 30. Results: Ethical approval for the study was obtained from the Institutional Ethics Committee of Datta Meghe Institute of Higher Education and Research on June 30, 2025. Participant recruitment began on November 1, 2025 and is ongoing at Acharya Vinoba Bhave Rural Hospital, Wardha. Data collection is expected to continue through 2027, with results anticipated to be reported in 2028. Conclusions: This protocol will generate prospective interventional data evaluating corneal astigmatism and visual outcomes of a protocol-defined simultaneous surgical procedure combining manual SICS and bare sclera pterygium excision in a high-volume rural ophthalmology setting. Findings will inform surgical planning, IOL power selection strategies, and patient counselling in resource-limited settings where SICS is the predominant cataract surgical technique. Clinical Trial: Clinical Trials Registry of India (CTRI): CTRI/2025/10/095785; Registered October 9, 2025 (Prospective).
Background: High myopia (spherical equivalent refractive error (SER) ≤ -6.00 diopters (D)) is a major global cause of visual impairment associated with structural ocular complications including myop...
Background: High myopia (spherical equivalent refractive error (SER) ≤ -6.00 diopters (D)) is a major global cause of visual impairment associated with structural ocular complications including myopic macular degeneration, retinal detachment, glaucoma, and cataract. Structural alterations including axial elongation, corneal remodeling, and fundus changes remain incompletely characterized across varying degrees of high myopia in Indian tertiary care settings. Objective: To evaluate ocular biometric parameters and fundus characteristics in patients with high myopia and examine their association with the severity of myopia. Methods: This hospital-based cross-sectional observational study will be conducted at the Department of Ophthalmology, Acharya Vinoba Bhave Rural Hospital (AVBRH), Jawaharlal Nehru Medical College (JNMC), Datta Meghe Institute of Higher Education and Research (DU) (DMIHER), Wardha, India. A minimum of 140 participants aged 10 to 50 years with high myopia (SER ≤ -6.00 D in at least one eye on cycloplegic refraction) will be consecutively enrolled over 2 years. Participants will undergo standardized ophthalmic assessment including best-corrected visual acuity (BCVA; converted to logMAR for analysis), cycloplegic refraction confirmed by autorefractometry, slit-lamp biomicroscopy, automated keratometry, ultrasound pachymetry, contact A-scan biometry, and dilated fundus examination. Fundus findings will be graded using the Meta-Analysis for Pathologic Myopia (META-PM) classification. Data normality will be assessed by the Shapiro-Wilk test. Associations between refractive error severity and ocular parameters will be evaluated using Pearson or Spearman correlation coefficients. Group comparisons will be conducted using one-way analysis of variance (ANOVA) or Kruskal-Wallis tests. Categorical comparisons will use the chi-square test. Inter-eye correlation will be addressed using generalized estimating equations (GEE). All analyses will be performed in SPSS version 23. Results: The study has received ethical approval from the Institutional Ethics Committee (IEC) of DMIHER (DU) (Ref. No. DMIHER(DU)/IEC/2025/393; July 9, 2025). Participant enrollment commenced in January 2026 and is ongoing. Results are anticipated for publication by 2027 to 2028. Conclusions: This study will provide region-specific data on ocular biometric and structural changes in high myopia to inform evidence-based clinical monitoring and management strategies. Clinical Trial: Registered with the Clinical Trials Registry of India (CTRI/2025/12/099253; registered December 16, 2025).
Background: Imaging quality control (QC) is a safety‑critical task in computed tomography (CT) operations, requiring technologists to assess image artifacts, protocol adherence, and acquisition qual...
Background: Imaging quality control (QC) is a safety‑critical task in computed tomography (CT) operations, requiring technologists to assess image artifacts, protocol adherence, and acquisition quality under time pressure.
Artificial intelligence based decision support systems are increasingly introduced to assist these tasks; however, poorly designed systems can degrade trust, increase cognitive workload, and undermine human accountability. In regulated healthcare environments, decision support must not only improve performance but also preserve human judgment and responsibility. Objective: This study aimed to evaluate how human‑centered design features : specifically confidence bands, concise reason codes, and frictionless override controls : influence trust calibration, cognitive workload, and task performance when using AI‑assisted decision support for CT imaging quality control. Methods: A mixed‑methods field study was conducted at a single CT imaging site.
Practicing CT technologists performed routine imaging QC tasks under two counterbalanced conditions: (1) a baseline workflow without AI assistance and (2) an AI‑assisted workflow using concept‑true decision support prototypes.
The AI‑assisted interface presented recommendations with qualitative confidence bands, brief reason codes, and one‑click accept or override controls with audit logging.
Quantitative measures included time‑on‑task, error and near‑miss rates, the System Usability Scale (SUS), and NASA Task Load Index (NASA‑TLX).
Trust and reliance were assessed using Likert‑scale items and observed override behavior. Semi‑structured interviews were conducted to capture qualitative insights. Results: AI‑assisted decision support was associated with reduced time‑on‑task and lower error and near‑miss rates compared to the baseline workflow when transparent explanations and confidence indicators were provided.
Cognitive workload, particularly mental and temporal demand, was lower in the AI‑assisted condition. Override behavior varied systematically with system confidence, indicating calibrated trust rather than blind reliance on automation. Qualitative findings highlighted the importance of visible reasoning, explicit uncertainty, and frictionless human control in supporting professional judgment. Conclusions: AI‑based decision support can improve CT imaging quality control without undermining human judgment when designed for trust, transparency, and accountability. Interfaces that make uncertainty explicit, explain recommendations, and preserve easy human override support calibrated reliance and reduced cognitive load. These findings offer practical human‑factors design guidance for deploying decision support in regulated healthcare operations Clinical Trial: Not applicable (non‑interventional field study).
Artificial intelligence (AI) is increasingly integrated into telehealth platforms that function as public health infrastructure, supporting disease surveillance, population-level screening, and health...
Artificial intelligence (AI) is increasingly integrated into telehealth platforms that function as public health infrastructure, supporting disease surveillance, population-level screening, and healthcare access in underserved communities. In the United States, telehealth regulation is largely determined at the state level, creating a patchwork of frameworks with inconsistent oversight of AI-enabled systems. This variation has consequences extending beyond clinical care into the equity of public health protections and the reliability of informatics infrastructure on which population health monitoring depends.
This article argues that fragmented state regulation creates governance gaps in AI-enabled telehealth. Drawing on a comparative assessment across all 50 states and informed by Salamon’s New Governance framework, the analysis evaluates variation in telehealth statutory frameworks, licensure models, transparency expectations, liability structures, and data governance protections.
The analysis finds that no U.S. state has established a comprehensive governance framework for AI-enabled telehealth. Of the 50 states, only 17 have implemented partial provisions related to AI, such as transparency requirements, algorithmic auditing, or liability considerations. However, most of these provisions remain vague and inconsistent. The remaining 33 states regulate telehealth without addressing the role of AI at all. This governance vacuum disproportionately affects the communities most dependent on AI-enabled telehealth, and it compromises population health data quality flowing into public health surveillance systems.
The article proposes targeted reforms including standardized disclosure requirements through model state legislation, distributed liability frameworks, mandatory algorithmic auditing, strengthened data governance standards, and an AI-Telehealth Interstate Compact. These reforms aim to ensure AI-enabled telehealth serves as reliable, equitable public health infrastructure rather than a source of uneven protections across populations.
Background: Perinatal mental health issues are now recognised as a global and significant public health concern. The global prevalence of Postnatal Depression (PND) is approximately 17.22%, and up to...
Background: Perinatal mental health issues are now recognised as a global and significant public health concern. The global prevalence of Postnatal Depression (PND) is approximately 17.22%, and up to 10% for fathers in Australia. While there is limited application of serious games to PND in fathers at present, there are analogous games for improving depression treatment, adherence, and engagement. Objective: This scoping review aims to examine technology-based interventions and serious games for PND and depression in fathers and non-birthing parents, identifying their design, effectiveness, and acceptability. Methods: Guided by the PRISMA-ScR framework, a comprehensive literature search was conducted between February and April 2025 across eight electronic databases. The screening process was conducted in three stages: title screening, abstract screening, and full-text screening. Articles were assessed to ensure they met all three inclusion criteria. Discrepancies or uncertainties during screening were resolved in discussion with a third reviewer, and consensus was reached in all cases after this discussion, which clarified inclusion eligibility. Results: There are only three studies met eligibility criteria following double screening. Across the included studies, findings indicate an emerging but limited evidence base. Although several interventions demonstrated promising signals of effectiveness and were generally well accepted by participants, the robustness of these findings is constrained by methodological weaknesses, heterogeneous outcome measures, and inconsistent reporting standards. There is very limited research focused on fathers and paternal PND and wellbeing. Evidence remains largely short-term, with most trials assessing outcomes only within the first ten weeks postpartum, leaving the long-term impact on paternal mental health, partner relationships, and father–infant bonding underexplored. Conclusions: The findings suggest that digital technologies provide a promising means of engaging fathers during the perinatal period by offering timely, accessible, and scalable support. Short message services, social media platforms, and online video resources can reduce barriers associated with time, geography, and stigma, making them well-suited to fathers who may otherwise be under-reached by conventional perinatal services. Importantly, features such as humour, baby-centred messaging, and daily digital check-ins demonstrate how interventions can be tailored to maintain engagement and encourage the translation of knowledge into everyday supportive behaviours.
Background: Naloxone is a life-saving opioid antagonist, but the alignment between real-time public engagement and overdose risk remains unclear across U.S. states. Google search data may offer novel...
Background: Naloxone is a life-saving opioid antagonist, but the alignment between real-time public engagement and overdose risk remains unclear across U.S. states. Google search data may offer novel infodemiologic insights to optimize public health responses. Objective: To characterize spatiotemporal trends in opioid overdose mortality, naloxone dispensing, and naloxone-related digital search interest across U.S. states and the District of Columbia (2019–2023), examine temporal associations between overdose mortality and naloxone search interest, and assess whether naloxone dispensing modifies this relationship. Methods: We conducted a retrospective longitudinal panel study integrating monthly opioid overdose mortality, Google Health Trends (GHT)–derived Naloxone search probability, and annual Naloxone dispensing rates for all 50 states and the District of Columbia from 2019 to 2023. We assessed spatiotemporal trends and temporal associations using Dumitrescu-Hurlin panel Granger causality, as well as statewise and rolling-window Granger analyses. Multivariable fixed-effects panel regression evaluated associations between overdose mortality, Naloxone dispensing, and search probability, including interaction effects. Results: From 2019 to 2023, the mean annual opioid overdose mortality rate was 31.2 per 100,000 population (range, 7.8–95.4), and the average annual Naloxone dispensing rate was 456 per 100,000 (range, 100–2,500), both with marked state-level variation. Naloxone search probability remained low overall (mean 66.8 ± 33.8 SD) but showed episodic spikes in several states. Panel Granger causality indicated that increases in overdose mortality significantly preceded increases in Naloxone search interest at 1- and 3-month lags (p < 0.05). Statewise Granger analyses after correcting variable ordering yielded no more significant states than expected by chance at any lag tested. In multivariable panel regression, overdose mortality (β = 0.29, p = 0.31), Naloxone dispensing (β = 0.010, p = 0.13), and their interaction (β = –0.0003, p = 0.37) were not significantly associated with Naloxone search probability. Conclusions: Panel-level Granger analysis identified significant temporal coupling between overdose mortality and Naloxone-related search probability at 1- and 3-month lags, though statewise analyses did not yield significant individual-state signals. Fixed-effects regression found no significant contemporaneous associations. These findings suggest that digital search signals capture short-term behavioral dynamics rather than structural access patterns. Integrating digital search surveillance with traditional overdose and dispensing metrics could enhance timely public health responses to opioid overdose risk.
Background: Suicide is a major public health concern in Malaysia. Relying solely on official mortality statistics often introduces significant reporting delays, hindering immediate public health respo...
Background: Suicide is a major public health concern in Malaysia. Relying solely on official mortality statistics often introduces significant reporting delays, hindering immediate public health response. In Malaysia, while the Suicide Mortality Rate (SMR) is the paramount metric for national health planning, official statistics suffer from a significant 1–2 year reporting lag, rendering prevention reactive. Objective: This study aimed to assess the utility of non-fatal intentional poisoning data—collected in near real-time by the National Poison Centre (NPC-USM)—as a sentinel proxy for estimating national SMR trends. Methods: Annual Incidence Rates (AIR) of NPC Suicidal Cases (2006–2024) were calculated using national population estimates. The NPC AIR was correlated with published Age-Standardized SMR (Lew et al., 2022) for the 2006–2019 period using Spearman’s Rank Correlation (rs). Simple Linear Regression (SLR) was utilized to model the associative relationship and generate scenario-based estimates for 2024. Results: A very strong positive correlation was found between the NPC AIR and the overall SMR (rs =0.800, p<0.001), and specifically with the Male SMR (rs =0.813, p<0.001), indicating strong trend alignment. SLR modeling showed the NPC AIR explained a significant portion of SMR variance (R2=0.763, p<0.001), resulting in a projected Age-Standardized SMR of 6.94 per 100,000 [95% CI: 6.12–7.76] for 2024. Analysis of age group distribution revealed that the 20–74 years demographic drives the strongest correlation with overall SMR (rs =0.769). The divergence between non-fatal attempts (+9.4%) and official fatalities (+81.0%) during the 2021 pandemic peak suggests a potential method substitution toward higher lethality, highlighting the NPC's utility as a responsive surveillance tool. Conclusions: We present an illustrative Clinicoeconomic Sensitivity Analysis demonstrating that the two-year strategic window provided by the NPC data allows for proactive prevention strategies. A conservative intervention scenario (10% reduction in mortality) suggests a potential societal value preservation of approximately RM 1.2 Billion. These findings suggest that NPC data is a robust, timely supportive indicator that should be integrated into Malaysia's national suicide prevention framework to bridge the gap in official mortality reporting.
Professionals, leaders, and institutions in healthcare and health research are rapidly adopting and integrating AI systems and chatbots into their regular work, but this poses risks for patients in th...
Professionals, leaders, and institutions in healthcare and health research are rapidly adopting and integrating AI systems and chatbots into their regular work, but this poses risks for patients in the case of patient and public involvement and engagement (PPIE). AI offers economical solutions for overstretched health systems and burned-out staff, already shows strengths in speeding up more long-term and minute research practices, and providing unique accessibility accommodations. However, AI can also be used to create personas and virtual PPIE panels, which can speak completely or partially for human patients with lived experience of conditions, thus minimising, distorting, or erasing their voices from collaborative research processes. AI pose risks through several distorting factors, including hallucinations, overconfidence, sycophancy, bias, sexism, and racism. Staley and Barron have argued that learning is the greatest outcome of PPIE. However, if researchers, professionals, and staff use AI chatbots in conjunction with or in lieu of human collaborators, the amount of learning that takes places is greatly reduced, according to AI expert and cultural critic, Ethan Mollick. In conclusion, we provide a checklist to guide professionals and researchers in ethical and responsible uses of AI that preserves the voices and roles of patients, members of the public, and lived experience.
Background: Chronic illness often disrupts individuals’ everyday lives and sense of self, particularly among young patients navigating identity formation. In the context of expanding digital media,...
Background: Chronic illness often disrupts individuals’ everyday lives and sense of self, particularly among young patients navigating identity formation. In the context of expanding digital media, online platforms have become important spaces where patients articulate illness experiences and seek support, yet how these processes unfold in non-Western contexts remains underexplored. Objective: This study investigates how young Chinese diabetics negotiate their experiences of diabetes, and reconstruct their identities in response to chronic illness in digital spaces.
Methods: A thematic analysis was conducted on 303 narrative posts from RedNote, a Chinese social media platform featuring intimate, user-generated storytelling. Methods: A thematic analysis was conducted on 303 narrative posts from RedNote, a Chinese social media platform featuring intimate, user-generated storytelling. Results: Four narrative types were identified: chaos (cognitive dissonance and disordered life), stigma (social withdrawal and discrimination), resilience (emotional fortitude and self-routinization), and solidarity (familial solidarity and reciprocal digital community). Conclusions: These narratives reveal diverse strategies through which young diabetics manage disruption and regain agency. The study extends biographical disruption theory by conceptualizing disruption as a dynamic, relational process of digital re-storying, offering insights for culturally sensitive health communication and online patient support.
Background: Digitalization is transforming the way we provide and experience medicine and healthcare. Experts have suggested various topics for medical curricula to keep pace with rapidly evolving kno...
Background: Digitalization is transforming the way we provide and experience medicine and healthcare. Experts have suggested various topics for medical curricula to keep pace with rapidly evolving knowledge; however, adapting these curricula remains a lengthy process that often lacks an interdisciplinary approach. Objective: This study examines the perspective of curriculum governing bodies and the boards responsible for curriculum operations at two medical universities towards the need for necessary curriculum changes to account for digitalization in medicine and the difficulties in adapting the curriculum to ever-growing knowledge. We identify and suggest ways for a more agile curriculum development. Methods: This study consists of an qualitative analysis of governing policy frameworks and a qualitative study involving 14 video interviews. The interviews were performed with members of university curriculum governing bodies and the boards responsible for curriculum operations. Results: Participants agreed that digitalization will reshape the medical profession by reducing physical contact, enhancing data-driven communication, and streamlining administrative processes. They highlighted the need for graduates to acquire digital literacy, critical evaluation skills, and a basic understanding of data and statistics. Yet, despite being designed as integrated program, participants noted curricula have become fragmented over time due to missing coordination between curriculum modules. Furthermore, current processes lead to a siloed perspective, where limited coordination between modules makes it difficult to implement new knowledge holistically. This lack of inter-module alignment emerged as a key barrier to coherent curricular change. Learning objectives were identified as a promising but underutilized tool for monitoring content, aligning modules, and ensuring that emerging topics like digitalization are integrated consistently. Conclusions: Participants agreed that current processes for monitoring and updating curricula are not efficiently designed and tend to be too static and focus on the advancement of subject-specific medical knowledge. To prepare current and future students for a rapidly changing world, curriculum processes should evolve from static, fragmented structures to more agile, integrated systems. By mapping the survey results to the curriculum development frameworks of Kern and Harden, we find that the challenge lies not so much in adding new content, but rather in designing curriculum processes that achieve a holistic overview. Strengthening the use of learning objectives as a dynamic monitoring and alignment tool offers a concrete opportunity to integrate rapidly changing knowledge holistically.
Background: Artificial intelligence (AI) has the potential to enhance clinical decision-making in high-acuity settings such as intensive care units (ICUs) and emergency departments (EDs). However, des...
Background: Artificial intelligence (AI) has the potential to enhance clinical decision-making in high-acuity settings such as intensive care units (ICUs) and emergency departments (EDs). However, despite promising performance, many AI-driven clinical decision support systems (AI-CDSS) face poor adoption due to issues of trust, workflow disruption, and alert fatigue. Understanding the human factors that shape clinician acceptance is critical to guide safe and effective implementation of AI-CDSS in acute care. Theoretical frameworks including the Systems Engineering Initiative for Patient Safety (SEIPS) 2.0 model and the Technology Acceptance Model (TAM) suggest that successful adoption requires addressing sociotechnical interactions among clinician trust, system design, organizational readiness, and task complexity, yet few empirical studies have applied these frameworks to AI-CDSS in acute care settings. Objective: This study aimed to evaluate emergency medicine and critical care clinicians’ perceptions of AI-CDSS and to identify key factors influencing adoption, including trust, design preferences, and workflow integration. Methods: A SEIPS 2.0 informed mixed-methods study evaluated ICU and ED clinicians from Emory Healthcare on perceptions of AI in clinical practice. An expert-reviewed survey (N=57) assessed clinician perceptions, trust, and implementation preferences. Semi-structured interviews (N=11) included A/B testing of AI-CDSS and clinical sepsis scenario to explore decision-making in context. Transcripts were thematically analyzed using Braun and Clarke's framework in ATLAS.ti. Quantitative data were analyzed descriptively. Results: Trust in AI varied significantly by patient acuity (Cochran's Q=30.40, p<0.0001): stable patients (75.4%, 95% CI: 62.9-84.8%), deteriorating patients (47.4%, 95% CI: 35.0-60.1%), and ICU/ED patients (43.9%, 95% CI: 31.8-56.7%). Internal consistency was acceptable-to-good across three scales (Cronbach's alpha: AI Perception=0.891, Trust=0.743, Implementation=0.740). Barriers included over-reliance, insufficient training, and data quality concerns. For the CDSS-AI design, clinicians preferred opt-in alerts (90%), evidence-linked recommendations (63%), and avoiding overt mention of AI increased acceptance (73%). Thematic analysis yielded 36 themes across six domains: trust and transparency, alert usability, workflow fit, data concerns, training needs, and perceived clinical impact. Clinicians favored AI-CDSS that preserved autonomy, minimized disruption, and provided transparent rationale. Conclusions: Adoption of AI-CDSS in critical care is not solely a technical issue, but a human-factors challenge centered on trust, transparency, and workflow compatibility. Applying the SEIPS 2.0 framework, we propose a phased implementation approach: beginning with lower-acuity applications where clinician trust is highest, then gradually extending to higher-acuity scenarios with enhanced transparency and override mechanisms. This graduated strategy addresses the critical interdependencies among people (trust), tools (design), organization (training), and task (clinical complexity) identified in this study.
Background: The integration of Generative Artificial Intelligence (GenAI) in healthcare is impeded by significant security challenges unaddressed by traditional frameworks, precisely the “data-in-us...
Background: The integration of Generative Artificial Intelligence (GenAI) in healthcare is impeded by significant security challenges unaddressed by traditional frameworks, precisely the “data-in-use” gap where sensitive patient data and proprietary AI models are exposed during active processing. Objective: To propose the Confidential Zero-Trust Framework (CZF), a novel security paradigm designed to address the data-in-use gap for GenAI healthcare workloads. Methods: We analyzed the healthcare threat landscape, regulatory requirements (such as HIPAA and GDPR), and the failure modes of traditional security architectures. Based on this analysis, we developed a multi-tiered architectural blueprint that synergistically combines Zero-Trust Architecture for granular access control with the hardware-enforced data isolation of Confidential Computing. Results: We detailed a blueprint for implementing the CZF on Google Cloud. The CZF provides a defense-in-depth architecture where data remains encrypted while in-use within a hardware-based Trusted Execution Environment (TEE). The framework’s use of remote attestation offers cryptographic proof of workload integrity, transforming compliance into a verifiable technical fact and enabling secure, multi-party collaborations previously blocked by security and intellectual property concerns. Conclusions: By closing the data-in-use gap and enforcing Zero-Trust principles, the CZF provides a robust and verifiable framework that establishes the necessary foundation of trust to enable the responsible adoption of transformative AI technologies in healthcare. Clinical Trial: n/a
Artificial intelligence is increasingly embedded in healthcare delivery, yet governance has not kept pace with its scale and influence. Algorithms now shape decisions about care allocation, treatment...
Artificial intelligence is increasingly embedded in healthcare delivery, yet governance has not kept pace with its scale and influence. Algorithms now shape decisions about care allocation, treatment duration, and patient prioritization, often operating with limited transparency and oversight. High-profile failures, including the Optum risk stratification algorithm and the nH Predict coverage system, show how governance weaknesses can produce large-scale and systematically unequal outcomes. Existing approaches remain analytically incomplete, typically explaining either why governance instruments fail or where breakdowns occur within the care delivery process, but rarely both together. This gap leads to recurring failures being treated as isolated incidents rather than structural patterns. To address this limitation, this paper proposes a diagnostic framework that integrates policy tool analysis with the structure–process–outcome model. The resulting 4×3 matrix links governance characteristics to stages of care delivery, enabling more precise identification of how failures emerge and propagate. Applied to two widely documented cases, the framework reveals consistent governance deficits and offers a structured basis for anticipating risks and strengthening oversight.
Background: Large language models (LLMs) such as ChatGPT, Claude, and Llama have demonstrated strong performance on general medical knowledge assessments, but their accuracy in specialty specific doma...
Background: Large language models (LLMs) such as ChatGPT, Claude, and Llama have demonstrated strong performance on general medical knowledge assessments, but their accuracy in specialty specific domains like Obstetrics and Gynecology (OBGYN) is less well characterized. Prior studies suggest high overall performance, but topic-specific proficiency across OBGYN subspecialties has not yet been evaluated, highlighting the need to assess their performance to inform safe integration into resident use and education. Objective: To benchmark the accuracy of contemporary LLMs on OBGYN knowledge using board-style question stems across subspecialty domains. Methods: We selected 50 questions from each of six Personal Review of Learning in Obstetrics and Gynecology (PROLOG) volumes, covering core OBGYN topics (total 300 questions). Three LLMs (ChatGPT-4, Claude 3.5, and Llama 3.1) were prompted to answer the entire set of 300 questions in topic-based blocks of 50. This was repeated over six independent sessions, totaling 1,800 question entries for each model, to obtain an average performance measure and minimize memory bias. Model responses to each individual question were graded against the answer key provided in the PROLOG volumes. We utilized a binary scoring system at the individual question level. A response was ‘correct’ only if it matched the single best answer as defined by the PROLOG volume, and ‘incorrect’ if it did not. Average performance across sessions was compared against the 2024 national Council on Resident Education in Obstetrics and Gynecology (CREOG) resident exam average as a contextual benchmark. Kruskal-Wallis tests, pairwise comparisons, and effect size comparisons using Cohen’s d were used to assess differences in performance across models and topics. Results: Overall accuracies were: 76% (Claude 3.5), 70% (ChatGPT-4), and 67% (Llama 3.1). Claude 3.5 outperformed the other models overall and in most topic areas, with the largest differences observed in Obstetrics and Reproductive Endocrinology. Accuracy was highest in Patient Management in the Office (84–86% across models) and lowest in Urogynecology and Pelvic Reconstructive Surgery (59–69%). Although comparisons are limited because PROLOG and CREOG questions are not identical, the reported national CREOG average serves as an indirect contextual benchmark. Within this context, average LLM performance on PROLOG questions (67%-76%) exceeded the reported national CREOG average across all resident levels (66%), but ChatGPT-4 (70%) and Llama 3.1 (67%) did not reach the average performance level of a PGY-4 resident (71%). Conclusions: LLM accuracy overlapped with reported national CREOG averages. Claude 3.5 outperformed ChatGPT-4 and Llama 3.1, exceeding PGY-4 accuracy. While promising as educational adjuncts, LLMs currently operate at a trainee-level and should complement, not replace, traditional clinical training.
Faculty mentorship programs are essential mechanisms for promoting career development, institutional retention, and academic leadership in medical universities. Yet the design, digital delivery, and s...
Faculty mentorship programs are essential mechanisms for promoting career development, institutional retention, and academic leadership in medical universities. Yet the design, digital delivery, and structured implementation of such programs within international medical institutions remain underexplored. This paper describes the rationale, design, and implementation of the Empower Xchange (EX) Foundations in Mentorship Certificate—a 16-hour, digitally delivered professional development program developed at St. George’s University (SGU), Grenada, West Indies, through a cross-departmental collaboration between the School of Medicine Faculty Mentoring Program and the Leadership Excellence in Academics and Development (LEAD) Division.
Drawing on faculty needs assessments, evidence-based pedagogical frameworks, and accreditation standards, the program delivers a structured mentorship training curriculum via the institution’s Learning Management System (Sakai), supplemented by Microsoft Teams and SharePoint. The certificate course is organized around five core design pillars: orientation and training, mentor-mentee responsibilities, learning resources, authentic engagement, and recognition and celebration. Instructional strategies include scenario-based tasks, reflective practice, peer mentoring circles, goal-setting frameworks, and portfolio building, organized through a mnemonic competency framework (3Ps, 2Ts, 1C). Mentorship goals are anchored across five developmental domains: leadership, teaching, curriculum development, research, and career advancement.
Early implementation outcomes indicate meaningful faculty engagement across career stages and departments within the School of Medicine. The paper presents a replicable, five-pillar framework that other international health professions education institutions may adapt to design their own digitally delivered mentorship certificate programs. Implications for faculty development practitioners, program designers, and institutional leaders are discussed, along with limitations and directions for future outcome evaluation.
Background: Loneliness is recognized as a global health threat. Older adults are vulnerable to loneliness due to life-changes common in old age. While individual risk factors of loneliness in old age...
Background: Loneliness is recognized as a global health threat. Older adults are vulnerable to loneliness due to life-changes common in old age. While individual risk factors of loneliness in old age are well-documented, contextual factors are scarcely explored, such as digitalization. Rapid digitalization underscores the need to explore the long-term effect of use of digital devices on loneliness. Objective: To explore whether and how the use of digital devices is associated with changes in loneliness over a ten-year period in a population-based sample of older adults. Methods: Data were obtained from the Swedish Adoption/Twin Study of Aging (SATSA) (N=771; mean age 69.4 years). Digital use use and loneliness was assessed across five waves between 2004 and 2014. Age, sex, education, living situation, self-rated health, and the personality trait openness were assessed at baseline. Growth Mixture Modeling was employed to identify latent trajectories of loneliness, and multinomial logistic regression predicted class membership based on baseline digital use and covariates. Results: Three latent loneliness classes were identified: Class 1 (10.5%; high intercept and significant increases in loneliness), Class 2 (33.2%; intermediate stable loneliness), and Class 3 (56.3%; low stable loneliness). Higher digital use at baseline significantly decreased the odds of belonging to the high-increasing loneliness group (Class 1) compared to the low-stable group (Class 3; OR 0.76, p=0.02, CI: 0.60-0.96). When comparing the two groups with higher loneliness levels (Class 1 vs. Class 2), digital use was the only significant predictor; higher use lowered the odds of experiencing increasing loneliness over time (OR 0.77, p=0.04). Differences between classes were not explained by the personality trait of openness to experience. Conclusions: Higher use of digital devices is associated with lower and more stable levels of loneliness over time. These findings suggest that digital technology might serve as an effective non-invasive tool to combat loneliness in older populations.
Background: Telerehabilitation (TR) is an important option for patients with post-COVID-19 condition (PCC). However, current evidence on its effectiveness remains inconsistent, and the impact of diffe...
Background: Telerehabilitation (TR) is an important option for patients with post-COVID-19 condition (PCC). However, current evidence on its effectiveness remains inconsistent, and the impact of different delivery modes is not fully understood. Objective: This study evaluated the effects of telerehabilitation on health-related quality of life (HRQoL), physical capacity, and symptom burden in adults with PCC, and to determine whether delivery mode (synchronous vs. asynchronous) modified these effects. Methods: We searched PubMed/MEDLINE, Cochrane Library, Web of Science, Scopus, Embase, EBSCO, and PEDro for randomized controlled trials (RCTs) published from January 2020 to December 2025. The primary outcome was HRQoL; secondary outcomes included functional capacity (6MWT, STS), dyspnea, fatigue, and HADS scores. Risk of bias was assessed using the Cochrane RoB 2 tool. Data were pooled using random-effects models with the Hartung-Knapp-Sidik-Jonkman (HKSJ) adjustment. Results: Twenty-three RCTs involving 2,320 participants were included. TR resulted in significant improvements in HRQoL (standardized mean difference [SMD] 1.26, 95% CI 0.07 to 2.45; P=.04), dyspnea (SMD 1.95, 95% CI 0.60 to 3.31; P=.005), and functional aerobic capacity (6MWT: mean difference [MD] 77.79 m, 95% CI 30.44 to 125.14; P=.001). Fatigue was also significantly reduced (SMD 0.89 95% CI: 0.16 to 1.62; P=.02), but no significant effects were observed for lower limb strength (STS: SMD 0.55, 95% CI -0.15 to 1.25; P=.12) or mental health outcomes (HADS: SMD 0.12, 95% CI −0.13 to 0.38; P=.36). No significant differences were observed be-tween delivery modes for most outcomes (P>.05), except for lower limb strength, where a significant subgroup difference was noted (P=.02). Conclusions: Telerehabilitation is effective for improving quality of life, aerobic capacity, and alleviating persistent symptoms in PCC patients. Asynchronous delivery appears sufficient for general conditioning, while synchronous supervision may be necessary for strength training. Clinical Trial: PROSPERO CRD42023490863; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023490863
Background: Automated outbreak detection can enhance infectious disease surveillance by enabling early identification of outbreaks and supporting timely public health measures. However, information on...
Background: Automated outbreak detection can enhance infectious disease surveillance by enabling early identification of outbreaks and supporting timely public health measures. However, information on its current use by national public health institutes (NPHI) remains limited. Objective: This paper aims to provide an updated and extended overview of automated outbreak detection usage in the European Union (EU) and the United Kingdom (UK) by: (1) assessing current demand, (2) examining the availability of key prerequisites within existing surveillance systems, and (3) identifying challenges and requirements for implementation. Methods: A mixed-method approach was applied as part of the Joint Action UNITED4Surveillance. Data were collected between April 2023 and January 2024 through an online survey sent to 25 countries, an in-person workshop with experts from 16 countries, and follow-up meetings with participating NPHIs. Additional information for selected countries was obtained from literature. Data were analysed descriptively. Results: Twenty-one countries completed the survey. Seven countries have established automated outbreak detection systems (AODS), four are planning implementation, and ten participated in a pilot project. All surveyed countries reported acting on detected surveillance signals. Most have suitable surveillance infrastructure, including case-based data (20/21), daily reporting (21/21), and at least three years of historical data (21/21). Main barriers to implementation include limited funding (15/21), insufficient IT capacity, and data quality issues (9/21). Despite heterogeneity in methods and system design, outputs and user requirements are largely similar across countries, with needs for flexible outputs, stratification, and user-friendly interfaces. Conclusions: While the specific methods in existing AODS differ, overall demands and outputs are similar, suggesting a single tool could serve multiple countries. Capacity building as part of EU-funded Joint Actions can bridge these gaps by developing sustainable tools and fostering cross-country collaboration.
Background Opioid-related drug–drug interactions (DDIs) are common in hospitalized patients and can lead to serious harm, especially when opioids are combined with central nervous system depressants...
Background Opioid-related drug–drug interactions (DDIs) are common in hospitalized patients and can lead to serious harm, especially when opioids are combined with central nervous system depressants. Electronic medical records (EMRs) often trigger DDI alerts to warn clinicians of potential DDIs, but the effect of DDI alerts on clinically relevant opioid DDIs and related patient harm remains uncertain. This study evaluated whether EMR-integrated opioid DDI alerts reduce clinically relevant interactions and associated harms in routine hospital care.
Objective This study aimed to address this gap by determining whether introducing these alerts reduces the prevalence of potentially and clinically relevant opioid-related DDIs, as well as the rate of DDI-related patient harm in hospitalized patients.
Methods This retrospective cohort study was a secondary analysis of a multicenter quasi-experimental controlled pre–post evaluation of EMR implementation across five Australian hospitals. Adult inpatients were randomly selected from all patients who stayed in study hospitals for a one-week period six months before and six months after EMR implementation. Inpatients were included if they had at least one prescribed and administered opioid and one concurrent medication. Interruptive opioid DDI alerts were active only at intervention sites post-EMR. Potential DDIs were identified using Stockley’s Interaction Checker; pharmacists adjudicated clinically relevant DDIs, and clinical pharmacologists assessed DDI-related harm and causality. Clustered logistic regression with generalized estimating equations, adjusting for demographic and clinical variables, estimated the effect of alerts involving opioids on three outcomes: clinically relevant opioid DDIs (primary), any potential opioid DDI, and opioid DDI-related harm.
Results Of 1,144 patients prescribed an opioid, 847 (74.0%) had at least one potential opioid DDI and 548 (47.9%) had at least one clinically relevant DDI. EMR alerts were associated with no significant change in clinically relevant DDIs (adjusted odds ratio 1.06, 95% CI 0.72–1.55; p=0.75). There was a significant reduction in potential opioid DDIs (adjusted odds ratio 0.55, 95% CI 0.41–0.74; p<0.001). Of all patients, there were 11 patients with a total of 38 DDIs experienced harm (0.6% of potential and 1.1% of clinically relevant DDIs), with most DDIs involving pharmacodynamic interactions with concomitant CNS depressants.
Conclusion EMR opioid DDI alerts reduced overall exposure to potential DDIs but did not decrease clinically relevant interactions or related harm. The low rate of harmful events highlights the limited clinical value of current alert systems and the burden of low-value warnings.
Digital health technologies are increasingly used to support health management, yet research focusing on educators’ engagement with digital health remains limited. Given that educators’ health lit...
Digital health technologies are increasingly used to support health management, yet research focusing on educators’ engagement with digital health remains limited. Given that educators’ health literacy and digital practices influence both their own wellbeing and their students’ health behaviors, understanding their interaction with digital health tools is essential. This scoping review maps the current literature on educators’ digital health and identifies gaps in knowledge. Following the Arksey and O’Malley framework, a systematic search for peer-reviewed articles yielded 17 eligible studies. Thematic analysis revealed three themes: 1) educators’ digital health literacy and its correlates; 2) educators’ experiences and challenges in using digital health technologies; and 3) professional support and interventions for educators’ digital health. The findings indicate uneven levels of educators’ digital health literacy, mixed experiences shaped by usability and contextual constraints, and insufficient institutional support for sustained engagement. This review highlights the need for targeted capacity-building efforts and context-sensitive interventions to enhance educators’ digital health competencies and to inform future research and policy development.
Background: The European Health Data Space (EHDS) will substantially increase cross-border health data sharing in the EU, tasking Health Data Access Bodies (HDABs) with the legal and ethical assessmen...
Background: The European Health Data Space (EHDS) will substantially increase cross-border health data sharing in the EU, tasking Health Data Access Bodies (HDABs) with the legal and ethical assessment of health data access requests. However, ethical evaluation of data access is often criticized as an opaque, inconsistent and difficult to operationalize, relying on list of principles with limited procedural guidance. As data access requests increase, the lack of standardized and actionable ethics review processes risks undermining transparency, consistency, and trust in health data governance. Objective: This scoping review (ScR) aimed to identify, map and synthesize existing tools and frameworks designed to operationalize ethical evaluation of health data access. It focused on tools moving beyond conceptual reflection by providing standardized, repeatable, or quantifiable processes that support actionable decision-making. Methods: The ScR was conducted in accordance with PRISMA-ScR guidelines, searching both academic databases and grey literature. Search results were imported into Covidence for screening when possible. For sources incompatible with Covidence, screening and data extraction were conducted manually, using Excel sheets. Two reviewers independently screened titles, abstracts and full texts, with disagreements resolved by consensus. To minimize bias, reviewers were aware of each other’s involvement but could not see each other’s decisions during the initial screening phase. Data were extracted and thematically analyzed to examine tool characteristics, intended users, evaluative criteria, patient involvement, use of quantification, and real-world applications. Results: The ScR retrieved 2215 results, of which 1887 were unique (82.15%). A total of 13 full text studies were included and analysed based on the following criteria: the type of tool, the actor meant to use it, what it measured, its actionable components, whether it contained quantified or quantifiable components compatible with (partial) automation, and real-life applications. These 13 tools differed in their approaches, varying from multiple choice questionnaires (n=5), qualitative questionnaires (n=3) and decision aids, frameworks or matrixes (n=5). Most of these tools were directed at data users (n=10) and mainly aimed at guiding reflection or generating reports for further ethics assessment (n=7). Very few directly involved the public or patients in their development (n=3). Some provided means to classify data use into risk categories with an associated lower or higher level of ethical scrutiny (n=5), at times incorporating quantification (n=5) in the review process. Finally, most tools had limited or no documented real-world implementation (n=9). Conclusions: An increasing number of tools and frameworks aiming at standardizing and operationalizing ethical assessment in data access are being developed in recent years, however their effectiveness remains untested in most cases. As data flows increase in the EHDS, and consequently, the need for streamlined ethics becomes more apparent, hybrid models incorporating both quantitative and deliberative components may play an important role in tackling the challenges associated with ethical data access management. Clinical Trial: N.A.
Background: AI has emerged as a promising technology in healthcare, offering potential benefits in assessment, diagnosis, drug discovery, and clinical trial for various diseases. Dementia, an incurabl...
Background: AI has emerged as a promising technology in healthcare, offering potential benefits in assessment, diagnosis, drug discovery, and clinical trial for various diseases. Dementia, an incurable condition characterized by progressive cognitive decline, remains a major neurodegenerative disorder with no effective treatment to slow its progression. This systematic review evaluates AI-based interventions in enhancing the quality of life for individuals with Mild Cognitive Impairment (MCI), which is a condition characterized by cognitive decline that is more pronounced than expected for an individual’s age but does not yet meet the criteria for dementia, or dementia. Objective: The primary objective of this review is to evaluate the existing evidence and suggestions on the effectiveness and usefulness of AI-powered applications in healthcare for supporting individuals with MCI or dementia, as well as their formal and informal caregivers. While the cognitive deficits observed in MCI may not be as marked as those observed in dementia, they can nonetheless have a notable impact on the lives of individuals and their carer. In this review, we consider outcomes in the domains of physical functioning, cognitive support, and psychological/behavioural well-being as indicators of quality of life. Based on our findings, our aim is to identify gaps in the current literature and outline future research directions for developers, healthcare providers, and decision makers. Methods: This systematic review was conducted following the PRISMA guidelines. We searched multiple databases, including PubMed, Scopus, Web of Science, Engineering Village, ACM Digital Library, and Medline, without restrictions on language or time. Eligibility criteria were defined using the PICOS framework and data extraction was performed using a standardized form. Results: From 10,514 records, 122 studies were included following inclusion criteria (112 primary studies and 10 secondary studies). Findings indicate that AI-powered technologies have been implemented to provide cognitive, physical, and psychological well-being support specifically for individuals with MCI or dementia and their caregivers. While AI-driven interventions have demonstrated potential benefits in improving the quality of life, challenges remain regarding accessibility, data privacy, bias in AI training datasets, and the need for inclusive design. Additionally, generative AI applications in dementia care are still in early development stages, with limited research on their long-term impact and real-world effectiveness. Conclusions: This review demonstrates that AI-based technologies hold measurable promises for improving quality of life in people with MCI and dementia by supporting cognitive, physical, and emotional needs. AI can assist patients and their formal and informal caregivers through monitoring, memory aids, and social interaction tools. A key emerging direction is caregiver-focused AI, including systems that support their well-being, education, and decision-making. Further validation is required to ensure reliability and long-term effectiveness, and future research should prioritize agentic, context-aware, and co-designed systems that adapt to the progressive nature of cognitive decline.
Background: The integration of artificial intelligence (AI) in long-term care (LTC) has been driven by workforce challenges and the potential to enhance resident-centered care and improve care quality...
Background: The integration of artificial intelligence (AI) in long-term care (LTC) has been driven by workforce challenges and the potential to enhance resident-centered care and improve care quality and safety. However, the evidence on AI in LTC remains fragmented across various intervention types and outcome domains. Objective: This umbrella review aims to synthesize evidence from systematic reviews regarding the use of AI in nursing care for older adults in LTC facilities, focusing on care quality, safety, nursing workflow, decision support, implementation barriers, and ethical concerns. Methods: The review followed the Joanna Briggs Institute guidelines and was registered in PROSPERO (CRD420251244061). A comprehensive search of Chinese and international databases was conducted up to February 2026. Eligible studies were systematic reviews (with or without meta-analysis) that focused on AI interventions in LTC for adults aged 60 years or older. AMSTAR 2 was used for methodological quality assessment. Results: Six systematic reviews published between 2019 and 2025 were included. AI applications in LTC nursing were categorized into five areas: social and companion robots, environmental sensors, wearable devices, fall detection, and robot-assisted medication management. The reviews most frequently reported positive outcomes in psychosocial health, particularly in reducing depression, loneliness, and improving social engagement. AI was also associated with benefits in fall surveillance, medication-related care, and nursing workflow. Common barriers included technical limitations, false alarms, privacy concerns, and workflow disruption. AMSTAR 2 quality assessment indicated that 3 reviews were of low quality, and 3 were critically low. Conclusions: AI holds promise as an adjunct to resident-centered care in LTC, rather than replacing direct nursing care. It can improve care quality and safety, optimize nursing workflows, and support decision-making. Further studies with rigorous designs and a focus on implementation are needed to strengthen the evidence base and address the identified challenges. Clinical Trial: PROSPERO CRD420251244061; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251244061.
Background: Metabolic disease, including type-2 diabetes, is a major public health concern. Reducing sugar-sweetened beverage consumption is a critical target for disease prevention and lifestyle modi...
Background: Metabolic disease, including type-2 diabetes, is a major public health concern. Reducing sugar-sweetened beverage consumption is a critical target for disease prevention and lifestyle modification. Workplace interventions offer a promising opportunity to address barriers to behavioral change while strengthening motivation and self-efficacy. Objective: We describe the protocol, fidelity and feasibility of a theory-informed brief intervention based on motivational interviewing principles to support reductions in sugar-sweetened beverage consumption among healthcare workers. We also report preliminary findings in proposed mechanisms of behavior change. Methods: Participants (N=314) allocated to the intervention arm of a multi-center randomized-controlled trial received a four-dose intervention consisting of an introductory phone call, a brief counseling session, and two booster calls, supplemented by at-home tools targeting stress and craving management. We assessed fidelity to motivational interviewing principles (competence scores) and describe feasibility via intervention dose adherence. Finally, we examined motivation for change and perceived self-efficacy over time. Results: More than 60% of enrolled participants completed all four doses, suggesting feasibility and high retention in the context of a remotely delivered workplace intervention. Health coaches achieved basic levels of proficiency in two of four competence scores: technical (M(SD): 3.23 (0.48)) and complex reflections (M(SD): 41.91% (15.17)). Preliminary findings indicate improvements in motivation for change (F (1.52, 304.65) = 32.21, p < .001) and self-efficacy (F (1.94, 391.42) = 7.60, p < .001) over time. Conclusions: This theory-grounded brief motivational intervention may offer a cost effective and accessible approach for employers to support behavioral change related to sugar-sweetened beverage consumption. Findings from the fully powered randomized-controlled trial are needed to determine if the brief intervention can effectively change behavior. This protocol establishes the intervention and its framework to inform future implementation. Clinical Trial: NCT05972109
Background: Emerging adults (EAs; aged 18-29 years) experience a high burden of mental health (MH) concerns during a developmental period marked by increasing autonomy and vulnerability to symptom ons...
Background: Emerging adults (EAs; aged 18-29 years) experience a high burden of mental health (MH) concerns during a developmental period marked by increasing autonomy and vulnerability to symptom onset. Many EAs face challenges in accessing and sustaining engagement with developmentally appropriate care. Digital measurement-based care (dMBC), which involves the routine use of patient-reported outcome measures (PROMs), has been introduced as an approach to support EA engagement and improve responsiveness of care through ongoing monitoring and informed clinical decision-making. However, its integration into routine care remains variable, and there is limited understanding of how EAs experience and engage with dMBC in practice. Objective: This study aimed to examine how EAs experience and engage with dMBC in routine MH care and to identify the key processes that shape its use in clinical settings. Methods: A descriptive qualitative design was used to generate formative evidence on EAs’ experiences with dMBC. Semi-structured interviews were conducted with 23 purposively sampled EAs who were either actively receiving care or had recently been discharged from one of two outpatient MH clinics in Southern Alberta, Canada. Within these settings, dMBC was implemented via a web-based platform to collect and review PROMs within a clinically staged-informed stratified care model. Under this model, clinical staging categorizes individuals based on the progression and complexity of their presentation to guide treatment intensity and service pathways. All participants completed baseline PROMs, with subsequent completion varying based on clinician discretion, participant use, and scheduled clinical time points at 6 months, 12 months, and discharge. Interview data were analyzed using thematic analysis. Results: Findings identified four interconnected processes shaping engagement with dMBC: Technology, Tracking, Translation, and Therapeutic Guidance. These processes were synthesized into a user-centered integration model (the “4Ts”). Engagement was influenced by the platform's usability and accessibility, the role of tracking in supporting reflection and awareness, the use of PROM data to facilitate communication, and the extent of clinician involvement in interpreting and applying results. Across the themes, the integration of PROM data into therapeutic interactions emerged as central to sustaining engagement, while variability in use reflected differences in individual capacity, perceived relevance, and clinical integration. Conclusions: dMBC functions most effectively when embedded within relational and clinical processes, rather than as a standalone digital tool. The 4Ts model offers a preliminary framework for understanding the conditions that support meaningful engagement in EA MH care. Ongoing co-design work is underway to mobilize these insights into practice-oriented strategies to support implementation.
Open Peer Review Period: Mar 28, 2026 - Mar 13, 2027
A brown seaweed, sea tangle (ST) (Laminaria japonica), is a dietary supplement with potential biological activities. If fermented using Lactobacillus brevis, it is converted to -aminobutyric acid (...
A brown seaweed, sea tangle (ST) (Laminaria japonica), is a dietary supplement with potential biological activities. If fermented using Lactobacillus brevis, it is converted to -aminobutyric acid (GABA)-rich fermented sea tangle (FST). The traditional Korean rice wine has been brewed using a variety of natural materials, and its physiological or functional properties have been well documented. But there is a paucity of data suggesting its beneficial effects when supplemented with GABA-rich FST. We therefore examined the biological properties of the Korean rice wine supplemented with GABA-rich FST. For the current experiment, the white rice was fermented using Aspergillus kawachii (AK), Aspergillus oryzae (AO), Rhizopus oryzae (RO) and Monascus purpureus (MP). Thus, the sample was prepared and then divided into the four groups: (1) Group 1: The first brew supplemented with 0.5% (w/v) GABA-rich FST (AK-1, AO-1, RO-1 and MP-1); (2) Group 2: The first and second brews supplemented with 0.25% (w/v) GABA-rich FST each (AK-2, AO-2, RO-2 and MP-2); (3) Group 3: The second brew supplemented with 0.5% (w/v) GABA-rich FST (AK-3, AO-3, RO-3 and MP-3); and (4) Group 4: No supplementation attempted (AK-4, AO-4, RO-4 and MP-4). Then, we performed assays of general chemicals, organic acid, free sugar, volatile flavor compounds, GABA, phenolic compounds and kojic acid. Moreover, we also performed assays of antioxidant, radical scavenging activity and fibrinolytic one. Furthermore, we also assessed alcohol dehydrogenase (ADH) zymogram and band pattern following yeast culture. The degree of antioxidant, radical scavenging activity was the highest in the AK-2, and it was approximately 11% higher as compared with the AK-4. The degree of antioxidant, radical scavenging activity was higher in the AK-2 or the AK-3 as compared with the AK-1 or the AK-4. The degree of fibrinolytic activity reached the highest level in the RO-2. Moreover, it was approximately 2 times higher in the RO-2 as compared with the RO-4. Furthermore, it was 2-3 fold higher in the RO-4 as compared with the AK-4, AO-4 and MP-4.
In conclusion, our results indicate that the Korean rice wine would have beneficial effects, such as antioxidant and fibrinolytic activities, if supplemented with GABA-rich FST.
Open Peer Review Period: Mar 28, 2026 - Mar 13, 2027
Background: We conducted this experimental study to examine whether fermented Angelica gigas (FAG) is effective in preventing oxidative stress in an Sprague-Dawley (SD) rat model of carbon tetrachlori...
Background: We conducted this experimental study to examine whether fermented Angelica gigas (FAG) is effective in preventing oxidative stress in an Sprague-Dawley (SD) rat model of carbon tetrachloride (CTC)-induced acute hepatic injury (AHI).
Methods: Male SD rats (n=36) were used to establish an animal model of CTC-induced AHI was established. They were given an intraperitoneal injection of CTC and then divided into the 6 experimental groups: NC group (n=6, olive oil), NCDG group (n=6, olive oil with 5% [w/w] AG), NFCDG group (n=6, olive oil with 5% [w/w] FAG), CC group (n=6, CTC), CCDG group (n=6, CTC with 5% [w/w] AG) and CFCDG group (n=6, CTC with 5% [w/w] FAG). Then, we compared serum alanine aminotransferase (ALT)/aspartate aminotransferase (AST) levels, serum and hepatic lipid levels and hepatic levels of thiobarbituric acid-reactive-substances (TBARS) and glutathione.
Results: Our results showed that the FAG was effective in significantly protecting the liver from CTC-induced hepatotoxicity and significantly inhibiting CTC-induced accumulation of lipid in the liver.
Conclusions: In conclusion, our results indicate that the FAG might be effective in preventing the hepatic injury due to oxidative stress in an animal model of CTC-induced hepatotoxicity.
Background: Antimicrobial resistance (AMR) poses a growing and serious threat to patient safety worldwide. Nurses, as the largest professional group in the global health workforce, play a central role...
Background: Antimicrobial resistance (AMR) poses a growing and serious threat to patient safety worldwide. Nurses, as the largest professional group in the global health workforce, play a central role in antimicrobial management. However, their contributions to antimicrobial stewardship (AMS) programmes remain poorly characterised and inconsistently measured. To date, no systematic review has combined a meta-analysis of the clinical effectiveness of nurse-led or nurse-involved AMS interventions with an implementation-focused synthesis to inform policy, practice, or future research. This gap is particularly relevant for the Gulf Cooperation Council and the Middle East and North Africa (GCC/MENA) healthcare systems, where the burden of AMR is high, and evidence of nursing contributions to AMS is limited. Objective: This protocol describes a convergent, parallel-streams mixed-methods systematic review to determine: (1) the effectiveness of nurse-led or nurse-involved antimicrobial stewardship (AMS) interventions on patient and antimicrobial outcomes in hospital and primary care settings globally; (2) the barriers and facilitators to implementing nurse-led AMS interventions or programmes; and (3) synthesis GCC/MENA evidence via sub-group analysis Methods: The review will follow the JBI convergent, parallel-streams mixed-methods methodology. Eleven databases will be searched from January 2000 to April 2026. The review will include primary research comprising: (1) quantitative studies (Stream 1), including randomised controlled trials, quasi-experimental studies, interrupted time series, and controlled before-and-after studies reporting clinical or process outcomes of nurse-led or nurse-involved AMS interventions. Where appropriate, meta-analysis will be conducted using a random-effects model. Outcomes will include antibiotic consumption, prescribing appropriateness, mortality, hospital length of stay, time to first antibiotic dose, and blood culture collection rates. (2) qualitative studies (Stream 2), which will be synthesised using thematic analysis guided by the Consolidated Framework for Implementation Research (CFIR 2.0) to identify barriers and facilitators of implementation. Findings from both streams will be integrated using a convergent joint display. Eight pre-specified subgroup analyses are planned, including comparisons between GCC/MENA countries and the rest of the world, and between intensive care and general ward settings. The certainty of evidence for quantitative outcomes will be assessed using GRADE. The review is registered with PROSPERO (CRD420261341653). Results: The protocol was registered with PROSPERO. Database searches are scheduled to commence in April 2026. Full-text screening is expected to be completed by June 2026, with data extraction and synthesis anticipated by October 2026. The completed systematic review to be submitted for publication in January 2027. Conclusions: This review will provide the first meta-analytic synthesis of the clinical effectiveness of nurse-led AMS, alongside a structured implementation analysis using CFIR 2.0. The findings will inform antimicrobial stewardship programme design, nursing education, and health policy, with particular relevance for GCC/MENA healthcare systems. Clinical Trial: PROSPERO: CRD420261341653; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=1341653
Background: African Americans (AAs) experience higher rates of hypertension (HTN) and related cardiovascular and stroke mortality compared with the general U.S. population. Interestingly, studies have...
Background: African Americans (AAs) experience higher rates of hypertension (HTN) and related cardiovascular and stroke mortality compared with the general U.S. population. Interestingly, studies have shown that AAs take more antihypertensive medications but are less likely to adhere to the prescribed medication regimens compared to the general population. Additionally, participants in qualitative studies have expressed that HTN self-management, particularly lifestyle modifications, should receive more attention than increasing medications. Objective: The objectives of this study are: 1) to evaluate the synergistic effect of a pharmacist and community health worker (Pharm+CHW) intervention compared to a Pharmacist alone (Pharm control group) to improve blood pressure (BP) control in a predominantly AA cohort aged ≥ 55 years in a prospective randomized trial (n=480); 2) to identify factors correlated with clinical BP outcomes. Methods: This is a randomized clinical trial to investigate novel behavioral intervention strategies targeting HTN self-management (e.g., medication management, blood pressure monitoring) and lifestyle modifications. The study has two components: a pharmacist-led medication management component (Pharm) and a community health worker support component (CHW). In the Pharm component, pharmacists will provide virtual Medication Therapy Management (MTM) services, which include: a comprehensive medication review, recommendations for lifestyle modifications, blood pressure self-monitoring education, and medication management app education. In the CHW component, CHW will support participants’ self-initiation of behavioral change(s), address challenges to lifestyle modification, and provide health education through workshops. The study intervention will be guided by a Community Advisory Board (CAB) consisting of six members. The study will recruit a cohort of 480 predominantly AA adults 55 years or older with HTN through community-and faith-based organizations and clinics in Houston, Texas, U.S. Participants will be prospectively randomized to one of the two parallel groups: 1) Pharmacist alone (control group; n=240); or 2) Pharmacist and CHW interventions (Pharm+CHW; n=240) for 24 months. Computer-generated randomization, with 1:1 allocation stratified by age, recruitment site, and HTN stage, will be applied. The primary self-monitoring BP outcome will be measured at 0-, 6-, 12-, 18-, and 24-months to evaluate the effectiveness of the proposed interventions. The secondary outcomes will include medication adherence, hypertension knowledge, perceived competency, body mass index, and physical activity. Results: The study was funded by the National Institute on Minority Health and Health Disparities of the National Institutes of Health under award number 2U54MD007605 in September 2025. The CAB has convened and offered support for recruitment efforts. Recruitment is ongoing, and the intervention is expected to begin in summer 2026. Conclusions: This project will fill an important knowledge gap regarding the synergistic effect of CHWs working in collaboration with pharmacists to help older adults use technologies (e.g., virtual visits, mobile medication management apps) and initiate lifestyle modifications for HTN self-management in older adult AAs. Clinical Trial: NCT07413159
Background: Routinely collected nursing real-world data (RWD) offer opportunities for nursing research to generate new knowledge and to improve quality care. Furthermore, advanced analytics with nursi...
Background: Routinely collected nursing real-world data (RWD) offer opportunities for nursing research to generate new knowledge and to improve quality care. Furthermore, advanced analytics with nursing routine data opens the development of predictive models that support risk detection and decision support. However, using RWD in nursing remains challenging due to insufficient data standardization, variable data quality, and difficulty for organizations implementing a targeted data strategy for secondary use of nursing data. To address this gap, we developed the NuDaK nursing data strategy framework as a practical guide to enable and optimize the secondary use of routinely collected nursing documentation data. Objective: To evaluate the completeness and applicability of the NuDaK framework for secondary use of nursing routine data in a clinical nursing setting. Methods: We conducted a formative feasibility study in collaboration with a large acute care hospital in Austria. The NuDaK framework was piloted on a surgical ward and evaluated across four implementation phases. Data were collected via phase-specific workshops and project meetings, continuous self-evaluation protocols by the project lead, and a final online focus group interview. Data were analyzed using deductive qualitative content analysis regarding completeness and applicability of NuDaK. Evaluation results were aggregated and used to update NuDaK. Results: The four core NuDaK aspects (“Purpose & Benefits,” “Data Set & Nursing Documentation,” “Data Modelling & Software Requirements,” and “Data Integration & Data Quality”) were confirmed as conceptually complete.
Based on the evaluation results, NuDaK was now updated: (i) adopting a circular structure with four temporal phases, (ii) defining interdisciplinary stakeholder involvement across phases, (iii) specifying professional roles and responsibilities across phases, and (iv) integrating an iterative nursing information model spanning dataset development, specification, and refinement. Further NuDaK additions comprise a data quality concept with operationalized dimensions and validation procedures. Conclusions: The NuDaK Nursing Strategy Framework was refined through real-world piloting. It now provides for organisations and research a nursing-specific, end-to-end nursing data strategy to enable systematic RWD use. While its feasibility was demonstrated in a real-world nursing setting, further implementation studies should assess transferability across heterogeneous institutions with different documentation systems, governance structures, and levels of digital maturity.
Background: Competition for specialty training in the United Kingdom has increased significantly in recent years. Current competition ratio data exists on separate webpages on the NHS England website,...
Background: Competition for specialty training in the United Kingdom has increased significantly in recent years. Current competition ratio data exists on separate webpages on the NHS England website, which makes comparing ratios across time and specialty difficult. This lack of accessible information can hinder career planning and, in turn, increases uncertainty around competition ratios - a topic at the forefront of current discussion among trainees. Objective: This project aimed to evaluate the user experience of a dashboard-based visualization tool illustrating the consolidated NHS data. Methods: Data from the NHS England website for the period 2013-2025 were collected. An interactive dashboard was then developed and published online at specialtytrainingcompetition.com. Usefulness of the dashboard was evaluated through usage data analytics and by inviting users to answer the question "how useful did you find this dashboard?" using a Likert-scale rating from 1 (not useful) to 5 (extremely useful) and a free-text box for "suggestions or feedback". Results: 5,700 unique users have visited the website at the time of writing. 401 valid usefulness ratings were collected. The mean usefulness score was 4.37 (SD=0.90), with a median of 5 (IQR 4-5), indicating the dashboard was a useful way to visualize competition ratio data - a finding supported by the free-text feedback. Conclusions: This study demonstrates the value of an open-access interactive dashboard to visualize UK specialty training competition ratios at a time when focus on competition has intensified. By consolidating recruitment data into an interactive format, the dashboard provides a practical means of exploring publicly available workforce data.
The terms transparency, explainability, and interpretability are ubiquitous in the health AI literature yet remain poorly and inconsistently defined, with fewer than 20% of papers using them offering...
The terms transparency, explainability, and interpretability are ubiquitous in the health AI literature yet remain poorly and inconsistently defined, with fewer than 20% of papers using them offering meaningful definitions. This paper argues that the imprecision matters — these are normative concepts, not mere technical descriptors, and their under-theorisation leaves the field vulnerable to regulatory frameworks imposed by those who understand neither the science nor the stakes. Drawing on W.B. Gallie's concept of essential contestability, and on pragmatic models of agreement from Rawls, Lukes, and Mouffe, the paper proposes that convergence on fixed definitions is neither likely nor necessary; what is needed instead is clarity about what kind of claim each term represents. The paper advances a tripartite taxonomy. Transparency-claims are structural: they concern the accessibility and visibility of a system's internals, independent of whether any observer understands what they see. Explainability-claims are relational: they concern the successful epistemic mediation between system output and a cognitively situated human observer, and are irreducibly dependent on the properties of that observer. Interpretability-claims are functional: they concern cognitive simulability, demanding that a human observer can mentally re-derive the model's reasoning, achieving epistemic closure rather than mere comprehension. Each category is developed through a survey of competing theoretical approaches — from post-hoc additive frameworks and counterfactual explanations to concept bottleneck models and Rashomon set analysis — and defended against plausible objections that would collapse the distinctions. The paper does not offer definitions of the three concepts but rather a meta-framework for classifying the epistemic work each performs, with the aim of enabling more disciplined and productive disagreement about their content.
Open Peer Review Period: Mar 27, 2026 - Mar 12, 2027
Background: The literature on value-based healthcare (VBHC) implementation has expanded rapidly but remains heterogeneous, making synthesis difficult. This study aimed to map the thematic landscape of...
Background: The literature on value-based healthcare (VBHC) implementation has expanded rapidly but remains heterogeneous, making synthesis difficult. This study aimed to map the thematic landscape of VBHC implementation and identify recurring domains, cross-cutting patterns, and gaps in the evidence. Objective: To map and synthesize the thematic landscape of the value-based healthcare (VBHC) implementation literature using computational thematic synthesis, identifying recurring domains, cross-cutting patterns, and evidence gaps. Methods: PubMed was searched without date restrictions and limited to English-language publications. Screening and full-text eligibility assessment were conducted manually in two stages. Eligible full texts were embedded using a text-embedding model (text-embedding-3-large, 3,072 dimensions), projected with UMAP, and clustered with HDBSCAN using consensus across 10 random seeds. Cluster stability was examined using an alternative embedding model, direct-space clustering, and negative controls. Results: A total of 8,957 records were screened, and 351 full-text articles were included. Computational synthesis identified 18 themes that grouped into four broader domains: condition-specific VBHC applications, measurement and costing approaches, organisational and workforce models, and health system policy and governance. Commentaries and framework papers were the most common design category (19.4%), while empirical studies were comparatively fewer across most themes. The United States and the Netherlands contributed the largest share of publications. Themes supported by stronger measurement infrastructure, including PROMs, ICHOM outcome sets, and TDABC, were more frequently associated with reported value improvements. Conclusions: This evidence-mapping study provides a broad overview of the VBHC implementation literature. Despite wide diffusion of the concept, much of the field remains centred on conceptual discussion rather than empirical evaluation. Future work should prioritise interventional designs and context-sensitive implementation frameworks, particularly in under-represented regions.
Background: There is an ongoing need for medical students to build skills beyond traditional clinical areas in order to best shape the evolving healthcare system and fill a variety of professional rol...
Background: There is an ongoing need for medical students to build skills beyond traditional clinical areas in order to best shape the evolving healthcare system and fill a variety of professional roles after graduation. With the emergence of artificial intelligence (AI), new learning methods are available for the delivery of medical education. Objective: To develop and evaluate the use of traditional (human) standardized patient engagement and Generative AI-generated standardized patient engagement as a low-stakes way for students to practice customer discovery interviews. Methods: An interactive classroom experience was created in the interprofessional Center for Experiential Learning and Simulation (iCELS) to simulate two different customer discovery interview scenarios with human standardized patients and AI-generated standardized patients. The customer discovery interview simulation with human standardized patients was conducted with first year medical students in the Entrepreneurship, Biodesign, and Innovation Pathway starting in 2023. In 2026, AI chatbots were added as an additional part of the simulation experience. Sample questions and answers were developed to generate a common interview experience. Student feedback was collected via a Qualtrics survey immediately after class. Students were asked to rate statements on a 4 or 5 point Likert scale and were also allowed to provide open-ended comments. Results: The students gave the simulation with standardized human patients high scores, with almost all agreeing or strongly agreeing that the exercise met learning objectives. Responses to the AI chatbot session had a bimodal distribution, with about 2/3 of students giving the simulation high scores and 1/3 of students giving it low scores. Conclusions: he first generation chatbot was able to replicate realistic customer discovery interviews in two different scenarios. In the future we will create transcripts of both the AI chatbot and human standardized patient interviews and use independent raters to score interview quality based on our scoring rubric. Finally, we plan to enhance the chatbots to provide a more immersive and realistic experience for students. The further enhancement of these chatbots will provide students with more opportunities to practice their customer discovery skills.
Background: Artificial intelligence (AI) has demonstrated considerable potential in cardiovascular medicine, including image interpretation, risk stratification, and procedural guidance. However, tran...
Background: Artificial intelligence (AI) has demonstrated considerable potential in cardiovascular medicine, including image interpretation, risk stratification, and procedural guidance. However, translation into routine clinical practice remains limited, particularly in interventional cardiology (IC). Beyond technical performance, successful implementation depends on clinicians’ knowledge, expectations and attitudes toward AI. Over the past five years, rapid developments in both medical and non-medical AI may have influenced these factors Objective: To assess longitudinal changes in interventional cardiologists’ perceptions of artificial intelligence, including knowledge, expectations, attitudes and implementation barriers. Methods: Semi-structured interviews were conducted as a longitudinal assessment, with an initial round in 2020 and follow-up in 2025 among the same cohort of ten interventional cardiologists. An identical questionnaire was administered at both time points to assess AI knowledge, expectations, attitudes toward engagement, perceived applications and implementation barriers. Results: Self-rated AI knowledge increased over time, accompanied by greater confidence that AI will play a meaningful role in IC and by shorter expected timelines to clinical impact. Expectations became more heterogeneous, while willingness to learn about AI, contribute to development and use AI in clinical practice remained high. Perceived barriers shifted from trust, generalizability and cost to digital infrastructure, regulatory and legal requirements and safety issues. Perspectives on clinical applicability evolved toward more specific use cases, including lesion assessment, procedural planning and workflow optimization Conclusions: Despite minimal current implementation of AI in IC, interventional cardiologists demonstrate increasing familiarity with AI, greater confidence in its future role and sustained willingness to engage with AI-enabled tools, suggesting growing readiness for structured clinical adoption of AI and informing future strategies for education, governance and responsible deployment in IC.
Background: Background: Immersive exercise technologies are increasingly proposed to support healthy aging, yet sustained adoption among older adults remains inconsistent. Prior work often treats acce...
Background: Background: Immersive exercise technologies are increasingly proposed to support healthy aging, yet sustained adoption among older adults remains inconsistent. Prior work often treats acceptance as a static decision and frequently aggregates virtual reality (VR) and mixed reality (MR), potentially obscuring modality-specific adoption mechanisms. Objective: Objective: This study aimed to examine how older adults’ adoption of immersive Tai Chi exercise evolves over time and whether VR and MR support sustained engagement through different mechanisms. Specifically, we investigated phase-specific barriers and facilitators across entry, adaptation, and adoption negotiation, as well as differences in trust development and continued use between VR and MR conditions. Methods: Methods: Guided by the Dynamic Barrier Framework (DBF), we conducted a qualitative-dominant mixed-methods longitudinal study of older adults completing repeated immersive Tai Chi training. Participants completed one initial session and six repeat sessions. Semi-structured interviews captured evolving barriers and facilitators across DBF phases (entry, adaptation, and adoption negotiation). Quantitative measures of subjective experience and cybersickness, together with session-level performance logs, were integrated for descriptive triangulation. Results: Results: Thirty-five participants were enrolled (VR: n = 16; MR: n = 19); 34 provided complete quantitative data (VR: n = 16; MR: n = 18). Across modalities, adoption unfolded through three sequential phases with systematic shifts in barriers and facilitators. Early resistance was driven more by uncertainty and perceived control than by physiological discomfort alone. VR engagement was more closely linked to immersive and affective value, whereas MR engagement emphasized skill adaptation and the emergence of trust over time. Performance logs indicated learning across repeated sessions, aligning with self-reported experience. Conclusions: Conclusions: Older adults’ immersive exercise adoption is dynamic and phase-sensitive. VR and MR may foster sustained engagement through differentiated mechanisms, suggesting the need for phase-tailored support strategies in community deployment. Clinical Trial: Trial Registration: Not applicable.
Background: Lung re‑expansion therapies are essential for preventing pulmonary complications but are frequently affected by low adherence due to their repetitive nature and limited supervision outsi...
Background: Lung re‑expansion therapies are essential for preventing pulmonary complications but are frequently affected by low adherence due to their repetitive nature and limited supervision outside clinical settings. Gamification has shown potential to enhance engagement in rehabilitation and digital health interventions; however, its application to respiratory physiotherapy remains underexplored. Objective: This study evaluates user experience, enjoyment, and engagement with a gamified mobile application designed to support lung re-expansion therapy in a preclinical, non-patient setting. Methods: A gamified mobile application integrating a digital spirometer was developed to guide deep‑inspiration exercises through game mechanics and feedback. Twelve non‑patient users completed a structured evaluation using an adapted questionnaire derived from the Game Experience Questionnaire and Player Experience of Need Satisfaction instruments. Enjoyment‑related indicators, including engagement, perceived control, clarity of instructions, challenge, frustration, and overall experience were assessed alongside descriptive performance metrics. Results: Participants reported high levels of engagement, perceived performance, clarity of instructions, and overall experience, alongside low levels of stress and frustration. Correlation analyses revealed strong associations between engagement and clarity of instructions, and between perceived performance and overall experience. Qualitative feedback identified technical synchronization and customization complexity as primary sources of frustration. Conclusions: The gamified application delivered a positive user experience and supported key enjoyment indicators associated with engagement, thereby establishing an experiential foundation relevant to adherence in subsequent patient‑centered evaluations. This preclinical study identifies usability strengths and limitations that inform further system refinement and future patient‑centered research.
Acute fibrinous and organising pneumonia (AFOP) is a rare subtype of idiopathic interstitial pneumonia, defined histologically by intra-alveolar fibrin accumulation and patchy organising fibrosis. Cli...
Acute fibrinous and organising pneumonia (AFOP) is a rare subtype of idiopathic interstitial pneumonia, defined histologically by intra-alveolar fibrin accumulation and patchy organising fibrosis. Clinical and radiological manifestations are non-specific, frequently resulting in initial misclassification as community-acquired pneumonia; conclusive diagnosis requires histopathological examination of lung tissue. Timely diagnosis and early intervention are critical, with glucocorticoids remaining the mainstay of treatment.We describe two AFOP cases diagnosed at our centre, both of which responded favourably to glucocorticoid treatment. A systematic review of the pertinent literature was undertaken to provide further insights into the disease.Both patients initially presented with pneumonia that did not respond to antibiotics. Timely lung biopsy established the diagnosis of AFOP, and subsequent glucocorticoid therapy resulted in rapid clinical improvement.Clinicians should remain vigilant for AFOP. In patients with pneumonia unresponsive to antibiotics, early pathological assessment is essential for accurate diagnosis. Prompt initiation of appropriate therapy can substantially affect prognosis.
Background: Artificial intelligence (AI) has shown significant potential in ICU nursing practice, enhancing efficiency, making decisions, and patient safety. However, evidence regarding the implementa...
Background: Artificial intelligence (AI) has shown significant potential in ICU nursing practice, enhancing efficiency, making decisions, and patient safety. However, evidence regarding the implementation factors of AI in ICU nursing remains limited, particularly from the perspective of nursing leadership. Objective: This study aimed to explore perceived barriers and facilitators to implementing AI in ICUs in China from the perspectives of ICU nursing managers, guided by the Consolidated Framework for Implementation Research (CFIR). Methods: A qualitative study using semi-structured, face-to-face interviews was conducted with 11 ICU nursing managers from tertiary hospitals across seven geographic regions in China. Interview questions were informed by the CFIR framework. Data were audio-recorded, transcribed verbatim, and analyzed using a combined deductive–inductive approach with NVivo software. CFIR constructs were coded and rated as barriers, facilitators, or neutral factors. Results: A total of 20 factors were identified across five CFIR domains, including 5 barriers, 13 facilitators, and 2 neutral influencing factors. Key barriers included high implementation costs, limited adaptability and complexity of AI systems, ethical and privacy concerns, shortages of interdisciplinary talent, and communication challenges between clinical and technical teams. Major facilitators encompassed perceived relative advantages of AI, supportive national policies, leadership engagement, a positive implementation climate, readiness for implementation, and nurses’ self-efficacy. Conclusions: AI implementation in Chinese ICUs is a complex socio-technical process influenced by multilevel contextual, organizational, and individual factors. While nursing managers hold generally positive attitudes toward AI, addressing structural, ethical, and workforce-related challenges is essential for sustainable integration. These findings provide theory-informed insights to support context-sensitive implementation strategies for AI in critical care nursing practice. Clinical Trial: None.
Background: Background: Artificial intelligence (AI) is quickly becoming a key part of digital health systems in oncology, supporting activities like cancer screening, clinical decision-making, and pa...
Background: Background: Artificial intelligence (AI) is quickly becoming a key part of digital health systems in oncology, supporting activities like cancer screening, clinical decision-making, and patient care management. Although AI has the potential to enhance care quality and efficiency, its adoption at cancer centers varies widely, raising concerns about disparities in digital health access and capacity. Objective: Objective: This research investigates the multiple factors influencing AI adoption as part of digital health implementation at National Cancer Institute (NCI)-designated cancer centers across the U.S., focusing on institutional readiness, policy environment, and geographic spread. Methods: Methods: A national dataset of 75 cancer centers was assembled using public sources to track AI use in screening, treatment, and patient care. AI adoption was measured as a composite index (0-3), indicating integration across clinical areas. Spatial patterns were analyzed with Moran’s I, and multilevel ordered logistic regression models examined links between AI adoption, institutional features (like number of physicians, hospital beds, center type), and contextual factors (such as socioeconomic status and state politics). Results: Results: No significant clustering of AI adoption was found geographically, implying limited regional diffusion. The size of the physician workforce was the most consistent predictor of AI adoption, emphasizing that organizational readiness is a key driver. Policy environment also influenced adoption: comprehensive cancer centers in Republican-controlled states showed higher AI uptake. Socioeconomic status at the community level was not significantly related. Conclusions: Conclusions: This study identifies institutional capacity and policy environment as primary constraints on scalable innovative digital health implementation in cancer institutions. These results point to structural barriers to broad digital health deployment and indicate that advancing AI-enabled cancer treatment will need focused investments in institutional capacity and policy support. Without these efforts, disparities in digital health infrastructure could restrict equitable access to AI-driven innovations in oncology.
Open Peer Review Period: Mar 26, 2026 - Mar 11, 2027
Background: Code-switching between Egyptian Arabic (ARZ) and English is ubiquitous in clinical settings across the Arab world, yet no dedicated benchmark exists for evaluating Automatic Speech Recogni...
Background: Code-switching between Egyptian Arabic (ARZ) and English is ubiquitous in clinical settings across the Arab world, yet no dedicated benchmark exists for evaluating Automatic Speech Recognition (ASR) systems under these conditions. Existing Arabic ASR benchmarks evaluate models on Modern Standard Arabic or single-dialect speech without medical vocabulary or code-switching. Objective: To introduce the Clinera ASR Benchmark, the first benchmark targeting medical ARZ-EN code-switched speech, and to evaluate commercial and open-source ASR systems on accurate recognition of medical terminology using both standard and novel medical-term-aware metrics Methods: We curated 683 utterances of Egyptian Arabic-English medical speech with dense medical-term annotations. We evaluated six ASR systems: the proprietary ElevenLabs Scribe v1, domain-adapted ArAZN-Whisper-S (244M parameters), Whisper-Large-V3, SeamlessM4T v2, Wav2Vec2 Large AR, and Whisper-Medium. We computed standard ASR metrics (WER, CER, MER) and proposed novel medical-term metrics (MT-Precision, MT-Recall, MT-F1, MT-wF1 weighted by inverse document frequency). Statistical comparisons used bootstrap confidence intervals and Wilcoxon signed-rank tests with Bonferroni correction. Results: ElevenLabs Scribe v1 achieved the lowest WER (27.8%) and highest MT-wF1 (60.5%), outperforming all open models by more than six times its size on medical-term recognition. General-purpose multilingual models achieved higher WER while almost entirely failing to recognize medical terms. Among open models, ArAZN-Whisper-S dominated on medical-term efficiency (26.0 MT-wF1 per 100M parameters). Even the best commercial system produced 11 fatal or high-risk medical errors affecting 1.6% of the corpus. Conclusions: Current ASR systems exhibit a critical gap between general transcription accuracy and medical-term recognition in code-switched Arabic-English speech. Our benchmark and novel MT-metrics provide the first standardized evaluation framework for this clinically important setting, revealing that even top-performing systems pose patient safety risks through medical terminology errors.
Background: Heart failure (HF) is a leading cause of hospitalizations, particularly in low- and middle-income countries (LMICs). Guideline-directed medical therapy (GDMT) can reduce adverse events by...
Background: Heart failure (HF) is a leading cause of hospitalizations, particularly in low- and middle-income countries (LMICs). Guideline-directed medical therapy (GDMT) can reduce adverse events by 64%, yet remains underutilized. Digital health interventions (DHIs) may improve GDMT use by enhancing access to care and patient self-efficacy, but their effectiveness depends on sustained patient engagement. Objective: To describe the development of a culturally-adapted DHI to support GDMT optimization within Brazil’s public health system (OPT-HF), using human-centered design (HCD) and reciprocal innovation principles. Methods: OPT-HF was developed through structured meetings with researchers and health professionals and HCD sessions with patients and caregivers, following the iDesign framework. A three-week mixed-methods pilot study assessed usability and acceptability (primary outcomes), and engagement, self-care behavior (European Self-Care Behavior Scale), and medication optimization (secondary outcomes). An Implementation Research Logic Model (IRLM) was created to define implementation determinants, strategies, mechanisms, and expected outcomes. Results: The OPT-HF intervention comprises a multicomponent mobile application featuring telemonitoring of vital signs, educational content, and GDMT tracking, integrated with a dashboard and teleconsultation program. Ten patients were enrolled [mean age 57 years, 40% female] in the pilot study. The intervention demonstrated high usability and acceptability. Engagement was high for daily monitoring tasks but lower for educational videos. Self-care scores improved from 41 to 73, exceeding adequacy thresholds, and 7 of 10 participants showed significant improvement in the medication optimization score. Qualitative analyses highlighted increased perceptions of safety and support, alongside barriers related to Bluetooth connectivity and access to videos and chat functions. These findings informed iterative refinements and IRLM development. Conclusions: The OPT-HF DHI showed good usability, acceptability, and preliminary effectiveness in improving HF self-care and GDMT optimization. Culturally tailored, HCD design was essential to enhance engagement. The IRLM supported the understanding of mechanisms underlying this multicomponent intervention and will inform future implementation. Clinical Trial: https://ensaiosclinicos.gov.br/rg/RBR-10vpf9bm
Traditional clinical simulation requires substantial infrastructure investment, limiting accessibility in resource-constrained settings. AI technologies hold promise for scalable simulation yet concer...
Traditional clinical simulation requires substantial infrastructure investment, limiting accessibility in resource-constrained settings. AI technologies hold promise for scalable simulation yet concerns about clinical accuracy and faculty displacement remain. We describe the “Lecturer-in-the-Loop” Clinical Dialectic (LLCD), a framework integrating AI-mediated Socratic simulation with faculty oversight and share our initial experience with final-year medical students at an academic hospital in Jamaica. We facilitated a teaching session with seven final-year medical students using Claude Opus (Anthropic). Students engaged with two sequential AI-generated pediatric cases: acute asthma exacerbation, then bronchiolitis. These clinical scenarios evolved dynamically based on student decisions. Through dialogue with the system, students asked questions, consolidated pathophysiology, proposed management plans, and requested clarification and elaboration on recommendations. Notably, students independently applied pharmacological reasoning from the asthma case to determine that bronchodilators were inappropriate for bronchiolitis, an unprompted transfer of mechanistic understanding across cases. Faculty provided continuous oversight: prompting students to articulate their clinical reasoning before committing to answers, reinforcing key learning points, and validating AI-generated content in real-time. When the AI generated equivocal or clinically inaccurate content, faculty insight transformed these moments into teaching opportunities about critical appraisal. The session ran approximately 3 hours with sustained student engagement. LLCD may represent a reproducible, low-cost approach to clinical simulation-based education that preserves the central role of faculty while leveraging AI’s dialogic capabilities. By positioning AI as a dialogic tool requiring expert validation rather than an autonomous teacher, the framework addresses safety concerns while enabling scalable simulation in resource-limited settings where high-fidelity simulation infrastructure remains inaccessible.
Background: Effective verbal communication is a core component of nursing care, particularly in dementia care such as Humanitude. However, manual evaluation of communication quality is time-consuming,...
Background: Effective verbal communication is a core component of nursing care, particularly in dementia care such as Humanitude. However, manual evaluation of communication quality is time-consuming, subjective, and difficult to scale in training settings. Large language models (LLMs) may enable automated and scalable analysis of verbal communication in caregiving. Objective: This study evaluated whether LLMs can reliably classify verbal communication in nursing care training sessions and detect differences in communication patterns across caregiver expertise levels. Methods: Care sessions involving simulated patients were conducted with 18 participants, including Humanitude instructors, intermediate practitioners, and novice nurses. Audio recordings were transcribed, segmented into utterances, and classified into 6 communication categories: positive/affectionate expression, request/suggestion, gratitude, explanation, question/confirmation, and none. Four human annotators independently labeled the utterances, and the same transcripts were analyzed using GPT, Claude, and Gemini. Agreement was evaluated using pairwise agreement rates and Cohen’s kappa coefficients. Model performance was further assessed against consensus labels derived from multiple annotators, and non-inferiority/equivalence was tested using two one-sided tests (TOST). Results: Inter-annotator agreement among the human annotators was moderate, with pairwise agreement rates ranging from 64.44% to 74.21% and Cohen’s kappa values ranging from 0.554 to 0.664. Among the evaluated LLMs, Claude showed the highest agreement with human annotations, followed by Gemini and GPT. Against consensus labels, Claude achieved the highest accuracy (0.836 for ≥2-annotator consensus; 0.902 for ≥3-annotator consensus), followed by Gemini (0.779; 0.837) and GPT (0.672; 0.732). TOST analysis showed that Gemini achieved statistical equivalence with human annotation (p=0.040), while Claude demonstrated non-inferiority and exceeded the human baseline (p=0.001). Across caregiver groups, instructors showed a higher proportion of positive/affectionate expressions, whereas novice caregivers showed a higher proportion of task-oriented and uncategorized utterances. Overall, LLM-based classification reproduced the general communication patterns observed in human annotations. Conclusions: LLM-based classification demonstrated reliability comparable to human annotation for caregiving communication analysis. Claude showed the strongest overall performance, and Gemini achieved statistical equivalence with human annotation. These findings suggest that LLM-based analysis may provide a scalable and objective approach to assessing communication behaviors in Humanitude training and support communication assessment in nursing and medical education. Clinical Trial: Gunma University Hospital (HS2024-044)
Background: ICH (Intracerebral hemorrhage) and acute IS (ischemic stroke) are life-threatening cerebrovascular disorders that sometimes share similar clinical presentations but require fundamentally d...
Background: ICH (Intracerebral hemorrhage) and acute IS (ischemic stroke) are life-threatening cerebrovascular disorders that sometimes share similar clinical presentations but require fundamentally different treatment approaches. Objective: This study aims to develop an advanced ML (machine learning) model that integrates patient laboratory data to rapidly differentiate between ICH and IS. Methods: We retrospectively analyzed clinical and laboratory data from 12,213 hospitalized patients at Xijing Hospital between 2013 and 2023, including 3,251 ICH and 8,962 IS patients, and 2,893 hypertensive individuals as controls. An external validation cohort comprising 154 ICH and 342 IS patients admitted to Xijing Hospital from January to December 2024 was constructed. The dataset was balanced using the BS2 (BorderlineSMOTE-2) technique. Three feature selection methods (RFECV-ADA, Lasso, and Boruta) were used to identify potential biomarkers, and Spearman correlation was used to assess intermarker relationships. Six ML models were trained using ten cross-validations. Predictive models were developed using supervised ML algorithms. Model performance was evaluated on the basis of the AUC (area under the curve), sensitivity and specificity. Feature contributions were interpreted via SHAP (SHapley Additive exPlanations) plots. Furthermore, an interactive interface was implemented using PyQt5. Finally, we screened genetic instruments related to candidate indicators and paired them with ICH and IS genome-wide association study data to conduct Mendelian randomization analysis. Positive Mendelian randomization findings were then subjected to colocalization analysis. Results: Ten features were identified for model training: white blood cell count, NEUT% (neutrophil percentage), CysC (cystatin C) levels, UA (uric acid) levels, TP (total protein), K+ (potassium) levels, sodium levels, chloride levels, fibrinogen degradation product and D-Dimer levels. The BS2_LightGBM_V10 model demonstrated excellent performance in differentiating ICH and IS patients (AUC = 0.926), ICH patients and controls (AUC = 0.979), and IS patients and controls (AUC = 0.923) with the test cohort. In the three-class classification task (distinguishing ICH patients, IS patients, and controls), model accuracy with the test cohort reached 79.07%. SHAP plots revealed that NEUT%, D-Dimer, CysC, and TP were the most influential features for model predictions. Mendelian randomization analysis indicated that UA (OR, 1.309 [95% CI, 1.117–1.534]) has causal relationships with the risk of ICH onset, whereas UA (OR, 1.0008 [95% CI, 1.0001–1.0015]), CysC (OR, 1.0011 [95% CI, 1.0001–1.0020]) and K (OR, 1.0040 [95% CI, 1.0003–1.0076]) have causal relationships with the risk of IS onset. Colocalization analysis revealed 18 genes that are linked to UA in the context of ICH. Finally, potential gene-targeting drugs were screened. Conclusions: This study developed a diagnostic model that utilizes ten routine laboratory indicators to accurately differentiate between ICH and IS. Among the considered biomarkers, UA was identified as a causal risk factor for both disorders.
The administrative burden in anesthesiology has reached a critical tipping point, as the digitization of healthcare via Electronic Health Records (EHRs) often forces clinicians to spend more time inte...
The administrative burden in anesthesiology has reached a critical tipping point, as the digitization of healthcare via Electronic Health Records (EHRs) often forces clinicians to spend more time interacting with screens than with patients. In the high-stakes, high-velocity environment of the Operating Room (OR), this "documentation tax" competes directly with cognitive vigilance. While "ambient AI scribes" that listen to and transcribe patient encounters are revolutionizing outpatient care, they remain largely ineffective in the perioperative setting, where care is a complex choreography of physical actions, physiological monitoring, and silent vigilance rather than mere conversation. This Viewpoint argues that the next generation of AI documentation in anesthesiology must evolve from unimodal "listening" to multimodal "sensing." We propose the concept of the "Visual Scribe," an ambient intelligence system integrating Computer Vision (CV) with audio and telemetry data to automatically document the physical reality of surgical care. Synthesizing current research on AI-enabled perioperative workflow analysis, we explore how CV algorithms—such as temporal action localization and pose estimation—can segment surgical cases into granular phases with superhuman precision. Contrasting the retrospective imprecision of manual documentation with the real-time capabilities of multimodal AI, we highlight how emerging architectures can accurately detect and timestamp critical "silent" events like patient transport, intubation, and incision. Automating these data points can drastically reduce clinician burnout, reveal hidden provider-level workflow variability, and enhance patient safety through real-time "sterile cockpit" monitoring. To address the ethical, medicolegal, and ergonomic implications of deploying "always-on" visual sensors, we emphasize the need for a paradigm shift in privacy engineering, utilizing edge-based skeletonization to mitigate surveillance concerns. Ultimately, by equipping the EHR with "eyes" as well as "ears," we can create a self-documenting operating room, transforming the EHR from a distractor into a silent, autonomous partner that restores the anesthesiologist’s unwavering focus to the patient.
Background: Aseptic loosening remains a leading cause of late failure after total hip and knee arthroplasty. Radiographic signs are often subtle or delayed, and definitive diagnosis frequently relies...
Background: Aseptic loosening remains a leading cause of late failure after total hip and knee arthroplasty. Radiographic signs are often subtle or delayed, and definitive diagnosis frequently relies on intraoperative findings at revision. Machine learning has been proposed as a tool to assist in detecting loosening or implant failure, but its clinical role and methodological robustness remain unclear. Objective: To understand if machine learning algorithms can help detect implant loosening and the need for revision in TKA/THA Methods: We conducted a scoping review of studies applying machine learning to detect implant loosening or mechanical failure following total hip or knee arthroplasty. Studies using imaging or mechanically derived data with a loosening or failure related endpoint were included. Data were extracted on joint studies, imaging modality, endpoint definition, modelling approach, and validation strategy. Results: Eight studies published between 2019 and 2025 met inclusion criteria. Most focused on total hip arthroplasty and relied on plain radiographs. Definitions of loosening varied substantially, including revision confirmed mechanical instability, composite prosthesis failure, expert interpreted radiographic suspicion, and quantified component displacement. Modelling approaches ranged from static image classification to longitudinal prediction and deep learning enabled automation of biomechanical measurement. Internal validation predominated, with external validation uncommon and reporting of sample size considerations, missing data, and reproducibility frequently limited. Conclusions: Machine learning can identify patterns associated with implant loosening or failure under controlled conditions, but current applications model heterogeneous constructs that do not align uniformly with mechanical diagnosis. Existing evidence supports a role for machine learning as decision support for triage or risk stratification rather than definitive diagnosis. Broader external validation, clearer alignment between clinical intent and modelled endpoints, and improved reporting will be required before reliable integration into routine arthroplasty practice. Clinical Trial: N/A
Background: Electronic Medical Records (EMR) can promote healthcare delivery, research, and policy. To maximize benefits, EMR must be complete, accurate, and ensure high data quality. The Light Wave H...
Background: Electronic Medical Records (EMR) can promote healthcare delivery, research, and policy. To maximize benefits, EMR must be complete, accurate, and ensure high data quality. The Light Wave Health Information Management System (LHIMS) is an electronic health platform implemented by Ghana's Ministry of Health. In September 2021, Korle Bu Teaching Hospital (KBTH) extended LHIMS to its Neonatal Intensive Care Unit (NICU), a leading NICU in Ghana. Since implementation, no validation studies have been conducted. Objective: This study assessed the completeness, validity, concordance, and accuracy of LHIMS EMR data at the KBTH NICU nine months post-implementation. Methods: This retrospective study compared EMR and paper-based records (PBR) of 222 newborns randomly selected from 1,025 NICU admissions between July 1 and December 31, 2022. A predefined set of 24 clinical variables across four domains (delivery, maternal, neonatal, and admission data) were analyzed. Data analysis was performed using Python (version 3.10) with pandas, numpy, and scipy libraries. Results: EMR data were significantly less complete than PBR across all 24 variables (37.0% vs 81.3%, p<0.01), except insurance status (91.4% EMR vs 22.5% PBR, p<0.01). Maternal data was the least complete domain in the EMR (2.5%). EMR concordance and accuracy were 42.6% and 31.4% respectively when missing values were included, but improved substantially when missing values were excluded (80.9% and variable-dependent, respectively). Validity was nearly perfect in both systems when missing values were excluded (EMR 89.2%, PBR 98.7%, p=0.02). Conclusions: KBTH NICU EMR data quality was poor during the study period, driven primarily by high rates of missing data rather than inaccurate data entry. The EMR is currently unreliable for clinical decision-making, research, or policy use. Targeted improvements including mandatory fields, workflow alerts, and staff engagement are needed to achieve a reliable transition from paper-based records.
Background: Software-based and artificial intelligence (AI)–enabled medical devices are increasingly networked and updateable, expanding the attack surface and making cybersecurity governance inters...
Background: Software-based and artificial intelligence (AI)–enabled medical devices are increasingly networked and updateable, expanding the attack surface and making cybersecurity governance intersect with quality management and postmarket oversight. Yet regulated device risk management remains primarily oriented toward patient-safety harms under ISO 14971–style frameworks. Objective: To compare how Korea’s Ministry of Food and Drug Safety (MFDS), the US Food and Drug Administration (FDA), and the European Union/Medical Device Coordination Group (EU/MDCG) define and operationalize cybersecurity for medical device software across premarket review and postmarket surveillance, and to identify informatics-relevant gaps between safety vigilance and vulnerability-focused cybersecurity practice. Methods: We conducted a qualitative comparative document analysis of publicly available laws, regulations, guidance, and standards relevant to medical device cybersecurity and AI-enabled software. Using a common analytic framework, we mapped (1) conceptual scope (definitions and lifecycle boundaries), (2) premarket operationalization (required artifacts and evidence such as threat modeling, software bills of materials, and vulnerability management plans), and (3) postmarket operationalization (monitoring, reporting, and update governance). Results: We analyzed 10 jurisdiction-specific regulatory and guidance documents (MFDS: 2; FDA: 4; EU/MDCG: 4) and mapped requirements into three domains (conceptual scope, premarket operationalization, and postmarket operationalization) across the total product life cycle. Across jurisdictions, cybersecurity converged on protecting confidentiality, integrity, and availability of data and device functions but was embedded in different regulatory architectures. MFDS emphasized documentation completeness aligned with ISO 14971 risk management; the FDA framed cybersecurity as quality-system and design-control activities spanning the total product life cycle, including statutory requirements for “cyber devices”; and the EU treated cybersecurity as an extension of safety under MDR/IVDR interpreted through MDCG guidance, with additional cross-sector obligations for subsets of manufacturers and service providers. A common limitation was that vigilance pathways were largely triggered by patient-harm thresholds, whereas vulnerabilities and near-miss security events were often managed through parallel information-security processes. Conclusions: Regulatory approaches show definitional alignment but operational fragmentation at the interface between patient-safety vigilance and vulnerability-centric cybersecurity practice. Integrating cybersecurity as an interoperable process within the quality management system—linking vulnerability monitoring, incident response, and software update controls to CAPA and change control—and expanding postmarket surveillance to incorporate vulnerability and performance signals may support more trustworthy deployment of regulated AI-enabled medical software.
Background: Cortisol is the body's primary glucocorticoid hormone and is deeply involved in metabolism, immune regulation, and stress response. Small shifts in hormonal release dynamics may serve as e...
Background: Cortisol is the body's primary glucocorticoid hormone and is deeply involved in metabolism, immune regulation, and stress response. Small shifts in hormonal release dynamics may serve as early indicators of metabolic dysfunction, yet standard measurement approaches often miss fast-changing, tissue-level details. Understanding how adipose tissue processes cortisol across different body mass index (BMI) categories may open new avenues for early metabolic risk stratification. Objective: This study aimed to characterize free cortisol release dynamics in men grouped by BMI (normal weight versus overweight) using continuous subcutaneous tissue sampling, sparse-recovery deconvolution, and Bayesian hidden-state estimation adapted for cortisol physiology. Methods: Subcutaneous free cortisol readings from 23 age-matched men were collected using the U-RHYTHM portable hormone sampler at 20-minute intervals over a 24-hour period (72 samples per participant). Participants were classified as normal weight (BMI 18.5-24.9 kg/m²) or overweight (BMI ≥25 kg/m²) following National Institutes of Health (NIH) cutoffs. A three-compartment pharmacokinetic model combined with a sparsity-promoting signal extraction pipeline was used to recover cortisol burst events from raw tissue data. A Bayesian hidden-state estimator, adapted from a cognitive-arousal tracking framework, then mapped cortisol dynamics onto an energy-mobilization index. Two-sample t-tests assessed group differences at an α = .05 significance level. Results: Signal extraction achieved high fidelity (R²=0.9875). Burst count, amplitude, and energy (ℓ0, ℓ1, ℓ2 norms) were statistically indistinguishable between groups across all time windows and meal periods (P>.05). However, cumulative cortisol exposure (area under the curve [AUC]) was significantly higher in normal-weight men over 24 hours (P=.009), during sleep (P=.020), and during waking hours (P=.036). Peak and trough tissue concentrations were significantly higher in normal-weight men (P=.016 and P=.003, respectively). The tissue clearance rate constant (θ4) was significantly elevated in overweight men (P=.040). After breakfast, normal-weight men exhibited significantly higher energy mobilization (P=.040) and a higher High Energy Index (P=.036). Conclusions: Cortisol burst frequency and magnitude are preserved regardless of weight status, indicating that hypothalamic-pituitary-adrenal (HPA) axis pulsatility is not altered by moderate overweight. Differences in cumulative cortisol exposure, peak and trough levels, and tissue clearance rates point to a peripheral, tissue-level mechanism rather than a central one. These findings suggest that adipose tissue cortisol dynamics particularly clearance kinetics and post-breakfast energy mobilization may serve as early biomarkers for metabolic risk screening. The Bayesian hidden-state framework originally developed for cognitive-arousal tracking translates effectively to cortisol physiology.
Open Peer Review Period: Mar 26, 2026 - Mar 11, 2027
Background: Emergency Medical Services (EMS) activations related to influenza-like illness (ILI) increased during the COVID-19 pandemic. Information about the characteristics of EMS activations relate...
Background: Emergency Medical Services (EMS) activations related to influenza-like illness (ILI) increased during the COVID-19 pandemic. Information about the characteristics of EMS activations related to ILI during the COVID-19 pandemic may inform prehospital planning for future pandemics. Objective: The objective of our study was to compare demographic, geographic, and other characteristics of EMS activations related to ILI using data from 2019 (before the pandemic) and 2020 (during the pandemic). Methods: We conducted a cross-sectional analysis of 2019 and 2020 EMS activation data from the National Emergency Medical Services Information System (NEMSIS). The outcome of interest was the number of EMS activations related to ILI. Rates (the number of EMS activations related to ILI divided by the total number of EMS activations initiated with a 9-1-1 call that resulted in patient contact multiplied by 1,000), confidence intervals, and P-values were calculated by year for age, sex, race / ethnicity, census region, census division, urbanicity, primary method of payment, patient disposition, barriers to patient care, and patient destination to assess whether there were significant differences in the rates between years. The percentage change per 1,000 EMS activations from 2019 to 2020 was calculated in addition to rates by quarter for each year to compare trends. Results: There were 21,278,998 total EMS activations in 2019 and 26,923,354 in 2020. The rate of EMS activations related to ILI was 153.4/1,000 (n=3,263,713) in 2019 and 174.6/1,000 (n=4,701,617) in 2020. Rates of EMS activations related to ILI in 2020 were statistically different (P<.001) from 2019 for all demographic, geographic, and other characteristics. The overall rate of EMS activations related to ILI increased by 13.8% from 2019 to 2020. Some of the largest percentage increases in rates between years were for EMS activations that included Hispanic or Latino patients (33.1%), EMS activations in the Middle Atlantic census division (42.0%), and EMS activations with language barriers between the patient and the EMS clinician (39.6%). Quarterly rates of EMS activations related to ILI for EMS activations that included Hispanic or Latino patients and for EMS activations with language barriers between the patient and the EMS clinician were larger during 2020 than during 2019 for all quarters. Conclusions: Compared to 2019, rates of EMS activations related to ILI during 2020 increased for EMS activations that included Hispanic or Latino patients, occurred in the Middle Atlantic census division, and demonstrated language barriers between the patient and the EMS clinician. Further studies are needed to investigate the health impacts of language barriers between the patient and EMS clinician during prehospital care. Prehospital planning efforts that include testing and implementing strategies to address potential language barriers between the patient and the EMS clinician during EMS activations are warranted.
Background: Age-related macular degeneration (AMD) is the leading cause of irreversible central vision loss in adults aged 50 years and above. In rural India, its true burden remains substantially und...
Background: Age-related macular degeneration (AMD) is the leading cause of irreversible central vision loss in adults aged 50 years and above. In rural India, its true burden remains substantially underestimated because prior prevalence data relied exclusively on fundoscopy, without confirmatory imaging. The Central India Eye and Medical Study (CIEMS, 2011) reported early AMD in 8.3% of adults aged 60 years and above in rural Wardha, yet no subsequent OCT- or FFA-confirmed estimate exists for this population. Objective: This study has three primary objectives: (1) To estimate the imaging-confirmed prevalence of AMD among adults aged 50 years and above attending a rural tertiary care hospital in Central India; (2) To identify demographic, behavioural, and systemic factors independently associated with AMD in this population; and (3) To characterise patterns of diagnostic delay and barriers to eye care in patients with AMD. Methods: A hospital-based cross-sectional study will be conducted at the Department of Ophthalmology, Acharya Vinoba Bhave Rural Hospital, Wardha, Maharashtra. Participant recruitment will begin on 20 March 2026 and continue until 19 March 2027, providing a 12-month enrolment period. A minimum of 126 participants will be enrolled through consecutive sampling supplemented by community screening camps. AMD will be graded by two masked retinal specialists using AREDS criteria, with spectral-domain OCT (Cirrus HD-OCT 5000, Carl Zeiss Meditec, Dublin, CA, USA) and fundus fluorescein angiography as confirmatory modalities. The primary outcome is age- and sex-adjusted AMD prevalence. Secondary outcomes include risk factor associations (multivariable logistic regression), diagnostic delay (median delay with nonparametric comparison between recruitment pathways), and visual acuity at first presentation. Inter-grader agreement will be quantified using Cohen's kappa. Results: The study was registered with the Clinical Trials Registry of India (CTRI/2026/03/106075) on 12 March 2026. Participant recruitment will begin on 20 March 2026 and continue until 19 March 2027 at Acharya Vinoba Bhave Rural Hospital, Wardha, Maharashtra, India. As this manuscript describes the study protocol, no participants have yet been enrolled. Data analysis is expected to begin in mid-2027, with dissemination of results anticipated in late 2027. Conclusions: This study will generate the first imaging-confirmed AMD prevalence data for rural Central India since 2011. Systematic quantification of smokeless tobacco exposure, occupational sunlight, and diagnostic delay will provide actionable evidence for integrating AMD screening into India's National Programme for Control of Blindness and Visual Impairment. Clinical Trial: Clinical Trials Registry of India - CTRI/2026/03/106075 (registered 12 March 2026)
Background: The Military Healthcare System mandates medical documentation at all echelons of care, however care providers in combat situations must prioritize lifesaving measures over record-keeping,...
Background: The Military Healthcare System mandates medical documentation at all echelons of care, however care providers in combat situations must prioritize lifesaving measures over record-keeping, leading to information gaps. Effective human machine teaming (HMT) solutions designed to autonomously document care delivery will serve as future force multipliers in tactical combat casualty care (TCCC) environments. Objective: To address this challenge, the United States Army Institute of Surgical Research commenced an effort to prototype HMT systems designed to passively document care delivery in TCCC environments. However, common artificial intelligence (AI) performance evaluation methods do not represent the temporal, repetitive and context dependent nature of real-world TCCC delivery. It was essential to conduct comprehensive assessments of the AI functions in a timely, synchronized manner. During the initial prototyping phase, developers were provided with annotated datasets from seventy-five TCCC simulations and given six months to develop their algorithms. Methods: To assess performance, the research team leveraged a reserved dataset. In the first phase of the assessment a standardized, repeatable performance methodology and framework was leveraged to evaluate individual algorithms that detect: (1) injury location; (2) visible medical objects, and (3) treatments administered by the care provider. Detection effectiveness included four metrics: modified accuracy; precision; recall; and F1 scores. Algorithm processing efficiency was evaluated by calculating lag time scores. A final composite score was used to quantify performance differences among the algorithms within a specific detection category.
The second phase of the evaluation integrated multiple algorithms into a centralized orchestration framework; enabling synchronized execution and consolidated outputs. System resource usage and throughput metrics were evaluated to characterize the efficiencies. Quantified memory consumption and processing unit resource utilization were assessed, followed by benchmarking the edge compute orchestration framework. Results: Results to date revealed that Medical Object Detection achieved the highest performance (mean F1≈0.42, range 0–0.71). Injury Detection and Localization showed lower performance (mean F1≈0.27, range 0–0.60), with higher recall than precision. Medical Procedure Detection yielded procedure-level mean F1 scores from 0.00 to 0.31 and simulation-level means from 0.00 to 0.33. Stronger results were observed for Nasopharyngeal Airway and Chest Seal Application (mean F1≈0.28, 0.31) medical procedures. The results to date are preliminary and serve as illustrative examples of the evaluation framework outputs. Conclusions: The preliminary results highlight the evaluation framework’s end-to-end, standardized results across core algorithm functions. While algorithm performance to date is modest, the framework demonstrates its capacity to capture both variability and recurring patterns across simulations, thereby highlighting strengths, limitations, and areas requiring refinement. It enables reproducible, cross data set comparisons, allowing evaluators to quantify algorithm performance. By leveraging both simulation-level evaluations and detection specific performance aggregated across simulations, the framework enables targeted identification of underperforming areas, supporting iterative and strategic AI model refinement.
Background: Medical education in rheumatology faces challenges in delivering effective clinical teaching, particularly in situations where bedside learning opportunities are limited. Augmented reality...
Background: Medical education in rheumatology faces challenges in delivering effective clinical teaching, particularly in situations where bedside learning opportunities are limited. Augmented reality (AR) technologies offer immersive learning environments that may enhance engagement and improve conceptual understanding of complex disease processes. Although AR has been explored in other areas of medical education, its application in rheumatology education remains largely unexplored. Objective: This pilot study aimed to develop an AR-based case scenario for teaching rheumatoid arthritis and to evaluate its feasibility, usability and acceptability among non-rheumatology healthcare learners. Methods: Non-rheumatology trainee junior doctors, medical students and nurses attended a teaching session comprising a 60-minute didactic lecture on the approach to inflammatory arthritis followed by an AR base-based scenario focused on rheumatoid arthritis. Learners interacted with a virtual patient avatar and performed a hand joint examination using a haptic feedback glove. The AR platform supported real-time verbal interaction regarding diagnosis, investigations and management. After the session, participants completed a questionnaire on usability, acceptability and perceived educational value. Descriptive statistics summarized demographics and survey responses. Results: Nineteen participants completed the session, including 15 junior non-rheumatology doctors (79%), 2 medical students (11%), one nurse (5%) and one rheumatology senior resident (5%). Most participants were aged 18 to 30 years (69.2%). Mean acceptability rating was 7.84 out of 10 (SD 1.2) and usability of the system was 79%. Most participants reported feasibility of the AR learning platform was 95%. Most participants reported improved understanding of rheumatoid arthritis (95%) and improved understanding of hand joint examination (74%). Majority of participants (85%) expressed interest in future AR-based tutorials. Conclusions: AR-based rheumatology teaching was feasible and well-accepted among non-rheumatology healthcare learners in this pilot study. Participants report high usability and perceived gains in understanding of inflammatory arthritis and hand joint examination. AR may complement traditional teaching, particularly when bedside exposure is constrained. Larger studies incorporating objective learning outcomes are needed to define its educational impact. Clinical Trial: augm
Background: Parents are increasingly using Infant Feeding Apps for information and decision making related to breastfeeding and other aspects of their baby’s care. Few studies have explored how user...
Background: Parents are increasingly using Infant Feeding Apps for information and decision making related to breastfeeding and other aspects of their baby’s care. Few studies have explored how users conceptualize data privacy risks in relation to feeding apps and how concerns are balanced with app use. Objective: This qualitative study forms part of the Infant Feeding App project exploring how feeding apps influence parent–child communication in Australian families. A focus of this investigation was users’ views and concerns around privacy practices and terms and conditions when using these apps. Methods: We included first time parents of children aged 0 – 4 years who were breastfeeding at the time of the first interview and previously or currently use one or more infant feeding apps. Twenty-four participants completed the first-round interview, of those, 22 returned for a second-round interview approximately six months later, and 7 participated in the focus group discussion. Reflexive thematic analysis was used to analyse the data. Results: This paper draws primarily on findings from the key category of ‘Data and/or Privacy Practices’. This category was further refined into two main themes: Lack of awareness or acceptance of the terms and conditions associated with the mobile app and the risk mitigation behaviours mothers took to protect their privacy. Conclusions: Despite privacy concerns, mothers engaged with the Infant Feeding App and these findings resonate with the growth in digital technology availability and engagement. This paper has highlighted that privacy, a fundamental human right is at risk when those engaging with mobile apps are not aware of how data about themselves and their child may be used. A call for a move toward privacy by design, transparency in privacy policies and an approach of non-alienation in the use of data are called for.
Background: Nonintrusive monitoring through WiFi signal sensing (WiFi sensing) has been proposed as an alternative to wearables and cameras to support aging in place while preserving privacy. However,...
Background: Nonintrusive monitoring through WiFi signal sensing (WiFi sensing) has been proposed as an alternative to wearables and cameras to support aging in place while preserving privacy. However, a persistent gap remains between laboratory performance and real-world deployment, where the scarcity of labeled data constrains supervised approaches. Objective: This study aims to evaluate the operational feasibility and perceived usefulness of a personalized, unsupervised WiFi-based anomaly detection system in a real-world, longitudinal deployment. Specifically, we examine how this technology can support care, follow-up, and social support for older adults by identifying behavioral deviations without the need for wearable devices or cameras. Methods: We deployed a WiFi-based monitoring system in the homes of 32 older adults (analytic cohort defined by availability of valid sleep and activity data). The analytics engine combined (1) a consensus novelty detection ensemble (CNDE) with SHAP-based explainability for acute sleep deviations and (2) trend analysis using moving average convergence divergence (MACD) for sustained changes; additionally, Prophet was used to detect atypical durations of stays. We distinguished technical alerts (device status/connectivity; resolved by technical support) from care alerts (signals about routines, sleep, and presence; intended for the care team). Operational outcomes were summarized from alert logs and from quality-controlled sleep/activity data available in the database. Results: Median follow-up was 326 days (IQR 247–410). We generated 2,214 care alerts (excluding technical system alerts), predominantly stay-based activity alerts (45.4%) and sleep alerts (daily sleep quality and sleep location alerts; 47.6% combined). Most alerts were mild (75.8%), with smaller proportions of moderate (10.4%) and severe (13.8%). Operationally (without clinical ground truth), the overall mean rate was 0.240 alerts per participant-day (combined sleep/activity exposure), with the burden concentrated in 18.9% of participant-days (≥1 alert). When stratified by domain using stream-specific exposure, sleep alerts showed 0.156 alerts per participant-day (13.9% of participant-days with ≥1 alert) and a higher proportion of high priority (severe+moderate: 42.2%), whereas activity alerts showed 0.126 alerts per participant-day (12.2% of participant-days with ≥1 alert; high priority: 4.3%). Care professionals reported practical usefulness for prioritization, proactive outreach, and early detection of functional changes, supported by multiple success cases reported during follow-up. We present selected illustrative vignettes, including an acute severe sleep anomaly temporally aligned with a reactive anxiety episode and a sustained deterioration trend that prompted follow-up and led to identification of an underlying health issue affecting sleep. Conclusions: In a real-world deployment, a personalized, unsupervised WiFi-based approach can be integrated into workflows and can generate actionable signals with a manageable operational load. These findings support the feasibility of unsupervised artificial intelligence as proactive support in gerontechnology focused on care and social support, although prospective designs with formal evaluation protocols for care processes and person-centered outcomes will be needed.
This study demonstrates that it is highly feasible to recruit and retain surgical patients across the aging and cognitive spectra for continuous perioperative neuromonitoring using NINscan, a multimod...
This study demonstrates that it is highly feasible to recruit and retain surgical patients across the aging and cognitive spectra for continuous perioperative neuromonitoring using NINscan, a multimodal wearable device originally developed for spaceflight.
Background: In the United States (US) and worldwide, chronic pain affects a vast number of people and is one of the leading reasons adults seek medical care. In the US, 24.3% of adults reported ch...
Background: In the United States (US) and worldwide, chronic pain affects a vast number of people and is one of the leading reasons adults seek medical care. In the US, 24.3% of adults reported chronic pain in the past three months in 2023. Chronic pain is defined as lasting three months or longer, significantly disrupting one’s daily functioning and quality of life. Chronic pain can also be accompanied by other conditions, including anxiety and depression. Health care providers should be aware of several limitations in their array of current treatment modalities. While opioid analgesics are used for moderate to severe pain management, they cause many serious adverse effects, including sedation, respiratory depression, and constipation, and a high risk of dependence and addiction. Other pain medications, such as non-opioid analgesics including nonsteroidal anti-inflammatory drugs, adjuvant analgesics, and corticosteroids, similarly cause a range of side effects and organ toxicity. Objective: We conducted a pilot study to explore the potential role of a non-invasive and safe transdermal audio-based therapeutic system among 24 police professionals aged 21 to 65 years old with chronic pain, and/or associated conditions of migraine, sleep disorders, stress/anxiety, and conventional headaches. Methods: This STROBE-guided pilot study evaluated the feasibility and perceived effectiveness of a self-administered Transdermal Acoustic Pain Suppression (TAPS) intervention among police personnel in West Palm Beach, Florida. Participants completed a flexible 30-day protocol integrated into their work schedules, with data collection spanning five months due to operational constraints. Descriptive and non-parametric analyses indicated patterns of symptom reduction, perceived pain relief, and user satisfaction, supporting the usability of TAPS in this occupational setting. Results: Our pilot study findings showed that following therapy, more than one-third of participants (37.5%) experienced condition improvement and half reported reduced symptoms, including notable pain reduction. Conclusions: The findings from this small pilot study support the rationale for larger analytic studies, designed a priori to test the hypothesis. Clinical Trial: Not applicable
Background: Psoriasis severity is commonly assessed using the Psoriasis Area and Severity Index (PASI), but PASI scores are frequently unavailable in real-world data due to inconsistent documentation...
Background: Psoriasis severity is commonly assessed using the Psoriasis Area and Severity Index (PASI), but PASI scores are frequently unavailable in real-world data due to inconsistent documentation and is often affected by interobserver variability. Medication-based proxies may offer a pragmatic alternative for severity stratification in registry-based research. Objective: This study aimed to develop a medication-based severity scale for psoriasis and evaluate its association with PASI scores in Danish real-world registry and electronic health record (EHR) data. Methods: We conducted a retrospective registry-based study including individuals with an ICD-10 diagnosis of psoriasis in Eastern Denmark between 2006 and 2016. Data were obtained from the Danish National Patient Registry, the Danish National Prescription Registry, and a population-wide EHR repository. PASI scores were extracted from unstructured clinical notes using regular expressions. Anti-psoriatic medications were categorized into mild, moderate, and severe groups based on Danish treatment guidelines. Correlations between PASI scores and the medication severity scale were analyzed. Results: Among 19218 individuals with psoriasis, 18848 had relevant prescriptions and 2884 had PASI scores. The automated PASI extraction algorithm demonstrated high performance (F1 score 0.98). A weak but statistically significant correlation was observed between PASI scores and the medication severity scale (r = 0.057, P = .001), indicating partial overlap between clinical severity and prescriptions. Moderate and severe therapies were generally associated with higher PASI values, though exceptions reflected treatment history and healthcare system structure. Conclusions: Automated extraction of PASI scores from EHR data is feasible and reliable in large-scale registry research. The modest association between PASI and medication-based severity highlights differences between clinical severity measures and real-world prescribing practices. Medication-based stratification may therefore serve as a complementary proxy for disease severity in registry-based studies when PASI scores are unavailable.
The line between tool and companion was once obvious, but conversational artificial intelligence (AI) is blurring it in ways few researchers anticipated. Large language model (LLM) chatbots and purpos...
The line between tool and companion was once obvious, but conversational artificial intelligence (AI) is blurring it in ways few researchers anticipated. Large language model (LLM) chatbots and purpose-built AI companion agents are now used by millions of people every day. They are not being used to simply retrieve information, but instead to offer emotional support, help process personal distress, and sustain what many describe as genuine relationships. Research puts the scale of this shift in sharp relief as nearly half (48.7%) of individuals with self-reported mental health concerns report having used an LLM for mental health support or therapy-related purposes [1]. This Viewpoint argues that these uses are best understood through three unique but overlapping relational frames: AI as therapist substitute, AI as companion or confidant substitute, and AI as romantic partner substitute. Drawing on empirical literature across digital mental health, psychology, communication, and human–computer interaction, and grounded in the values of participatory medicine, this paper examines why people turn to AI for these intimate purposes, what they appear to gain, and what clinicians, designers, developers, and policymakers should examine more carefully as the practice evolves. The picture that emerges is neither straightforwardly optimistic nor dismissive. Therapeutic chatbots can produce real symptom reduction for users, AI companionship can ease loneliness in genuine, if bounded, ways, and the emotional relief some people experience in these interactions is not an artifact of naivety. But the same systems that lower the barriers to disclosure also lower the barriers to harm. AI chatbots regularly hallucinate clinical guidance, validate dysfunctional beliefs, handle crises without accountability, and may cultivate the very isolation they seek to relieve. Responsible integration requires something more demanding than a disclaimer. Instead, it requires transparent design, thoughtful escalation pathways, ongoing evaluation, and a commitment to the human connection that participatory medicine places at the center of good care.
Background: Mental health disorders represent a major public health challenge worldwide and substantially affect work ability, productivity, and social functioning. Public-sector employees are particu...
Background: Mental health disorders represent a major public health challenge worldwide and substantially affect work ability, productivity, and social functioning. Public-sector employees are particularly exposed to psychosocial demands, yet data on their mental health literacy and supportive behaviors remain scarce. Mental Health First Aid (MHFA) aims to improve knowledge, attitudes, and behaviors (KAB) toward psychological distress among non-specialists, but evidence among civil servants and on skill retention over time is limited. Objective: This study aimed to (1) determine knowledge, attitudes, and behaviors related to psychological distress among French civil servants; (2) examine associations between KAB scores and MHFA training exposure and (3) explore gradients according to time since training as a proxy for skill retention. Methods: A nationwide cross-sectional survey was conducted in November 2025 among French civil servants. Participants completed an online questionnaire adapted from a validated KAB instrument. The scores for the three dimensions were normalized on a 0–40 scale, yielding a total KAB score ranging from 0 to 120. They were categorized as inadequate (0–70), marginal (71–88), or adequate (89–120). Multivariable linear regression models were used to identify factors associated with KAB outcomes, adjusting for sociodemographic and professional variables. Results: Among 338,560 eligible civil servants, 6,526 participants (1.93%) were included and 4,679 (1.38%) fully completed the questionnaire. Participants were predominantly female (64.6%), aged 40–69 years (92.3%), and belonged to professional category A (47.2%). The mean total KAB score in the overall population was 82.88 (SD 19.14), corresponding to a marginal level. Mean subscores were 31.31 (SD 5.26) for knowledge, 29.72 (SD 11.52) for attitudes, and 21.86 (SD 7.96) for behaviors.
Participants who had completed MHFA training (11.6%) showed significantly higher total KAB scores than untrained participants (95.56 [SD 14.17] vs 81.21 [SD 19.09]; p<.001), with higher scores for knowledge (33.31 vs 31.04), attitudes (34.35 vs 29.10), and behaviors (27.87 vs 21.06; all p<.001). Among trained participants, a decreasing gradient was observed with increasing time since training (<6 months, 6–12 months, >12 months; p<.001), although scores remained higher than in untrained individuals beyond 12 months. In adjusted models, MHFA training was strongly associated with higher KAB scores (β = 14.63 for <6 months; β = 13.61 for 6–12 months; β = 10.33 for >12 months; all p<.001). Lower scores were observed in professional categories B and C compared with category A and among males. Conclusions: Mental health literacy and supportive behaviors among French civil servants remain heterogeneous and, on average, marginal. MHFA training is associated with substantially higher KAB scores, even beyond 12 months, supporting its role as a public health literacy intervention in occupational settings. The attenuation of scores over time highlights the relevance of reinforcement strategies to sustain mental health competencies within the civil service.
Open Peer Review Period: Mar 24, 2026 - Mar 9, 2027
Background: Clinical trials are essential for evaluating medical interventions, yet approximately 80% fail to meet enrollment targets on time. The challenge of matching patients to suitable trials inv...
Background: Clinical trials are essential for evaluating medical interventions, yet approximately 80% fail to meet enrollment targets on time. The challenge of matching patients to suitable trials involves reviewing unstructured clinical notes against complex eligibility criteria containing dozens of inclusion and exclusion conditions. AI methods promise to automate this matching at scale, but each approach involves fundamental tradeoffs between speed, accuracy, interpretability, and auditability. Objective: This technical narrative review surveys the landscape of AI approaches to patient-trial matching, tracing the evolution from rule-based systems through sparse retrieval, dense retrieval, cross-encoder reranking, knowledge graphs, and large language model approaches. We identify critical gaps and provide an integrative framework for understanding tradeoffs across methods. Methods: We searched PubMed, IEEE Xplore, ACL Anthology, and arXiv using terms including "clinical trial matching," "patient recruitment AI," "eligibility criteria NLP," and "trial-patient retrieval" for literature published 2015 to 2026. We synthesized algorithmic approaches, commercial platforms, evaluation methodologies, and explainability requirements through a technical narrative review. Results: We identified seven algorithmic paradigms: rule-based systems, machine learning classifiers, BM25 sparse retrieval, BERT-based dense retrieval, cross-encoder reranking, large language models (including TrialGPT achieving 87.3% criterion-level accuracy), and knowledge graph approaches. We catalogued sixteen commercial platforms and documented the TREC Clinical Trials Track and n2c2 2018 benchmarks. Each method resolves the core tradeoff differently: BM25 offers speed without semantic understanding; LLMs offer flexibility without auditability; hybrid architectures distribute tradeoffs across components. Conclusions: Six critical gaps define the frontier: absence of operational-scale benchmarks, temporal and Boolean reasoning limitations, tension between LLM flexibility and deterministic auditability, multi-method explainability disclosure requirements, proxy-label governance, and data heterogeneity across EHR systems. The matchmaker's dilemma of balancing competing goods with no perfect solution frames both progress and unresolved challenges in this rapidly evolving field.
Background: Background: Global awareness of chronic kidney disease (CKD) remains limited, and health information literacy among CKD patients is generally suboptimal. Developing effective health inform...
Background: Background: Global awareness of chronic kidney disease (CKD) remains limited, and health information literacy among CKD patients is generally suboptimal. Developing effective health information delivery strategies has become a clinical priority. Objective: Objective: To evaluate the impact of a WeChat mini-program-based intervention on health information literacy, self-management behaviors, and renal function indicators in CKD patients. Methods: Methods: A single-group prospective before-and-after study was conducted, adhering to the STROBE guidelines. A total of 57 CKD patients were enrolled, with health information literacy assessed using the CKD Health Information Literacy Questionnaire at baseline and 6 months post-intervention. Renal function parameters were monitored concurrently. Results: Results: Compared with baseline, the intervention group showed significant improvements in total health information literacy score (P=0.041), CKD knowledge reserve (P=0.007), and health behaviors (acceptance of medical promotion: P=0.042; regular health check-ups: P=0.028). Significant improvements were also observed in renal function-related indicators, including urinary protein (P=0.023) and urinary protein-to-creatinine ratio (P=0.044). No significant differences were found in platelet count, hemoglobin, estimated glomerular filtration rate (eGFR), or albumin levels. Subgroup analysis revealed that employed patients had significantly higher knowledge reserves (P=0.022); patients with college education showed improved total health literacy scores (P=0.042); patients with primary school or college education had enhanced knowledge reserves (P=0.009, P=0.005); primary school-educated patients had significant differences in platelet counts (P=0.027); and younger patients demonstrated improvements in knowledge reserves (P=0.005), urinary protein (P=0.031), and urinary protein-to-creatinine ratio (P=0.017). Conclusions: Conclusions: The dedicated WeChat mini-program effectively enhances health information literacy, disease knowledge, and self-management behaviors in CKD patients, with associated stabilization of renal parameters, suggesting potential for delaying disease progression. Large-scale randomized controlled trials are warranted to validate these findings. Clinical Trial: Trial Registration: Registration number: ChiCTR2100053103
Background: Stunting remains a critical public health challenge in rural Indonesia, with a national prevalence of 19.8% among children under five years. Despite high levels of digital access, parents'...
Background: Stunting remains a critical public health challenge in rural Indonesia, with a national prevalence of 19.8% among children under five years. Despite high levels of digital access, parents' capacity to critically evaluate and act on digital health information remains limited. The socio-institutional factors that determine whether digital health promotion translates into preventive behavior are poorly understood in community-based rural settings. Objective: This study examined: (1) the level of perceived eHealth literacy among parents in a rural West Java community; (2) socio-cultural and institutional factors shaping the relationship between digital health literacy and parental health behavior; and (3) implications for community-integrated digital health promotion strategies. Methods: A convergent mixed-methods design was employed. Quantitative data were collected using the eHealth Literacy Scale (eHEALS; n=175) from purposively sampled parents in Posyandu-active households via structured face-to-face interviews. Qualitative data comprised 15 in-depth interviews, three focus group discussions (total n=24), and four structured Posyandu observation sessions, analyzed thematically using NVivo 12 Plus. Reporting followed COREQ standards. Results: Mean eHEALS score was 22.8 (SD 4.7); 76.6% of parents were in the moderate perceived eHealth literacy category, 20.0% low, and 3.4% high. Formal educational attainment was not a statistically significant predictor of perceived eHealth literacy (chi-square=7.113, df=6, P=.311). Four qualitative themes emerged: limited evaluative digital competencies despite broad access; socio-cultural influences on message uptake; Posyandu as the primary institutional mediator; and community-based digital empowerment as a policy priority. Conclusions: Parents consistently reported greater behavioral responsiveness to health messages reinforced through Posyandu cadres, suggesting that community-based mediation is a critical pathway linking digital health promotion to preventive behavior. Digital health strategies should be embedded within trusted local health structures rather than deployed as stand-alone interventions Clinical Trial: Not applicable
Background: Prolonged sedentary behavior has become a pervasive occupational health concern in China. Zero-Time Exercise (ZTEx), which embeds brief movements into routine tasks, offers a promising low...
Background: Prolonged sedentary behavior has become a pervasive occupational health concern in China. Zero-Time Exercise (ZTEx), which embeds brief movements into routine tasks, offers a promising low-threshold strategy to reduce inactivity. However, the psychological mechanisms underlying ZTEx adoption remain unclear, which may limit empirical guidance for the development of theory-informed and targeted workplace interventions among sedentary occupational groups. Objective: To examine the psychological mechanisms underlying intention to adopt ZTEx among Chinese sedentary workers, and to test a theory-informed sequential mediation model in which exercise motivation influences ZTEx-related intention through self-efficacy and physical activity triggers. Methods: A cross-sectional online survey was conducted among 790 adults in sedentary occupations across 20 Chinese provinces. Validated instruments measured exercise motivation, self-efficacy, physical activity triggers and ZTEx-related intention. Sequential mediation analyses (PROCESS Models 4 and 6) examined direct, indirect, and chain effects of motivation on intention via self-efficacy and triggers, adjusting for sociodemographic covariates. Results: Exercise motivation showed significant positive associations with self-efficacy, triggers, and ZTEx intention (all p < 0.001). Three mediation paths were identified: (1) via self-efficacy (B = 0.104, 95% CI [0.057, 0.155]); (2) via triggers (B = 0.157, 95% CI [0.110, 0.205]); and (3) a sequential effect through self-efficacy and triggers (B = 0.025, 95% CI [0.013, 0.039]). A direct effect remained significant (B = 0.167, 95% CI [0.088, 0.246]). The full model explained 28.5% of the variance in ZTEx-related intention. Conclusions: Guided by the COM-B framework and the Fogg Behavior Model, this study delineates a structured pathway linking exercise motivation, self-efficacy, and physical activity triggers in shaping ZTEx-related intention among adults in sedentary occupations. These findings indicate that interventions integrating motivational enhancement, efficacy support, and context-sensitive prompts may support the incorporation of low-threshold physical activity into routine sedentary work contexts.
Background: The February 6, 2023 Kahramanmaraş-centered earthquakes devastated Malatya province and created an acute collective trauma context in which psychosocial recovery remains a pressing public...
Background: The February 6, 2023 Kahramanmaraş-centered earthquakes devastated Malatya province and created an acute collective trauma context in which psychosocial recovery remains a pressing public health priority. Despite growing recognition that cultural resources may buffer against disaster-related psychological deterioration, empirical evidence from non-Western, earthquake-affected populations remains sparse — and age-differentiated patterns of cultural meaning-making in such contexts are largely unexplored. Whether intangible cultural heritage functions as a similarly protective resource across cohorts with divergent socialization histories carries direct implications for the design of population-level psychosocial interventions in earthquake-affected communities. Objective: This cross-sectional study examined the associations between attitudes toward intangible cultural heritage, sense of belonging, and life satisfaction among earthquake-affected adults in Malatya, Türkiye, and tested whether these associations and the underlying constructs differed between young adults (aged 18–30) and older adults (aged 65 and above). Methods: A total of 404 adults directly affected by the February 6, 2023 earthquakes and residing in Malatya province were recruited through purposive sampling (young adults: n=203; older adults: n=201). Life satisfaction was assessed using the Satisfaction with Life Scale (SWLS), sense of belonging with the General Belongingness Scale (GBS), and cultural heritage attitudes with the Intangible Cultural Heritage Attitude Scale (ICHAS). Measurement comparability across age groups was evaluated via Tucker's congruence coefficients (φ). Group differences were examined with Mann-Whitney U and Kruskal-Wallis tests; associations among constructs were assessed with Spearman correlations; and predictors of life satisfaction were identified through hierarchical multiple regression. Effect sizes and bootstrap-corrected 95% confidence intervals were reported throughout. Results: Cultural heritage attitudes (β=.125, 95% CI 0.06–0.19) and sense of belonging (β=.261, 95% CI 0.19–0.33) were significant independent predictors of life satisfaction across the sample (model R²=.420, adjusted R²=.412). No significant age-group differences were detected in cultural heritage attitudes (U=19,567.50, p=.477, r=.04) or sense of belonging (U=18,648.50, p=.132, r=.08). Life satisfaction was significantly lower among older adults than young adults (U=10,570.50, Z=−8.397, p<.001, r=.42). Educational attainment and income level were the primary sociodemographic predictors of life satisfaction. A Simpson's paradox was identified in the household size data: the aggregate-level association between household size and life satisfaction was reversed within each age group when examined separately, with larger households associated with reduced belonging and life satisfaction among young adults only. Conclusions: Attitudes toward intangible cultural heritage and sense of belonging constitute robust, age-stable predictors of post-disaster well-being, suggesting that these psychosocial resources remain intact across the life course even under conditions of collective trauma. The significant age gap in life satisfaction, however, points to the differential material and economic vulnerability of older survivors for the targeting of post-disaster public health interventions in earthquake-affected Malatya and comparable settings. Clinical Trial: Not applicable
Background: The COVID-19 lockdown accelerated the adoption of telepsychiatry, including synchronous videoconferencing in adult mental health care. It is still unclear whether videoconferencing works e...
Background: The COVID-19 lockdown accelerated the adoption of telepsychiatry, including synchronous videoconferencing in adult mental health care. It is still unclear whether videoconferencing works equally well for all user groups. This study examines clinicians’ and clients’ experiences with videoconferencing during the first Dutch lockdown (April 2020) and one year later (April 2021), focusing on the appraisal of online individual (OIT) and online group treatment (OGT) in outpatient adult mental health care, covering first-line (primary) and second-line (specialized) services. Objective: To examine the appraisal of OIT and OGT among clients and clinicians in 2020 and 2021, and to explore subgroup differences by gender, age and diagnosis/profession. Methods: Two independent cross-sectional surveys were conducted in 2020 (clients: n = 350; clinicians: n = 146) and 2021 (clients: n = 357; clinicians: n = 122) at a Dutch mental health service. Participants evaluated their OIT and OGT experiences using newly developed service-evaluation questionnaires. For the appraisal score, we only used items that explicitly compared videoconferencing with face-to-face (FTF) care; principal axis factoring was used to derive appraisal scores. Hierarchical regression tested the added predictive value of survey year for appraisal of videoconferencing: step 1 included participant variables (gender, age, diagnosis/profession), and step 2 added survey year. Exploratory subgroup analyses examined within-year variation in appraisal by gender, age, and diagnosis/profession. Results: Despite a prevailing preference for FTF treatment, clients reported significantly higher appraisal with OIT in 2021 compared to 2020 (B = 0.22, P = .001), while appraisal of OGT did not differ significantly between years. Clinicians reported higher appraisal in 2021 than in 2020 for both OIT (B = 0.69, P < .001) and OGT (B = 1.40, P = .003). Exploratory subgroup analyses based on gender, age, or diagnosis found no meaningful differences in appraisal for either clients or clinicians. Conclusions: Appraisal of videoconferencing was higher in 2021 than in 2020, most clearly among clinicians, yet both clients and clinicians still preferred FTF care on average. At present, videoconferencing appears best suited as a complementary option, with relevance for expanding access to mental health care globally in a rapidly changing landscape.
Background: Artificial intelligence (AI) systems designed to enhance polyp detection during colonoscopy have shown promise in clinical trials, but the extent to which various systems improve detection...
Background: Artificial intelligence (AI) systems designed to enhance polyp detection during colonoscopy have shown promise in clinical trials, but the extent to which various systems improve detection of polyps of different sizes remains unclear. Objective: The objective of this article is to compare efficacy of various AI-assisted systems during colonoscopy. Methods: We searched PubMed, Cochrane Library and Web of Science for randomized controlled trials (RCTs) comparing AI-assisted colonoscopy with white-light colonoscopy, then literature screening was conducted and network meta-analysis (NMA). NMA was conducted using StataMP 14 and Review Manager 5.3 was used for the quality assessment of included studies. Results: NMA of 13 RCTs (4,156 participants) revealed marked size-dependent efficacy of AI-assisted systems for increasing mean polyp detection. For diminutive polyps, EndoScreener ranked first [SUCRA 77.7%, probability of being best 33.3%] and significantly increased mean polyp count versus standard colonoscopy (SMD 1.22, 95%CI 0.08 to 2.36). For small polyps (6-9mm), standard colonoscopy unexpectedly achieved highest ranking (SUCRA 91.5%), with AI systems showing minimal incremental value. For large polyps (≥10mm), CAD EYE and standard colonoscopy performed comparably (SUCRA 78.9% vs 80.0%), with no AI system demonstrating significant improvement. No head-to-head trials directly compared AI systems, indirect comparisons revealed no differences between AI platforms. Conclusions: Current evidence supports size-dependent efficacy of AI-assisted colonoscopy, with maximal benefit for diminutive polyps (≤5mm) and diminishing returns as polyp size increases. EndoScreener demonstrates the most consistent evidence for improving diminutive lesion detection. These findings support targeted AI implementation for screening populations where small lesion detection impacts surveillance intervals. Direct comparative trials between AI systems are needed to establish optimal platform selection. Clinical Trial: The protocol was registered with PROSPERO, registration number: CRD420251266932.
Abstract
NHS England's early digital-first agenda has been absorbed into a broader reform of modern general practice built around multiple access routes, structured triage, and routine use of online...
Abstract
NHS England's early digital-first agenda has been absorbed into a broader reform of modern general practice built around multiple access routes, structured triage, and routine use of online consultation systems. The central question is therefore no longer whether digital access should exist, but under what conditions it improves care. This review reassesses digital-first primary care in England through the linked lenses of access, equity, communication, continuity, and trust. Recent UK evidence shows that digital routes can reduce friction for routine and bounded problems, improve administrative convenience, and widen options for contacting general practice. However, these gains are conditional rather than universal. Patients with limited digital confidence, language barriers, multimorbidity, or needs that are diagnostically or relationally complex are less well served when digital systems become the default route rather than one option within a broader access model. New English studies also show that patients judge access less by speed alone than by whether they can reach an appropriate professional in an acceptable mode and, where possible, maintain continuity with a known clinician. At the same time, staff studies indicate that digital access reform can redistribute work, create hidden facilitation labour, and strain relationships within practice teams. We argue that the most useful analytic and policy endpoint is no longer digital-first primary care, but patient-centred hybrid care. In this review, that term denotes a multi-channel model in which route of access, consultation modality, continuity, and safety are deliberately matched to patient need rather than subordinated to technological throughput. Future research should prioritize subgroup-sensitive outcomes, continuity, trust, safety, and implementation burden.
Background: National surveillance of commercial retail environments is limited by data sources that are updated infrequently and capture narrow dimensions of food access. Google Places API offers cont...
Background: National surveillance of commercial retail environments is limited by data sources that are updated infrequently and capture narrow dimensions of food access. Google Places API offers continuously updated, programmatically accessible data on business locations across the US, but its validity as a population-level exposure measure has not been systematically evaluated. Objective: To develop and evaluate a Google Places–derived US Built Retail Environment Index (UBER) as a scalable measure of county-level commercial retail infrastructure in the contiguous United States and to estimate its spatial association with age-adjusted diabetes prevalence. Methods: We constructed the US Built Environment Retail (UBER) Index using Google Places API data capturing alcohol outlets, fast-food and convenience stores, grocery and health food stores, and fitness and recreation facilities across 1,701 US counties. Principal component analysis generated a single composite index. We assessed Construct validity against USDA Food Access Research Atlas and County Health Rankings benchmarks and estimated spatial associations with age-adjusted diabetes prevalence (CDC PLACES, 2025) using spatial error models adjusting for area deprivation, urbanicity, and census division. Quantile regression and tract-versus-county comparisons evaluated robustness. Results: The first principal component explained 92.9% of variance among the four indicators with near-equal loadings (range: 0.492–0.506), indicating the index captures overall commercial retail density rather than any single establishment type. Correlations with existing food-environment benchmarks were weak (|r| < 0.20), confirming that the index measures a distinct dimension of the built environment not reflected in current surveillance tools. Each SD increase in the UBER Index was associated with 0.24 percentage points higher diabetes prevalence (95% CI, 0.16–0.32; P < .001). Associations were stable across quantiles of diabetes prevalence. County- and tract-level analyses showed discordant effect directions, indicating scale-dependent ecological confounding. Conclusions: Google Places API data can generate a reliable, spatially structured index of commercial retail infrastructure associated with county-level diabetes prevalence. The UBER index captures a dimension of the built environment not represented in existing surveillance systems and may support scalable digital monitoring of commercial environments relevant to chronic disease prevention.
Background: Noncommunicable diseases (NCDs) cause a very significant health and economic burden. As they are associated with modifiable behavioral risk factors such as physical inactivity and poor die...
Background: Noncommunicable diseases (NCDs) cause a very significant health and economic burden. As they are associated with modifiable behavioral risk factors such as physical inactivity and poor diet, more evidence on effective health behavior modification methods is needed. Fully online delivery of clinical trials can provide a practical and scalable way to evaluate interventions that aim to modify relevant lifestyle factors. The emergence of online delivery methods presents opportunities and challenges that need to be better understood to inform future research. Objective: This umbrella scoping review aimed to summarize current evidence on the opportunities and challenges provided by lifestyle intervention clinical trials that are delivered fully online. Methods: Evidence was synthesized from existing peer-reviewed review papers to map the digital delivery methods in online lifestyle intervention trials, focusing on technologies, recruitment, engagement and retention strategies, and reported strengths, limitations, and future directions. Using PRISMA-ScR guidelines, PubMed, EMBASE, CINAHL, Web of Science, and Scopus were searched for reviews published between January 2013 and May 2025. Predominantly (>50%) hybrid, telehealth, or acute condition focused interventions were excluded. Results: Eligible reviews (n=39) discussed digital interventions targeting diet, physical activity, or both, for lifestyle improvement, chronic disease prevention or management. The most common hardware used in online lifestyle clinical trials were smartphones and wearables, with the most frequent software modes being web-based platforms, mobile apps, and SMS. Successful engagement strategies often integrated behavior changes techniques, such as goal setting, self-monitoring, personalized feedback, and human support into the intervention design, or had behavior change techniques as a feature of the technology itself. Reported strengths of conducting clinical trials online included improved accessibility, scalability, cost-efficiency and personalization, whereas limitations discussed were poor engagement and retention, digital literacy barriers, and rapid technological change outpacing evaluation capabilities. Interventions that used theory-based designs, particularly those using Social Cognitive Theory, the Transtheoretical Model, and the Theory of Planned Behavior, were reportedly most successful in improving behavioral outcomes. Engagement and retention varied considerably across online trials, suggesting that the success of these studies may depend less on the online delivery modality itself and more on how interventions and technologies are designed, including the integration of behavioral theory and behavior change techniques. Conclusions: This review shows that online delivery of lifestyle intervention trials is a feasible and potentially advantageous method as it can improve reach, increase scalability, be cost-efficient, and allow more personalization of the intervention. To further improve the conduct of online clinical trials, future research should address increased use of behavioral change theory, equitable access to clinical trial participation, management of data privacy and security, intervention fidelity, and use of novel technologies such as artificial intelligence in a field that is rapidly evolving. Clinical Trial: The protocol was prospectively registered with the Open Science Framework https://osf.io/umcfv; 5 September 2023
Background: Clinical guidelines recommend an integrated, person-centered care model with better control of modifiable risk factors and coexisting conditions in patients with atrial fibrillation (AF),...
Background: Clinical guidelines recommend an integrated, person-centered care model with better control of modifiable risk factors and coexisting conditions in patients with atrial fibrillation (AF), but many persons with AF receive insufficient risk factor management. Digital health technologies may provide valuable support in addressing this gap. Objective: Our aim was to evaluate a co-designed digital platform for supporting person-centered management of modifiable risk factors in individuals with AF. Methods: This is a mixed-methods study including a standardized quantitative questionnaire used to score the usability of digital tools, the System Usability Scale (SUS), and a qualitative, descriptive, manifest content analysis of individual interviews. Results: Twenty-two patients hospitalized for AF were included (age 68 (48-79) years; 32% female; BMI 27.7 (20.8-35.0) kg/m2; paroxysmal/persistent AF (36%/64%); AF duration 4 (0.5-18 years). Relevant comorbidities were hypertension (77%), heart failure (36%), diabetes mellitus type 2 (14%), and ischemic heart disease (18%). Usability was rated high, with a mean SUS score of 75 (±18.2), indicating above-average user acceptance. Participants’ requirements were summarized into four main categories and ten subcategories. First, they value a clear layout with simple design and easy navigation. Second, they appreciate positive content, which is informative, inclusive and motivating. Third, they request personalized information on different aspects and provided on different levels. Fourth, they desire individualized medical recommendations that are personalized and flexible but open to individual choice. Conclusions: To improve digital management of lifestyle-related risk factors and comorbidities, individuals with AF seek a solution with a clear layout, positive content, personalized information, and individualized medical recommendations.
Background: Nursing students are the future workforce, and their readiness to use digital health is important. Prior studies focus on knowledge and attitudes; however, they do not examine the wide ran...
Background: Nursing students are the future workforce, and their readiness to use digital health is important. Prior studies focus on knowledge and attitudes; however, they do not examine the wide range of digital health literacy levels that may influence the attitudes of nursing students toward using telehealth in clinical settings. Objective: This study aimed to determine the relationship between nursing students’ literacy of digital health and their attitudes toward adopting telehealth in practice. Methods: A cross-sectional design was employed, and the sample for this study consists of undergraduate nursing students who are pursuing a Bachelor of Nursing at a selected Saudi Arabian University. An online survey was used with two scales, which are the Digital Health Care Literacy Scale and the Nurse's Attitudes Towards the Use of Telehealth Scale. Results: A total of 273 students participated (mean age 21.3 years, SD1.9). Most of the nursing students demonstrated a high digital health literacy level (184/273, 67.4%; mean DHLS score 11.9/15). Digital health literacy was a significant predictor of positive telehealth attitudes (AOR=1.48, 95% CI 1.28–1.71; P<.001). Male students were significantly less likely to report positive attitudes compared with females (AOR=0.62, 95% CI 0.39–0.97; P=.038). However, academic year and participation in telehealth workshops and informatics courses were not associated with higher literacy levels. Conclusions: Higher levels of literacy appear to be associated with more positive attitudes toward telehealth usage in practice. However, current formal education and workshops were not associated with influencing digital health literacy. This demonstrates the necessity of providing restructured digital training and development in nursing education. These may enhance telehealth readiness and support future digital healthcare delivery.
Background: Aged Care Onsite Pharmacist roles are progressively rolling out across Australia. Distinct from pharmacist practice in hospital and community pharmacy, the role is professionally isolated...
Background: Aged Care Onsite Pharmacist roles are progressively rolling out across Australia. Distinct from pharmacist practice in hospital and community pharmacy, the role is professionally isolated from other pharmacists, limiting opportunities for peer learning and collaboration, and practice-specific professional development. Objective: To co-design a training program that enhances the professional practice of aged care onsite pharmacists in line with the diverse interests of stakeholders. Methods: Focus group-style co-design workshops and stakeholder consultations will be employed in this project. The ‘Double-Diamond’ framework will be employed to structure the process, using four stages of alternating convergent and divergent exploration of stakeholder perspectives. A consumer advisory group has been engaged, consisting of persons with lived experience of aged care settings, for consistent observation and overarching guidance of the project. Exploratory workshops with pharmacists, allied health and medical professionals, residents, and families of residents will be conducted. Workshops will be audio- and video-recorded, transcribed verbatim and undergo thematic analysis. Insights obtained from overarching themes as well as individual perspectives will be used to design a pilot program, which will be presented and discussed for feedback during follow-up focus groups. A pilot phase will then be undertaken with aged care onsite pharmacists, who will be further involved in feedback and workshop-style focus groups to collaboratively refine the program. Results: Focus group-style workshops and oversight from a consumer advisory group will guide the development of a novel education intervention for practising aged care onsite pharmacists in Australia. Conclusions: This will be the first co-designed workplace-based training program available for pharmacists undertaking the novel, complex role of aged care onsite pharmacists. It is expected to be of benefit for developing clinical and non-clinical skills, facilitate integration into existing teams, and offer a pathway to professional recognition. Clinical Trial: Ethical approval was sought and obtained through the Adelaide University Human Research Ethics Committee (Approval #206543). All participants will provide informed consent prior to participation. The study is conducted in accordance with the National Statement on Ethical Conduct in Human Research (2023).
This research was funded through the Medical Research Future Fund (MRFF) 2022 Quality, Safety and Effectiveness of Medicine Use and Medicine Intervention by Pharmacists (Grant ID: MRFMMIP000022).
Background: Cigarette smoking remains highly prevalent among people living with type 2 diabetes mellitus (T2DM) and is independently associated with accelerated cardiovascular disease, worsened microv...
Background: Cigarette smoking remains highly prevalent among people living with type 2 diabetes mellitus (T2DM) and is independently associated with accelerated cardiovascular disease, worsened microvascular complications, and premature mortality. Despite strong evidence supporting pharmacological and behavioural cessation interventions, the integration of structured tobacco treatment into routine diabetes care remains inconsistent across healthcare systems. Furthermore, ongoing controversies surround the potential role of tobacco harm reduction (THR) strategies—including electronic nicotine delivery systems (ENDS)—for individuals who are unable or unwilling to achieve complete abstinence from tobacco.
Objective: This protocol describes an international, modified Delphi study designed to generate expert consensus on optimising smoking cessation care and on the place of THR approaches in the management of T2DM, and to prioritise pragmatic, evidence-informed recommendations for clinical practice and future research.
Methods: A multidisciplinary Steering Committee will develop a structured set of consensus statements informed by the DiaSmokeFree evidence base and framed using PICO-oriented questions across eight clinical domains. We will recruit 30–50 international experts from relevant scientific societies and clinical disciplines. Participants will complete two to three anonymous online rating rounds over approximately two to three months. Each statement will be rated on a 6-point Likert scale (1 = strongly disagree to 6 = strongly agree). Consensus will be defined a priori as ≥85% of respondents rating within a single agreement category (1–2, 3–4, or 5–6). Statements not reaching consensus will be revised on the basis of quantitative feedback and free-text comments and re-rated in subsequent rounds.
Ethics and dissemination: Ethical approval will be sought where required by local regulations. Participation is voluntary with informed consent. Results will be reported in accordance with the Conducting and REporting DElphi Studies (CREDES) guidance and disseminated as an open-access consensus statement, a suite of practical clinical tools, and a prioritised research agenda to support the integration of evidence-based tobacco treatment and harm reduction into T2DM care.
Background: Reviewing the prescription drug monitoring program (PDMP) before signing a controlled medication prescription is a best practice to improve opioid safety and is legislatively mandated in m...
Background: Reviewing the prescription drug monitoring program (PDMP) before signing a controlled medication prescription is a best practice to improve opioid safety and is legislatively mandated in most states. Mandating provider actions have unintended costs including workflow interruptions and misapplying provider time. The evidence supporting PDMP effectiveness is mixed, exacerbating the knowledge gap regarding mandating PDMP use. Prior PDMP evaluations have been limited by low rates of PDMP use and an inability to link encounter level PDMP review with patient outcomes. Clinical decision support (CDS) is an effective implementation strategy which is advantageous in collecting clinical data on PDMP use and prescribing decisions. Objective: This study aims to evaluate if user-centered CDS, which imports PDMP data into existing workflows, improves PDMP use and patient safety while reducing provider work. Methods: This is an electronic health record (EHR)-embedded, randomized control trial of 2 clinician-facing active choice CDS alerts to facilitate mandatory PDMP review vs usual care. One CDS, “mandated alert,” interrupts providers when prescribing an opioid or benzodiazepine with a link suggesting providers open the EHR-integrated state PDMP interface. Results: Utilizing user-centered design, we developed a second “smart” mandated CDS with the same rules-based logic and suggestion to check the PDMP, but also displays patient-specific data imported from the PDMP (number of active narcotic, sedative, and stimulant filled prescriptions). The aim of adding PDMP data to CDS is to facilitate PDMP utilization while decreasing unnecessary provider work when additional PDMP information is not needed. Providers were randomized and balanced by setting (349 inpatient, 354 emergency department and 751 outpatient). Both CDS alerts will be implemented within a single health system with a shared EHR and compared to usual care (no CDS). The primary outcome will be PDMP use. Secondary outcomes will be evaluated relative to encounter level PDMP use and include time spent prescribing, controlled medication prescription completion, and future opioid use by patients. Conclusions: This is a study protocol for a pragmatic, EHR-embedded randomized clinical trial optimizing a CDS implementation strategy to improve PDMP utilization while decreasing provider work. Implementation and effectiveness outcomes will be examined using the RE-AIM framework. Clinical Trial: NCT06215560 registered 5/6/24
Background: Globally, healthcare treatment trends are shifting from inpatient to outpatient settings owing to medical advancements and cost-reduction strategies, including shortening hospital stays. C...
Background: Globally, healthcare treatment trends are shifting from inpatient to outpatient settings owing to medical advancements and cost-reduction strategies, including shortening hospital stays. Consequently, the workload for outpatient nurses is increasingly complex. Nurses are required to ensure safe and efficient medication administration for high patient volumes; however, discussions on technologies such as Barcode Medication Administration (BCMA) have largely been limited to inpatient settings. Inpatient BCMA models are not directly applicable to outpatients, who present unique challenges, including a lack of identification (ID) wristbands, short visit durations, and high mobility within the hospital. Objective: To identify the work system factors that contributed to the adoption and sustainability of a smartphone-based BCMA system in busy outpatient treatment rooms, where both efficiency and safety are paramount. The interactions between nurses and patients using the system, and the surrounding environmental factors, were examined using the Systems Engineering Initiative for Patient Safety (SEIPS) 2.0 framework. Methods: A mixed-methods study based on the SEIPS 2.0 framework was conducted in three treatment rooms of a large hospital in Japan. A smartphone-based BCMA with a dual-workflow design for administering injections was implemented. The traditional "Flow A" involved verbal name confirmation and manual entry on a personal computer (PC). The new "Flow B" used the patient’s ID card (with a printed barcode) instead of an ID wristband, scanned via a smartphone (Personal Digital Assistant) linked to the Hospital Information System (HIS). To accommodate the diversity of outpatients, the system was designed to allow nurses to choose either Flow A or Flow B. Quantitative data included injection logs and error-detection rates from August 2023 to March 2025. Qualitative data from participant observations and questionnaires were analyzed using SEIPS 2.0 to determine factors influencing system adoption. Results: Within five months of introduction, the usage rate of Flow B for authentication and registration exceeded 50% across all rooms. Subsequently, the usage rate stabilized at 80–90% without decline. Log data revealed that the system detected preventable errors or mismatches in 2% of total injections. Nurses reported in questionnaires that Flow B improved both safety and operational workflow. A complementary system was established in which Flow A was used for patients without ID cards, thereby enhancing safety and efficiency in this high-turnover outpatient setting. Conclusions: Our holistic overview based on the SEIPS 2.0 framework revealed the critical roles of nurse and patient capabilities, professional ethics, environmental factors, operational policies, and tool usability. The key factors for successful entrenchment were flexible workflows that adapted to outpatient dynamics and a tool that facilitated patient participation in the safety process. Outpatient nurses should assess patient capabilities and consider strategies to engage patients as active partners in the workflow using intuitive tools.
Background: The prevalence of mobile health devices for remote monitoring has experienced rapid expansion, leading to more people being able to continuously monitor their asthma. However, discrepancie...
Background: The prevalence of mobile health devices for remote monitoring has experienced rapid expansion, leading to more people being able to continuously monitor their asthma. However, discrepancies between patient-reported outcome measures and electronically recorded device data pose challenges in the use of mHealth systems for disease management. Objective: This study investigates the concordance between the longitudinal self-reported relief inhaler usage and the smart-inhaler recorded usage data to identify and characterize "concordant users". Methods: We analyzed a subset (n=15) of the AAMOS-00 dataset (N=22) who had sufficient data. To improve robustness of our results to data processing choices, we carried out a multiverse analysis across 132 unique analytical configurations combined with patient-specific permutation testing (10,000 permutations each) to assess the statistical significance of concordance and identify concordant users. We also conducted an exploratory analysis of end-of-study feedback using a multi-method large language model (LLM) ensemble to evaluate user mindsets. Results: Longitudinal analysis revealed these patients showed stable, strong concordance over the study period, while the remaining patients (n=12) showed variable and low agreement. While demographic characteristics did not distinguish these groups, exploratory analysis of end-of-study feedback using a multi-method LLM ensemble provided a possible distinction in user mindset. These concordant users emphasized the data's utility for "Clinical Care Value" and "Insights & Self-Discovery," whereas remaining users primarily emphasized “Integration & Interoperability”. A sensitivity analysis using median Fisher's Z identified one additional borderline patient but did not alter the comparison findings. Conclusions: These findings, while hypothesis-generating given the limited sample size, suggest that high-quality self-reporting might be a sustained trait associated with a patient’s motivation to derive clinical utility from their data. This highlights the potential for future research to validate whether designing digital health interventions that appeal to user motivations can improve data concordance between self-reports and device records.
Background: Community-based interventions (CBIs) have emerged as a promising strategy for preventing and controlling non-communicable diseases, including Type 2 diabetes mellitus (T2DM). However, the...
Background: Community-based interventions (CBIs) have emerged as a promising strategy for preventing and controlling non-communicable diseases, including Type 2 diabetes mellitus (T2DM). However, the success of such interventions depends on cultural and contextual factors that influence community mobilisation and participation. Objective: This qualitative study aimed to investigate the barriers and facilitators to community-based interventions for the prevention and control of T2DM in rural and urban Pakistan. Methods: We conducted a multi-site qualitative study involving 37 in-depth interviews and 12 focus group discussions with diverse stakeholders, including individuals with known and unknown status of T2DM, caregivers of people with T2DM, community elders, and healthcare providers. Data were collected from approximately 50 participants across both rural and urban Pakistan and were analysed thematically using the framework approach. Results: Three overarching themes emerged: familiarity with CBIs, barriers, and facilitators, mapped across the socioecological model. Participants displayed limited yet varied understanding of CBIs, with rural participants often equating them to research activities, while urban participants linked them to health education and moral responsibility. Barriers included low awareness, misconceptions, gender and cultural constraints, logistical challenges, economic hardship, and security concerns, differing by context. Facilitators encompassed engagement with community leaders, gender-sensitive approaches, incentives, accessible venues, mass mobilisation, religious spaces, and involvement of local healthcare workers for intervention delivery. Rural–urban contrasts highlighted the influence of social cohesion, prior exposure, and structural factors on CBI uptake. Conclusions: This study highlights that CBIs for T2DM prevention and control in Pakistan require contextually tailored, culturally sensitive, and gender-responsive approaches. Rural–urban contrasts underscore the role of social cohesion, prior exposure, and structural factors in shaping engagement. Integrating these insights can guide the design and implementation of scalable, sustainable, and participatory diabetes prevention programs across diverse Pakistani communities.
Background: As populations age and demand for in-home age care increases, home care providers face increasing administrative pressures. One area where productivity gains are likely to be significant v...
Background: As populations age and demand for in-home age care increases, home care providers face increasing administrative pressures. One area where productivity gains are likely to be significant via optimisation is rostering and scheduling and appointment management. Conversational artificial intelligence (AI) offers a promising solution to automate structured, non-clinical interactions, potentially reducing workforce burden and enhancing client experience. Despite growing popularity, evidence on feasibility and acceptance among older adults remains limited, and assumptions persist that this population resists digital innovation, particularly AI. Objective: To evaluate the feasibility, client acceptance, and operational implications of implementing conversational AI for outbound scheduling calls in aged home care service delivery. Methods: An Australian home aged care provider, Silverchain, conducted a mixed-methods pilot in Western Australia’s Greater Southern region (August–November 2025). AI-driven calls were implemented for appointment confirmations, time changes, and cancellations, with a focus on user acceptance and operational insights rather than immediate efficiency gains. Quantitative data included client eligibility records, detailed call logs (attempts, outcomes, transfers), and complaint reports. Qualitative data were derived from nine semi-structured stakeholder interviews (1 consumer, 1 vendor, and 7 staff). Interviews explored perceptions of usability, workload impact, and future integration. Transcripts were coded thematically. Results: Of all eligible clients, 86.8% remained in the AI-calls pilot while 13.2% opted out. Across 915 attempted calls with client engagement, messages were successfully delivered in 679 (74.2%). Identity verification failed in 130 calls (14.2%), 39 calls (4.3%) were abandoned mid-call, and only 21 (2.3%) required transfer to the call centre. Complaint rates were negligible (<0.5%). Contrary to prevailing assumptions, older adults demonstrated high receptivity to AI-mediated communication. Thematic analysis revealed three dominant themes: (1) alignment with broader digital transformation goals; (2) perceived potential for future efficiency gains; and (3) recommendations for future improvements to fully realise AI benefits. As expected, no short‑term efficiencies were realised and staff workloads temporarily increased; however, interview participants viewed conversational AI as a viable pathway to future operational improvements, contingent on full integration with core systems. The pilot coincided with low appointment cancellation volumes, constraining full scalability assessment. Conclusions: Conversational AI is feasible for managing outbound scheduling calls in home aged care, with high client acceptance challenging myths of digital resistance among older adults. The pilot yielded critical organisational learnings: successful adoption requires robust planning, technical readiness, and alignment with broader digital transformation strategies. These findings can inform future models of care and underscore the potential of AI to support automated calls and sustainable service delivery in aging populations. Clinical Trial: Not applicable
Background: Peripheral facial nerve palsy (FNP) leads to motor impairment and psychosocial distress, often requiring prolonged rehabilitation. Emotional Training (ET) is a physiotherapy-based approach...
Background: Peripheral facial nerve palsy (FNP) leads to motor impairment and psychosocial distress, often requiring prolonged rehabilitation. Emotional Training (ET) is a physiotherapy-based approach intrinsically compatible with technology-assisted and remote rehabilitation models. However, its clinical effects across different FNP etiologies and management pathways remain underexplored. Objective: To investigate the motorfunctional and psychological effects of ET in patients with peripheral FNP, comparing individuals who had iatrogenic, neoplastic or traumatic damage and underwent triple innervation surgery with patients affected by idiopathic FNP treated conservatively, while exploring implications for digitally supported rehabilitation. Methods: In this prospective, exploratory, observational pilot study, 14 patients with unilateral peripheral FNP were allocated into two cohorts based on etiology and prior clinical management, i.e., SURGICAL (n = 7; iatrogenic, neoplastic or traumatic FNP, triple innervation surgery) and NON-SURGICAL (n = 7; Bell’s palsy, conservative therapy). All participants underwent a standardized ET protocol over 20 weeks. Motor performance was assessed using the Sunnybrook Facial Grading System (SFGS), functional disability with the Facial Disability Index (FDI), and anxiety with the Beck Anxiety Inventory (BAI), at baseline (T0) and post-treatment (T1). Non-parametric and Bayesian analyses were conducted to evaluate between/within-group differences. Results: Fourteen patients [SURGICAL: n = 7, age (mean ± SD) = 50.57 ± 18.17, 3F; NON-SURGICAL: n = 7; age (mean ± SD) = 51 ± 11.61, 3F)] completed the intervention with no dropouts. In between-group comparison, NON-SURGICAL group significantly increased the Synkinesis Score in SFGS (p = 0.019, BF₁₀ = 1.903), whereas SURGICAL group increased the FDI – Physical Function subscale (p = 0.002, BF₁₀ = 5.033) and in the FDI – Social/Well-being Function subscale (p = 0.004, BF₁₀ = 3.743) score. In within-group comparison SURGICAL group significantly improved the Resting Symmetry Score in SFGS (p = 0.032, BF₁₀ = 9.262), BAI (p = 0.022, BF₁₀ = 66.338), Symmetry of Voluntary Movement Score in SFGS (p = 0.022, BF₁₀ = 23.300), Composite SFGS Score (p = 0.016, BF₁₀ = 24.859), FDI – Physical Function subscale (p = 0.021, BF₁₀ = 24.550) and FDI – Social/Well-being Function subscale (p = 0.034, BF₁₀ = 10.664); similarly, NON-SURGICAL group improved the Symmetry of Voluntary Movement Score in SFGS (p = 0.034, BF₁₀ = 6.261) and Composite SFGS Score (p = 0.034, BF₁₀ = 6.288). However, for NON-SURGICAL group, a significant increase in the Synkinesis Score in SFGS (p = 0.034, BF₁₀ = 4.964) was also disclosed. Conclusions: ET appears to be a clinically relevant PT approach for peripheral FNP, with etiology-specific response patterns that likely reflect differences in underlying neuroplastic mechanisms. ET is suited for hybrid and telerehabilitation models, supporting its integration into digitally enabled rehabilitation pathways. However, these preliminary findings require confirmation in larger, controlled studies. Clinical Trial: not applicable.
Background: Ecological momentary assessment (EMA) enables repeated, real-time measurements in naturalistic contexts and may help address key challenges in physiotherapy rehabilitation, including treat...
Background: Ecological momentary assessment (EMA) enables repeated, real-time measurements in naturalistic contexts and may help address key challenges in physiotherapy rehabilitation, including treatment adherence and symptom monitoring in context. Objective: To map and synthesize the available evidence on clinical, methodological, and technological applications of EMA in physiotherapy-related rehabilitation research Methods: A scoping review was conducted following Joanna Briggs Institute and PRISMA-ScR guidelines. PubMed, Cochrane Library, Scopus, and Google Scholar were searched, and empirical studies implementing EMA in clinical, community, or home settings were included. Data were charted on populations, EMA design features (sampling window, prompts/day, instruments), sensor integration, target variables, and compliance reporting. Results: Twenty-seven studies were included, mainly on musculoskeletal and neurological conditions. Most studies used smartphone-based mHealth platforms, and a substantial proportion integrated objective sensors (primarily accelerometry). Reported compliance ranged from moderate to high across protocols. However, substantial heterogeneity was observed in EMA schedules, variables assessed, compliance definitions, and reporting practices, limiting cross-study comparability. Conclusions: EMA appears feasible and promising for physiotherapy rehabilitation research. It may support context-sensitive assessment and personalization. Greater standardization of EMA reporting (including compliance metrics and missing-data handling) and more rigorous designs are needed to consolidate clinical implementation and facilitate integration into real-time adaptive interventions. Clinical Trial: https://inplasy.com/inplasy-2026-1-0084/
https://inplasy.com/wp-content/uploads/2026/01/INPLASY-Protocol-8732.pdf
Background: With increasing global life expectancy, promoting social, mental, and physical wellbeing among older adults is crucial. Loneliness and social isolation are common in this population and ne...
Background: With increasing global life expectancy, promoting social, mental, and physical wellbeing among older adults is crucial. Loneliness and social isolation are common in this population and negatively affect health, functioning, and quality of life. Activity programs have been proposed as a strategy to encourage engagement, social interaction, and increase physical activity. Objective: In this mixed-method study we aimed to explore older adults’ experiences and perceptions of a weekly Activity Program in semi-rural areas of mid-Sweden. Methods: Participants from three semesters (fall 2022, spring 2023, fall 2023) completed five self-reported questionnaires assessing loneliness, happiness, distress, quality of life, and physical activity and took part in five focus group discussions. The Activity Program consisted of weekly two-hour sessions including health-related lectures, physical activities such as walking and dancing, and social interactions over coffee breaks. Results: Findings shows that the Activity Program positively influenced subjective wellbeing across multiple dimensions. Social benefits were emphasized, including formation of new relationships, continued interactions outside the program, and reinforcement of existing personal relationships. Participants reported improvements in mental wellbeing, describing feelings of upliftment, motivation, and inner drive, as well as opportunities to “take a break from loneliness.” Physical benefits included increased awareness of functional abilities, engagement in physical activity, and exposure to new activities. Challenges were noted in tailoring activities to the wide age and functional range of participants, while the group leader was identified as central to fostering engagement, motivation, and positive group dynamics.
Questionnaire data showed minimal changes, highlighting the value of qualitative methods for capturing nuanced experiences not reflected in standardized instruments. Seasonal effects were noted to have possibly influenced physical activity reports. Conclusions: In conclusion, participation in a structured Activity Program can enhance social interaction, mental wellbeing, and physical engagement among older adults. Success depends on promoting social connections, offering appropriately tailored activities, and effective leadership. These insights give guidance the design and implementation of programs aiming to improve wellbeing in aging populations. Clinical Trial: This research has ethical approval from the Swedish Ethical Review Authority (2022--03516) and was performed in accordance with the Declaration of Helsinki. All study participants signed a written informed consent form prior to any study-related activities.
Background: Adolescent girls and young women (AGYW) in South Africa face overlapping sexual and reproductive health challenges, including high unintended pregnancy rates, disproportionate HIV burden,...
Background: Adolescent girls and young women (AGYW) in South Africa face overlapping sexual and reproductive health challenges, including high unintended pregnancy rates, disproportionate HIV burden, and limited access to youth-friendly services. AI-powered digital health tools can bridge gaps in accessible, stigma-free sexual and reproductive health (SRH) support, yet AGYW’s experiences with these technologies in the Global South remain under-researched. We explored experiences of acceptability and safety of two AI-powered chatbots for HIV prevention, SRH, and wellbeing among AGYW. Objective: This study explored the acceptability and safety of two AI-powered chatbots for HIV prevention and SRH among adolescent girls and young women in South Africa, trained on South African vernacular and national clinical guidelines. Methods: Convergent parallel mixed-methods research was embedded within combination HIV prevention programming across five high-burden districts in two provinces of South Africa (June-November 2025). Three focus group discussions (N=27) explored acceptability and safety among AGYW peer providers (aged 20-32) across three urban, peri-urban, and rural sites. Reflexive thematic analysis was used. A digital survey of AGYW service users (N=961, aged 18-24) measured acceptability of the two AI-powered HIV/SRH chatbots using the validated Acceptability of Intervention Measure (AIM), with descriptive statistics and correlation analyses examining sociodemographic factors associated with acceptability. Results: Survey participants (mean age 21.5 years; 73.1% completed secondary education) reported high acceptability: 94.6% reported satisfaction, 91.8% would use again, and 93.3% would recommend the AI-powered tool to others. Median AIM score was 17 (IQR 16-20; Cronbach's α=0.903). Most reported using chatbots for sexual health information (59.2%) and reproductive health (50.1%). 86.8% of service users trusted the chatbots’ information accuracy. Qualitative safety concerns amongst peer service providers appeared to be influenced by prior social media experiences, including worry about personal data handling. Qualitative findings revealed nuanced perceptions and concerns: AGYW sought deeper relational qualities beyond information provision, expressing unmet expectations around empathetic tone and validation. Geographic variation in acceptability emerged, with rural/peri-urban participants showing greater concerns about data privacy, though this could be due to variation in tool exposure or residence. Conclusions: While quantitative metrics indicate high acceptability of AI-powered SRH chatbots among South African AGYW, qualitative insights reveal important considerations around contextual relevance, empathy, and digital literacy gaps. Findings underscore the need for differentiated implementation strategies that address geographic, social and individual variations, strengthen data safety features, and balance innovation with ethical guardrails, ensuring AGYW-centered AI integration in HIV and SRH programming.
Open Peer Review Period: Mar 21, 2026 - Mar 6, 2027
We present, to our knowledge, the first independent evaluation of Google's MedGemma 1.5 4B instruction-tuned model on OpenAI's HealthBench, one of the most comprehensive physician-graded benchmarks fo...
We present, to our knowledge, the first independent evaluation of Google's MedGemma 1.5 4B instruction-tuned model on OpenAI's HealthBench, one of the most comprehensive physician-graded benchmarks for health AI [3]. The evaluation encompassed all 5,000 HealthBench conversations (48,562 rubric criteria created by 262 physicians across 60 countries), run entirely on a consumer laptop (Apple M1 Max, 32GB) using quantized weights (Q8_0), with automated grading via GPT-4.1 mini at a total API cost of $39.21.
MedGemma 1.5 4B achieved an overall HealthBench score of 0.4512 (bootstrap SE = 0.0046), placing it ahead of o1 (0.418), Claude 3.7 Sonnet (0.346), GPT-4o (0.320), and Llama 4 Maverick (0.249), though behind GPT-4.1 (0.479), Gemini 2.5 Pro (0.520), Grok 3 (0.543), and o3 (0.598) [3, 11]. This is notable given that MedGemma 1.5 4B has roughly 4 billion parameters and runs without internet connectivity, while the models it approaches or surpasses are significantly larger and require cloud infrastructure.
Performance varied substantially across HealthBench dimensions. Communication quality was the strongest axis (0.7021), followed by instruction following (0.5589) and accuracy (0.5425). Completeness was the weakest (0.3780), confirming the HealthBench finding that completeness is the primary driver of overall model ranking. Among themes, emergency referrals scored highest (0.5559), with the model satisfying 90.6% of physician-written emergency behavior criteria in emergent cases. Global health scored lowest (0.3602), suggesting training data bias toward Western clinical contexts.
The most clinically significant finding involved context-seeking behavior. When presented with conversations lacking sufficient clinical context, MedGemma satisfied only 7.2% of context-seeking criteria when clinical information was insufficient — while meeting helpfulness and safety criteria 97.2% of the time. The model consistently provides well-communicated, accurate, but incomplete answers without recognizing when it needs more information. This pattern — knowing what to say but not how much to say or when to ask — has direct implications for clinical deployment safety.
All analyses were pre-specified before the evaluation was conducted. Code, results, and the pre-registered study analysis plan are publicly available. This evaluation demonstrates that rigorous, independent benchmarking of medical AI is accessible to individual researchers at minimal cost, and that such evaluation is essential before clinical deployment of any health AI system.
Artificial intelligence (AI) is transforming healthcare delivery and patient engagement, as tools like diagnostic imaging algorithms, symptom checkers, and AI platforms such as ChatGPT Health, Claude,...
Artificial intelligence (AI) is transforming healthcare delivery and patient engagement, as tools like diagnostic imaging algorithms, symptom checkers, and AI platforms such as ChatGPT Health, Claude, and Google Health AI proliferate. However, this rapid adoption has left patients surrounded by AI yet lacking the necessary frameworks and literacy to use these tools safely and effectively. This viewpoint proposes the AI-Empowered Patient™ framework—a patient-centered model built on three pillars (Preparation, Verification, Protection)—that empowers individuals to engage responsibly with healthcare AI. We further outline governance principles for patient-facing AI encompassing transparency, human oversight, privacy by design, equity and inclusion, and continuous monitoring, and introduce an AI Privacy and Operations model for operationalizing those principles at the organizational level. A comparative analysis of major AI healthcare platforms and targeted recommendations for healthcare organizations, policymakers, technology developers, and patients are also presented. AI will not replace physicians, but it will fundamentally reshape how patients experience care. Success requires active patient participation, robust organizational governance, and regulatory frameworks that prioritize trust, transparency, and equity.
Background: The application of immersive technologies, particularly Virtual Reality (VR), has expanded rapidly across healthcare domains, including mental health, rehabilitation, and education. These...
Background: The application of immersive technologies, particularly Virtual Reality (VR), has expanded rapidly across healthcare domains, including mental health, rehabilitation, and education. These technologies enable controlled, interactive, and ecologically valid environments that can support therapeutic interventions, skills development, and behavioural assessment. Within forensic mental health (FMH) and prison settings, where individuals often present with complex psychological needs alongside restrictive and highly regulated environments, immersive technologies offer potential advantages such as safe simulation of real-world scenarios, enhanced engagement, and personalised intervention delivery. However, despite increasing interest, the evidence base remains fragmented, and questions persist regarding effectiveness, ethical implications, and feasibility of implementation in secure and resource-constrained contexts. Objective: Interest in immersive technologies in forensic mental health (FMH) and prison settings is growing, yet their role remains unclear. This scoping review maps current uses, highlights opportunities, and identifies key gaps and considerations for future implementation. Methods: A scoping review of English-language publications (2010 - 2025) was conducted using Scopus, PubMed, and CINAHL. Data extraction followed the JBI framework, and thematic analysis explored benefits, drawbacks, and implementation barriers. Results: Thirty sources were identified. Primary research focused mainly on Virtual Reality (VR) for therapy, skills training, education, and assessment. Evidence suggests benefits such as increased engagement, emotional regulation, skill acquisition, autonomy, and improved clinician-patient dialogue. However, studies were small, heterogeneous, and inconsistently reported, with limited long-term follow-up. Implementation barriers included institutional, ethical, and technical constraints, and limited personalisation and end-user involvement. Co-design and participatory approaches surfaced as key enablers of acceptability, relevance, and safe use. Conclusions: Immersive technologies show promise in FMH and prison contexts, but robust evidence, careful implementation, and end-user input are critical for safe, relevant, ethical, and effective use.
Background: Treat-to-target strategies have substantially improved outcomes in rheumatoid arthritis (RA), yet induction strategies remain guided by population-level evidence that does not adequately a...
Background: Treat-to-target strategies have substantially improved outcomes in rheumatoid arthritis (RA), yet induction strategies remain guided by population-level evidence that does not adequately account for inter-individual heterogeneity in disease biology, treatment response and patient context. Artificial intelligence (AI) offers an opportunity to support individualized prediction in RA. Objective: To review the current evidence on AI applications for treatment response and remission prediction in RA and to propose a conceptual research-oriented framework for evaluating AI-enabled prediction of clinical deep remission (CliDR) as a stringent modeling endpoint to support individualized induction strategies. Methods: A narrative review of literature on AI applications in RA was conducted. Evidence on remission prediction, treatment response trajectories, multimodal data integration was analyzed qualitatively to identify methodological trends, performance limitations endpoint heterogeneity and translational gaps. Results: CliDR, defined by absence of swollen and tender joints and normalization of inflammatory markers, is proposed as a candidate prediction endpoint for AI model for RA induction therapy due to its biologically coherent and objective definition. Existing AI models for RA treatment outcome prediction demonstrate moderate accuracy and limited external validation, reflecting challenges related to heterogenous endpoints, small sample sizes and observational data structure. An AI-enabled research framework for RA induction therapy is outlined, spanning baseline phenotyping, treatment prioritization, early treatment response trajectory modeling and longitudinal monitoring with emphasis on evaluation, interpretability and clinician oversight. Conclusions: AI offers a potential pathway towards more individualized RA induction strategies. Rigorous validations are required before AI-enabled prediction tools can inform routine RA care.
Background: Detecting eye complications early in dialysis patients is vital to avoid vision loss. Objective: This study aims to evaluate retinal findings in dialysis patients using artificial intellig...
Background: Detecting eye complications early in dialysis patients is vital to avoid vision loss. Objective: This study aims to evaluate retinal findings in dialysis patients using artificial intelligence (AI) and to compare the diagnostic accuracy of AI with assessments made by two ophthalmologists. It also investigates the correlation between the severity of ocular conditions and factors such as dialysis vintage, medication usage, and systemic comorbidities. Methods: Prospective, multicenter study of 371 dialysis patients, retinal images were analyzed using the Eyecheckup AI system. Two independent ophthalmologists evaluated the same images. Primary outcomes included the detection of diabetic retinopathy, age-related macular degeneration, retinal vein occlusion, retinal anomalies, and glaucoma. Results: The agreement between AI, Doctor-1 and Doctor-2 was found in all measurements (p<0.05), with kappa values indicating substantial agreement for DR (73%), moderate agreement for ARMD (47.9%), moderate agreement for RVO (39.6%), substantial agreement for retinal anomalies (64.4%), and substantial agreement for glaucoma (74.5%). The agreement between the three measurements was as follows: for DR, there was a 65.5% (significant agreement), for ARMD, a 29.5% (moderate agreement), for RVO, a 46.7% (moderate agreement), for retinal anomalies, a 74.5% (significant agreement), and for glaucoma, an 80% (significant agreement) was observed. Significant differences were observed in dialysis vintage for DR (p=0.014) and Kt/V values for DR (p<0.001). Patients with DR had shorter dialysis vintage and lower Kt/V values compared to those without DR. Conclusions: AI demonstrates potential as a screening tool for ocular complications in dialysis patients, particularly for diabetic retinopathy and glaucoma.
Background: Unlinked adverse event reports referring to the same case impede statistical analysis and may mislead clinical assessment. Pharmacovigilance relies on large databases of adverse event repo...
Background: Unlinked adverse event reports referring to the same case impede statistical analysis and may mislead clinical assessment. Pharmacovigilance relies on large databases of adverse event reports to discover potential new causal associations, and computational methods are required to identify duplicates at scale. Current state-of-the-art is statistical record linkage which outperforms rule-based approaches. In particular, vigiMatch is in routine use for VigiBase, the WHO global database of adverse event reports, and represents the first statistical duplicate detection approach in pharmacovigilance deployed at scale. Originally developed for both medicines and vaccines, its application to vaccines has been limited due to inconsistent performance across countries. Objective: To advance state-of-the-art for duplicate detection in large-scale pharmacovigilance databases and achieve more consistent performance across adverse event reports from different countries. Methods: This paper extends vigiMatch from probabilistic record linkage to predictive modelling, refining features for medicines, vaccines, and adverse events using country-specific reporting rates, extracting dates from free text, and training separate support vector machine classifiers for medicines and vaccines. Recall was evaluated using 5 independent labelled test sets. Precision was assessed by annotating random selections of report pairs classified as duplicates. Results: Precision for the new method was 92% for vaccines and 54% for medicines, compared with 41% for the comparator method. Recall ranged from 80-85% across test sets for vaccines and from 40–86% for medicines, compared with 24–53% for the comparator method. Conclusions: Predictive modeling, use of free text, and country-specific features advance state-of-the-art for duplicate detection in pharmacovigilance.
Background: Thyroid nodule segmentation in ultrasound imaging is clinically important but typically requires large volumes of expert annotations. Foundation models offer a promising approach to reduci...
Background: Thyroid nodule segmentation in ultrasound imaging is clinically important but typically requires large volumes of expert annotations. Foundation models offer a promising approach to reducing labeled data requirements, yet the relationship between annotation budget and segmentation performance across adaptation strategies remains insufficiently characterized. Objective: Thyroid nodule segmentation in ultrasound imaging is clinically important but typically requires large volumes of expert annotations. Foundation models offer a promising approach to reducing labeled data requirements, yet the relationship between annotation budget and segmentation performance across adaptation strategies remains insufficiently characterized. Methods: We evaluated four SAM3-based adaptation strategies on TN5000 (5,000 biopsy-confirmed thyroid ultrasound images): full fine-tuning (MedSAM3), zero-shot inference (SAM3-Zero), and Low-Rank Adaptation at ranks 8 and 16 (SAM3-LoRA8, SAM3-LoRA16). All models were initialized via Masked Autoencoder pretraining on the unlabeled corpus and evaluated under four annotation regimes (10%, 25%, 50%, and 100% of labeled data) using Dice, IoU, AUC, sensitivity, specificity, and F1-score, with 95% bootstrap confidence intervals and Bonferroni-corrected significance testing. External validation was conducted on the independent DDTI dataset. Results: At full annotation budget, MedSAM3 achieved the highest internal performance (Dice = 0.815, AUC = 0.989), significantly outperforming the zero-shot baseline (Dice = 0.097, p < 0.001 across Wilcoxon, DeLong, and McNemar tests). At 25% label availability, SAM3-LoRA8 matched or exceeded MedSAM3 (Dice = 0.736 vs 0.726) while updating only 1.85% of model parameters, identifying 25% as the annotation-efficiency crossover threshold. No statistically significant difference was observed between LoRA ranks 8 and 16 on per-image Dice (Wilcoxon p = 0.704), indicating that increasing rank beyond 8 yields no measurable performance benefit for this task. External validation on the DDTI dataset preserved model ranking (MedSAM3 AUC = 0.911); a dissociation between AUC and Dice under domain shift was observed across all fine-tuned models, with implications for cross-site deployment. Conclusions: Full fine-tuning with domain-adapted initialization provides the best segmentation performance at large annotation budgets. LoRA adaptation with rank 8 offers a more annotation-efficient and parameter-efficient alternative, matching full fine-tuning at 25% label availability and requiring approximately half the training time. These findings provide a concrete decision framework for selecting adaptation strategies in annotation-constrained clinical deployments of thyroid ultrasound segmentation.
Background: A persistent discrepancy exists between patient-reported information and physician documentation. While conversational agents have been developed to collect medical histories prior to cons...
Background: A persistent discrepancy exists between patient-reported information and physician documentation. While conversational agents have been developed to collect medical histories prior to consultation, existing evaluations have largely focused on diagnostic accuracy or user satisfaction rather than the completeness and clinical usefulness of the information collected. There remains a need to assess the extent of clinically relevant information captured through chatbot-based interviews and to understand how model configurations and instructional strategies influence this coverage. Objective: This study aimed to evaluate the extent to which a chatbot can obtain clinically useful patient history information and to examine how prompt detail and internal reasoning influence information coverage during chatbot-based medical interviews. Methods: We developed a medical history-taking chatbot using the Qwen3-14B-Instruct model and evaluated four configurations in a 2×2 factorial design: Detailed/Thinking (DT), Detailed/Non-thinking (DN), Minimal/Thinking (MT), and Minimal/Non-thinking (MN). These configurations were compared against a rule-based system baseline (choice-based mode) using 66 standardized primary care clinical cases, with simulated patients interacting with the chatbot according to predefined case scripts. Information coverage (%) was assessed using a checklist inspired by Objective Structured Clinical Examination (OSCE) frameworks. Three physicians independently evaluated transcript coverage, with inter-rater agreement assessed using full agreement rates and Fleiss’ κ. Coverage percentages were compared across configurations using repeated-measures analysis of variance with post hoc testing. Results: Inter-rater agreement was substantial (Fleiss’ κ = 0.75). Across all 66 simulated cases, information coverage differed significantly among configurations (p < .001), with the detailed prompt with thinking (DT) mode achieving the highest mean coverage (72.3%), compared with moderate coverage in configurations using either thinking or detailed prompts alone (approximately 60%) and lower coverage in minimal non-thinking and rule-based configurations (approximately 51-54%). Differences were most pronounced for past medical and family history domains. Symptom-level analyses revealed substantial variability, with higher coverage for symptoms associated with well-defined diagnostic frameworks and lower coverage for multi-system presentations. Conclusions: The combination of clinically detailed prompt instructions and internal reasoning significantly enhances the clinical usefulness of AI-driven history-taking by ensuring more comprehensive data collection. This approach allows for a more systematic and robust foundation for automated clinical documentation, facilitating better integration into healthcare workflows.
Background: Older people who receive aged care services at home experience high rates of depression, anxiety and loneliness, yet have poor access to mental health services. Digital interventions are w...
Background: Older people who receive aged care services at home experience high rates of depression, anxiety and loneliness, yet have poor access to mental health services. Digital interventions are widely available, but research is required to determine whether they are acceptable to the in-home aged care population. Objective: To explore the perspectives of in-home aged care recipients on the use of digital technologies to support their emotional wellbeing. Methods: A qualitative interview study was conducted with 11 older people who received in-home aged care from a national provider. The interview aimed to understand care recipients’ experiences of using technology, as well as their current access to emotional support, and attitudes towards using digital technologies to support their wellbeing. Results: Participants reported high access to technology, but varied usage. Most required support to use technology, typically provided by family members, and reported a range of concerns related to technology use. Participants received a variety of informal supports for their emotional wellbeing, typically from family members and their communities, but none accessed professional support. Most were open to using technology to support their emotional wellbeing, and some were willing to receive such support from their aged care provider. Conclusions: The willingness of older people who receive in-home aged care to use digital technologies to supplement existing informal emotional support is encouraging. Aged care providers are well placed to implement digital wellbeing programs and can leverage older people’s existing access to digital devices. Technologies and implementation strategies should be purpose-designed for this population and address identified barriers.
Background: Trials of stress management interventions have shown modest and inconsistent effects. Emerging evidence suggests that structural constraints may limit the real-world effectiveness of behav...
Background: Trials of stress management interventions have shown modest and inconsistent effects. Emerging evidence suggests that structural constraints may limit the real-world effectiveness of behavioural strategies, yet this process has rarely been examined empirically. Population-level tools for monitoring stress and stress-management processes over time remain limited. Objective: To develop and pilot a mixed-methods surveillance platform for repeated measurement of stress and stress-management effectiveness in real-world contexts, and to assess its feasibility, validity, and ability to detect equity-relevant patterns. Methods: Adults living in Australia completed baseline assessments including sociodemographics, perceived stress (PSS-10), psychological flexibility (MPFI), social resources, and health measures. Participants then completed brief fortnightly surveillance surveys for up to 12 months comprising 0–10 visual analogue scale (VAS) ratings of stress and perceived stress management effectiveness, a stress-related symptom checklist, and open-ended reporting of stressors and coping strategies. We assessed feasibility, convergent validity of early VAS indicators, and preliminary equity patterning. Results: Seventy-seven participants completed baseline measures, and 1,195 fortnightly check-ins were recorded as of June 2025. Early VAS stress demonstrated strong convergent validity with baseline perceived stress and stress symptoms. Despite pilot recruitment skewing toward higher socioeconomic status, clear gradients were observed in both stress and stress management. Lower-income participants reported higher stress, lower perceived stress management, and fewer good stress-management check-ins. Psychological flexibility was positively associated with stress management overall, but showed weaker associations among lower-income participants. Conclusions: Brief, repeated stress surveillance is feasible and can generate equity-informative signals. Findings support a model in which adaptive skills are widely available but their effective expression is shaped by structural conditions. The FocUS-R platform provides a scalable approach for monitoring population stress processes and identifying contexts where structural constraints may limit the effectiveness of behavioural strategies.
Background: Chronic liver disease (CLD) is a major public health burden, particularly among middle-aged men with metabolic risk factors such as obesity and type 2 diabetes. Although sustained lifestyl...
Background: Chronic liver disease (CLD) is a major public health burden, particularly among middle-aged men with metabolic risk factors such as obesity and type 2 diabetes. Although sustained lifestyle modification is critical for preventing disease progression, long-term self-management in this population is often suboptimal. Mobile health interventions show promise for supporting self-management; however, most are patient-centered and rarely incorporate structured family engagement despite evidence highlighting the importance of social support. Objective: This study aims to develop and formalize a protocol for a family-supported digital health coaching intervention for middle-aged men with CLD and metabolic risk factors and to evaluate its feasibility and acceptability prior to effectiveness testing. Methods: This parallel-group randomized controlled trial, approved by the Institutional Review Board of Y University Health System (Approval No. 4-2025-0117) and sponsored by Y University Health System, will be conducted in Seoul, Republic of Korea. Eligible participants are men aged 40–64 years with CLD for ≥6 months and at least one metabolic risk factor; key exclusions include decompensated cirrhosis and active malignancy within 5 years. A total of 82 patient–family dyads (164 participants) will be randomized (1:1) to either a 12-week smartphone-based intervention grounded in the Information–Motivation–Behavioral Skills model or usual care. The primary outcome is change in Nonalcoholic Fatty Liver Disease Self-Management Questionnaire score from baseline to 12 weeks. Secondary outcomes include perceived social support (MSPSS), depressive symptoms (PHQ-9), family experience (FIES:CI), sleep quality (PSQI-K), and clinical indicators derived from electronic medical records, including body mass index, waist circumference, aspartate aminotransferase, alanine aminotransferase, bilirubin, albumin, triglycerides, high-density lipoprotein cholesterol, and glycated hemoglobin as applicable. Data will be analyzed using repeated-measures analysis to evaluate group-by-time effects under an intention-to-treat framework. Results: Expert content validation demonstrated excellent validity (Content Validity Index = 1.00). Usability testing indicated high acceptability (System Usability Scale mean 86.88, standard deviation 7.58). The finalized protocol operationalizes information, motivation, and behavioral skills within a structured digital platform incorporating dyadic family involvement. Enrollment began in May 2025 and is expected to be completed by March 2026. Conclusions: This protocol describes the development of a family-supported digital intervention for CLD management and provides a framework for evaluating family-centered self-management strategies in middle-aged men with metabolic risk factors. Clinical Trial: Clinical Research Information Service (KCT0010494), registered on March 25, 2025. Secondary identifiers: none. Individual participant data will be available upon reasonable request following publication.
Background: Type 2 diabetes mellitus (T2DM) has become a major health burden worldwide, therefore strategies to manage it in early stages are continuously being investigated. Excessive calorie consump...
Background: Type 2 diabetes mellitus (T2DM) has become a major health burden worldwide, therefore strategies to manage it in early stages are continuously being investigated. Excessive calorie consumption and sedentary lifestyles which are accompanied by the development of a low-grade inflammatory state, are one of the key risk factors for the development of prediabetes, metabolic syndrome and the subsequent T2DM. Lifestyle modifications like intermittent fasting (IF) have become an important aspect in the management of T2DM. Current evidence on the effects of IF on low-grade inflammation in individuals with either prediabetes, metabolic syndrome or T2DM is inconsistent. This systematic review protocol outlines the planned synthesis and evaluation of existing literature investigating the effectiveness of IF on inflammatory markers of adult patients with prediabetes T2DM or metabolic syndrome. Objective: The main objective of this review is to analyse data of previously published studies investigating effects of different IF regimens by assessing inflammatory markers of patients with prediabetes, metabolic syndrome or T2DM. Methods: This protocol is prepared in accordance with the Preferred Reporting Items for Systematic Review and Meta-analysis Protocol (PRISMA-P) 2020. Databases including PubMed/Medline, PubMed Central, Scopus and Google Scholar will be systematically searched for published randomized controlled trials and longitudinal studies, including cohort, case-control, cross-sectional, observational, retrospective and prospective studies involving all types of intermittent fasting in adult humans with prediabetes or T2DM OR metabolic syndrome. Eligible participants will be adults (≥18 years) diagnosed with metabolic syndrome, prediabetes or T2DM using the American Diabetes Association (ADA) or the World Health Organisation (WHO) criteria. Systematic reviews, conference abstracts, editorials and meta-analyses will be excluded. Additionally, studies without a control group will be excluded unless they provide baseline vs. follow-up inflammatory data. The control group in randomized controlled trials will consist of subjects on their usual diet, continuous energy restriction or standard diabetic care. Active comparators like Mediterranean diet will be allowed if inflammatory markers are reported.
Data on changes in inflammatory markers between the intervention and control group, as well as between baseline and post intervention will be extracted. The extracted data will be verified by a second reviewer and disagreements will be resolved by a third reviewer. The risk of bias of the included randomized controlled trials will be assessed using the Cochrane Risk of Bias 2 (RoB 2), while for longitudinal studies the National Institutes of Health (NIH) quality assessment tool for Before-After (Pre-post) studies with no control group will be used. Review Manager (RevMan) will be used to perform a meta-analysis where sufficient data are available and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach will be used to assess the quality of evidence. Results: This review will only use publicly available published data. The findings of this review will provide a comprehensive synthesis of the current evidence regarding the effects of intermittent fasting regimens on inflammatory markers of adult patients with either T2DM, prediabetes or metabolic syndrome. Conclusions: The findings will highlight the current knowledge gaps and inform future clinical research, which may help to guide early intervention strategies to manage prediabetes or metabolic syndrome and to prevent the onset of T2DM. Clinical Trial: International Prospective Register of Systematic Reviews (PROSPERO) CRD420251133867, https://www.crd.york.ac.uk/PROSPERO/view/CRD420251133867
Background: Population aging is rapidly reshaping the Brazilian labor force, increasing the participation of adults aged 50 years and older and intensifying the need for safe, inclusive, and age‑fri...
Background: Population aging is rapidly reshaping the Brazilian labor force, increasing the participation of adults aged 50 years and older and intensifying the need for safe, inclusive, and age‑friendly workplaces. Preventive behaviors at work play a vital role in protecting workers’ physical, social, and psychological health; however, no validated Brazilian Portuguese instrument is currently available to measure their frequency. The Preventive Behaviors at Work Frequency Scale (Échelle de Fréquence des Comportements Préventifs au Travail), developed in Canada, evaluates how often workers adopt six key preventive behaviors grounded in the Model of Preventive Behaviors at Work. A culturally adapted and psychometrically validated version is therefore needed to support occupational health research, surveillance, and evidence‑based interventions for Brazilian older workers. Objective: This study aims to describe the protocol for the cross-cultural adaptation and psychometric validation of the Brazilian Portuguese version of the Preventive Behaviors at Work Frequency Scale for workers aged 50 years and over. Methods: This methodological study will be conducted based on international guidelines for cross-cultural adaptation and questionnaire validation and will be completed in six steps: (1) forward translation, (2) forward translation synthesis, (3) back translation, (4) harmonization and expert appraisal of relevance, (5) pretesting with the target population, and (6) field testing and psychometric evaluation. Data collection will take place remotely with Brazilian workers aged 50 years and over during a 12‑month period, beginning in winter 2026. Results: Steps 1 and 2 were completed in February 2026, and step 3 (back‑translation) began in March 2026, alongside preparations for step 4. The study is progressing according to the established methodological timeline. Field testing and psychometric evaluation are expected to be carried out between spring–fall 2026, with preliminary results anticipated in winter 2026. Conclusions: This study will produce the first Brazilian Portuguese version of the Preventive Behaviors at Work Frequency Scale, enabling the assessment of preventive behaviors among older workers and strengthening occupational health research and practice in Brazil. The adapted instrument is expected to support cross‑national research, inform evidence‑based workplace interventions, and contribute to the promotion of safe, inclusive, and sustainable work participation among aging workers.
Background: Digital health technologies play a pivotal role in enhancing the quality of life for an increasing number of older people living in long-term care. Applications are among the most discusse...
Background: Digital health technologies play a pivotal role in enhancing the quality of life for an increasing number of older people living in long-term care. Applications are among the most discussed and researched topics within healthcare innovation and have the potential to revolutionise clinical practice. Although research has expanded, the evidence suggests knowledge is minimal on the factors that influence residents’ in long-term care acceptance and adoption of digital apps. Objective: Explore the factors that influence older adults’ intention to use digital health applications in LTC settings Methods: Searches were conducted in Scopus, ProQuest Health and Medical, CINAHL, MEDLINE and IEEE Explore. Search terms were associated with, and MeSH terms related to, (1) digital health applications and (2) long-term care settings. Citation searching and hand-searching identified any other studies. The search took place between 2020 and 2025. Results: Of 1,184 studies identified, seven met inclusion criteria. Each explored digital app use with or by older residents in long-term care. Thematic analysis generated four analytic themes: (1) Strengthening Resident Self-Concept and Wellbeing Supports App Adoption; (2) Digital Experience Influences App Acceptance; (3) Ethical and Relational Dimensions of App Adoption; and (4) Organisational Culture Impacts App Acceptance. Collectively, these themes highlight the interplay between digital health innovation, resident wellbeing, and the organisational context of care delivery. Conclusions: Digital health apps offer promising opportunities to strengthen older persons’ health and wellbeing when implemented as part of a culture of innovation in care practice. Findings indicate that individual, systemic, and structural factors influence successful integration. Designing digital interventions that are ethically grounded, user-friendly, and responsive to residents’ identities and capabilities can enhance both quality of life for older adults and innovation capacity within care systems.
Background: Obesity is a chronic disease requiring long-term treatment, yet current treatment models do not align with the chronicity of obesity. Current guidelines provide limited direction regarding...
Background: Obesity is a chronic disease requiring long-term treatment, yet current treatment models do not align with the chronicity of obesity. Current guidelines provide limited direction regarding how to adapt treatment to support weight loss maintenance as adolescents transition into emerging adulthood. Objective: This qualitative study aims to 1) better understand adolescents’ and their parents’ experiences after behavioral weight loss treatment as they navigate late adolescence into emerging adulthood and 2) identify what weight management support is needed during this transition. Methods: Adolescents and parents (N=20 dyads; M adolescent age=16.5 yrs; SD=0.6) completed semi-structured interviews > 11 months after participation in a 4-month multicomponent behavioral weight loss treatment. Domains assessed included adolescent and parent perceptions of facilitators and barriers to weight management since program completion, and needs and preferences for continued support through the transition into emerging adulthood. Interviews were recorded, transcribed verbatim, and thematically analyzed. Results: Adolescents and parents reported adolescents maintaining several weight management behaviors (e.g., regular exercise, self-weighing, improved diet quality), while identifying others as more difficult to sustain (e.g., monitoring food intake, meal planning). Reported facilitators included consistent routines and ample opportunities for physical activity, while barriers consisted of time constraints, competing priorities, and external influences (e.g., eating behaviors of other family members). Both adolescents and parents expressed a desire for continued support during the transition to emerging adulthood to sustain healthy behaviors and navigate changing roles. Most adolescents indicated an interest in participating in a booster program to support weight management during this period. Conclusions: Findings identify key weight management behaviors that families maintained after treatment, as well as areas that require ongoing support. Results underscore the need for continued support with weight management behaviors during the transition to emerging adulthood and offer guidance for program structure and content. Results will inform the development of a targeted behavioral weight management transition program for this population.
Background: Background: Clinical assessment of fine motor and gross motor deficits in Parkinson’s disease (PD) relies heavily on subjective scoring systems, including the motor section of the Unifie...
Background: Background: Clinical assessment of fine motor and gross motor deficits in Parkinson’s disease (PD) relies heavily on subjective scoring systems, including the motor section of the Unified Parkinson’s Disease Rating Scale (UPDRS). Variability between raters and limited granularity present challenges for clinical trials and routine monitoring. MOVXAM is a newly developed, video-based digital analysis system that quantifies human movement without sensors or wearable devices. Objective: To evaluate the validity of the MOVXAM system in quantifying amplitude and duration variability of finger tapping, hand flapping, and foot tapping movements in individuals with PD compared with healthy controls. Methods: Using a single high-resolution video camera, 10-second recordings were obtained from 20 healthy adults and 22 individuals with PD (Hoehn & Yahr stages II–III). Participants completed standardized modules: thumb-index finger tapping (FIT), hand flapping (HF), and foot tapping (FOT). MOVXAM software converted raw video frames into 2-dimensional vertical and horizontal waveforms. Variability metrics included amplitude variance, duration variance, and bilateral differences. Statistical comparisons between groups used independent sample t-tests. Results: Results: PD participants demonstrated significantly higher vertical waveform amplitude variance across most modules, including LFIT, RFIT, RHF, LFOT, and RFOT (p < 0.01). Duration variance was significantly greater in PD for both FIT and FOT (p < 0.001). Bilateral performance differences were markedly larger in PD for FIT and HF (p < 0.01). Horizontal waveform measures showed no significant group differences. Conclusions: Conclusion: MOVXAM provides a reliable, sensor-free method for quantifying motor variability in PD. Vertical waveform amplitude and duration variability, particularly during finger tapping and foot tapping, effectively distinguish PD from control performance. The system offers objective, remote-ready digital biomarkers that complement clinical UPDRS scoring and may support research and clinical monitoring. Clinical Trial: IRB- Brany 17-02-213-458
Background: Artificial intelligence (AI) capabilities in therapeutic domains are advancing at an unprecedented rate, with evidence suggesting AI has reached human-level performance in several clinical...
Background: Artificial intelligence (AI) capabilities in therapeutic domains are advancing at an unprecedented rate, with evidence suggesting AI has reached human-level performance in several clinical psychology competencies years ahead of conservative forecasts. Professional training systems operate on 5–10-year adaptation cycles, while AI capabilities double every 90–200 days, creating a critical temporal mismatch that could lead to major structural challenge of the clinical psychology workforce Objective: This study aimed to (1) synthesise current evidence on AI capabilities across 13 clinical psychology competencies, (2) project human parity timelines using an empirically grounded exponential growth model, and (3) develop probability-weighted scenario plans for clinical psychology through 2030 to guide proactive professional adaptation. Methods: A two-phase methodology was employed. First, a narrative evidence synthesis of peer-reviewed literature (n=30+ studies, including RCTs, systematic reviews, and meta-analyses) was undertaken across 13 competencies defined by APA accreditation standards and the Benchmarks model, identifying current AI performance measures, human benchmarks, effect sizes, and limitations. Second, the Intuitive Logics framework for scenario planning was applied to develop three probability-weighted exploratory scenarios (AGI Emergence, 35%; Capability Plateau, 45%; Partial/Narrow AGI, 20%), incorporating AI capability data, expert forecasting, prediction market data, infrastructure investment analysis, and professional workforce metrics. A sensitivity analysis tested robustness across four doubling-time assumptions (90, 135, 180, and 240 days) using an exponential growth model. Results: Evidence synthesis across 30 studies revealed that AI has already reached, or will reach, human parity in at least three foundational competencies (session management, outcome monitoring, and protocol-driven CBT) by the current year (2026). Under the base case 135-day doubling trajectory, 10 of 13 competencies are projected to reach human parity by 2030. Two competencies, clinical judgement/complex case formulation and deep relational work, are projected to remain below human parity beyond 2036 due to theoretical barriers (metacognition, embodiment, authentic vulnerability). Sensitivity analyses confirmed robustness: across all four doubling-time scenarios, 6–11 competencies reach parity by 2030, and the critical professional adaptation window closes between 2029 and 2035. Three scenario analyses identified convergent findings across all trajectories, including perceptual indistinguishability between AI- and human-led therapeutic interactions by 2027–2028, and inadequate professional adaptation velocity in all scenarios. Conclusions: Clinical psychology faces a structural disruption driven by a fundamental temporal mismatch between the acceleration of AI capabilities and the pace of professional training. The critical adaptation window is estimated at 2026–2031 under base-case assumptions and is unprecedented in compression compared to previous professional transformations. Immediate priorities include curriculum restructuring towards AI-resistant competencies (clinical judgement and deep relational work), development of AI integration skills, and establishment of supervisory frameworks. Scenario-based planning, rather than singular prediction, is recommended as the appropriate strategic tool given the radical uncertainty of AI capability trajectories.
Background: Surgery resident selection is a resource-intensive process. The advent of generative artificial intelligence (GAI) offers a new possibility to aid in resident selection, increasing the eff...
Background: Surgery resident selection is a resource-intensive process. The advent of generative artificial intelligence (GAI) offers a new possibility to aid in resident selection, increasing the efficiency of file review without the burden of creating a customized machine-learning algorithm. Objective: Our study aimed to compare file review of general surgery applicants by GAI to file review by our program’s residency selection committee (RSC). Methods: GPT-4o, an open access GAI software, was used to score deidentified 2023-2024 Canadian Resident Matching Service (CaRMS) application files to our program based on our RSC’s file review scoresheet. GAI scores were compared to RSC-assigned scores for each application element including CVs, personal letters, and reference letters. Rank lists generated from both sets of scores were compared using Spearman’s rank correlation. GPT-4o was then used to create ten generic application files. These were scored by GAI and compared to GAI scores for the 2023-2024 CaRMS applicants using the Wilcoxon rank-sum test. Results: A total of 124 application files were included. Median GAI file review scores were consistently higher than RSC-assigned scores (24.46 vs. 17.54 y, p<0.05) and had less variance between applicants (6.96 vs. 20.80, p<0.05). The interrater reliability between GAI scores and RSC scores was poor across all application elements (0.16). Rank lists generated by GAI and RSC scores demonstrated a weakly positive correlation for each application element (0.25 to 0.37, p<0.05). Rank lists based on total file review scores demonstrated a moderately positive correlation (0.44, p<0.05). Median scores for GAI-created files compared to CaRMS applicant files were statistically similar for application CVs (6.88, p=0.25), but were significantly higher for other application elements and global scores (27.51 vs. 24.46, p<0.05). Conclusions: GAI in its current form cannot reliably replicate human file review. Further research is needed to determine the potential role for GAI in residency selection.
Background: Individuals with mild intellectual disabilities (ID) often exhibit significant deficits in motor proficiency, coordination, and reaction time, which negatively impact their functional inde...
Background: Individuals with mild intellectual disabilities (ID) often exhibit significant deficits in motor proficiency, coordination, and reaction time, which negatively impact their functional independence. While virtual reality (VR) exergames offer a highly immersive and motivating environment for physical activation, objective evidence regarding their impact on specific motor scales in this population remains limited. Previous research has established a structured teaching methodology (WISH and WON protocols) to achieve gameplay independence. However, there is a need to quantify the physiological and motor benefits resulting from such interventions. By utilizing standardized tools like the Bruininks-Oseretsky Test of Motor Proficiency (BOT-2) and specialized reaction time measures, we can evaluate how systematic VR-based training influences motor control and cognitive processing speed. Objective: The primary objective of this study was to evaluate the effectiveness of a 16-session structured VR exergame intervention (based on WISH and WON protocols) in improving motor proficiency and shortening simple reaction time among adolescents with mild intellectual disabilities. Methods: This pilot study used a multisession, single-group pre-test/post-test research design involving 8 adolescents with mild ID (mean age 17.63 years, SD 1.77). The intervention consisted of 16 VR exergame sessions using the "OhShape!" game, conducted according to the WISH and WON protocols during physical education classes. Motor proficiency was assessed at pre-test and post-test) using the short form of the BOT-2, covering eight subtests including bilateral coordination, balance, and strength. Simultaneously, simple reaction time (SRT) to light and sound stimuli was measured using a standardized Alfa-Electronics reaction time meter. Results: Total BOT-2 scores significantly improved from a baseline mean of 69.25 (SD 6.80; range 55–74) to 80.50 (SD 5.50; range 72–88) at the post-test (P < .05; r = 0.74). Based on the standardized 5-point proficiency scale, the group mean rose from 1.88 (SD 0.64; range 1–3; below average) at baseline to 2.88 (SD 0.35; range 2–3; average) post-intervention (P < .01; r = .89). Similarly, the mean SRT decreased from 426.50 ms (SD 52.3; range 350–510) to 392.10 ms (SD 38.4; range 340–450) at the final assessment (P < .05; r = .72). Conclusions: This pilot study suggests that the structured WISH and WON training protocols for VR exergames may effectively enhance motor proficiency and cognitive processing in adolescents with mild ID. The observed gains, significantly exceeding MCID thresholds, provide preliminary evidence that immersive VR can serve as a potent supportive tool in special education. Further large-scale research is warranted to confirm these long-term functional benefits.
Background: Cognitive behavioral therapy is effective in reducing posttraumatic stress disorder (PTSD) symptoms and posttraumatic cognitions in women who have experienced sexual assault. Online interv...
Background: Cognitive behavioral therapy is effective in reducing posttraumatic stress disorder (PTSD) symptoms and posttraumatic cognitions in women who have experienced sexual assault. Online interventions are a potentially effective and accessible treatment modality for this population, but engagement issues have been documented. Design strategies such as gamification and personalization to the needs of a target population may help address these challenges by enhancing acceptability, user experience and engagement. Objective: This pilot study evaluated a gamified, online cognitive restructuring intervention personalized to women who have experienced sexual assault. A mixed-methods design was used to assess (1) changes in PTSD, anxiety, depression, posttraumatic cognitions, and self-blame, as well as perceived changed reported by participants; (2) acceptability and user experience, as well as participants’ subjective experiences, and (3) quantitative engagement, as well as perceived engagement reported by participants. Methods: Seventeen women who have experienced sexual assault were given access to the five-session intervention. They completed self-reported questionnaires evaluating symptoms and cognitions at three time points during treatment. At post-treatment, they also completed questionnaires about acceptability and user experience and participated in a qualitative interview. Usage data was collected to describe engagement with the intervention. Results: Twelve participants completed the intervention and all three measure points. Quantitative results showed significant reductions in severity of all symptoms and endorsement of negative cognitions (all P<.05), high acceptability and user experience, and good engagement. Qualitative findings revealed perceived improvements, including reduced anxiety and avoidance, and increased ability to restructure one’s thoughts. Personalized content was described as relevant to participants’ needs, as well as validating and empowering. Gamification features were perceived as supporting learning and engagement. However, some participants reported design-related challenges, including insufficient relevance for repeated sexual trauma and discouragement from unmet gamified goals. Conclusions: Overall, these findings highlight personalization and gamification as promising design considerations for online interventions targeting PTSD symptoms in women who have experienced sexual assault. Future research should aim to isolate the specific effects of design strategies such as personalization and gamification on effectiveness, user experience, acceptability, and engagement through controlled experimental trials.
Background: Assessing medication literacy in children is essential for evaluating school-based health education initiatives, yet traditional measurement tools often fail to engage young respondents, p...
Background: Assessing medication literacy in children is essential for evaluating school-based health education initiatives, yet traditional measurement tools often fail to engage young respondents, potentially compromising data quality. Gamified assessments offer a promising alternative, but their psychometric properties, comparability to conventional formats, and responsiveness to intervention effects require systematic evaluation. Objective: This study developed and evaluated a gamified assessment tool for measuring medication literacy across four domains (Knowledge, Attitude, Perceived Behavioral Control, and Behavioral Intention) in elementary school children through three sequential phases: psychometric validation, methodological comparison with a traditional paper-based questionnaire, and application in an intervention context. Methods: In Phase 1, content validity was first established through a two-round expert review. The refined tool was then administered to 81 students to assess construct validity (Rasch analysis) and test-retest reliability (one-month interval). Phase 2 employed a randomized crossover design with 85 students to assess equivalence (ICC, Bland-Altman, decision consistency) and superiority (data quality, enjoyment, preference) compared to paper-based administration. We adapted the CHERRIES framework to report on user recruitment, engagement metrics, and data completeness, as these principles remain applicable to digital survey modalities. Phase 3 involved 85 students in a pre-post intervention design, with responsiveness evaluated using generalized linear mixed models. Results: The tool demonstrated strong content validity (S-CVI/Ave = 1.00), adequate construct validity (unidimensionality supported, item fit within 0.5–1.5, point-measure correlations ≥0.67), and good test-retest reliability (ICC: 0.76–0.92). Equivalence with paper-based administration was confirmed across all domains (ICC: 0.94–0.99; mean bias near zero; κ: 0.87–0.97). While data quality was comparable (validity rates: 100% vs 90.7%, P = .13), the gamified tool yielded substantially higher enjoyment and preference ratings (Cohen's d = 2.00 and 1.62, respectively). The tool detected significant intervention effects across all four domains (β: 1.74–2.86; IRR = 1.52, all P < .001), with moderate to large effect sizes and consistent improvements across classrooms despite baseline differences. Conclusions: The gamified assessment is a psychometrically sound, methodologically robust alternative to traditional questionnaires. It achieves measurement equivalence while enhancing child engagement—a dual advantage that supports both data quality and feasibility in pediatric research. By demonstrating consistent intervention effects across diverse classrooms, the tool further holds potential to advance health equity in school-based medication literacy education.
Background: While exergaming is known to boost physical activity (PA) levels and psychological factors linked to sustained PA participation, little research has explored the effects of adding narrativ...
Background: While exergaming is known to boost physical activity (PA) levels and psychological factors linked to sustained PA participation, little research has explored the effects of adding narrative elements to exergames on PA and psychological outcomes. Objective: This study investigated the acute effect of narrative and non-narrative exergaming on moderate-to-vigorous PA (MVPA) and light PA (LPA). It also examined differences in energy expenditure (EE), perceived exertion (RPE), PA-related psychological outcomes, and potential sex differences. Methods: Fifty-nine college students (52.5% female; Mage = 24.3 ± 3.4 years) completed two separate 20-minute exergaming sessions: 1) Nintendo Switch’s Let’s Get Fit (non-narrative) and 2) Ring Fit Adventure (narrative). PA and EE were measured with ActiGraph accelerometers. Enjoyment, situational motivation, self-efficacy, and exercise-induced feelings were self-reported using validated questionnaires after each session. A series of repeated measures ANOVA, with sex as a between-subjects factor and exergame type as a within-subjects factor, was conducted. Results: Significant main effects of session were observed for MVPA (F1,114 = 92.24, p < .001;η² = 0.45), LPA (F1,114 = 93.62, p < .001;η² = 0.45), EE (F1,114 = 57.89, p < .001;η² = 0.34), enjoyment, (F1,57 = 121.94, p < .001;η² = 0.22) and self-efficacy (F1,57 = 11.41, p = .02;η² = 0.09. A significant main effect of session was observed for negative feeling (p < .05). Post hoc Tukey tests indicated that the narrative exergaming session produced significantly higher enjoyment, self-efficacy, and LPA, and lower negative feeling (p < .01) than the non-narrative session. In contrast, non narrative exergaming elicited significantly higher MVPA, and EE (p < .01). A significant sex × session interaction also emerged for intrinsic motivation, negative feeling, and situational motivation (p < .01). Post hoc analyses showed that females demonstrated higher intrinsic motivation during the narrative session (p < .01), and lower negative feelings during the narrative session (p < .01). Conclusions: The findings suggest that narrative exergaming may increase enjoyment, self-efficacy, intrinsic motivation, and lower negative feelings during PA among college students, highlighting its potential to support long-term exercise engagement, while non-narrative exergaming may promote greater physical intensity. Clinical Trial: UTK IRB-23-07880-XP
Background: Physical exercise has long been recognized as an effective strategy for promoting healthy aging, but most existing studies have primarily involved community-dwelling, relatively healthy ol...
Background: Physical exercise has long been recognized as an effective strategy for promoting healthy aging, but most existing studies have primarily involved community-dwelling, relatively healthy older adults. Whether these benefits extend to older adults with chronic diseases remains uncertain. Objective: This study aimed to evaluate the effects of maintaining or initiating physical exercise on multiple aspects in older outpatients with chronic illnesses. Methods: We enrolled outpatients aged ≥65 years from a Geriatric Medical Center. Patients were excluded if they had experienced a severe illness requiring hospitalization in the year prior or during the observational period. A total of 118 participants were classified into no-exercise (n=42), low-intensity exercise (n=51), and median/vigorous-intensity exercise (n=25) groups according to the metabolic equivalents of their daily physical activity. Baseline and 1-year follow-up physical performance, laboratory data, and individual domains of quality of life (QoL) were compared among patients with different exercise habits. Results: There were no statistically significant differences in baseline age, sex, body mass index, Charlson Comorbidity Index, or the prevalence of common chronic diseases among the three groups with different intensity physical activity. However, a significant trend toward higher hemoglobin levels was observed with increasing exercise intensity. Additionally, the median/vigorous-intensity group showed significantly better physical performance, less depressive mood (GDS-15), and better physical functioning and vitality domains of QoL compared to no or low-intensity exercise groups. After 1 year of follow-up, participants who maintained moderate-to-vigorous-intensity exercise exhibited significantly higher hemoglobin levels, better QoL, and faster gait speed. Notably, 11 participants transitioned from other groups to the median-to-vigorous-intensity exercise group. Significant improvements in hemoglobin levels and the vitality domain of QoL were observed in these participants. Conclusions: This study found most older adults with chronic diseases engaged in insufficient physical exercise and further underscores that maintaining or newly initiating regular median-to-vigorous intensity exercise later in life can still confer meaningful health benefits. Clinical Trial: N/A
Background: Cognitive complaints (CC) in individuals without dementia may predict future cognitive and functional decline. The GERO cohort aims to identify multidimensional risk factors associated wit...
Background: Cognitive complaints (CC) in individuals without dementia may predict future cognitive and functional decline. The GERO cohort aims to identify multidimensional risk factors associated with the prognosis of CC in older adults who are free of dementia. Longitudinal studies on cognitive complaints are essential for understanding their prognosis; however, such studies often face challenges in recruiting and retaining participants, which impacts the strength of the inferences drawn. Addressing these challenges is crucial for guiding future research efforts. The purpose of the study is to report the recruitment process at baseline and the attrition rates (i.e., participant dropout over time) at follow-up in the Chilean GERO cohort. Objective: To describe the baseline recruitment process and quantify attrition during follow-up in the Chilean GERO cohort, and to identify baseline factors associated with loss to follow-up among older adults with cognitive complaints who were free of dementia. Methods: The GERO cohort is a prospective study involving older adults without dementia, aged 70 years and above, living in three municipalities in Santiago, Chile. The study included assessments of cognitive performance, functionality, psychosocial factors, and other medical indicators. Baseline assessments were conducted from May 2017 to July 2021, with follow-up evaluations from December 2018 to December 2023. Results: Initially, 17,759 households were approached. Significant recruitment challenges included difficulties establishing contact, age limitations for participation, and high refusal rates, resulting in 291 participants being recruited at baseline. The participants were predominantly older women (80%), with an average age of 76.8 ± 5.1 years and 8.9 ± 4.8 years of education. The study attrition rate over three years was 42%. Factors associated with loss to follow-up included refusal to participate, illness, death of the participant, lack of contact after multiple attempts, and relocation. Univariate analysis revealed that age, cognitive status, and activities of daily living (ADL) were significant predictors of attrition. In contrast, in the multivariable analysis, only age and Instrumental Activities of Daily Living remained significant predictors of loss to follow-up. Conclusions: Our findings highlight the need to address barriers to recruitment and retention in aging studies. They emphasize the importance of age and functional ability as predictors of attrition, underscoring the need for strategies to mitigate these challenges in future research. Clinical Trial: NCT04265482 in ClinicalTrials.gov.
Background: Recovery High Schools (RHS) integrate academics and therapeutic support for youth with substance use disorders, yet staff often have limited visibility into students’ day-to-day health b...
Background: Recovery High Schools (RHS) integrate academics and therapeutic support for youth with substance use disorders, yet staff often have limited visibility into students’ day-to-day health behaviors between check-ins. Wearable technology can generate objective indicators (e.g., activity, sleep) that may support individualized recovery care planning, but RHSs have not yet implemented these tools. Objective: This pilot study evaluated the feasibility, engagement, acceptability, and perceived impact of integrating wearable-derived health reports into care planning at one RHS in the Northeast United States. Secondary aims explored changes in psychosocial outcomes (school belonging, social identity, recovery capital, and social-emotional learning). Methods: Thirteen adolescents (Mages 15-21) and school staff (n = 3) participated in a single-site, mixed methods 3-month pilot. Data included baseline and monthly surveys, Fitbit wear-time and activity data collected via Fitabase, weekly staff surveys, and staff interviews. Outcomes were summarized descriptively, and qualitative feedback was synthesized to characterize workflow integration and implementation barriers to care. Results: Survey retention declined from 100% to 46.2% at Month 3; 2 students left the school during the study. Wearable engagement was inconsistent, with 69.2% (n = 9) participants wearing the device at least once, declining to 23.1%-30.8% (n = 3-4) by Weeks 9-12. Staff adopted weekly device report usage at a high rate (88.2% of survey weeks) and described them as helpful for care planning and opening dialogue with students regarding sleep, activity, and substance use. Key implementation challenges included inconsistent student device wear and logistical barriers on assessment days. Conclusions: Wearable-derived health reports were acceptable to RHS staff and feasibly integrated into care planning workflows, though sustained student device engagement was a critical barrier. Future implementation studies should prioritize structures including device-use protocols, enhance syncing tech support, and strategies to sustain adolescent engagement across the full study period. Clinical Trial: https://osf.io/q6jt4/overview?view_only=eb1606b3bfa44e658383a63ab734fde1
Background: Medical students experience high rates of anxiety and depression, yet stigma, time constraints, and limited access to support often delay help-seeking. Digital self-help interventions may...
Background: Medical students experience high rates of anxiety and depression, yet stigma, time constraints, and limited access to support often delay help-seeking. Digital self-help interventions may provide an accessible and low-threshold approach to supporting student wellbeing. Objective: To evaluate the feasibility, acceptability, and preliminary effectiveness of a conversational agent–based cognitive behavioral therapy (CBT) self-help intervention for anxiety and depression among medical students Methods: A two-week multinational, unblinded, pilot prospective study was conducted with 138 medical students enrolled at an Indian and Bulgarian university. Participants scoring ≥10 on the Generalized Anxiety Disorder-7 (GAD-7) or Patient Health Questionnaire-9 (PHQ-9) were randomized to either a conversational agent–based CBT self-help intervention (Woebot; n=69) or a self-help psychoeducational leaflet control (n=69). Anxiety and depression were assessed at baseline and post-intervention. Feasibility outcomes included recruitment, adherence, and engagement metrics. All data were collected in de-identified form following electronic informed consent. Results: Recruitment and retention were feasible, with 138 participants completing baseline and post-intervention measures. The intervention group demonstrated significantly greater reductions in anxiety and depression scores compared with controls (GAD-7 and PHQ-9, p<0.001). Engagement with the conversational agent was high, with participants completing most assigned interactions, and the intervention was rated as acceptable and user-friendly. Conclusions: A conversational agent–based CBT self-help intervention is feasible, acceptable, and shows preliminary effectiveness in reducing anxiety and depression among medical students. Larger and longer-term studies are warranted to confirm these findings and inform implementation in educational settings. Clinical Trial: Vardhman Mahavir Medical College & Safdarjung hospital, New Delhi – S. No. IEC/VMMC/SJH/Thesis/06/2022/CC-68
Background: Diabetic foot ulcers (DFUs) are a major complication of diabetes mellitus (DM) and contribute substantially to morbidity, disability, and the healthcare burden worldwide. Appropriate footw...
Background: Diabetic foot ulcers (DFUs) are a major complication of diabetes mellitus (DM) and contribute substantially to morbidity, disability, and the healthcare burden worldwide. Appropriate footwear plays an essential role in preventing diabetic foot complications. In many Asian countries, including Indonesia, sandals are commonly used because of climatic, cultural, and socioeconomic factors. However, most commercially available sandals are not designed to meet the therapeutic requirements needed to protect diabetic feet. Although wound care nurses play a crucial role in diabetic foot prevention and patient education, the perspectives of both nurses and patients regarding the design of diabetic foot sandals remain insufficiently explored. Objective: This study aimed to explore the perspectives of wound care nurses and patients regarding the structural and functional design of diabetic foot sandals in Indonesia. Methods: A descriptive qualitative study was conducted in two phases: eliciting expert perspectives and validating user needs. Wound care nurses were recruited using total sampling during a national wound care forum and completed open-ended questionnaires. Patients with diabetic foot ulcers without gangrene were purposively recruited from two wound care clinics in eastern Indonesia and participated in face-to-face interviews following a brief walking trial using a prototype diabetic sandal. Data were documented using field notes and analyzed thematically to identify perceptions related to the structural and functional design of diabetic foot sandals. Results: A total of 93 wound care nurses and 11 patients with DFUs participated in the study. Nurses emphasized the importance of soft and durable materials, pressure-distributing insoles, adjustable straps, stable sole structures, and protective sandal designs. Functional considerations included comfort, safety, ease of use, and support for daily activities. The patients highlighted the importance of lightweight design, adequate width, open or semi-open structures, and walking comfort. Both groups also emphasized affordability, practicality, and usability as important aspects of diabetic sandal design, although some differences were observed in structural and model preferences. Conclusions: The perspectives of wound care nurses and patients highlight both shared priorities and complementary insights in the design of diabetic foot sandals. Integrating clinical safety, functional usability, cultural suitability, and affordability is essential for developing contextually appropriate diabetic footwear for people with diabetes in Indonesia. Clinical Trial: Not Applicable
Background: Degenerative thoracic myelopathy (DTM) encompasses a spectrum of degenerative spinal pathologies that result in symptomatic thoracic spinal cord compression. Whilst widespread availability...
Background: Degenerative thoracic myelopathy (DTM) encompasses a spectrum of degenerative spinal pathologies that result in symptomatic thoracic spinal cord compression. Whilst widespread availability of magnetic resonance imaging (MRI) is beneficial for diagnosis, clinical history and examination remain as important as ever in localising neurological problems. This is particularly essential in the distinction between symptomatic pathology and the increasing burden of incidental findings that result from large volumes of imaging. The prevalence and spectrum of degenerative thoracic spinal pathology in healthy individuals is currently largely uncharacterised. Objective: The aim of this systematic review was therefore to synthesise available evidence on degenerative thoracic spinal pathologies on MRI in healthy individuals and to improve understanding of the natural history of DTM. Methods: A systematic review was registered with PROSPERO (CRD42023484442) and performed following PRISMA guidelines. MEDLINE and Embase were searched from inception to March 2025. All studies investigating degenerative thoracic spinal pathologies on MRI in healthy adults were eligible for inclusion. Duplicate title and abstract screening, data extraction and risk of bias assessments were conducted. Results: Of 3574 records screened, 11 studies satisfied eligibility criteria for inclusion in the final synthesis. There were seven cross-sectional studies, three case-control studies and one cohort study. A total of 3805 individuals were included: 40.3% (1533/3805) were male, with a mean age of 58.2 years. The most prevalent degenerative pathologies identified on MRI were multilevel (two or more consecutive or non-consecutive discs) disc degeneration (two studies; mean prevalence of 54.2%; range 50%-55%), single level disc degeneration (four studies; mean prevalence of 29.7%; range 7%-72.5%), ossification of the ligamentum flavum (two studies; mean prevalence of 7.2%; range 2.1%-11.9%), inflammatory end-plate changes (two studies; mean prevalence of 4.5%; range 3%-20%), and disc herniation (three studies; mean prevalence of 2.5%; range 0.8%–40%). In individuals with multilevel disc pathology the affected discs were most frequently adjacent. Overall, disc degeneration increased with age and findings were more frequently seen in the lower than the upper thoracic spine. Conclusions: Degenerative disc disease is the most prevalent form of degenerative thoracic spinal pathology in healthy individuals. It increases with age and occurs most frequently in the lower thoracic spine. However, data on degenerative spinal pathology in healthy individuals is currently sparse, limiting understanding of the natural history of symptomatic disease. We found significant heterogeneity between studies and inconsistent use of terminology; future work should include large-scale imaging studies of healthy individuals across broad populations and should seek to promote the use of standardised terminology by researchers and medical professionals.
Background: The Oncology Care Model aims to improve the value of oncology care. However, concerns exist that the Oncology Care Model participation could accelerate consolidation among smaller oncology...
Background: The Oncology Care Model aims to improve the value of oncology care. However, concerns exist that the Oncology Care Model participation could accelerate consolidation among smaller oncology practices, threatening market competition. Objective: To evaluate whether participation in the Oncology Care Model is associated with changes in oncology market competition across U.S. regions, using the Herfindahl–Hirschman Index to assess competition within Dartmouth Health Referral Regions. Methods: A cross-sectional analysis was conducted using Medicare claims data to identify oncology practices participating in the OCM. Market competition was assessed using the Hirschman-Herfindahl Index (HHI) across Dartmouth Health Referral Regions (HRRs) from 2015 to 2019. Demographic and socioeconomic factors were incorporated using American Community Survey data. Results: Market competition, as measured by HHI, remained stable from 2015 to 2019 across regions with varying levels of OCM adoption, indicating no significant impact on competition. Patient demographics and area-level socioeconomic factors minimally influenced HHI trends. Conclusions: The findings suggest that OCM adoption does not drive significant practice consolidation. This supports the OCM as a model that enables both smaller and larger practices to deliver high-value, patient-centered oncology care without undermining market competition. Clinical Trial: Not applicable
Background: Spanish-speaking populations in the United States face significant barriers to accessing mental health care, including language discordance, cultural mismatch, and limited availability of...
Background: Spanish-speaking populations in the United States face significant barriers to accessing mental health care, including language discordance, cultural mismatch, and limited availability of bilingual providers. Although digital mental health tools offer scalable solutions, most are not culturally or linguistically adapted, limiting their effectiveness and engagement among Spanish-speaking users. Objective: This study describes the translation and cultural adaptation of the Digital Clinic (DC) manual and associated digital tools for Spanish-speaking populations, with the goal of improving accessibility, engagement, and equity in digital mental health care. Methods: Guided by the Ecological Validity Framework (Bernal et al., 2009), the translation process addressed eight dimensions: language, persons, metaphors, content, concepts, goals, methods, and context. A stepwise approach was implemented, including initial translation by a bilingual clinician, secondary translation of digital and clinician-facing materials, and collaborative review to ensure linguistic accuracy, cultural relevance, and clinical fidelity. Existing Spanish-language clinical resources, including the Unified Protocol, were used to guide terminology and conceptual alignment. Results: The adapted manual preserved the core therapeutic components of the original model while incorporating culturally relevant language, tone, and examples. Key challenges included translating complex clinical terms and ensuring cross-regional comprehensibility. The final version improved clarity, cultural resonance, and usability for both clinicians and patients, while maintaining flexibility within a structured, short-term care model. Conclusions: Culturally and linguistically adapting digital mental health interventions is essential for equitable care. The Spanish-language Digital Clinic manual demonstrates a scalable approach to improving engagement and access for underserved populations. Future efforts should focus on multilingual infrastructure, clinician training, and region-specific adaptations to expand the reach and impact of digital mental health care.
Background: Falls are a leading cause of injury and mortality among older adults, with slopes posing particular risk due to elevated biomechanical demands. However, objective tools for slope-specific...
Background: Falls are a leading cause of injury and mortality among older adults, with slopes posing particular risk due to elevated biomechanical demands. However, objective tools for slope-specific fall risk assessment remain lacking. Objective: This study aimed to develop and validate a fall risk prediction model for slope walking in older adults using multimodal biomechanical data acquired through wearable devices, with the goal of enhancing the objectivity and precision of fall risk assessment. Methods: Eighty-six community-dwelling older adults aged ≥60 years were recruited and classified into faller and non-faller groups based on fall history in the previous 12 months. Plantar pressure, hip-knee-ankle joint kinematics, and lower limb muscle electromyographic signals were collected during walking on a 10° slope. LASSO regression (λ1se criterion) was employed to identify independent predictors, and multivariate logistic regression was used to construct the prediction model with a corresponding nomogram. Internal validation was performed using Bootstrap resampling (1,000 iterations), and external validation was conducted with an independent sample of 37 participants. Model performance and clinical utility were comprehensively evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). Results: Compared with non-fallers, fallers demonstrated significantly different sEMG root mean square (RMS) amplitudes, joint range of motion, and plantar pressure parameters during both uphill and downhill walking (P < 0.05). Through sequential univariate screening, LASSO regression, and multivariable logistic regression analysis, three independent predictors were retained: uphill vastus lateralis RMS (OR = 1.076), downhill knee range of motion (OR = 0.952), and downhill heel-medial (H-M) peak force (OR = 0.891). The model achieved an AUC of 0.859 (95% CI: 0.777–0.941) in the training cohort and 0.838 (95% CI: 0.703–0.974) in the external validation cohort. Calibration curves and DCA demonstrated satisfactory model calibration and clinical utility. Conclusions: Uphill vastus lateralis RMS, downhill knee range of motion, and downhill H-M peak force constitute independent risk factors for slope-related falls among older adults. The multimodal biomechanical prediction model exhibited favorable discriminative ability and calibration, providing an evidence-based foundation for early screening and targeted intervention strategies in this population.
Background: Public reporting systems are designed to enhance transparency, accountability, and informed decision-making by publishing comparative performance data on healthcare providers. Several high...
Background: Public reporting systems are designed to enhance transparency, accountability, and informed decision-making by publishing comparative performance data on healthcare providers. Several high-income countries, including the United States, United Kingdom, Germany, Sweden, and the Netherlands, have successfully implemented such systems, thereby enabling patients and payers to make informed choices among healthcare providers. However, the nationwide implementation of public reporting systems remains challenging in other countries, and the barriers to their adoption are not well understood. The purpose of this study is to investigate the factors impeding adoption in Finland. Despite Finland’s longstanding tradition in the development of digital health solutions and electronic patient records, it has yet to establish a comprehensive public reporting system.
Public reporting systems publish comparative performance about healthcare providers to improve transparency, accountability, and informed decision-making. Many Global North countries (e.g, the US, UK, Germany, Sweden, and the Netherlands) have implemented these systems, enabling patients and payers to compare provider, but other countries still struggle to implement them or to gain full benefit from them. National-level factors that prevent adoption and full benefits are still poorly understood; Finland is a useful case because, despite increasing use of digital solutions, it has not been able to implement a robust public reporting system. Objective: The aim of this study was to identify the factors that hinder public reporting systems adoption in Finland using organizational diffusion of innovation framework, specifically adapted to health care service context, expanded with a new ‘data regulation’ construct. Methods: This qualitative study is based on insights from expert stakeholders within the Finnish healthcare system. The data collection involved 24 in-depth interviews, each offering a unique perspective on the Finnish healthcare system. The data were analyzed using an abductive Gioia approach, generating first-order, informant-centric concepts, clustering them into second-order themes, and distilling aggregate dimensions. Results: The expert stakeholders described a strong need and readiness for public reporting systems, but the adoption is hindered by five interrelated factors: (1) political climate; (2) professional resistance, especially among doctors; (3) resource constraints that limit investment , including the ability to develop user-friendly public reporting systems; (4) information systems and data challenges, including fragmented and inconsistent systems, inconsistent documentation; (5) restrictive and burdensome data regulation (GDPR and Finnish privacy laws). Conclusions: This study shows that the adoption of public reporting systems in Finland is constrained less by lack of perceived need than by political, professional, resource, data-infrastructure and regulatory barriers. By extending Diffusion of Innovation (DOI) framework with data regulation construct. This extension helps explain how highly regulated environments can slow adoption of data-centric healthcare innovations even when benefits are recognized.
Background: Cesarean section (C-section) is the most common surgical procedure in the United States, yet its use varies widely across regions and institutions. While clinical risk factors are importan...
Background: Cesarean section (C-section) is the most common surgical procedure in the United States, yet its use varies widely across regions and institutions. While clinical risk factors are important, growing evidence suggests that delivery decisions are also shaped by geographic context and health system characteristics. Understanding these systemic drivers is critical for improving obstetric equity and aligning C-section use with clinical need. Objective: To examine the extent of geographic and institutional variation in C-section use across the United States and to evaluate the performance and implications of machine learning–based risk prediction across clinically defined risk groups. Methods: This population-based study analyzed 38·1 million U.S. births from the 2013–2022 National Vital Statistics System Natality Detailed Files. Interpretable logistic regression models with county fixed effects and supervised machine learning approaches were used to examine factors associated with C-section delivery and to predict C-section risk in the full population and in low-risk and high-risk subgroups. County-level health system capacity and socioeconomic characteristics were linked from national administrative sources. Model performance was assessed using accuracy, recall, and area under the receiver operating characteristic curve (AUC), with probability thresholds optimized to balance overall performance and clinical relevance. Results: C-section rates exhibited substantial and persistent geographic variation, with consistently higher utilization in the U.S. South despite a modest national decline over time. After adjustment for detailed maternal risk factors, county fixed effects explained a large share of residual variation. Machine learning models demonstrated good predictive performance (AUC 0·81–0·83), but performance varied markedly across risk groups, with substantially lower accuracy in low-risk pregnancies. Threshold optimization revealed clinically meaningful trade-offs between sensitivity and specificity. Conclusions: C-section use in the United States is strongly influenced by geographic and institutional context in addition to maternal risk. Policies narrowly focused on low-risk populations may fail to address—and may inadvertently exacerbate—system-level drivers of variation. Risk-adjusted, context-aware prediction tools may support more equitable and clinically appropriate obstetric decision-making.
Background: Intravenous fluid therapy is widely used in emergency departments (ED) to treat hypovolemia but is invasive and associated with potential complications. Oral fluid therapy represents a non...
Background: Intravenous fluid therapy is widely used in emergency departments (ED) to treat hypovolemia but is invasive and associated with potential complications. Oral fluid therapy represents a non-invasive alternative; however, evidence regarding its feasibility and ability to achieve prescribed fluid volumes in ED patients remains limited. Objective: To evaluate the feasibility of oral fluid therapy in ED patients requiring fluid replacement and to determine whether oral fluid therapy is non-inferior to intravenous fluid therapy in achieving prescribed fluid volumes. Methods: This investigator-initiated, multicenter, open-label randomized controlled feasibility and non-inferiority trial will enroll 250 adult ED patients prescribed ≥1000 ml intravenous crystalloid therapy. Participants are randomized 1:1 to receive either oral fluids or intravenous crystalloid. The primary feasibility outcome is protocol adherence. The primary clinical outcome is the proportion of patients achieving the prescribed fluid volume during the ED stay, analyzed using a non-inferiority framework. Secondary outcomes include total fluid volume received, blood pressure changes, hospital-free days, peripheral intravenous catheter use, acute kidney injury, severe electrolyte imbalance, and additional feasibility measures. Analyses will follow the intention-to-treat principle. Results: Recruitment began in October 2025 at three Danish EDs and is ongoing. As of March 2026, we have enrolled 85 patients. Conclusions: This trial will evaluate whether oral fluid therapy is feasible in ED patients and whether it can achieve prescribed fluid volumes comparable to intravenous therapy. The findings will inform the design of a future definitive effectiveness trial. Clinical Trial: ClinicalTrials.gov ID NCT07361952
Background: Breast cancer surgical and reconstructive decision-making is a complex, preference-sensitive process that requires patients to balance oncologic safety, aesthetic outcomes, recovery burden...
Background: Breast cancer surgical and reconstructive decision-making is a complex, preference-sensitive process that requires patients to balance oncologic safety, aesthetic outcomes, recovery burden, and long-term quality of life. Despite the growing emphasis on shared decision making (SDM), existing patient decision aids (PDAs) in breast reconstruction are often static, text-heavy, and insufficiently responsive to individual patient values and emotional needs. Artificial intelligence (AI) offers an opportunity to develop adaptive, patient-centered decision-support tools that integrate clinical evidence, patient narratives, and personalized feedback. Objective: This study protocol describes the development and early feasibility testing of an AI-supported, narrative-driven digital decision aid designed to facilitate shared decision-making for patients considering breast cancer surgery and reconstruction. Methods: A multiphase mixed-methods design will guide development and preliminary evaluation. Phase I involves qualitative semi-structured interviews with breast cancer patients and clinical stakeholders to identify key informational needs, emotional challenges, and experiential factors influencing decision-making. Interview transcripts will undergo inductive thematic analysis to inform the conceptual framework, content structure, and narrative integration of the decision aid. Phase II is a pilot mixed-methods feasibility study involving 50 patients with early-stage breast cancer considering surgical and reconstructive options. Participants will use the digital decision aid and complete validated measures including the Decisional Conflict Scale and Decision Regret Scale, along with investigator-developed usability and acceptability assessments. Semi-structured exit interviews will provide qualitative feedback on usability and perceived value. Results: Findings will inform refinement of the decision aid and guide the design of future effectiveness trials. Conclusions: This study outlines a rigorous, stakeholder-informed framework for developing an AI-supported decision aid for breast cancer surgical decision-making. If successful, this approach may enhance shared decision-making and serve as a model for ethically grounded AI-supported decision tools in other preference-sensitive clinical contexts.
Open Peer Review Period: Mar 16, 2026 - Mar 1, 2027
Background: Artificial intelligence (AI) is rapidly reshaping healthcare by supporting earlier diagnosis, assisting clinical decision-making, and improving operational efficiency. However, most system...
Background: Artificial intelligence (AI) is rapidly reshaping healthcare by supporting earlier diagnosis, assisting clinical decision-making, and improving operational efficiency. However, most systems remain deployed within human-in-the-loop workflows, and hospitals lack a standardized framework to evaluate fairness, reliability, accuracy, and real-world safety. Prior failures illustrate how ambiguous objectives and unvalidated proxy targets can produce inequitable outcomes and erode clinical trust. Objective: This paper proposes a unified, model-type-aware minimum evaluation and reporting standard capable of assessing both traditional classification models and generative large language models (LLMs) via transparent reporting of performance markers, subgroup fairness analyses, and hallucination detection. Methods: We developed the framework by synthesizing recurring, documented failure modes of healthcare AI with widely used regulatory and risk-management concepts, iteratively mapping risks to concrete evidence artifacts that developers can produce, evaluators can audit, and purchasers can compare across vendors. Results: The resulting standard comprises three layers: universal disclosures applicable to all healthcare AI systems (U1–U5), minimum evaluation requirements for clinical ML models (C1–C6), and minimum evaluation requirements for LLM/RAG systems (G1–G6), supported by lifecycle governance expectations for post-deployment monitoring, versioning, and rollback. Conclusions: Current FDA pathways provide a foundation but remain insufficient for governing continual-learning systems and generative models in clinical workflows. We propose that the FDA extend these mechanisms to require mandatory disclosure of training data provenance and standardized benchmarks for clinical safety and relevance. Establishing such a framework is crucial to ensure that AI advances deliver autonomously safe and trustworthy healthcare.
Background: Rising rates of youth mental health difficulties have led to an increased focus on initiatives for improving the mental wellbeing of young people. Digital mental health interventions (DMHI...
Background: Rising rates of youth mental health difficulties have led to an increased focus on initiatives for improving the mental wellbeing of young people. Digital mental health interventions (DMHIs), including serious games and the use of extended reality interventions, show promise, offering novel opportunities for delivering effective, evidence-based support. However, the factors that influence engagement and mechanisms of change remain underexplored, particularly for augmented reality (AR) games. Objective: This study aimed to explore processes of engagement and identify mechanisms of change in Dragons of Afterlands, a co-designed AR multiplayer serious game for adolescent wellbeing. A secondary aim was to generate a theoretical model to highlight psychological mechanisms of change that can inform future development of similar interventions. Methods: A qualitative study using constructivist Grounded Theory was conducted within a mixed-methods intervention trial. Thirty-one young people aged 11–14 years from two UK secondary schools participated in focus groups, live gameplay audio recordings, field observations, and written feedback regarding the four-week intervention. Data were analysed iteratively using line-by-line, focused and theoretical coding to identify engagement factors and change mechanisms, and how these factors interplayed to promote wellbeing. Results: Engagement was driven by the interaction between individual factors, social processes, digital features and game design factors, and the environmental context. The novelty of AR gameplay, narrative immersion, interactive opportunities and progression, supported sustained motivation, although technological glitches and limited relatability of content were considered barriers to engagement. Social interactions emerged as a central engagement driver and mechanism of change. The multiplayer format fostered social development, including connection with others, communication and confidence, and the combined digital and physical environment created a psychologically safe space for disclosure, shared problem-solving and rehearsal of coping strategies, with some evidence of skill transfer beyond sessions. Cognitive-emotional development was facilitated through challenges embedded in the game, storytelling, and structured choices that promoted reflection, perspective-taking, emotional awareness and problem-solving. Positive affect arose from enjoyment from playing, and spending time with others in a calm and supportive environment. This functioned both as reinforcement for engagement and a psychological mechanism of change. Together, social, cognitive-emotional, and affective processes cited, may contribute to hedonic and eudaimonic aspects of wellbeing. Conclusions: This study provides the first qualitative model integrating engagement processes and psychological mechanisms within an AR serious game for adolescent wellbeing. Findings suggest that combining immersive digital features with real-world social interaction may enhance both sustained engagement and therapeutic impact. Robust technology, personalisation and relatable content appear important for optimising change processes. The proposed model offers a framework to guide refinement and future evaluation of AR-based wellbeing interventions.
Background: Many diseases often develop through sequences of related conditions over time. Identifying how diagnoses occur over time may help detect early risk signals before severe outcomes arise. Cl...
Background: Many diseases often develop through sequences of related conditions over time. Identifying how diagnoses occur over time may help detect early risk signals before severe outcomes arise. Clinically significant patterns are limited due to many population-level studies focus on disease co-occurrence rather than the temporal order of diagnosis. Objective: To identify and validate temporal disease associations using frequent pattern mining and statistical validation techniques in a nationwide patient-record database. Methods: We analyzed health records of 3,987,382 Finnish patients. The records were transformed into temporal disease sequences by taking the date that the first record was entered into the database. To identify the most common patterns for each unique disease, we applied the FP-Growth algorithm by using a support threshold of 5% or the minimum number of 5 patients. To validate each pattern, we applied a combination of relative risk, 95% confidence interval, and relative width to measure precision. Results: We identified several clinically interpretable temporal disease connections. Such as, acute kidney failure was mostly preceded by chronic kidney disease with a RR = 15.13 and sepsis with RR = 9.76. It is also grouped with heart failure-related combinations with RR = 10.76. Patients with diabetic foot ulcer with type 1 or type 2 diabetes have a significant risk of getting osteomyelitis with a relative risk of 157.02 for type 1 and 84.84 for type 2. At the block level, cerebrovascular diseases were linked to hypertension (RR = 2.47), atherosclerosis (RR = 2.52), and dementia (RR = 2.96). Drug poisoning patterns were also connected to psychiatric diagnoses, including mood disorders (RR = 7.24) and combinations of alcohol and mood disorders (RR = 18.25). Across these patterns, confidence intervals were narrow, and relative width values were low. The generated patterns and statistical measures are publicly available in a web interface for research purposes: https://cs.uef.fi/ml/impro/disease-pattern/ Conclusions: Frequent pattern mining, when integrated with RR/CI and precision filtering, produces clinically interpretable temporal connections that could help in decision-making and hypothesis development. External validation with other datasets and cohorts is essential.
Background: Given the rapid growth of digital healthcare, greater transparency in engaging end-users in developing implementation plans is needed to improve the sustained delivery and effectiveness of...
Background: Given the rapid growth of digital healthcare, greater transparency in engaging end-users in developing implementation plans is needed to improve the sustained delivery and effectiveness of digital healthcare. PREVENT is a patient-centered digital health tool designed to engage patients with overweight or obesity in conversations about behavior change to lower their cardiovascular disease (CVD) risk. We are implementing PREVENT in a rural federally qualified health center network with eight clinics. Objective: Objective: The objective of this paper is to present a multi-method approach used to engage clinic teams, including healthcare team members and administrators, to co-develop an implementation plan for the PREVENT digital health tool. Methods: Methods: Healthcare team members (e.g., clinicians, community health workers) and administrators (e.g., informatics professionals, clinic managers, CEO) were engaged through qualitative interviews, advisory board meetings, and site visits to develop an implementation plan prior to implementation. Qualitative interviews were coded using the Consolidated Framework for Implementation Research to identify potential barriers and facilitators of implementing PREVENT and paired with learnings from previous trials of PREVENT to inform the selection and tailoring of implementation strategies. Direct clinic observations generated clinic workflow maps, roles and responsibilities of team members, clinic resources (e.g., technology, space), and an understanding of the clinic and community context and readiness for implementation. A mixed-methods evaluation plan was developed using validated measures, input from the advisory board, and implementation research logic models. Results: Results: Implementation of the PREVENT tool was facilitated by a positive clinic culture, alignment with chronic disease priorities, an easy-to-use and adaptable design with EHR integration, and CHWs’ experience addressing social needs. Key barriers included limited technology infrastructure, variable staffing across clinics, patients’ digital access and literacy challenges, and the need for additional training and support for care team members. An implementation plan that includes thirteen strategies across six implementation strategy clusters was created to leverage facilitators and address barriers. A multi-modal evaluation plan was created to examine implementation and effectiveness. Conclusions: Conclusions: This paper provides an example of how to develop implementation and evaluation plans tailored to the healthcare context that engage end users to increase the impact of a digital health intervention. This work may be replicated to support the successful implementation of other digital health tools, particularly in under-resourced, complex healthcare contexts such as rural clinics where resources are less available.
Background: The prevalence of mental health disorders is steadily increasing, while informative biomarkers remain lacking. Although artificial (AI) intelligence shows promise for revealing latent patt...
Background: The prevalence of mental health disorders is steadily increasing, while informative biomarkers remain lacking. Although artificial (AI) intelligence shows promise for revealing latent patterns in data, available datasets in the computational psychiatry community are still insufficient. Digital technologies and informatics tools could fill this gap, offering new strategies for collecting large real-world data. Furthermore, computational infrastructures provide scientists with access to e-services and powerful computational resources. Objective: We present the NewPsy4U web platform, which integrates data, AI pipelines, and mobile applications into an efficient environment from the end-user’s perspective. Methods: NewPsy4u is built on a LAMP architecture (Linux, Apache, MySQL, PHP) and is hosted at the IRCCS-FBF High Performance Computing (HPC) Center. These resources enable the execution of classical and generative AI algorithms required for computational psychiatry. The platform implements the highest standard protocols, while access is granted following registration and approval. The data repository complies with the FAIR principles (Findable, Accessible, Interoperable, Reusable), and the datasets provided are structured according to the OMOP Common Data Model or BIDS standard, enabling standardized data storage and interoperability.
Mobile technology is integrated into NewPsy4u, allowing users to collect patient data using the experience sampling method (ESM). All mobile data are synchronized within the NewPsy4u web-based portal, where they can be managed for research purposes. Results: NewPsy4u is designed to provide access to pipelines and multiple datasets. The platform is freely accessible and its AI algorithms are released as open-source tools to promote transparency, reproducibility, and collaborative development. All AI algorithms available on the platform are offered as group or single-case second opinion tools. Multimodal patient records can be hosted in the web-platform with either open or restricted access. NewPsy4u currently includes data from 1900 patients diagnosed with various psychiatric conditions, collected at multiple time points. The platform architecture supports integration of multimodal data (sociodemographic, clinical, imaging and digital information captured by a mobile app) and serves as both a data-sharing solution and a hypothesis testing environment. Conclusions: NewPsy4u is a platform developed to support both research and clinical settings, offering an integrated suite of digital tools for psychiatry.
Universities globally face rising demand for mental health services while counseling capacity remains limited. Artificial intelligence (AI) is increasingly proposed as a scalable solution through pred...
Universities globally face rising demand for mental health services while counseling capacity remains limited. Artificial intelligence (AI) is increasingly proposed as a scalable solution through predictive analytics, conversational agents, and automated screening. However, rapid AI deployment within university mental health infrastructures raises critical ethical, governance, and equity concerns. This Viewpoint examines how AI integration may reshape institutional mental health systems and introduces a governance framework to guide responsible implementation. Synthesizing interdisciplinary literature from digital psychiatry, global mental health, health informatics, and higher education policy, we identify emerging ethical and structural risks associated with AI-driven mental health technologies. We propose the concept of algorithmic stratification: the differential classification, prioritization, or management of student populations through algorithmic systems embedded within institutional care pathways. This phenomenon operates through four interconnected mechanisms, namely algorithmic bias, surveillance disparities, infrastructural exclusion, and diminished human connection, that form a self-reinforcing cycle. Without structural safeguards, AI risks producing a two-tiered system, with augmented human therapy for privileged students and automated triage for marginalized populations. To address these challenges, we introduce the AUGMENT framework: Accessibility without surveillance, User-centered co-design, Governance and auditability, Model transparency, Equity adaptation, Non-replacement of human care, and Tiered integration. The AUGMENT framework is proposed as a formative governance tool for institutions implementing AI-supported mental health services, designed to guide iterative evaluation and adaptation. Artificial intelligence offers significant potential to enhance the scalability of university mental health services, but its integration requires strong governance safeguards. The AUGMENT framework provides a structured approach to ensure AI strengthens mental health systems while minimizing risks of algorithmic inequity. Future research should prioritize real-world evaluation of AI-supported mental health systems and develop institutional governance models emphasizing transparency, equity, and human-centered care.
Background: Informal caregivers play a central role in long-term care but face elevated risks of stress, depressive symptoms, and caregiver burden, alongside barriers to accessing preventive support....
Background: Informal caregivers play a central role in long-term care but face elevated risks of stress, depressive symptoms, and caregiver burden, alongside barriers to accessing preventive support. Low-threshold digital public health interventions may offer scalable, evidence–based support, however, evidence on feasibility, usability, and contextual fit remains limited, particularly for intersectionally diverse caregiver populations. Objective: This study describes the development and implementation of an intersectionality–informed WhatsApp–based chatbot intervention (PflegeBot) and evaluates its feasibility, usability, and acceptability among informal caregivers in Germany. Methods: A single-arm, non-randomized feasibility study with pre-post assessments was conducted between December 2023 and October 2024. Informal caregivers aged ≥18 years were recruited via community, institutional, and online channels. PflegeBot, a bilingual (German/Turkish) WhatsApp-based chatbot delivering 84 scheduled messages over 12 weeks, including psychoeducation, self-care, and CBT-informed self-help elements, plus optional FAQ and “ask” functions, was co-designed with caregivers and stakeholders. This paper reports the feasibility evaluation. Outcomes were self-assessed via online survey. Primary outcomes were usability (System Usability Scale, SUS) and user satisfaction; secondary outcomes included perceived stress (PSS-4), caregiver burden (ZBI-4), depressive symptoms (CES-D), and loneliness. Descriptive and exploratory pre-post analyses were conducted, complemented by qualitative feedback from open-ended survey items interpretation workshops. Intersectionality-informed principles guided intervention development and the Conceptual Model for the Relationship of Social Networks and Social Support to Health informed content structure and sequencing. Results: Of 140 individuals entering the study dataset, 102 (mean age of 56.2 years (SD 12.2); 82.4% women) met eligibility criteria at baseline and 70 initiated the chatbot. Sixty-seven actively received the intervention; 57 provided at least one follow-up data point and 49 completed the full follow-up survey. Mean usability was good (mean SUS 73.5, SD 13.1). Higher usability scores were descriptively observed among participants with higher educational attainment, with no consistent variation by caregiving language or caregiving characteristics. Satisfaction was high across key features. Exploratory pre-post analyses showed small reductions in perceived stress, depressive symptoms, and loneliness, alongside a modest increase in caregiver burden. Qualitative findings highlighted heterogeneity in perceived relevance and engagement depending on caregiving context and available resources. Message frequency, timing, and content personalization emerged as key aspects of acceptability. Implementation challenges included platform-related verification procedures, changing terms of service, and the introduction of messaging costs. Conclusions: A co-designed, intersectionality–informed caregiver intervention delivered via WhatsApp was feasible and acceptable in this sample. While no definitive conclusions about effectiveness can be drawn, findings support further evaluation in controlled trials and highlight important considerations for the sustainable implementation of messenger–based digital public health interventions.
Background: Temporal fluctuations in distress and suicidal ideation, across daily, weekly, and seasonal cycles, may influence the use and effectiveness of digital suicide prevention tools. Understandi...
Background: Temporal fluctuations in distress and suicidal ideation, across daily, weekly, and seasonal cycles, may influence the use and effectiveness of digital suicide prevention tools. Understanding patterns of app engagement, perceived suffering, and affective expression can inform the design of proactive, personalized digital interventions impacting on adherence and efficacy. Objective: To examine temporal patterns of engagement with two core components of the suicide prevention app SERO, specifically the safety plan and the PRISM™-S self-assessment, using three years of interaction log data, assessing variations across circadian, weekly, and seasonal cycles and evaluating the sentiment of free-text responses submitted immediately after PRISM™-S self-assessments. Methods: We analyzed anonymized interaction logs from the SERO app collected over three years (November 2022–December 2025). Engagement metrics included frequency of use of the safety planning functionality and PRISM™-S self-assessment entries. Free-text responses provided after PRISM™-S assessments were analyzed using automated sentiment classification. Temporal analyses examined variations by hour of day, day of week, and season. One-way ANOVAs, post hoc tests, and Pearson correlations were used to examine patterns and associations between perceived suffering and sentiment. Results: A total of 1,076 users engaged with the SERO app’s safety planning functionality, generating 3,502 entries, with Coping Strategies and Warning Signs showing the highest mean interactions and Personal Beliefs the lowest. Separately, 1,212 app users accessed the PRISM™-S self-assessment, producing 2,329 entries (mean distance 12.91 cm, 95% CI 12.39-13.42), with most app users recording only one or two registrations. Safety planning engagement showed clear diurnal patterns, peaking in the afternoon (14:00-15:00) and lowest at night (00:00-03:00), whereas PRISM™-S scores were stable across time. Sentiment analysis revealed predominantly negative affect (mean=-0.41), correlated with PRISM™-S distance, and most negative at night (specifically at 23:00, and 2:00-5:00). Seasonal effects were small but significant for PRISM™-S , with lowest perceived suffering in summer. Conclusions: Digital suicide prevention tools can support consistent, routine-like coping, but periods of increased vulnerability, particularly at night, may be underaddressed. Integrating automated sentiment analysis alongside self-assessments could enable personalized, time-adaptive interventions that detect changes in emotional state and deliver timely, tailored support, strengthening proactive engagement and resilience.
Background: The healthcare workforce is facing numerous challenges, with retaining skilled, qualified, and experienced practitioners being paramount. Exit interviews are a long-established practice wi...
Background: The healthcare workforce is facing numerous challenges, with retaining skilled, qualified, and experienced practitioners being paramount. Exit interviews are a long-established practice with evidence of effectiveness across a range of settings, but are rarely implemented among health professionals. Research on the use of exit interviews mirrors this pattern. This scoping review will address this gap by systematically mapping the use of exit interviews in the literature on health professionals and trainees. Objective: This protocol describes the scoping review process, with a primary question: What is known about the use of exit interviews in health professions education? Methods: This review will follow the Joanna Briggs Institute methodology and will report using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist. The review will involve systematic searches of academic and grey literature databases. The review will include any studies that utilise or describe exit interviews in the context of health professions education, broadly defined. No data limit will be applied. Titles, abstracts, and full texts will be screened in a two-stage process. At each stage, an initial pilot stage of 10% of identified sources will be reviewed by two independent screeners. A Kappa score indicating substantial agreement (0.61-0.80) will be required before the remaining screening can be completed by a single reviewer. This will be supported by the Rayyan systematic review management platform. Data extraction will be performed by one reviewer and checked by a second, with disagreements resolved by consensus. A further AI-powered verification of data extraction will be performed. The findings will map existing approaches, highlight research gaps and inform future research using exit interviews. Results: No results as this is a protocol. Conclusions: Ethics and Dissemination
This scoping review will use publicly available data; therefore, no ethical approval is required. The findings from the completed review will be submitted for publication in a peer-reviewed journal and for presentation at relevant conferences. Clinical Trial: This protocol is registered on the Open Science Framework: https://doi.org/10.17605/OSF.IO/PT2RX
Background: Rich, standardised metadata is essential for improving the findability, accessibility, interoperability, and reusability (FAIR) of health research resources. The European Health Data Space...
Background: Rich, standardised metadata is essential for improving the findability, accessibility, interoperability, and reusability (FAIR) of health research resources. The European Health Data Space (EHDS) requires harmonised catalogue metadata across countries, yet national implementations demonstrating how DCAT‑based standards can be operationalised in practice are still limited. Objective: To develop, document, and demonstrate a DCAT‑based metadata schema for a National Health Data Catalogue, using the Dutch Health‑RI National Health Data Infrastructure as a concrete implementation example, and to assess how this approach supports interoperability and FAIR‑aligned metadata publication. Methods: Metadata requirements were gathered from Dutch health institutions and mapped to DCAT, DCAT‑AP, HealthDCAT‑AP and DCAT‑AP‑NL. A multi‑stakeholder modelling process involving semantic experts, ontology engineers and data stewards produced a core schema and domain‑specific extensions. RDF and SHACL were used for validation, and FAIR Data Points enabled decentralised metadata publication. Several pilot use cases were onboarded to evaluate applicability, interoperability and usability in real‑world settings. Results: The resulting schema comprises DCAT‑aligned classes and expanded mandatory and recommended fields aligned with HealthDCAT‑AP, the metadata model supported by EHDS. The model supports both general catalogue metadata and evolving domain‑specific extensions. Pilot implementations across Dutch institutions demonstrated improved metadata consistency, enhanced resource discoverability and successful interoperability between local FAIR Data Points and the National Health Data Catalogue. Conclusions: The developed DCAT‑based schema provides a scalable, standards‑aligned foundation for a National Health Data Catalogue and supports cross‑infrastructure interoperability mandated by the EHDS. The Dutch implementation shows how structured metadata and coordinated national governance can enhance FAIRness and improve access to health research resources. The approach offers a practical template for national Health Data Access Bodies across Europe. Future work includes completing domain‑specific extensions, increasing automation in metadata generation and validation, and strengthening documentation and training to support sustainable, community‑driven adoption.
Background: Despite advances in continuous glucose monitoring (CGM) and automated insulin delivery (AID) systems, many individuals with type 1 diabetes (T1D) fail to achieve recommended glycemic targe...
Background: Despite advances in continuous glucose monitoring (CGM) and automated insulin delivery (AID) systems, many individuals with type 1 diabetes (T1D) fail to achieve recommended glycemic targets. Although behavioral factors (e.g., physical activity, sleep, diet, insulin timing) influence glucose outcomes, the behavioral context under free-living conditions remains insufficiently characterized. Objective: To examine associations between real-world behavioral patterns and glycemic outcomes in individuals with type 1 diabetes using automated insulin delivery systems by analyzing multimodal data from wearable sensors, mobile food logs, and continuous glucose monitoring. Methods: We conducted a prospective observational study involving 19 adults with T1D using AID systems over a 30-day period. Participants wore a smartwatch to capture behavioral metrics, including step counts, exercise duration, and sleep duration, and used a custom mobile application to log time-stamped food intake. Wearable and mobile app data were integrated with AID system data to construct a multimodal dataset. Behavioral–glycemic relationships were analyzed using a complementary framework combining unsupervised clustering and correlation analyses across individuals. Results: Clustering revealed distinct groups with similar overall activity and intake patterns but different percentages of time-in-range (TIR ≈ 69–86%), indicating that comparable behavioral profiles were associated with different levels of glycemic control. Insulin timing relative to meals consistently differentiated glycemic profiles, whereas physical activity and carbohydrate intake alone showed weaker separation. Correlation analysis identified average meal–bolus delay as one of the strongest behavioral correlates of glycemic outcomes, with a negative association with TIR (ρ ≈ −0.67). Sleep duration showed a moderate positive association with TIR and lower variability, while activity- and intake-related measures were strongly interrelated but less directly associated with glycemic metrics. Conclusions: Glycemic differences appear to be more closely associated with how behaviors are coordinated—particularly insulin timing relative to meals—than with exercise or carbohydrate intake alone. These findings highlight the importance of incorporating behavioral context to explain heterogeneity in real-world diabetes management and support personalized, behavior-aware strategies.
Background: Diabetes is a chronic metabolic condition, characterized by impaired blood glucose regulation. It is often linked to serious health complications and comorbidities that significantly affec...
Background: Diabetes is a chronic metabolic condition, characterized by impaired blood glucose regulation. It is often linked to serious health complications and comorbidities that significantly affect quality of life, requiring effective management, continuous monitoring, and advanced data analytics. Notably, tailored diabetes management can be enhanced by digital twins (DTs), which serve as an adaptive digital replica of patients using clinical, physiological, and lifestyle data. Objective: This review explores diabetes-related DTs by examining their patient representation levels. We aim to synthetize the current state of the art and outline the foundations of a holistic multi-level, multi-functional personalized digital twin for diabetes management. Methods: We investigate requirements for a patient-centred holistic DT and classify existing approaches into three representation levels: 1) Data representation, involving structured, context-aware, and AI-ready data architectures, that support data analysis, enable semantic interoperability, relationship extraction, and real-time bidirectional data exchange between patient and virtual replica. 2) Process representation, primarily based on mechanistic models simulating glucose-insulin-meal, and exercise-glucose dynamics. 3) Data-driven representation, focusing on individualization through predictive modelling of disease onset, adverse events, and generation of explainable, personalized recommendations. The literature is synthetized to provide a perspective of a holistic multi-level DT and to identify research gaps. Results: Digital twins accompany patients throughout their lifecycle and span a wide range of use cases from long-term disease prediction to timely prediction of severe events. However, patient-centered DTs remain at an early developmental stage. Most existing systems function primarily as simulation tools and lack comprehensive integration of data, process, and data-driven representations. Key gaps include limited use of standardized semantic data models and ontologies, insufficient real-time bidirectional architectures, and fragmented integration of mechanistic and machine learning models, which are often treated as independent rather than complementary components. Conclusions: Although digital twins show substantial potential for advancing personalized diabetes care, current implementations remain fragmented and incomplete. Future research should prioritize the development of holistic, multi-level digital twins that integrate interoperable data infrastructures, mechanistic simulations, and data-driven models into cohesive, patient-centered systems capable of supporting lifelong disease management.
Background: A gap exists between the development of digital health technologies (DHTs) and the actual needs and capabilities of patients with stroke, which could hinder the successful adoption and imp...
Background: A gap exists between the development of digital health technologies (DHTs) and the actual needs and capabilities of patients with stroke, which could hinder the successful adoption and implementation of DHTs. It is essential to understand clinical perspectives in order to effectively integrate DHTs into post-stroke care. Objective: To synthesise evidence on clinical perspectives on the use of DHTs in post-stroke self-management and rehabilitation, and to frame these perspectives within the Non-adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework. Methods: A systematic literature search was conducted across six electronic databases (PubMed, MEDLINE, Embase, Scopus, CINAHL and Web of Science) from their inception until October 2025. Two reviewers independently screened the records. Eligible studies were English-language, peer-reviewed, primary research using qualitative, quantitative or mixed methods designs, which reported on the clinical perspectives of DHTs in post-stroke care. Study quality was independently appraised using the Mixed Methods Appraisal Tool (MMAT). Qualitative content analysis was undertaken to identify and categorise determinants, which were subsequently mapped to the NASSS framework. Results: Of 15,262 records identified, 42 studies (24 quantitative, 8 qualitative, and 10 mixed-methods), published between 2019 and 2025, met the inclusion criteria. Overall, patients, caregivers, healthcare professionals, and members of the public reported generally positive attitudes towards the use of DHTs in post-stroke care. However, multiple unmet needs and expectations related to implementation were identified. Regulatory and political contexts were notably underrepresented across studies. Based on these findings, key perspectives and recommendations were synthesised to adapt and extend a conceptual model within the NASSS framework. Conclusions: Future research and implementation efforts should address the diverse needs of stroke patient subgroups when integrating digital interventions into clinical practice. Implementation strategies should explicitly assess alignment with the NASSS framework and demonstrate how stakeholder priorities and concerns are incorporated throughout the development process. Clinical Trial: The protocol was registered with PROSPERO under registration number CRD420251112158.
Background: Hormone receptor–positive (HR+) breast cancer exhibits limited and heterogeneous clinical benefit from immune checkpoint inhibitors. While peripheral blood single-cell profiling provides...
Background: Hormone receptor–positive (HR+) breast cancer exhibits limited and heterogeneous clinical benefit from immune checkpoint inhibitors. While peripheral blood single-cell profiling provides a minimally invasive approach to monitoring systemic immune dynamics, its utility in predicting treatment response remains to be fully established. Objective: This study aims to develop and evaluate a multimodal machine learning framework that integrates peripheral blood single-cell transcriptomes and T-cell receptor (TCR) encodings to predict response to chemo-immunotherapy in patients with early-stage HR+ breast cancer. Methods: I analyzed the GSE300475 cohort, comprising longitudinal samples from 4 patients (11 total samples; 100,067 cells). The feature set included principal components of gene expression, TCR k-mer and physicochemical encodings, and quality control covariates. I compared several classification algorithms, including logistic regression, tree-based baselines, and sequence-aware deep models, using leave-one-patient-out cross-validation for cell-level evaluation. Model interpretability was assessed via SHAP (SHapley Additive exPlanations) for tree models and gradient-based attributions for neural networks, with uncertainty quantified through nonparametric bootstrapping. Results: The multimodal models achieved high cell-level discrimination, with a peak area under the receiver operating characteristic curve of 0.97 and an accuracy of 91.7%. Transcriptomic signatures related to cytotoxicity and interferon response were the primary drivers of model predictions. The integration of TCR encodings provided complementary signals that improved model calibration. Sensitivity analyses confirmed the robustness of these findings to imputation and initialization variations, though the results remain exploratory due to the small cohort size. Conclusions: These proof-of-concept results suggest that combining peripheral single-cell multimodal profiling with interpretable machine learning can identify coherent predictive signatures of immunotherapy response. Future research in larger independent cohorts is necessary to validate these biomarkers for clinical use.
Background: Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental condition in children and adolescents, for which conventional treatments present certain limitations. Whil...
Background: Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental condition in children and adolescents, for which conventional treatments present certain limitations. While digital therapeutics (DTx) have developed rapidly, the relative efficacy of different DTx modalities for this population remains to be established. Objective: To systematically compare the efficacy of four digital therapeutics (DTx) modalities (single-task, cognitive-motor dual-task, AI-integrated single-task, and AI-integrated cognitive-motor dual-task) on core symptoms and executive functions in children and adolescents with ADHD within the dual framework of task design and AI empowerment. Methods: We systematically searched PubMed/MEDLINE, PsycINFO, Web of Science, EMBASE, Scopus, ProQuest Dissertations and Theses, the Cochrane Library, and grey literature from ClinicalTrials.gov for randomized controlled trials published from January 2000 to February 2026, without language restrictions. A snowballing method was also employed. Risk of bias was assessed using the Cochrane RoB 2.0 tool. Data were analyzed using Bayesian network meta-analysis in R software (version 4.2.3). Heterogeneity was assessed using I² statistics, and publication bias was evaluated using Egger's test. Subgroup analyses, meta-regression, and sensitivity analyses were performed to explore sources of heterogeneity. Results: A total of 32 studies involving 2,819 patients were included. Network meta-analysis showed that AI-integrated cognitive-motor dual-task DTx appeared to be the most effective modality for improving core symptoms and executive functions, demonstrating the highest probability of being the best treatment on the Attention Deficit/Hyperactivity Disorder-Rating Scale (ADHD-RS) [Surface Under the Cumulative Ranking Curve (SUCRA): 57.5%; Mean Difference (MD): -3.03, 95% Confidence Interval (95% CI): -5.59 to -0.47], the Swanson, Nolan, and Pelham Rating Scale, Version IV - Inattention subscale (SNAP-IV-PI) [SUCRA: 58%; MD: -5.58, 95% CI: -8.76 to -2.39], the Swanson, Nolan, and Pelham Rating Scale, Version IV - Hyperactivity-Impulsivity subscale (SNAP-IV-PHI) [SUCRA: 81.6%; MD: -6.84, 95% CI: -10.37 to -3.31], and the Behavior Rating Inventory of Executive Function (BRIEF) [SUCRA: 67.4%; MD: -7.75, 95% CI: -10.06 to -5.43]. Moreover, this modality significantly outperformed conventional pharmacotherapy across all outcomes. Subgroup analyses revealed that intervention duration emerged as a potential source of heterogeneity for the SNAP-IV (both PI and PHI subscales) and BRIEF, while mean participant age was identified as a potential source of heterogeneity for the SNAP-IV-PI and BRIEF (all P < 0.05). Sensitivity analyses indicated that individual studies influenced heterogeneity. Of note, all outcome measures reported were based on parent versions of the scales. Conclusions: AI-integrated cognitive-motor dual-task DTx may be the most effective intervention for improving core symptoms and executive functions in children and adolescents with ADHD. Subgroup analyses suggested that Intervention duration and age emerged as moderators of treatment outcomes, warranting consideration in clinical practice. Clinical Trial: CRD420261304236
Background: Health communicators and researchers are increasingly exploring partnerships with social media influencers as a strategy to improve the reach and engagement of health messaging. However, p...
Background: Health communicators and researchers are increasingly exploring partnerships with social media influencers as a strategy to improve the reach and engagement of health messaging. However, practical guidance on how health communicators can identify, recruit, and collaborate with influencers is limited. Objective: The aim of this paper is to provide a detailed description of how to work with social media influencers to disseminate health messages and to highlight lessons learned that may help others overcome challenges associated with this communication channel. Methods: We conducted a process evaluation of an Instagram influencer campaign promoting colorectal cancer screening between March and July 2025. We reviewed publicly available guidance on collaborating with social media influencers for health promotion and summarized key recommendations. Using a paid influencer marketing platform, we identified and contacted nano- and micro-influencers (1,000–50,000 followers). Participating influencers created and posted an Instagram Reel and shared it to their Instagram Story. We documented the recruitment process, vetting criteria, negotiations, content review procedures, and engagement metrics and examined associations between influencer characteristics and engagement outcomes. Results: We sent 1,907 outreach emails to potential influencers; 72 expressed interest and we negotiated terms with 52 before finalizing agreements with 22 and receiving Reels from 16 (0.84%). Outreach emails that specified compensation and project details upfront were the most effective strategy (completed posts from 2.0% of outreach emails compared with 0.7% for emails that did not include compensation and 0% for emails sent only after pre-vetting influencers). Recruiting these influencers required approximately 2.5 months of outreach and a paid influencer marketing platform subscription costing $1,647. Influencer payments ranged from $200 to $500 (mean $389). The 16 influencer videos generated 89,764 total views (mean 5,610 per video) and approximately 232 visits to the campaign website. We found no significant associations between influencer payment or follower count and video views or engagement rates. Conclusions: Partnering with influencers to disseminate health messages on social media can result in relatively high engagement with health messages, including among audiences who may not actively seek health information. However, implementing influencer campaigns using a commercial influencer marketing platform required substantial recruitment effort, including large volumes of outreach and lengthy negotiation timelines. In our campaign, fewer than 1% of outreach emails resulted in completed posts. Providing compensation and project expectations in the initial outreach email substantially improved recruitment success. Influencers with relatively fewer followers may generate similar reach and engagement at lower cost, but may be less experienced and require more clarifications in the negotiation process. Establishing clear expectations for deliverables and revisions may help prevent delays and improve content quality. The guidance in this study can help health communicators develop more realistic implementation plans and budgets when considering influencer-based health communication campaigns.
Background: Chronic primary pain is a complex condition involving biological, psychological, and behavioral mechanisms and is commonly associated with emotional distress and reduced quality of life (Q...
Background: Chronic primary pain is a complex condition involving biological, psychological, and behavioral mechanisms and is commonly associated with emotional distress and reduced quality of life (QoL). Digital mental health interventions (DMHIs) offer scalable and accessible solutions for delivering psychological care in chronic pain management; however, evidence regarding their effectiveness across delivery modalities and outcome domains remains heterogeneous. Objective: This systematic review aimed to (1) evaluate the effectiveness of DMHIs on clinical (pain intensity, disability) and psychological outcomes (QoL, anxiety, depression, catastrophizing, and self-efficacy) in adults with chronic primary pain; (2) examine whether specific digital delivery modalities are differentially associated with particular outcomes; and (3) identify methodological gaps to inform future research and implementation. Methods: A systematic literature search was conducted in PubMed, Scopus, PsycINFO, Cochrane Library, Web of Science, and Google Scholar following PRISMA guidelines. Two independent reviewers screened randomized controlled trials (RCTs) and assessed risk of bias using the Cochrane Risk of Bias 2.0 tool. Given substantial heterogeneity in study designs, interventions, and outcome measures, a narrative synthesis was performed. Results: Twenty-two RCTs were included. DMHIs were effective in improving psychological functioning and pain-related disability, often independently of changes in pain intensity, particularly when grounded in evidence-based psychotherapeutic frameworks such as cognitive behavioral therapy and acceptance and commitment therapy. Guided web-based interventions demonstrated the most consistent benefits, whereas unguided interventions showed smaller effects. Mobile applications and virtual reality–based interventions also showed positive effects on emotional functioning, self-management, and pain interference. Interventions incorporating some form of human guidance were generally associated with superior outcomes. Conclusions: DMHIs represent a promising, scalable, and person-centered approach to improving psychological well-being and functional outcomes in adults with chronic primary pain, particularly when integrated into stepped-care or hybrid care models. Clinical Trial: CRD420251010767
Background: Data and information problems (incompleteness, inaccuracy, misinterpretation, limited accessibility) continue to impair clinical decision-making, organizational processes, and interdiscipl...
Background: Data and information problems (incompleteness, inaccuracy, misinterpretation, limited accessibility) continue to impair clinical decision-making, organizational processes, and interdisciplinary collaboration in healthcare. While existing standards, data governance approaches, and human-centered design methods offer valuable guidance, they provide limited support for interdisciplinary teams to systematically identify and discuss such problems in practice. Objective: This research describes the formative development and evaluation of a human-centered framework and supporting question catalogue designed to support interdisciplinary teams in identifying and addressing data and information problems in healthcare. This framework was developed by adapting principles from ISO 9241-210 for the context of healthcare data and information work. Methods: A sequential mixed-methods design was applied across three studies. Study 1 (N = 88) explored how interdisciplinary participants understand and experience data and information problems. Study 2 (N = 14) assessed experts’ familiarity with key concepts and their perceptions of framework use for data and information problems. Study 3 comprised two online focus-group rounds (N = 15), using consensus measurement, qualitative discussion, and SWOT analysis to evaluate clarity, perceived usefulness, and interdisciplinary applicability of the framework and question catalogue. Results: Across studies, data problems were primarily associated with quality and integrity, while information problems centered on accessibility, structuring, comprehension, and communication. Participants encountered such problems frequently but rarely used systematic frameworks to address them. In Study 3, experts perceived the tailored framework and question catalogue as relevant, interdisciplinary, and useful for structured discussion. Iterative refinement improved clarity and consensus. Participants emphasized the need for layered access, prioritization mechanisms, and navigation support. Conclusions: The findings suggest that the framework and its supporting question catalogue constitute a feasible and promising early-stage tool for interdisciplinary reflection on data and information problems in healthcare. Further refinement and pilot testing in real-world settings are needed.
Background: Rates of sexually transmitted infections (STIs) are elevated for Black men who have sex with men (BMSM) and those taking HIV pre-exposure prophylaxis (PrEP), and thus STI prevention strate...
Background: Rates of sexually transmitted infections (STIs) are elevated for Black men who have sex with men (BMSM) and those taking HIV pre-exposure prophylaxis (PrEP), and thus STI prevention strategies are critically important. PCheck is a PrEP adherence app tailored for BMSM PrEP users, adapted with STI prevention features. Objective: This pilot trial was conducted to assess app acceptability, feasibility, and usability. Methods: We enrolled BMSM PrEP Users ages 18-35 in a randomized controlled pilot study (1:1 randomization, later amended to 2:1). Surveys were administered using questions from validated questionnaires. In-depth interviews (IDIs) were conducted with 12 participants to discuss specific app features and the potential of adapting PCheck for doxycycline post-exposure prophylaxis (doxy PEP). Results: From June 2022 to June 2023, 68 BMSM participants were enrolled, with 40 randomized to receive PCheck. Among these, 30 (75.0%) completed the Week 48 survey. Most participants reported that they were mostly or very satisfied with PCheck (19/30, 63.3%) and that PCheck would mostly or very much work for long-term usage (21/30, 70.0%). In IDIs, participants reported enjoying the app, particularly how it increased their accountability. Most participants had not heard of doxy PEP, but all thought it sounded beneficial. All participants thought the app would facilitate doxy PEP use, for example, by providing resources, pill reminders, sexual activity tracking, and general education about this STI prevention strategy. Conclusions: In a small pilot study, results provide encouraging evidence of PCheck’s usability and feasibility, and its potential to be further developed as an app to promote uptake and adherence to doxy PEP. Clinical Trial: NCT05395754
Background: Human papillomavirus (HPV) is a leading cause of preventable cancers, yet HPV vaccination rates remain well below national targets, particularly in rural and highly religious communities....
Background: Human papillomavirus (HPV) is a leading cause of preventable cancers, yet HPV vaccination rates remain well below national targets, particularly in rural and highly religious communities. Social media plays a dual role as both a platform for health education and a conduit for vaccine misinformation. In Utah, where internet use is high across geographic and religious lines, online discourse from news outlets may shape public perceptions of HPV vaccination. However, little is known about how the linguistic framing of HPV vaccine information varies across rural and urban, or more and less religious, online news ecosystems. Objective: This study aimed to evaluate how HPV vaccine information is framed in online news outlet posts on social media in Utah, comparing linguistic patterns across rural versus urban areas and communities with higher versus lower religiosity. Methods: We collected 851 Facebook posts related to HPV and HPV vaccination from 23 Utah-based news outlets published between March 2012 and March 2022. After removing duplicates (n=531 unique posts) and screening for relevance, 36 posts specifically addressing HPV or HPV vaccination were retained for analysis. Posts were coded by rurality (Rural-Urban Commuting Area codes) and religiosity (county-level religious affiliation prevalence) of the originating news outlet. Natural language processing was applied using Linguistic Inquiry and Word Count (LIWC-22) software to estimate emotional valence, including positive emotion, negative emotion, and overall emotionality. Narrative arc analysis was conducted to characterize the structural and psychological patterns of online discourse. Summary statistics and independent t tests compared linguistic features across geographic and religious contexts. Results: Only 5 of Utah’s 29 counties had news outlets that posted about HPV vaccination over the study period. Urban outlets produced more posts than rural ones (69.7% vs 30.3%), and nearly 70% of posts came from less religious areas. Posts from urban and less religious areas used significantly more positive language (mean 2.04 vs 1.28 words; P=.003), while posts from rural and more religious areas used more emotional language (mean 1.17 vs 0.63 words; P<.001) and more negative emotional language (mean 0.73 vs 0.42 words; P=.02). Threat-based language was significantly more prevalent in rural posts (P=.03), and rural posts contained significantly more references to death (P<.01) and religion (P<.01). Narrative arc analysis revealed that online HPV vaccine discourse followed a consistent structural pattern with descriptive, information-rich comment sections, fluctuating cognitive tension, and a conclusive, nonprogressive ending. Conclusions: In Utah, rural and religious communities are exposed to HPV vaccine information that is less positive and more emotionally charged than that in urban and less religious communities. These linguistic patterns may reinforce vaccine hesitancy and contribute to persistently low HPV vaccination rates in underserved areas. Tailored public health messaging that accounts for the emotional and cultural dimensions of vaccine discourse in rural and religious communities is needed to improve HPV vaccine equity.
Background: Passive exoskeletons are implemented in occupational settings to reduce physical strain and muscle activity during demanding tasks. Surface electromyography (sEMG) is commonly used to quan...
Background: Passive exoskeletons are implemented in occupational settings to reduce physical strain and muscle activity during demanding tasks. Surface electromyography (sEMG) is commonly used to quantify these effects, but sEMG signal acquisition in exoskeleton applications is often compromised by mechanical artifacts from exoskeleton–sensor interaction. Existing standards for sEMG electrode placement and data processing rarely address these challenges. Additionally, extensive signal filtering can remove relevant muscle activity information. Objective: This experimental study aimed to evaluate custom 3D-printed covers (‘boxes’) for dry sEMG electrodes, designed to protect sensors from simulated exoskeleton related perturbations. We hypothesized that these covers would reduce motion artifacts and improve signal integrity without affecting skin physiology. Methods: Twenty-three healthy adults participated. Dry sEMG electrodes (Delsys Trigno Mini/ Avanti) were attached to the right and left vastus medialis muscles. A 3D printed cover (‘small Box or large Box’ depending on sensor size) was attached to the skin over the sensors. Each participant performed knee extension tasks under six conditions, combining two cover states (NoBox, Box) and three types of mechanical perturbation (none, vertical load, lateral load). Loads of 3.35 kg simulated typical exoskeleton-induced disturbances. sEMG data were rectified and normalized to maximal voluntary contraction (%MVC). Signal quality was evaluated using RMS amplitude in %MVC, standard deviation, frequency-domain SNR via fast Fourier transformation (FFT), and time synchronization using dynamic time warping (DTW). Skin temperature and electrical resistance due to skin moisture were monitored. Linear mixed-effects models and paired t-tests were used for analysis. Results: Application of the boxes resulted in reduced mean muscle activation during mechanical perturbations (vertical: –3.61% MVC, lateral: –2.17% MVC; both P <.001), lower within-trial variability (e.g., lateral perturbation ΔSD: –5.86, P <.001), and higher SNR compared to uncovered conditions (ΔSNR up to +1.10, P <.001). Skin temperature, resistance, and baseline noise were not influenced by the box (all P >.05). The large cover yielded inconsistent results, with some reduction in variability during perturbation (lateral ΔSD: –2.86, P <.001), but lower SNR and more variability in unperturbed trials compared to NoBox. Conclusions: The small 3D-printed box stabilizes the sEMG signal by mechanically shielding the sensor from external disturbances and reducing motion artifacts. Consistency and reliability of sEMG recordings are enhanced without negatively affecting local skin temperature and resistance. The medical adhesive, used to attach the box to the skin, further improves signal quality by limiting lateral dissipation and providing a localized measurement area beneath the box. In contrast, inadequate anatomical fit of the large box may introduce additional variability rather than reduce disturbances. Our study demonstrates that a small, anatomically designed protective cover can effectively reduce motion artifacts and improve sEMG signal quality during mechanical perturbations, without interfering with physiological skin parameters. This approach represents a promising method for enhancing the reliability of sEMG measurements in exoskeleton studies. Clinical Trial: German Clinical Trials Register (Germany), DRKS00035777; https://drks.de/search/en/trial/DRKS00035777
Background: Online patient reviews are widely used by consumers to assess the quality of direct-to-consumer teleconsultation (DTCT) services, particularly in settings where objective quality informati...
Background: Online patient reviews are widely used by consumers to assess the quality of direct-to-consumer teleconsultation (DTCT) services, particularly in settings where objective quality information is limited. However, whether these reviews validly reflect actual clinical and patient-centered care quality remains unclear. Objective: This study aimed to evaluate the validity of online physician reviews in reflecting the quality of care delivered on China’s three largest DTCT platforms. Methods: We conducted a cross-sectional study using unannounced standardized patients (USPs) to objectively assess the quality of DTCT services. Thirty-three USPs were trained to present 11 standardized clinical cases and completed 542 DTCT consultations between physicians on three major Chinese platforms. Technical quality was assessed using a clinical guideline adherence checklist, and patient-centered quality was measured using the Patient–Patient-Centered Care Chinese version (PPPC-CN) scale. Online review quality was defined as the positive review rate displayed on each physician’s profile. Agreement between online reviews and measured quality was evaluated using Intraclass Correlation Coefficients (ICCs), with additional rank correlation analyses. Results: Of the 542 consultations initiated, 530 were completed and 404 physicians had publicly available review data. Among all encounters, 53.14% (288/542) were phone-based and 46.86% (254/542) were text-based consultations. The median positive review rate was 99.9% (interquartile range [IQR], 99.4%–100%). Median guideline adherence was low (0.16; IQR, 0.08–0.26), and median patient-centered quality was modest (PPPC-CN score 2.1; IQR, 1.98–2.79). Diagnoses were completely correct in 40.92% (196/530) of consultations. Unnecessary examinations occurred in 1.7% of encounters, and medication prescribing was appropriate in 79.04%. The median consultation time was 13 minutes (IQR, 7–64.69), and the median registration fee was 29.9 yuan (IQR, 26.1–39.9). Agreement between positive review rate and guideline adherence (ICC= 0.002; 95% CI, −0.006 to 0.013) and between positive review rate and patient-centered quality (ICC= 0.014; 95% CI, −0.043 to 0.083) was negligible and far below accepted validity thresholds. Correlations between positive review rate and diagnostic accuracy were weak but statistically significant (Spearman ρ = 0.168; Kendall τ = 0.141; both P < 0.05). Limitations include the use of standardized cases rather than real patients and the focus on publicly visible review metrics. Conclusions: Online reviews on major platforms were overwhelmingly positive but showed almost no alignment with actual provider performance. DTCT providers demonstrated low guideline adherence and modest patient-centered quality. More research on improving the review frameworks is urgently needed to fill the gap between patient feedback and service quality. Clinical Trial: The study has been approved by the Southern Medical University Ethics Committee ([2022] No. 013) and registered with the China Clinical Trial Registry (ChiCTR2200062975).
Background: The volume of biomedical evidence makes it difficult for physicians to access up-to-date information quickly during routine practice. Large language models (LLMs) have shown promise for cl...
Background: The volume of biomedical evidence makes it difficult for physicians to access up-to-date information quickly during routine practice. Large language models (LLMs) have shown promise for clinical support, but most evaluations use multiple-choice or simulated settings and do not assess real-world use by practicing clinicians. Evidence-traceable tools that combine LLMs with real-time retrieval of curated sources could support clinical question-answering; external validation of such tools in routine practice is lacking. Objective: To evaluate the effect of Arkangel AI use on response time and the validity of physicians’ answers to open-ended clinical questions, compared with traditional search methods without artificial intelligence support. Methods: Physicians were randomly assigned to two study groups—Group A (Arkangel AI–assisted search) and Group B (traditional search methods). A total of 202 physicians initiated the study, and 71 completed all responses and were included in the final analysis. Each participant solved four clinical cases, each comprising four open-ended questions. Responses were evaluated by clinical specialists blinded to group assignment using six predefined validity criteria. The association between Arkangel AI use and response validity was assessed using multivariable logistic regression, adjusting for academic and sociodemographic characteristics. Results: Physicians who used Arkangel AI had higher validity scores than those using traditional search. For total validity, the median was 2.83 (IQR 2.52–3.00) in Group A and 2.46 (IQR 2.21–2.67) in Group B (median difference 0.38; 95% CI 0.17–0.54; Mann-Whitney U test, P<.001). The effect size was large (Cliff delta 0.59; 95% CI 0.34–0.80), with a 79% superiority probability for Group A. In the multivariable model, the association between Arkangel AI use and higher response validity showed a positive trend (adjusted OR 2.42; 95% CI 0.82–7.16) but did not reach statistical significance (P=.11). Response times were comparable between groups, with no significant difference in time per question or number of searches. Conclusions: LLM-assisted clinical search with Arkangel AI was associated with higher response validity and comparable response times in this sample of practicing physicians. The findings support the potential role of evidence-based conversational agents as decision-support tools in medical education and clinical practice and justify further studies with larger samples. Clinical Trial: N/A
Background: Breast cancer has become the most common cancer worldwide, and newly diagnosed breast cancer patients are in particular need of health information. The results of this study will help clin...
Background: Breast cancer has become the most common cancer worldwide, and newly diagnosed breast cancer patients are in particular need of health information. The results of this study will help clinical providers understand the complete process related to online health information behavior among newly diagnosed breast cancer patients and guide them in developing targeted information behavior intervention. Objective: The purpose of this study was to identify online health information behavior patterns in newly diagnosed breast cancer patients. The purpose of this study was to identify the specific characteristics of patients' online health information behaviors using a grounded theory approach, with the goal of ultimately identifying a behavioral pattern that depicts the trajectories of change among these behaviors. Methods: Thirty-two patients with newly diagnosed breast cancer who underwent breast surgery at The First Affiliated Hospital of Anhui Medical University were interviewed semistructurally per procedural grounded theory from August 2021 to May 2022. The data were processed through three-level coding, continuous comparison, and dimensional analysis until theoretical saturation was achieved. This manuscript adheres to the COnsolidated criteria for REporting Qualitative research (COREQ) guidelines. Results: An online health information behavior pattern was identified for newly diagnosed breast cancer patients. The complete online health information behaviors (core categories) of patients during the period from breast cancer diagnosis to treatment included the following aspects: information acquisition behavior, including active information search, avoidance of information, and encounter information; information evaluation behaviors, including information evaluation and no evaluation; information processing behaviors, including information rejection, information storage, and information utilization; and information processing outcomes, including information termination and continuous searching for information. Conclusions: This study identified a pattern of online health information behavior for newly diagnosed breast cancer patients. The findings will help clinical health care professionals understand the complete process related to online health information behavior among newly diagnosed breast cancer patients, discover entry points for interventions for online health information behavior , and develop targeted information behavioral interventions to help patients improve their e-health literacy, enhance their decision-making ability and information utilization effectiveness, and promote their recovery.
Background: The rapid integration of Artificial Intelligence (AI) in healthcare, particularly for diabetes risk prediction, holds significant promise for improving patient outcomes. However, the "blac...
Background: The rapid integration of Artificial Intelligence (AI) in healthcare, particularly for diabetes risk prediction, holds significant promise for improving patient outcomes. However, the "black-box" nature of deep learning models remains a primary barrier to clinical adoption. While the field of Explainable AI (XAI) has introduced numerous techniques to enhance transparency, clinical adoption continues to be stifled by a fundamental reliance on clinician trust. Despite an increase in research focusing on technical interpretability, there remains a critical measurement gap: existing literature frequently fails to evaluate or quantify how specific XAI techniques such as SHAP (Shapley Additive Explanations) and Counterfactual Analysis actually influence the multi-dimensional constructs of trust held by medical professionals. Without mapping technical explanations to validated human-centric trust models, the clinical utility of XAI remains theoretical rather than evidentiary. Objective: The primary objective of this study is to evaluate the impact of different XAI modalities on clinician trust within the context of diabetes prediction. Specifically, this research seeks to operationalize and map SHAP (feature attribution) and Counterfactual (actionable "what-if" scenarios) explanations to the Asan et al (2020) Trust Framework. The study aims to determine how these specific explainability techniques influence the clinicians trust.
Ultimately, this work aims to provide a structured approach for measuring the effectiveness of XAI in fostering the professional trust required for real-world clinical deployment. Methods: The study was conducted in four distinct phases. First involved a foundational exploration of trust measurement by integrating psychological, behavioral, and cognitive aspects of human-AI interaction. Next, these general trust constructs were aligned with specific clinical trust measurement requirements to filter out essential trust measurement metrics.
Phase 3 involved the development of a structured clinician trust measurement questionnaire, specifically tailored to evaluate the SHAP and Counterfactual explanations generated by decision-support model of diabetes prediction model of our previous work titled "Enhancing Clinical Trust in Diabetes Prediction: A Multi-Directional Counterfactual and SHAP-based Decision Support Model".
Phase 4 consisted of primary data collection from practicing clinicians using a hybrid approach of Google Forms and hard-copy questionnaires. Participants evaluated the model’s explanations against real patient profiles collected from Hiwot Fana Comprehensive Specialized University Hospital. Finally, the collected data were analyzed to quantify the relationship between XAI modalities and the filtered clinical trust measurements. Results: Clinicians reached a positive consensus on core diagnostic pillars, specifically where SHAP-driven feature importance and Counterfactual Analysis aligned with clinical intuition. This transparency bolstered decision certainty and appropriate reliance. However, a "trust ceiling" persisted regarding outlier resilience, as clinicians remained skeptical of the model’s performance with atypical lab results and data asymmetry. While institutional accreditation was the strongest driver of overarching trust, cloud-connectivity bottlenecks hindered platform suitability. Qualitatively, practitioners advocated for "Communicative AI," favoring structured written summaries over abstract visual plots for faster bedside interpretation. Conclusions: Clinician trust in diabetes AI is multi-dimensional and conditional, rather than binary. While explainability (XAI) fosters informed certainty, Institutional Accreditation remains the primary catalyst for professional adoption. The disparity between high logical trust and low outlier resilience suggests that clinicians currently view AI as a "routine collaborator" rather than a surrogate for complex edge-case reasoning. To bridge the remaining trust gaps, future developments must move beyond abstract visualizations toward "Communicative AI" utilizing natural language summaries and edge-computing architectures to ensure the tool is both medically intuitive and infrastructure-resilient in resource-limited settings. Clinical Trial: First
Background: Despite the fact that the primary cause of adolescent dental caries is high levels of sugar intake, a significant portion of digital interventions lacks the potential to affect the domesti...
Background: Despite the fact that the primary cause of adolescent dental caries is high levels of sugar intake, a significant portion of digital interventions lacks the potential to affect the domestic settings where eating patterns are formed. New mHealth tools are sometimes lacking in theoretical richness and omissive of the family unit, providing a great gap in behavioral support. Objective: The aim of this objective was to address these constraints by creating and testing the Family-Centered Sugar-Control Mini-Program (FCSC-MP) achievement of providing sustained sugar intake control by applying the theory-informed family collaboration via a WeChat-based platform. Methods: we operationalized an integrated behavioral model that is a synthesis of the Self-Determination Theory (SDT), Theory of Planned Behavior (TPB), and Social Cognitive Theory (SCT). To enhance this scheme, we organized two rounds of Delphi consultation involving 15 professionals in the field of public health, clinical dentistry as well as medical informatics. Scientific rigour and feasible assessment of the program were conducted with the help of the Kendall coordination coefficient (W) and the content validity indices (I-CVI, S-CVI). Results: A high level of expert response (100%) was also achieved and high consensus (Kendall W = 0.105-0.107, P＜0.05). Remarkably enough, the last six-module framework, including Family Account & Sharing, Adaptive Goal Management, Gamified Challenges, a Knowledge Hub, Multi-Tiered Rewards, and a Progress Dashboard demonstrated good content validity as the S-CVI of the framework has an average of 0.93. Combined, these modules serve as an ecosystem with synergy, where family support becomes not a passive element, but a deliberate intervention mechanism. Conclusions: Our work proves the fact that FCSC-MP is able to bridge the gap between abstract behavior theory and user-oriented digital design. Through a set of multi-theory mechanisms embedded in a unified, family-based unit of digital functionality, we provide a scalable and tested framework of how to handle the eating habits of adolescents in the digital era. Clinical Trial: N/A
Background: Hypoglycemia is an acute diabetic condition in which blood glucose drops below 70 milligrams per deciliter. Consequences of hypoglycemia include seizures, coma, and death. Hypoglycemia is...
Background: Hypoglycemia is an acute diabetic condition in which blood glucose drops below 70 milligrams per deciliter. Consequences of hypoglycemia include seizures, coma, and death. Hypoglycemia is easily avoided if emerging episodes are identified early enough, so accurate prediction can be highly beneficial to patients with diabetes. Prior work shows that timely prediction and intervention of hypoglycemic episodes is possible with continuous glucose monitoring (CGM) data and deep learning technologies. However, deep learning typically requires substantial amounts of training data, motivating aggregation of CGM data from many patients, while CGM data itself is highly sensitive and may not be easily shared. To address this tension between data needs and privacy, we develop an ensemble-based federated learning approach (FedEnsemble) for hypoglycemia prediction that requires no sharing of raw CGM data. In our framework, nodes in a centralized federated learning network use averaged model weights of all nodes as their initial weights for local training, and at the end of each communication round, the central server sends updated local model weights to all nodes, which then form an ensemble model for evaluation. On temporal validation within an 89-patient type 1 diabetes cohort, FedEnsemble achieves a balanced accuracy of 86.90%, which is within 0.02% of the gold-standard centralized model (trained on pooled data) and outperforms baseline federated averaging (FedAvg) by several percentage points. We further evaluate generalization in two settings: (1) a patient-disjoint holdout of 22 unseen patients from the same clinical population, and (2) an external AZT1D cohort, where models trained on 89 pediatric patients (ages 1.5–20 years) from Texas Children’s Hospital [1] are applied without retraining to older adults (ages 40–80 years) on automated insulin delivery in AZT1D [2]. In both scenarios, FedEnsemble maintains high balanced accuracy and consistently outperforms FedAvg, with lower false positive and false negative rates. Thus, our proposed privacy-preserving federated ensemble method not only matches centralized performance on the training cohort, but also generalizes well to new patients and an age-shifted external cohort, making it a promising contribution to AI-based healthcare. Objective: Among people with diabetes, hypoglycemia, or low blood sugar, can lead to seizure, coma, and death. It is a short-term condition that can emerge quickly and with little warning. Prediction of emerging hypoglycemia using real-time continuous glucose monitoring (CGM) data is an active area of research. Deep learning technologies have been successful for building prediction models, but these require significant amounts of data, often more than can be provided by a single patient. Thus, data from many patients is typically combined to train and test such models. But CGM is private health data, sometimes not easily shared. Thus, federated learning is useful for maintaining privacy. This paper proposes a federated ensemble approach that avoids data sharing. We show that it achieves performance equivalent to centralized deep learning with all data combined. Methods: Ensemble-based Federated Learning
This work proposes a federated framework (FedEnsemble) that incorporates ensemble learning using the Snorkel technique. As with the FedAvg algorithm, during the federated training process, at the beginning of each communication round, the central server sends global model weights to all nodes and the nodes train on their local training data for a certain number of epochs. The updated weights are then sent back to the server, which takes a weighted average to update the global model. At the end of each federated learning round, the central server distributes all trained model weights to all nodes. At each node, these trained models are considered as weak classifiers and are used to predict on its test data.
These weak classifiers are chosen as components for the ensemble model. The predictions of these models are combined as a column matrix. In the column matrix, number of rows is equal to the number of data points and columns are binary prediction labels of each of the models. This label column matrix is sent as input to Snorkel. Snorkel learns from these labeling functions and outputs an integrated label for each of the data points in a node’s test data.
If the ensemble model’s performance metrics are above a certain threshold or if it reaches convergence, the federated learning process is terminated else the above process repeats until termination criterion is met. The architecture of ensemble federated learning algorithm is shown in Figure 8.
The major difference between FedAvg algorithm and our proposed method is that a global model with averaged model weights is used for evaluation at each node in the former while an ensemble model built by Snorkel using predictions of trained individual models are used for evaluation in our method. The aggregating local models tend to over-fit and suffer from high variance during training and prediction when local datasets are heterogeneous by size or distribution [54, 55]. Also, the ensemble model has more parameters than the single global model, they can store a lot of information and hence they perform better than the global model. Results: Results
2.1.1 Temporal Validation Results
In this experiment, we perform temporal validation on the same 89-patient cohort, training each model on earlier CGM data and evaluating on temporally held-out data from the same patients.
Table 2 compares the Central, FedEnsemble, and FedAvg models in terms of balanced accuracy, sensitivity, and specificity, averaged across patients. FedEnsemble attains a balanced accuracy of 86.90% (SD 5.60%), which is essentially identical to the Central model (86.92% 4.94%) and clearly higher than FedAvg (82.48% 6.42%). FedEnsemble also achieves slightly higher sensitivity than the Central model (87.25% vs. 86.12%) with comparable specificity (86.56% vs. 87.72%), and both sensitivity and specificity are superior to those of FedAvg (81.67% and 83.28%, respectively).
Table 3 reports the corresponding false positive and false negative rates. Relative to FedAvg, FedEnsemble substantially reduces both FPR (13.44% vs. 16.72%) and FNR (12.75% vs. 18.33%). Compared with the Central model, FedEnsemble trades a modest increase in FPR (13.44% vs. 12.28%) for a lower FNR (12.75% vs. 13.88%). Overall, these temporal validation results indicate that FedEnsemble closely matches the performance of the Central model while outperforming standard FedAvg, despite not requiring centralized aggregation of raw patient data.
Figure 9 shows how balanced accuracy varies across patients with different levels of hypoglycemic readings in their data. The figure uses color to indicate patients for which FedEnsemble outperformed the central model (red when FedEnsemble performs best, blue when the central model performs best). Finally, Figure 10 illustrates the balanced accuracy as a function of the number of communication rounds for FedEnsemble and FedAvg.
Table 2: Classification metrics: Balanced accuracy (BA), sensitivity and specificity of Central, FedEnsemble and FedAvg models with Standard Deviation (SD)
BA ± SD Sensitivity ± SD Specificity ± SD
Central 86.92% ± 4.94% 86.12% ± 5.30% 87.72% ± 5.21%
FedEnsemble 86.90% ± 5.60% 87.25% ± 6.53% 86.56% ± 4.96%
FedAvg 82.48% ± 6.42% 81.67% ± 6.69% 83.28% ± 6.43%
Table 3: Classification metrics: False positive rate (FPR) and False negative rate (FNR) of Central, FedEnsemble and FedAvg models with Standard Deviation (SD)
FPR ± SD FNR ± SD
Central 12.28% ± 5.21% 13.88% ± 5.30%
FedEnsemble 13.44% ± 5.05% 12.75% ± 6.83%
FedAvg 16.72% ± 6.82% 18.33% ± 7.29%
Figure 9: Comparing Percentage Improvement in Balanced Accuracy of FedEnsemble over FedAvg for Patients in Different Hypo-percentage Ranges
Figure 10: Rounds vs Balanced Accuracy: Comparing Performances of FedAvg And FedEnsemble Models
2.1.2 Within-Dataset Generalization on a Patient-Disjoint Holdout (n=22)
In this experiment, we assess within-dataset generalization by evaluating the pre-trained FedEnsemble and FedAvg models on a patient-disjoint holdout cohort of 22 patients from Texas Children's Hospital. Both models are trained on the remaining patients in the cohort and then applied, without further retraining, to these 22 unseen patients.
Table 4 summarizes the resulting classification performance of FedEnsemble and FedAvg in terms of balanced accuracy, sensitivity, and specificity, averaged across the 22 holdout patients. FedEnsemble achieves higher balanced accuracy than FedAvg (88.80% vs. 86.69%), along with slightly higher sensitivity (83.63% vs. 82.29%) and specificity (93.96% vs. 91.09%), with comparable standard deviations. These findings indicate that FedEnsemble provides more accurate and reliable hypoglycemia prediction than standard FedAvg on unseen patients drawn from the same clinical population.
Table 5 reports the corresponding false positive and false negative rates for the same holdout cohort. FedEnsemble attains a lower false positive rate than FedAvg (6.03% vs. 8.90%) and a slightly lower false negative rate (16.36% vs. 17.70%), showing that the gains in balanced accuracy translate into fewer misclassifications in both classes for these disjoint patients. Finally, Figure 11 further illustrates how balanced accuracy evolves over communication rounds for FedEnsemble and FedAvg when the models, trained on the non-holdout patients, are evaluated on this 22-patient test cohort. Finally, Figure 11 further illustrates how balanced accuracy evolves over communication rounds for both FedEnsemble and FedAvg.
Table 4: Classification metrics: Balanced accuracy (BA), sensitivity and specificity of Central, FedEnsemble and FedAvg models with Standard Deviation (SD)
BA ± SD Sensitivity ± SD Specificity ± SD
FedEnsemble 88.80% ± 12.05% 83.63% ± 20.45% 93.96% ± 5.07%
FedAvg 86.69% ± 11.39% 82.29% ± 19.48% 91.09% ± 6.42%
Table 5: Classification metrics: False positive rate (FPR) and False negative rate (FNR) of Central, FedEnsemble and FedAvg models with Standard Deviation (SD)
FPR ± SD FNR ± SD
FedEnsemble 6.03% ± 5.07% 16.36% ± 20.45%
FedAvg 8.90% ± 6.42% 17.70% ± 19.48%
Figure 11: Rounds vs Balanced Accuracy (Within-dataset patient-disjoint holdout (n=22)): Comparing Performances of FedAvg And FedEnsemble Models
2.1.3 AZT1D Patients Results
In this experiment, we evaluate the generalization performance of the pre-trained FedEnsemble and FedAvg models on the external AZT1D cohort. Both models are trained on the 89-patient cohort from Texas Children's Hospital [1] and then applied, without further retraining, to the AZT1D patients.
Table 6 summarizes the resulting balanced accuracy, sensitivity, and specificity, averaged across AZT1D patients. When transferred to this external cohort, FedEnsemble achieves higher balanced accuracy than FedAvg (88.32% vs. 86.40%), as well as slightly higher sensitivity (86.61% vs. 86.08%) and noticeably higher specificity (90.02% vs. 86.71%), with similar standard deviations. These findings indicate that FedEnsemble generalizes better than FedAvg to the AZT1D population.
Table 7 reports the corresponding false positive and false negative rates. FedEnsemble attains a lower false positive rate than FedAvg (9.98% vs. 13.29%) and a slightly lower false negative rate (13.39% vs. 13.92%), showing that the gains in balanced accuracy translate into fewer misclassifications on this external dataset. Figure 12 further illustrates how balanced accuracy evolves over communication rounds for FedEnsemble and FedAvg when the models, pre-trained on the Texas Children's Hospital cohort, are evaluated on the AZT1D patients. Finally, Figure 12 plots balanced accuracy versus communication rounds for the pre-trained FedEnsemble and FedAvg models when evaluated on the AZT1D cohort.
Table 6: Classification metrics: Balanced accuracy (BA), sensitivity and specificity of Central, FedEnsemble and FedAvg models with Standard Deviation (SD)
BA ± SD Sensitivity ± SD Specificity ± SD
FedEnsemble 88.32% ± 4.74% 86.61% ± 5.66% 90.02% ± 4%
FedAvg 86.40% ± 4.62% 86.08% ± 4.62% 86.71% ± 4.69%
Table 7: Classification metrics: False positive rate (FPR) and False negative rate (FNR) of Central, FedEnsemble and FedAvg models with Standard Deviation (SD)
FPR ± SD FNR ± SD
FedEnsemble 9.98% ± 4% 13.39% ± 5.66%
FedAvg 13.29% ± 4.69% 13.92% ± 4.62% Conclusions: In this work, we address the problem of predicting hypoglycemia in a setting where continuous glucose monitoring (CGM) data cannot be freely shared across patients or institutions. To this end, we develop a federated ensemble learning architecture (FedEnsemble) that allows each patient to train a local model on their own data while sharing only model parameters. These locally trained models are then combined in an ensemble, so that each patient can benefit from the information contained in the broader cohort without exposing their raw data.
Across the 89-patient cohort, FedEnsemble achieves predictive performance that is essentially equivalent to a centralized model trained on pooled data, while clearly outperforming standard federated averaging (FedAvg) in terms of balanced accuracy and related classification metrics. In additional analyses, we also observe that using a smaller subset of influential nodes as ensemble components can further improve performance, suggesting that not all nodes contribute equally to the final prediction quality. Taken together, these results demonstrate that federated ensemble learning is a practical and privacy-preserving way to deliver high-quality hypoglycemia prediction, and they point toward future work on selectively weighting or choosing nodes to further enhance performance and efficiency.
Background: Approximately 1 in 100 children worldwide are diagnosed with Autism Spectrum Disorder (ASD), and 46% to 89% experience significant feeding difficulties. Mobile health applications (mHealth...
Background: Approximately 1 in 100 children worldwide are diagnosed with Autism Spectrum Disorder (ASD), and 46% to 89% experience significant feeding difficulties. Mobile health applications (mHealth apps) are a potential tool for scalable support. However, there is nascent literature on the quality and relevance of mHealth apps for managing ASD-related feeding challenges. Objective: This study aimed to identify and evaluate the scope, content, and quality of free English-language mHealth apps available in the Africa region for addressing feeding difficulties in children with ASD. Methods: A systematic search was conducted on the Apple App Store and Google Play Store between September and October 2024. Applications were included if they were free, in English, updated within the past year, explicitly focused on feeding in children with autism, available in the Africa region, and had more than 100 downloads. We identified intervention strategies in the included apps using the Behavior Change Wheel (BCW) framework and evaluated their quality using the Mobile App Rating Scale (MARS). Results: Of the 326 applications identified, only two iOS apps (EduKitchen-Toddlers and Autism Food Coach 2) met all inclusion criteria. Relaxing the inclusion criteria did not yield additional results. EduKitchen–Toddlers Food Games provided child-centered interactive games and sensory-friendly visuals, while Autism Food Coach 2 provided structured caregiver tools, visual meal plans, and progress tracking. Both apps aligned with multiple BCW intervention functions. EduKitchen–Toddlers provided education, training, enablement, incentivization, and modeling intervention functions, while Autism Food Coach 2 provided education, training, enablement, persuasion, incentivization, and environmental restructuring based on the Behavior Change Wheel framework. The apps received MARS scores of 3.7 and 3.9, respectively, indicating acceptable to good usability and content quality. The apps’ shortcomings included limited customization for diverse user needs and the absence of documented clinical validation. Conclusions: There is a critical shortage of mHealth apps for feeding difficulties in children with ASD that are both evidence-based and of high quality. Future development must integrate robust clinical validation and comprehensive, caregiver-centered support features to address this significant gap. Clinical Trial: Not applicable
Background: In technically constrained clinical settings, conventional electronic health record (EHR) storage and data extraction methods often limit the implementation of real-time analytics for moni...
Background: In technically constrained clinical settings, conventional electronic health record (EHR) storage and data extraction methods often limit the implementation of real-time analytics for monitoring clinical performance measures. These limitations pose challenges for interoperability between EHR systems and analytics platforms, particularly in contexts with restricted time, resources, and specialized technical personnel. Objective: To describe the design and implementation of an EHR-driven, web-based data collection workflow for real-time analytics of acute myocardial infarction care, and to assess its feasibility, data completeness, and ability to generate actionable clinical and managerial indicators in routine practice within a high-complexity healthcare institution. Methods: A descriptive implementation study was conducted integrating the institutional EHR with REDCap to support structured data collection, automated data extraction, and longitudinal follow-up. The workflow incorporated rule-based patient identification, manual validation by clinical staff, and interoperability with a business intelligence platform to enable real-time visualization of predefined clinical performance measures. Results: The implemented workflow enabled automated identification and daily updating of patients with suspected acute myocardial infarction, structured longitudinal data capture, and real-time visualization of clinical and operational indicators through interactive dashboards. The system demonstrated feasibility in routine practice, acceptable data completeness, and adaptability to clinical and managerial information needs without requiring additional data warehousing infrastructure. Conclusions: An EHR-driven, web-based data collection workflow integrated into routine acute myocardial infarction care is feasible and supports the generation of real-time clinical and managerial indicators. This approach provides a practical framework for data-driven monitoring and quality improvement in high-complexity healthcare settings with limited technical resources.
Background: Septic arthritis constitutes a rheumatologic emergency that necessitates prompt and precise diagnosis across various medical specialties. The potential for neurosymbolic multi-LLM architec...
Background: Septic arthritis constitutes a rheumatologic emergency that necessitates prompt and precise diagnosis across various medical specialties. The potential for neurosymbolic multi-LLM architectures, which integrate neural language models with formal knowledge-graph reasoning, to match the expertise of board-certified specialists and to outperform single-model (uni-LLM) approaches in clinical vignettes of septic arthritis remains an area for further investigation. Objective: This study aimed to evaluate the diagnostic reasoning performance of SepticJoint-Reason, a multi-layer neurosymbolic pipeline, compared with board-certified specialists and constituent uni-LLMs on American Board–style septic arthritis questions, with emphasis on hallucination elimination and the neurosymbolic advantage over both human experts and standalone AI models. Methods: We developed SepticJoint-Reason, a five-stage neurosymbolic pipeline integrating Claude Opus 4.5 (Anthropic), GPT-4.1 (OpenAI), and Gemini 2.5 Pro (Google DeepMind) with a Neo4j musculoskeletal infection ontology (52,418 nodes; 203,672 edges), Lean 4–style proof-trace generation, adaptive compute allocation, and hallucination blocking. We benchmarked SepticJoint-Reason against 30 board-certified specialists (10 rheumatologists, 10 orthopedic surgeons, 10 infectious disease physicians) and against each constituent uni-LLM on 30 American Board–style septic arthritis questions across six clinical subtypes: etiology and risk factors, clinical presentation and diagnosis, synovial fluid analysis, microbiology and laboratory, management and antibiotic therapy, and complications and prognosis. Analyses included non-inferiority testing (δ = 5%), Fleiss’ κ (30 raters), Cohen’s κ (435 pairs), inter-specialty ANOVA, item-level concordance, question-subtype analysis, neurosymbolic versus uni-LLM comparisons, error typology with Fisher’s exact tests, ablation with McNemar’s tests, and counterfactual robustness analysis. Results: SepticJoint-Reason correctly answered 27 of 30 questions (90.0%; 95% CI, 73.5–97.9). The pooled specialist panel achieved a mean accuracy of 76.8% (95% CI, 74.0–79.4), with individual scores ranging from 63.3% to 90.0%. The pipeline met the non-inferiority threshold and demonstrated statistical superiority (difference, +13.2 percentage points; 95% CI, 7.1–19.3; P<0.001). Uni-LLM accuracies were: Claude Opus 4.5, 73.3%; GPT-4.1, 70.0%; Gemini 2.5 Pro, 66.7%—all significantly inferior to the neurosymbolic pipeline (all McNemar P<0.05). Fleiss’ κ was 0.38 (fair-to-moderate). Question-subtype analysis revealed the pipeline’s greatest advantage on management and antibiotic therapy items (100% vs. 72.0%; P = 0.009) and microbiology questions (100% vs. 70.7%; P = 0.014). Inter-specialist ANOVA showed significant group differences (F = 8.94; P<0.001), with infectious disease specialists achieving the highest accuracy (81.3%). Ablation confirmed knowledge-graph verification as the dominant accuracy driver (+10.0%; McNemar P = 0.004). Conclusions: A neurosymbolic multi-LLM reasoning pipeline significantly outperformed both board-certified specialists and constituent uni-LLMs on American Board–style septic arthritis questions. Knowledge-graph–grounded verification and multi-model consensus were the primary drivers of the neurosymbolic advantage, particularly on complex management and microbiological reasoning items.
Background: Transition-age youth (18-25 years) intersecting with the criminal legal system face compounding challenges, including untreated behavioral health conditions and fragmented care systems. Wh...
Background: Transition-age youth (18-25 years) intersecting with the criminal legal system face compounding challenges, including untreated behavioral health conditions and fragmented care systems. While the Sequential Intercept Model (SIM) provides a diversion framework, few initiatives have been adapted for young adults through community engagement. Objective: Co-develop a youth-focused adaptation of the Sequential Intercept Model (SIM) through a community-based participatory process. We engaged cross-sector stakeholders including individuals with lived experience to identify system gaps, barriers to diversion for transition-age youth (18–25), and generate actionable, community-driven strategies to strengthen equitable, developmentally responsive diversion pathways Methods: We conducted community-based participatory research in state in the Mid-Atlantic region of the United States using the Partnership Academy model to develop a youth-focused SIM. One virtual session brought together stakeholders, including individuals with lived experience, providers, and justice professionals. Using structured dialogue techniques, we mapped resources and gaps across six SIM intercepts. Results: While numerous programs exist (CHAMPS, CRT, ACE, CARES), effectiveness is limited by systemic barriers rather than service scarcity. Key findings included 984 trained crisis intervention officers with inconsistent availability, 30% family decline rates for diversion programs, absent behavioral health screening protocols, and surveillance-focused probation approaches. Stakeholders identified four collaboration priorities: data sharing infrastructure, shared accountability, standardized school partnerships, and centralized resource access. Conclusions: System fragmentation, not service scarcity, represents the primary barrier to effective diversion. Recommendations include developing youth-specific services for ages 18-25, implementing trauma-informed workforce development, establishing structured diversion protocols, and redesigning probation toward positive youth development. This
community-engaged approach provides a stakeholder-driven roadmap for building equitable, developmentally responsive diversion systems.
Background: : Diabetic patients undergoing cataract surgery face heightened risks due to compromised corneal endothelium, impaired healing, and poor pupillary dilatation. The choice of ophthalmic visc...
Background: : Diabetic patients undergoing cataract surgery face heightened risks due to compromised corneal endothelium, impaired healing, and poor pupillary dilatation. The choice of ophthalmic viscosurgical device (OVD) during phacoemulsification may significantly affect surgical outcomes in this vulnerable population; however, comparative evidence remains limited. Objective: This study aims to compare surgical outcomes of sodium hyaluronate ophthalmic solution (Hyloject) versus hypromellose ophthalmic solution USP (Hyprosol) as OVDs in topical phacoemulsification among diabetic patients with cataract at a tertiary care centre in central India. Methods: This is a prospective comparative non-randomised interventional study to be conducted at the Department of Ophthalmology, Acharya Vinoba Bhave Rural Hospital, Sawangi, Wardha, Maharashtra, India. A total of 174 diabetic patients (87 per group) aged above 40 years with senile cataract undergoing phacoemulsification will be enrolled using alternate sequential allocation. The primary outcome is change in corneal endothelial cell density assessed by specular microscopy; secondary outcomes include visual acuity recovery, intraocular pressure changes, and postoperative complications, all assessed preoperatively and at day 1, day 15, and 1 month postoperatively. Results: The study protocol was approved by the Institutional Ethics Committee of Datta Meghe Institute of Higher Education and Research (Deemed to be University) on July 9, 2025 (Ref. No. DMIHER(DU)/IEC/2025/370), with a subsequent amendment approved on February 21, 2026 (Ref. No. DMIHER(DU)/IEC/2026/018). The study was registered with the Clinical Trials Registry India on October 15, 2025 (CTRI/2025/10/096085). Enrollment commenced in November 2025; as of March 5, 2026, 35 participants have been enrolled, with 13 allocated to Group 1 (sodium hyaluronate) and 12 to Group 2 (hypromellose); the remaining participants are awaiting surgery and postoperative follow-up. Data collection is projected to be completed by August 2027, with final results expected to be submitted for publication by October 2027. Conclusions: This study is expected to provide comparative evidence on the safety and efficacy of two commonly used OVDs in diabetic cataract surgery, informing evidence-based selection of viscoelastic agents to optimise outcomes in this high-risk population. Identifying the optimal OVD for endothelial protection in diabetic eyes may carry significant clinical relevance in regions such as India, where the dual burden of diabetes and cataract is substantial and cataract surgery volumes are high. Clinical Trial: Clinical Trials Registry of India (CTRI): CTRI/2025/10/096085. Registered October 15, 2025.
Background: Attention-Deficit/Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder. First-line treatments are often associated with adverse effects and limited accessibility. Game-...
Background: Attention-Deficit/Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder. First-line treatments are often associated with adverse effects and limited accessibility. Game-based digital therapeutics (DTx) have emerged as promising adjunctive treatments. Objective: This pilot study aimed to evaluate the preliminary efficacy, feasibility, and safety of Focus Run, a novel game-based digital therapeutic (DTx) designed to improve core symptoms and executive function in Chinese children with ADHD. Methods: This prospective, single-arm pilot study enrolled 25 children aged 4–14 years with ADHD. Participants completed a 4-week home-based intervention using Focus Run (25 minutes/day, 5 days/week). We assessed outcomes at baseline and week 4, including parent-reported symptom and function scales (SNAP-IV, WFIRS, BRIEF), objective cognitive tests (DCT, BOKE STARS), and prefrontal hemodynamics measured by fNIRS during a Go/No-Go task. Changes from baseline to post-intervention were analyzed using paired t-tests or Wilcoxon signed-rank tests. Results: After the 4-week intervention, the SNAP-IV total score decreased significantly from baseline (mean reduction of 7.96 points; 95% CI -10.76 to -5.16; P < .001; Cohen's d = 1.17), with the largest effect size on the inattention subscale (Cohen's d = 1.30, P < .001). The response rate, defined as ≥30% improvement from baseline, was 40% for the inattention subscale and 20% for the total score. Significant improvements were also observed in specific domains of WFIRS and BRIEF (P < .05). The DCT showed improved accuracy (P < .001), and fNIRS revealed enhanced Go trial accuracy (P = .001) without significant hemodynamic changes. The intervention was highly feasible, with a mean adherence rate of 110.65% to the prescribed training duration. Three participants (12%) reported mild, transient visual fatigue; no serious adverse events occurred. Conclusions: This study provides preliminary evidence that Focus Run is a safe and effective digital therapeutic for improving core ADHD symptoms and specific executive functions in Chinese children, supporting its potential as a feasible adjunctive intervention. Clinical Trial: Chinese Clinical Trial Registry ChiCTR2500104023; https://www.chictr.org.cn/showproj.html?proj=198625
Background: Serious games and digital cognitive training interventions are increasingly explored as scalable approaches to support individuals with major depressive disorder (MDD). However, many trial...
Background: Serious games and digital cognitive training interventions are increasingly explored as scalable approaches to support individuals with major depressive disorder (MDD). However, many trials evaluating game-based interventions rely on weak comparators, making it difficult to distinguish intervention-specific effects from engagement or expectancy effects inherent to digital interventions. Objective: This study aimed to evaluate the feasibility and effectiveness of a fully remote serious game-based cognitive control training intervention for adults with MDD using a closely matched active control game. Methods: We conducted a fully remote, parallel-group, randomized controlled trial comparing a cognitive control-oriented videogame intervention (Legends of Hoa’manu) with a matched active control videogame. Adults with current MDD (N=57) were randomized to complete a 6-week home-based training program (30 sessions, 30 minutes each). Assessments were conducted at baseline, during training, post-intervention, and at 1-month follow-up. Co-primary outcomes were depressive symptoms measured using the Montgomery-Åsberg Depression Rating Scale (MADRS) and cognitive control assessed using the Adaptive Cognitive Evaluation battery. Secondary outcomes included rumination, well-being, functional capacity, and engagement metrics. Results: Retention rates were high, with 81% (46/57) of participants completing post-intervention assessments and 77% (44/57) completing follow-up assessments. Participants in the experimental condition reported higher interest/enjoyment and effort during gameplay compared with the control condition. Depressive symptoms showed significant reductions from baseline to post-intervention across both intervention arms (P < .001, η²p = 0.49), and cognitive control scores improved modestly over time (P = .045; η²p = 0.09). No significant group × time interactions were observed for the primary outcomes, suggesting comparable improvements across conditions when using a closely matched active control game. Exploratory analyses indicated that, within the experimental group, higher perceived competence following the intervention was associated with greater reductions in depressive symptoms. Conclusions: These findings support the feasibility of scalable, home-based serious game interventions for depression. The lack of superiority over a closely matched control highlights the importance of rigorous comparator design in serious game trials and suggests that engagement-related mechanisms may contribute to clinical benefit. These findings highlight the need for future studies to examine moderators and mechanisms that could guide personalization of digital mental health interventions. Clinical Trial: Israeli Ministry of Health Clinical Trials Registry MOH_2021-07-20_005979.
Regulatory misalignment represents a critical barrier to digital health innovation. However, the Innovative Health Initiative Joint Undertaking (IHI-JU) INTERCEPT (GA n. 101194766), an initiative invo...
Regulatory misalignment represents a critical barrier to digital health innovation. However, the Innovative Health Initiative Joint Undertaking (IHI-JU) INTERCEPT (GA n. 101194766), an initiative involving European public and private sectors aimed at intercepting Crohn’s disease prior to the manifestation of symptoms-advocates that the development of AI-powered digital platforms with compliance inherently integrated 'by design' is not only achievable but also imperative as the lack of systematic approaches for integrating regulatory requirements creates inefficiencies and delays in market access. In this perspective, we outline INTERCEPT’s strategic plan for an evolutionary digital infrastructure, coupled with AI-driven analytics, which is meticulously aligned with EU 2016/769 General Data Protection Regulation – GDPR, data privacy regulation, medical device regulation (EU 2017/745 Medical Device Regulation) and recommendations from the Medical Device Coordination Group – MDCG of the European Commission), Health Technology Assessment (HTA) criteria, and the recently enacted EU AI legislation (Regulation EU 2024/1689), which entered into force in 2024 with staggered implementation deadlines extending through 2027.
Although presently in the start-up phase with clinical activities about to commence, INTERCEPT functions as a practical blueprint.
Methods: Our analysis followed a multi-phase methodology comprising three sequential components. First, we conducted systematic regulatory landscape mapping to identify and categorize the most relevant EU frameworks (GDPR, Data Governance Act, EHDS, Clinical Trials Regulation, AI Act, and MDR) applicable to digital health innovation. Second, we implemented a structured stakeholder engagement through comprehensive surveys administered to all Work Package leaders (n=10), assessing both regulatory relevance and anticipated regulatory risk using high/medium/low categories, coupled with implementation timeline mapping (2025-2029). Third, we established iterative feedback cycles through structured dialogue sessions with project coordinators to identify current and anticipated regulatory challenges, implementation barriers, and mitigation strategies. Through retrospective analysis of survey responses, regulatory
deliverables, and stakeholder feedback, we extracted and synthesized core principles into a generalizable regulatory-aligned innovation framework.
Conclusions: The INTERCEPT regulatory-aligned framework aims to enhance digital health innovation by leveraging systematic regulatory integration to support project development and enable faster, more reliable market access for Software as Medical Device solutions, and contributes to regulatory science theory by establishing systematic principles for proactive compliance integration and offers practical guidance for digital health innovators navigating the complex EU regulatory landscape. Future research should validate the framework across diverse therapeutic areas and assess long-term impact on market success rates.
Background: Primary posterior capsular opacity (PCO) is opacity identified on the posterior capsule immediately after cortical clean-up during cataract surgery, encompasses fibrotic posterior capsule...
Background: Primary posterior capsular opacity (PCO) is opacity identified on the posterior capsule immediately after cortical clean-up during cataract surgery, encompasses fibrotic posterior capsule plaque and early lens epithelial cell (LEC)-proliferative changes. In rural populations where advanced cataracts predominate, intraoperative capsular pathology is common yet poorly characterised across cataract morphologies. Existing studies conflate distinct opacity types and rely on unmasked subjective assessment. Objective: This study aims to determine the incidence of primary PCO across all cataract morphologies operated on by a single surgeon at a rural tertiary care centre and to describe its anatomical distribution across posterior capsule zones. Methods: This is a 2-year, prospective, observational, single-surgeon study at Acharya Vinoba Bhave Rural Hospital (AVBR Hospital), Wardha, India. A total of 142 adults (minimum n = 129; 10% attrition allowance) will be enrolled. Intraoperative PCO will be classified as Type A (plaque) or Type B (LEC-proliferative) using a pre-specified decision tree, graded on a 4-point scale (0–3) by two independent masked observers using retroillumination photographs (operating microscope + slit-lamp). EPCO grading will be applied at postoperative day 1, week 6 (±1 week), month 6 (±2 weeks), and month 12 (±1 month). Primary outcome is PCO incidence (Wilson score 95% CI). Secondary outcomes include anatomical distribution, longitudinal best-corrected visual acuity (BCVA in logMAR) by linear mixed-effects model, and 12-month Nd:YAG capsulotomy rate. Multivariable logistic regression will adjust for surgical technique, IOL type and edge design, cataract morphology, and age. Missing data will be handled by multiple imputation (MICE). Results: Ethics approval was obtained from the Institutional Ethics Committee of Datta Meghe Institute of Higher Education and Research (Ref: DMIHER(DU)/IEC/2025/374) on June 30, 2025. The study was prospectively registered with the Clinical Trials Registry of India (CTRI/2025/11/097770) on November 20, 2025. Participant recruitment began in November 2025 at Acharya Vinoba Bhave Rural Hospital, Wardha, India. As of March 2026, recruitment is ongoing. The planned recruitment period is 24 months, with a target sample size of 142 participants. Data collection is expected to conclude by late 2027, and the final study findings are anticipated to be published in 2028. Conclusions: This protocol provides a rigorous, objectively graded, morphology-stratified framework for characterising intraoperative capsular pathology in a rural Indian cataract population, with direct implications for surgical planning and Nd:YAG resource allocation. Clinical Trial: Clinical Trials Registry of India (CTRI/2025/11/097770) on November 20, 2025
Background: Ocular surface disease (OSD) is a prevalent yet underdiagnosed comorbidity in glaucoma patients undergoing long-term topical therapy. Benzalkonium chloride (BAK), the most commonly used op...
Background: Ocular surface disease (OSD) is a prevalent yet underdiagnosed comorbidity in glaucoma patients undergoing long-term topical therapy. Benzalkonium chloride (BAK), the most commonly used ophthalmic preservative, exerts cytotoxic effects on the corneal and conjunctival epithelium, contributing to tear film instability, goblet cell loss, and chronic inflammation. Despite reported OSD prevalence rates of 30–78% in this population, routine ocular surface screening remains inconsistently implemented in glaucoma clinics. Comprehensive data on OSD burden, clinical subtypes, and treatment gaps in the Indian setting are particularly scarce. Objective: This study aims to determine the prevalence and clinical patterns of OSD among glaucoma patients on topical medications, identify associated risk factors (including preservative exposure, polypharmacy, and treatment duration), compare ocular surface parameters with healthy controls, and document existing gaps in OSD screening and management in a tertiary care ophthalmology setting. Methods: This is a prospective hospital-based comparative cross-sectional study to be conducted at the Department of Ophthalmology, Acharya Vinoba Bhave Rural Hospital, Sawangi, Wardha, India, from December 2025 to November 2027. A total of 110 participants (55 glaucoma patients on topical therapy and 55 age-matched controls ≥40 years, accounting for 10% attrition) will be enrolled using consecutive sampling. Ocular surface assessment will include the Ocular Surface Disease Index (OSDI) questionnaire, tear breakup time (TBUT), Schirmer's test, and fluorescein/lissamine green ocular surface staining. The study follows the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) reporting guidelines and has received institutional ethics committee approval (DMIHER(DU)/IEC/2025/375, dated July 9, 2025). The study is registered with the Clinical Trials Registry – India (CTRI/2025/11/098126). A participant flow diagram will be included in the final results manuscript. Results: Ethics approval was obtained (DMIHER(DU)/IEC/2025/375; July 9, 2025). The study is prospectively registered with the Clinical Trials Registry – India. Participant enrolment will commence on December 1, 2025. Final results are anticipated in November 2027. Conclusions: This protocol describes a systematic approach to quantify the burden of OSD in glaucoma patients receiving topical therapy in an Indian tertiary care center. Findings are expected to inform evidence-based screening protocols and guide integrative management strategies that balance intraocular pressure (IOP) control with ocular surface health. Clinical Trial: Clinical Trials Registry – India (CTRI/2025/11/098126; November 26, 2025).
Background: Healthcare service quality is inherently multidimensional, yet document-level text analysis methods such as Latent Dirichlet Allocation (LDA) force patient reviews into single dominant top...
Background: Healthcare service quality is inherently multidimensional, yet document-level text analysis methods such as Latent Dirichlet Allocation (LDA) force patient reviews into single dominant topics. This simplification may systematically discard evaluative information when patients discuss multiple service dimensions with varying sentiments within the same review. Objective: This study compared document-level topic modeling (LDA) with GPT-based aspect-level sentiment analysis (ABSA) to address three research questions: (1) How much information is lost when collapsing multi-aspect reviews to single topics? (2) How prevalent are mixed-sentiment reviews, and what quality tensions do they reveal—both cross-aspect trade-offs and within-aspect ambivalence? (3) Do positive and negative reviews exhibit different structural patterns in aspect co-occurrence? Methods: We analyzed 2024 Google Reviews from 24 medical centers in Taiwan. Both LDA (K=7 topics) and GPT-based ABSA were applied to the same 5,467 reviews, ensuring fair comparison on identical data. The ABSA design employed structured prompts to extract aspects from seven predefined quality dimensions. Quality validation achieved Cohen κ=.82 against human annotation. Mixed-sentiment reviews were identified as those containing both positive and negative aspect evaluations, and cross-polarity couplings were analyzed to identify recurring trade-off patterns. Rating-stratified network analysis compared aspect co-occurrence patterns between positive reviews and negative reviews using Jaccard similarity. Results: Reviews discussed an average of 2.05 distinct aspects (SD=0.97), producing 51.2% information loss under LDA's single-topic assignment. Among multi-aspect reviews, 11.0% exhibited cross-aspect mixed sentiment, with Technical–Functional Divergence—praising Professional Quality while criticizing functional dimensions—appearing in 49.9% of these mixed-sentiment cases. Network analysis revealed differential bundling: operational dimensions co-occurred more strongly in negative reviews, whereas clinical dimensions co-occurred more strongly in positive reviews. Conclusions: Document-level topic modeling discards more than half of the evaluative information patients provide. Our findings reveal that patients cognitively decouple clinical competence from service delivery—Technical–Functional Divergence appeared in half of mixed-sentiment cases—and that positive and negative reviews organize quality dimensions differently. We recommend a complementary approach: topic modeling for exploratory discovery and ABSA for diagnostic assessment. For healthcare quality improvement, hospitals should separate clinical signals from operational signals in feedback dashboards.
Background: Two-fold increases in the prevalence of youth anxiety and depression over the last two decades have mirrored exponential growth in opportunities for adolescent online social interaction vi...
Background: Two-fold increases in the prevalence of youth anxiety and depression over the last two decades have mirrored exponential growth in opportunities for adolescent online social interaction via social media, short messaging service (SMS) and internet text messaging apps on smartphones. However, studies to date of self-reported online social interaction time have produced conflicting results. Understanding the role of dispositional and developmental differences in individuals’ responses to online versus offline social interactions, may help elucidate whether and how online social interaction is related to anxiety and depression. Objective: This study aimed to investigate the relationship between older adolescents’ and emerging adults (18-24-year-olds) mental health and (1) objectively measured time spent on smartphones and online social interaction apps; (2) momentary affective and affiliative responses to online and offline social interactions; and (3) the moderating role of developmentally and dispositionally elevated social sensitivity. Methods: Smartphone, social media (eg, Instagram), SMS and internet (eg, WhatsApp) text messaging app time from participants’ screen use settings, as well as symptoms of anxiety and depression, and social sensitivity were measured in 190 older adolescents and emerging adults (mean age: 20.4 years). Participants then completed a novel ecological momentary assessment (EMA) capturing affective and affiliative responses to recent online or offline social interactions 3x daily for 1 week. Symptoms of mental health were assessed again after 1 month. Results: Total online social interaction (combined social media and text messaging) app time, but not total smartphone time, was associated with greater anxiety, at both baseline and 1 month later. Affective and affiliative responses were less positive for online social interactions compared to in-person interactions. Affective and affiliative responses to in-person, but not online, social interactions were negatively associated with depression across the 1-month study period. Finally, social sensitivity did moderate the relationship between affective and affiliative responses to social media interactions and depression at baseline. Conclusions: These findings emphasize the need to investigate individual factors influencing for whom online social interaction is harmful or beneficial. To do this, this study provides a novel, ecologically valid tool for understanding young people’s momentary responses to online and offline social interactions, as well as initial evidence for stronger associations between in-person than online social interaction responses and mental health. It also introduces evidence of social sensitivity as a potential, developmentally relevant vulnerability to the effects of online social interaction.
Background: Continuous renal replacement therapy (CRRT) is a life-sustaining critical care intervention widely used for hemodynamically unstable individuals with acute kidney injury. Recent efforts, i...
Background: Continuous renal replacement therapy (CRRT) is a life-sustaining critical care intervention widely used for hemodynamically unstable individuals with acute kidney injury. Recent efforts, including standardized procedures, structured documentation, and quality monitory, have shown small improvements in CRRT delivery and safety. However, fragmented workflows and paper-based documentation limit the sustainable implementation of these improvements in routine practice. Objective: This study aimed to design and evaluate a CRRT information system to support standardized procedures, structured documentation, and quality monitory. Methods: A user-centered design approach, informed by Design Science Research (DSR) methodology, guided a multi-step process of identifying problems, defining objectives, and designing and evaluating the information system. The approach to design involved close collaboration within a nurse-led, 10-member multidisciplinary team comprising nephrologists, nurses, information technology specialists, and information engineers. Evaluation included six months of real-world clinical use with ongoing feedback collected through a dedicated WeChat workgroup and a System Usability Scale (SUS) survey of 27 CRRT care team members. Results: A role-based CRRT information system was developed, comprising 14 clinical modules and 6 core functions. The system embedded a continuous data-processing pipeline that enabled automated capture of treatment-related data directly from CRRT machines, creation of structured nursing documentation, and generation of quality indicators from structured data. During demonstration, workflow refinements—including dual-nurse verification and enhanced device data transmission—were incorporated following pilot testing. Over six months of clinical use, 42 user-reported issues were identified across three domains: data retrieval and calculation, fidelity of automatically generated clinical documentation, and interface appearance. Quantitative usability survey (n=27) demonstrated excellent usability (mean SUS score 95.19, SD 5.09). Conclusions: A CRRT information system integrating standardized clinical procedures, structured documentation, and ongoing quality monitoring supported complex clinical practice and management beyond simple digitization. Workflow-aligned, data-flow–enabled design may help future critical care information systems better support clinicians working in information-intensive environments.
Background: Digital health has provided caregivers with access to supportive resources without space-time restrictions. Caregivers’ digital health engagement behaviors can help them track their own...
Background: Digital health has provided caregivers with access to supportive resources without space-time restrictions. Caregivers’ digital health engagement behaviors can help them track their own health and that of care recipients as well as communicate with others. While digital health tools have become more prevalent since COVID-19, the trend of caregiver engagement has been less explored. Objective: This study examined the trends and factors associated with selected digital health engagement behaviors in family caregivers in the United States (U.S.), using the Health Information National Trends Survey (HINTS) datasets collected in 2019, 2020, and 2022. Methods: Our cross-sectional data analysis included 1,676 family caregivers. Dependent variables were: 1) access to online medical records (caregiver’s, care recipient’s); and 2) health-related use of social media (sharing health information, interacting with others, watching health-related videos). Independent variables were survey year, demographic, socioeconomic, caregiving, and Internet technology factors. Weighted multivariable logistic regression analyses were conducted. Results: Among 1,676 caregivers (2019: n = 570; 2020: n = 412; 2022: n = 694), access to online medical records increased from 2019 to 2022. Access to caregivers’ own records rose from 48.7% to 72.6% (P<.001), and access to care recipients’ records increased from 30.8% to 44.5% (P<.001). Health-related social media use also increased, including sharing health information (22.5% vs 39.1%, P<.001), interacting with others (16.6% vs 27.0%, P<.001), and watching health-related videos (49.5% vs 60.9%, P=.005). In adjusted analyses, higher education (college graduate vs ≤high school: OR = 2.75, 95% CI 1.56–4.85, P<.001) and having health insurance (OR = 2.40, 95% CI 1.24–4.68, P=.010) were associated with access to caregivers’ records. Female sex (OR = 1.96, 95% CI 1.36–2.84, P<.001) and spousal caregiving (OR = 2.14, 95% CI 1.26–3.65, P=.005) were associated with access to care recipients’ records. High-speed internet access was strongly associated with digital engagement outcomes (e.g., sharing health information: OR = 3.98, 95% CI 2.15–7.35, P<.001). Conclusions: Digital health engagement—including access to online medical records and the use of social media for health-related purposes—among U.S. family caregivers increased following the COVID-19 pandemic. These findings suggest that healthcare professionals and researchers should consider multifaceted factors, such as age, race/ethnicity, geography, education, insurance coverage, and digital access, when designing and implementing digital health tools and technology-based interventions. Future research should evaluate how digital technologies, automation of systems “talking” to other systems, including artificial intelligence (AI) can better support caregivers’ health information needs and care coordination. The varying learning curves for individuals and groups could further be explored for effective and efficient adoption and utilization.
Background: Problematic digital use among youth is associated with mental health concerns, yet the affective and behavioral mechanisms linking self-esteem to problematic digital use remain insufficien...
Background: Problematic digital use among youth is associated with mental health concerns, yet the affective and behavioral mechanisms linking self-esteem to problematic digital use remain insufficiently characterized. Objective: To examine whether depressive and anxiety symptoms and objectively measured smartphone behaviors are associated with the relationship between self-esteem and problematic digital use among adolescents and young adults. Methods: This cross-sectional observational study was conducted between April 2022 and January 2023 in academic institutions in Grenoble, France. Participants were 171 adolescents and young adults aged 11 to 25 years using Android smartphones who completed self-report questionnaires alongside passive smartphone monitoring. Measures included self-esteem, depressive symptoms, anxiety symptoms, recreational smartphone time, delay to first connection in the morning, and nighttime digital disconnection (digital sleep).
Problematic digital use was modeled as a latent construct encompassing excessive use, emotional regulation, and reactivity and assessed using a validated self-report scale. Results: Among 171 participants (meanage, 17.6 years, SD 3.0; 57% female), depressive symptoms mediated the association between self-esteem and problematic digital use (β = −0.33; 95% CI −0.45 to −0.21), with larger indirect effects than anxiety symptoms (β = −0.13; 95% CI −0.22 to −0.04). Recreational smartphone time was positively associated with problematic digital use (β = 0.28), whereas digital sleep was independently associated with lower problematic digital use (β = −0.24). Conclusions: Lower self-esteem was indirectly associated with problematic digital use primarily through depressive symptoms, which showed stronger associations than anxiety symptoms. Objective smartphone behaviors were independently associated with problematic digital use. Clinical Trial: This trial was registered at ClinicalTrials.gov (NCT07293208).
Background: : Digital health interventions (DHIs), including telemedicine and artificial intelligence–enabled health tools, are increasingly integrated into health care systems worldwide. While thes...
Background: : Digital health interventions (DHIs), including telemedicine and artificial intelligence–enabled health tools, are increasingly integrated into health care systems worldwide. While these technologies have the potential to improve access and efficiency, unequal access to digital resources and health capabilities may create disparities in their use. Evidence on population-level determinants of digital health use remains limited in rapidly digitalizing health systems such as China. Objective: This study aimed to examine social and structural determinants of DHI use among adults in mainland China using the World Health Organization’s Social Determinants of Health (SDoH) framework. Methods: This cross-sectional study analyzed data from a nationally representative survey conducted in mainland China in 2024 among adults aged ≥18 years. The primary outcome was self-reported ever use of digital health interventions, including telemedicine, digital health applications, and AI-enabled health tools. Explanatory variables were categorized into five SDoH domains: economic stability, education and health-related capabilities, health care access and quality, neighborhood and built environment, and social and community context. Multivariable logistic regression models were used to estimate adjusted odds ratios (aORs) and 95% confidence intervals (CIs) for associations between social determinants and DHI use. Results: Among 34,672 participants, 14,565 (42.0%) reported ever using a DHI. Higher household income (≥6001 CNY vs ≤3000 CNY: aOR, 1.37; 95% CI, 1.29–1.46), higher educational attainment (bachelor’s degree or above vs junior high school or below: aOR, 1.49; 95% CI, 1.38–1.61), higher health literacy (per SD increase: aOR, 1.10; 95% CI, 1.07–1.13), and higher eHealth literacy (per SD increase: aOR, 1.20; 95% CI, 1.17–1.24) were associated with greater odds of DHI use. Health insurance coverage was associated with higher DHI use (aOR, 1.22; 95% CI, 1.11–1.34), whereas individuals aware of but not enrolled in family doctor services had lower odds (aOR, 0.65; 95% CI, 0.60–0.70). Difficulty paying medical expenses was associated with higher DHI use (aOR, 1.31; 95% CI, 1.22–1.41), while rural residence was associated with lower odds (aOR, 0.94; 95% CI, 0.89–1.00). Conclusions: DHI use in China is strongly associated with socioeconomic resources, health-related capabilities, and access to health care. These findings highlight the importance of addressing structural and social determinants to promote equitable adoption of digital health technologies in rapidly digitalizing health systems. Clinical Trial: NA
Background: MiniMed 780G insulin delivery system has demonstrated improved HbA1c, time-in-range (TIR) and quality of life (QoL) in clinical trials but real-world studies are lacking in Southeast Asia....
Background: MiniMed 780G insulin delivery system has demonstrated improved HbA1c, time-in-range (TIR) and quality of life (QoL) in clinical trials but real-world studies are lacking in Southeast Asia. Objective: Our study aims to evaluate the efficacy, safety and the impact on QoL of MiniMed 780G in adults with type 1 diabetes (T1D) over 24 weeks. Methods: A prospective, single centre pilot study was conducted in patients with T1D on multiple daily injections or conventional pump therapy. Primary endpoints include the change in HbA1c at 12 and 24 weeks. Secondary endpoints include CGM glycaemic metrics, total daily insulin dose (TDD), quality of life (QoL) scores and adverse events. Results: 9 patients (5 males) with a mean age of 35 ± 10.9 years completed the study. Mean HbA1c improved from 7.3% (56.3 mmol/mol) to 6.9% (51.2 mmol/mol) at 12 weeks and 7% (53.0 mmol/mol) at 24 weeks respectively. All patients achieved the target TIR of >70%, TAR of <25% and TBR of <4% at 12 and 24 weeks. Reduction in TDD were observed for all patients. Conclusions: Our study demonstrated the usability, safety and consistency of MiniMed 780G in improving glycaemic outcomes in Asian adults with T1D independent of their baseline insulin therapies. Clinical Trial: Not applicable
Background: State abortion laws have been in flux since the Dobbs v. Jackson Women’s Health Organization decision. Therefore, it is important to anticipate how the future physician workforce will na...
Background: State abortion laws have been in flux since the Dobbs v. Jackson Women’s Health Organization decision. Therefore, it is important to anticipate how the future physician workforce will navigate abortion-related care. Osteopathic medical students are understudied despite comprising a growing share of the healthcare workforce in the United States. Objective: To measure personal abortion-related values among osteopathic medical students at a Western U.S. College of Osteopathic Medicine and evaluate the impact of these values on their perceptions of future clinical decision-making across diverse clinical scenarios. Methods: A cross-sectional, anonymous survey was distributed to all osteopathic medical students (N=1,285) from September – November 2022. Measures included demographics, a 6-point abortion attitudes “spectrum score”, and responses to standardized first- and second-trimester abortion clinical scenarios. Analyses included descriptive statistics, chi-square, and multivariable ordinal logistic regression. Results: Of the 1,285 eligible students, 247 responded (19.2%). The majority were female (56.5%), Democrat (46.9%), and religious (49.2%). Most students expressed supportive stances on abortion and chose to “provide” the abortion in 65% of first trimester and 59% of second-trimester scenarios. Spectrum scores were strongly associated with gender, political affiliation, and religiosity (all P <.001). Students with moderate spectrum scores showed the greatest uncertainty, particularly in second-trimester cases. Conclusions: Medical students may be approaching abortion-related care through a patient-centered lens rather than through a religious or political framework. Medical education across the U.S. must provide case-based abortion-related education sufficient to build students’ clinical decision-making confidence.
Background: Depression affects over 280 million people globally; nevertheless, access to evidence-based psychotherapy remains severely limited by workforce shortages and stigma. Large language model (...
Background: Depression affects over 280 million people globally; nevertheless, access to evidence-based psychotherapy remains severely limited by workforce shortages and stigma. Large language model (LLM)-based chatbots promise to overcome the rigidity of rule-based systems; however, their ability to deliver structured psychological interventions with clinical fidelity remains largely unverified. Unlike psychotherapists, who undergo rigorous fidelity assessments using validated clinical instruments, LLM-based chatbots have not been subjected to equivalent evaluation standards. Objective: This study aimed to evaluate the clinical fidelity of an LLM-based chatbot delivering behavioral activation for depression to young people and to identify limitations and opportunities for refinement through clinical expert assessment. Methods: We developed an LLM-based chatbot (GPT-4o) by implementing a seven-phase behavioral activation protocol for young people aged 14–29 years with depressive symptoms. We created 48 artificial users (GPT-4o) derived from clinical patient vignettes, systematically varied across seven characteristics, including depression severity, gender, and attitudes toward mental health chatbots. Ten licensed psychotherapists or advanced psychotherapy trainees (mean age 30.1 years, SD 4.12; 70% female) independently assessed sessions using the Quality of Behavioral Activation Scale (Q-BAS), a validated 14-item fidelity instrument (0–6 scale; ≥3 indicates satisfactory delivery), supplemented by therapeutic capability ratings and qualitative feedback. Results: The chatbot completed all seven intervention phases across all 48 sessions. The mean holistic session quality rating was 3.94 (SD 1.23) and the mean Q-BAS rating was 4.03 (SD 1.18). Thirteen of the 14 components exceeded the satisfactory threshold. Component adequacy rates ranged from 97.9% for mood assessment (n=47/48) to 56.2% for explaining positive reinforcement (n=27/48). The highest-rated components were mood assessment (M=5.42, SD=1.09) and planning activities (M=4.98, SD=1.41); the lowest were explaining positive reinforcement (M=2.92, SD=2.30) and encouraging observation of activity–mood connections (M=3.02, SD=2.04). Variance decomposition showed that 36.8% of the Q-BAS variance was attributable to session differences (variance=1.27, 95% CI 0.82–2.02) and 12.0% to component differences (variance=0.42, 95% CI 0.18–0.98). Message safety received the highest therapeutic capability rating (M=6.90, SD=0.37), with 92% of sessions rated at maximum. Therapeutic rapport received the lowest rating (M=5.13, SD=1.45). Artificial users with negative chatbot attitudes were rated as significantly more authentic than those with positive attitudes (W=387.50, p=.036), without significantly affecting fidelity scores (p=.275). Qualitatively, psychotherapists consistently identified insufficient clinical reasoning as the primary limitation, particularly the failure to verify whether activities and rewards were therapeutically appropriate. Conclusions: Although large language model-based chatbots can execute structured therapeutic protocols with satisfactory fidelity while maintaining high message safety, clinical reasoning remains a critical gap. Prompt-level refinements, including granular task breakdown, template-based content, embedded clinical decision rules, and explicit redirection mechanisms, were proposed to address the identified shortcomings.
Open Peer Review Period: Mar 7, 2026 - Feb 20, 2027
Background: Current acute stroke management guidelines focus primarily on time-based imaging windows and the pharmacological suppression of acute hypertension. This paper proposes an alternative parad...
Background: Current acute stroke management guidelines focus primarily on time-based imaging windows and the pharmacological suppression of acute hypertension. This paper proposes an alternative paradigm based on a first-principles theoretical derivation of hydraulic states. Objective: The primary goal of this framework is to establish the physiological feasibility of a "Stability Corridor" for Cerebral Perfusion Pressure (CPP) to maximize neuronal salvage. Methods: The methodology utilizes a first-principles biophysical derivation incorporating the Monro-Kellie Doctrine and Laplace's Law. It modifies the Cushing Reflex sequence, framing the terminal rise in intracranial pressure (ICP) as a result of a systemic blood pressure spike driven by ischaemic vasoparalysis. Results: The derivation identifies two phases of hydraulic failure: a "Masked Influx" where the 0.05 alpha extracellular space (ECS) buffer is exhausted, followed by a "Terminal Spike" in ICP. It establishes a "Stability Corridor" by identifying the Ischaemic Floor for collateral flow and the Elastic Limit to prevent vascular tearing. Conclusions: By modulating ICP to keep CPP within the Stability Corridor and using GFAP biomarkers as proxies for hydraulic integrity, clinicians can theoretically maintain cerebral perfusion and prevent the "Hydraulic Breach" of macro-haemorrhage. Clinical Trial: N/A (Theoretical Paper)
Background: Chikungunya virus (CHIKV) is a mosquito-borne arbovirus that causes acute febrile illness frequently associated with severe polyarthralgia and long-term disabling sequelae. In 2024–2025,...
Background: Chikungunya virus (CHIKV) is a mosquito-borne arbovirus that causes acute febrile illness frequently associated with severe polyarthralgia and long-term disabling sequelae. In 2024–2025, La Réunion Island experienced a major resurgence of CHIKV transmission after more than a decade without documented autochthonous circulation. The live-attenuated chikungunya vaccine VLA1553 (IXCHIQ®, Valneva) was recently approved based primarily on immunogenicity data and a validated immune correlate of protection. However, real-world evidence regarding vaccine effectiveness, safety, and population-level impact during active outbreaks remains limited. Objective: The CHIK-RE-VAC study aims to evaluate the real-world effectiveness, safety, immunogenicity, and cost-effectiveness of the VLA1553 chikungunya vaccine during an ongoing epidemic on La Réunion Island. Methods: CHIK-RE-VAC is an ambispective, observational, multicenter, phase IV cohort study conducted across hospital and outpatient care clusters on La Réunion Island. Eligible adults are enrolled into vaccinated and unvaccinated groups according to their decision to receive vaccination in accordance with national recommendations. The study integrates both prospective recruitment and a retrospective cohort of individuals vaccinated prior to study initiation. Participants are followed for up to 12 months after vaccination or index date with active surveillance, including weekly symptom monitoring and triggered clinical visits. The primary outcome is vaccine effectiveness against laboratory-confirmed symptomatic chikungunya infection at 6 and 12 months. Secondary outcomes include severe disease, hospitalization, adverse events following vaccination, persistent symptoms, health-related quality of life, vaccine acceptability, and cost-effectiveness. A nested immunogenicity substudy evaluates neutralizing antibody responses and cellular immune responses over time. Statistical analyses will use propensity score–based weighting and generalized estimating equation models accounting for clustering. Results: Recruitment began in April 2025 following the launch of a government-funded vaccination campaign targeting populations at high risk of severe chikungunya. An amendment introducing a retrospective cohort was approved in July 2025 to include individuals vaccinated prior to study initiation and to enhance recruitment and statistical power. As of March 2026, 260 participants have been enrolled across the prospective and retrospective components. Recruitment and follow-up are ongoing. Conclusions: The CHIK-RE-VAC study will provide the first comprehensive real-world evaluation of the effectiveness, safety, immunogenicity, and cost-effectiveness of the VLA1553 chikungunya vaccine during an active epidemic. The findings are expected to inform vaccination strategies, public health preparedness, and policy decisions regarding the deployment of chikungunya vaccines in outbreak settings. Clinical Trial: EU Clinical Trials Register: EU-CT 2025-521307-43-00; ClinicalTrials.gov: NCT06928753.
Background: Simulated or standardized patients (SP) have long been a cornerstone of medical education, offering controlled, repeatable, and ethically sound training scenarios. With the rise of skills...
Background: Simulated or standardized patients (SP) have long been a cornerstone of medical education, offering controlled, repeatable, and ethically sound training scenarios. With the rise of skills labs and standardization efforts, SPs have become widely implemented across faculties. However, certain limitations—such as the representation of dynamic or complex clinical conditions—remain. Advances in virtual reality (VR) technology now offer new possibilities to complement traditional simulation formats. While both modalities are well established, little is known about their respective strengths and limitations or the potential for mutual substitution or integration in medical education. Objective: This study investigates the strengths and limitations of VR and SPs in medical education to inform conclusions about their potential complementarity and ability to address training gaps. Methods: This scoping review was conducted in accordance with the JBI methodology and PRISMA 2020 guidelines. A dual search strategy was developed to investigate VR and SP simulations separately across four databases (PubMed, Web of Science, ScienceDirect, and Cochrane Library). Studies were screened independently by multiple reviewers using predefined inclusion criteria. Data extraction and analysis followed a structured, iterative process, with results to be presented in tabular and narrative form.
Eligible studies examine the use of VR and SPs in the education of medical students and physicians, with a focus on comparing their respective strengths and limitations across various training contexts. Results: The search strategy is expected to identify studies examining the use of virtual reality versus actor-based simulation in medical education. Results will be reported using a PRISMA flow diagram and summarized in structured tables. Findings will be synthesized narratively and categorized according to advantages, limitations, and distinct characteristics of each simulation modality. If feasible, sub-analyses based on study design, educational setting, or outcome type will be conducted. Conclusions: This scoping review will provide a comprehensive overview of the strengths, limitations, and distinct characteristics of virtual reality and actor-based simulation in medical education. The findings are expected to inform educators and curriculum developers about optimal use cases, identify gaps in the literature, and guide future research on simulation-based training methods. Clinical Trial: not possible as scoping review
Background: Simulation-based learning (SBL) constitutes a valuable component of medical training, with immersive 3D simulations and physical simulations being commonly used. Immersive 3D simulation tr...
Background: Simulation-based learning (SBL) constitutes a valuable component of medical training, with immersive 3D simulations and physical simulations being commonly used. Immersive 3D simulation training has been proven to enhance procedural skills and their transfer, whereas physical simulators allow for haptic feedback. However, direct comparative evidence between these two simulation modalities is scarce and often does not address cognitive, motivational and perceived learning outcomes, which also have an effect on learning outcomes. Objective: The objective of this study is to determine the differences in procedural performance alongside cognitive load, motivation, and perceived learning between when teaching novice trainees endourological laser-based procedures using immersive 3D simulation and physical simulation. Methods: We will conduct a single-center, two-group randomized controlled trial with 100 medical students who are currently in their final year of undergraduate medical studies. Each participant will complete one training session on either the immersive 3D simulation or the physical simulator followed by one post-training test on the physical simulator. Technical performance will be assessed using a blinded, Objective Structured Assessment of Technical Skills (OSATS) based composite including efficiency, safety and task completion. Furthermore, we will measure motivation, cognitive load, and perceived learning. To analyze group differences in the primary and secondary outcomes we will use independent sample t-tests. Results: Recruitment will start in September 2026. We will begin with the training sessions and post-training tests in November 2026. We expect to complete data collection by December 2026, and finish analyzing by February 2027. Consequently, we expect to publish the results in April 2027. Conclusions: By integrating technical performance with cognitive, motivational, and perceived learning outcomes this study will clarify not only whether, but also how, immersive 3D and physical simulation differently support learning in endourological training. Furthermore, it aims to inform the evidence-based development of simulation curricula in medical education.
Background: Artificial intelligence (AI) is increasingly shaping health care, yet the AI preparedness of midwifery students remains underdocumented. Evidence is needed to inform midwifery-specific cur...
Background: Artificial intelligence (AI) is increasingly shaping health care, yet the AI preparedness of midwifery students remains underdocumented. Evidence is needed to inform midwifery-specific curriculum development and to clarify how students understand and operationalize AI in training and placements. Objective: This mixed-methods study aimed to assess French midwifery students’ AI readiness, training needs, and ethical/regulatory concerns. Methods: We conducted a national sequential explanatory mixed-methods study during the 2024–2025 academic year. A web-based survey (five previously translated/adapted questionnaires) was disseminated via midwifery schools/universities in France (30/33, 91%, institutions confirmed dissemination and responses were received from these 30 institutions). Eligible participants were students enrolled in years 2–5 of the French midwifery curriculum. We computed mean theme scores (1–5) with 95% confidence intervals (CIs) and assessed internal consistency using Cronbach α. Analyses were restricted to fully completed questionnaires. Semi-structured interviews were conducted with volunteer students from one midwifery school in Eastern France (n=8), transcribed verbatim, anonymized, and analyzed using thematic analysis. Mixed-methods integration used a joint display. Results: Of 414 survey entries, 190 were fully completed and kept for analysis (190/414, 46.1%). Mean theme scores for AI skills and knowledge were below the neutral midpoint, ranging from 1.20 (95% CI 1.09–1.23) for familiarity with advanced AI techniques to 2.89 (95% CI 2.48–3.31) for analytical concepts in AI for health. Perceived ability to use AI for clinical purposes was low (2.05, 95% CI 1.01–3.09). In contrast, students strongly endorsed AI education (belief that students and professionals should be trained: 4.11, 95% CI 3.96–4.26) and emphasized evidence and safety requirements (up to 4.04, 95% CI 3.84–4.24). Item-level results suggested “AI label ambiguity”: general AI familiarity showed higher agreement (91/190, 47.9%) than familiarity with specific concepts such as machine learning (20/190, 10.5%) or deep learning (13/190, 6.8%). Interviews aligned with these patterns, indicating rare exposure to explicitly identified AI-supported workflows in placements and describing mainly academic and informal uses of generative tools. Participants emphasized patient safety, accountability, and preservation of human judgment. Conclusions: French midwifery students report a substantial AI readiness gap characterized by both low technical preparedness and limited situated exposure during placements, despite strong demand for training and high salience of safety and governance. Findings support implementing a structured, progressive curriculum linked to midwifery-relevant clinical scenarios and aligned with placement ecosystems. Future measurement should explicitly distinguish generative AI practices from regulated clinical AI systems and capture safe-use behaviors to improve construct validity.
Background: Scaling lung cancer screening from controlled trials to nationwide implementation requires interoperable digital infrastructure capable of coordinating primary care, radiology, pulmonology...
Background: Scaling lung cancer screening from controlled trials to nationwide implementation requires interoperable digital infrastructure capable of coordinating primary care, radiology, pulmonology, and centralized governance. Although low-dose computed tomography (LDCT) reduces lung cancer mortality in high-risk populations, few countries have embedded screening programs directly within national health information systems to enable standardized workflows, real-time monitoring, and data-driven quality control. Objective: To describe the digital architecture, interoperability framework, and real-world performance of the Croatian National Lung Cancer Screening Program (CNLCSP), implemented as a native extension of the Central Health Information System of the Republic of Croatia (CEZIH). Methods: This retrospective observational implementation study analyzed structured program data collected between October 2020 and December 2025. The CNLCSP targets individuals aged 50–75 years with ≥30 pack-years of smoking history who are current smokers or former smokers who quit within 15 years. The program operates entirely within CEZIH through role-specific modules for general practitioners (GPs), radiologists, pulmonologists, and national coordinators. Core digital functionalities include electronic eligibility verification, paperless referral and scheduling, structured radiology and pulmonology reporting based on modified I-ELCAP guidelines, AI-assisted volumetric nodule analysis integrated into the reporting workflow with mandatory radiologist second reading, secure DICOM-based telemedicine image transfer, and a centralized analytics module providing real-time dashboards of predefined quality indicators, including radiation dose metrics. Results: From October 2020 to December 2025, over 54,000 individuals were screened, generating more than 80,000 LDCT examinations across 27 radiology centers and 6 pulmonology centers, involving more than 2,000 GPs. Positive radiological findings were reported in 4.45% of examinations. Continuous digital monitoring supported a mean effective radiation dose of 0.85 mSv, below the program limit of 1.5 mSv. The interoperable CEZIH-based infrastructure enabled expansion from 16 to 27 radiology centers while maintaining standardized reporting and centralized oversight. Conclusions: Embedding lung cancer screening as a native component of a national health information system enables scalable implementation, structured data capture, AI-supported clinical workflows with human oversight, and real-time governance. The Croatian model illustrates how digital integration within existing health infrastructure can support population-level screening and may serve as a transferable informatics framework for other health systems.
Background: Conventional heart failure (HF) management is challenged by high loss to follow-up, fragmented care, and insufficient multidisciplinary collaboration (MDT), contributing to a 30% readmissi...
Background: Conventional heart failure (HF) management is challenged by high loss to follow-up, fragmented care, and insufficient multidisciplinary collaboration (MDT), contributing to a 30% readmission rate during the vulnerable post-discharge period. While the integration of remote monitoring and telehealth signals a paradigm shift towards proactive intervention, the effectiveness of a nurse-led, mHealth-based multidisciplinary model in this critical phase requires further validation. Objective: This randomized controlled trial evaluated a nurse-led, app-based multidisciplinary telemanagement program for improving self-care, symptoms, and clinical outcomes in vulnerable-phase HF patients. Methods: A single-blind, randomized controlled trial was conducted. 100 heart failure patients (left ventricular ejection fraction ≤50%) from a tertiary hospital in Beijing were randomly assigned to either an intervention group (n=50) or a control group (n=50). The intervention group received a 3-month, nurse-led, multidisciplinary telemanagement program via a cardiovascular health management APP. This program included structured education, personalized care plans (medication, self-monitoring, follow-up), automated reminders, and proactive monitoring. A core component was the nurse-coordinated multidisciplinary case discussion (involving doctors, pharmacists, and nurses) triggered by abnormal patient data. The control group received routine heart failure outpatient follow-up. The Self-Care of Heart Failure Index (SCHFI), the Memorial Symptom Assessment Scale-Heart Failure (MSAS-HF), B-type natriuretic peptide (BNP) levels, and NYHA functional class were assessed at baseline and 3 months. Results: After the 3-month intervention, the intervention group demonstrated significantly greater improvements compared to the control group in the SCHFI total score and its three subscales (self-care maintenance, management, and confidence), the MSAS-HF total score and its subscales (physical, psychological, and heart failure-specific symptoms), and BNP levels (t=2.302 to 3.953, -2.204 to -2.841, Z=-3.354, P < 0.05). Moreover, a significantly higher proportion of patients in the intervention group achieved NYHA class I (84.0% vs. 66.0%; χ²=4.320, P < 0.05). Conclusions: This nurse-led, mHealth-facilitated multidisciplinary telemanagement program led to significant improvements in self-care, symptom burden, NYHA functional class, and BNP among patients with heart failure during the vulnerable post-discharge period. By demonstrating these benefits, the model effectively overcomes critical limitations inherent in traditional post-discharge management approaches.
Background: Automated systems for detecting adverse drug reactions (ADRs) are increasingly common and carry high expectations from policymakers, researchers, healthcare professionals, and patients, ye...
Background: Automated systems for detecting adverse drug reactions (ADRs) are increasingly common and carry high expectations from policymakers, researchers, healthcare professionals, and patients, yet evidence of their effectiveness and safety remains limited Objective: The aim of this systematic review was to identify the ethical, legal, organizational, social, and environmental implications of these systems. Methods: We conducted a systematic using the VALIDATE framework, we conducted a three-step approach: (1) defining scope through literature review and stakeholder consultation; (2) systematic review; (3) environmental inquiries. Results: Stakeholders prioritized research on feasibility, barriers, facilitators, alarm management, staged implementation, confidentiality, cybersecurity, and bias detection. The systematic review of ten studies revealed that leveraging new data sources and developing privacy-protection technologies is essential for upholding ethical and legal standards. Cybersecurity risks could expose patient information to unauthorized parties, while biases in training datasets can compromise fairness. Integrating ADR detection into clinical workflows and medication management systems can improve resource optimization and reporting rates. Establishing a positive reporting culture, supported by education and training for healthcare teams, is crucial to enhance ADR reporting. Conclusions: Careful planning is critical when implementing an early ADR detection system. Incorporating co-design methodologies can help align these automated systems with stakeholder needs and improve medication safety. Clinical Trial: Not requiered
Background: Bangladeshi adolescents, who constitute a fifth of the country's population, experience barriers in accessing sexual and reproductive health (SRH) information. Previous studies have shown...
Background: Bangladeshi adolescents, who constitute a fifth of the country's population, experience barriers in accessing sexual and reproductive health (SRH) information. Previous studies have shown that mobile health (mHealth) interventions provide adolescents with timely access to evidence-based curricula, gamified, and interactive content, sessions, and information. The widespread adoption of mHealth technologies among adolescents and their willingness to embrace emerging technologies are encouraging specialists to employ mHealth approaches to share health information. Despite the high mobile phone usage among adolescents in Bangladesh, there are a few mHealth interventions specifically targeting their SRH needs. Objective: We aimed to assess changes in SRH knowledge and awareness among adolescents in Bangladesh following exposure to "Mukhorito", an interactive mobile app-based intervention. Methods: This pilot study employing a pre-post non-randomized experimental approach was conducted in three selected secondary schools in Feni, Bangladesh, from June 2023 to March 2024. 46 students from class 9 across the three schools were recruited, with a minimum of 10 per school. Bivariate analyses were performed to assess the association between SRH knowledge and awareness scores with other covariates. Significantly associated covariates for both scores were used in building the adjusted linear regression models. Results: The adjusted models indicated a significant improvement in the end-line group compared with the baseline group for both knowledge (1.2 units; 95% CI: 0.8-1.6 units) and awareness scores (1.0 units; 95% CI: 0.3-1.5 units), indicating a high level of intervention effect. Conclusions: These findings demonstrate the potential of mobile app-based innovations to improve adolescent SRH education within a national program in resource-constrained settings, specially where conventional methods may be less effective.
Background: The integration of artificial intelligence (AI) into intraoperative surgical imaging represents an emerging frontier in digital health. Despite advances in preoperative computed tomography...
Background: The integration of artificial intelligence (AI) into intraoperative surgical imaging represents an emerging frontier in digital health. Despite advances in preoperative computed tomography (CT)–based surgical planning, real-time translation of imaging data into actionable intraoperative guidance remains limited by CT-to-body divergence—a fundamental information gap between preoperative digital models and the dynamic surgical field. This divergence, driven by lung deflation under anesthesia and positional changes, represents a critical digital-to-physical registration challenge that current preoperative imaging workflows fail to address in real time. This study evaluated the performance and safety of the LungVision system, a portable AI-driven digital platform that integrates preoperative CT data with real-time fluoroscopic image fusion, for intraoperative tumor localization during thoracoscopic lung resection. Objective: This study aimed to evaluate the clinical feasibility, localization accuracy, and safety of the LungVision system—an AI-augmented fluoroscopic navigation platform—for real-time intraoperative localization of small pulmonary nodules during thoracoscopic surgery. Methods: A prospective single-center study enrolled fourteen patients with pulmonary nodules requiring localization prior to thoracoscopic resection between March and September 2024. The platform comprises a passive radiopaque positioning board, an AI-powered computing unit for real-time image processing, and a tablet-based interface for procedural planning and augmented visualization. All patients received dual localization with either preoperative CT-guided dye injection or Archimedes virtual bronchoscopic navigation, followed by intraoperative localization with the LungVision system and video-assisted thoracoscopic surgery. Demographic data, lesion characteristics, procedural performance, and procedure-related complications were collected. Results: The mean patient age was 57.2 years, and 92.9% were non-smokers. Most nodules were peripherally located (85.7%), with a mean diameter of 9.3 ± 5.3 mm and a mean CT attenuation of −320.1 Hounsfield units. LungVision successfully localized all target lesions intraoperatively, with a mean navigation time of 38.6 minutes. Complete resection was achieved in all cases, and 71.4% of nodules were pathologically malignant. No intraoperative or localization-related complications were observed. The system was integrated into the existing operating room without additional infrastructure modifications. Conclusions: The LungVision system demonstrated high accuracy and safety for intraoperative localization of small, hypodense pulmonary nodules. By minimizing CT-to-body divergence and integrating seamlessly into existing bronchoscopic and surgical workflows, this AI-driven platform represents a scalable and infrastructure-light alternative to conventional localization strategies, warranting further evaluation for broader clinical implementation.
Background: Current AI interventions in mental health positions LLMs to act as therapists, raising concerns regarding simulated emotional bond and clinical safety. These systems risk patients becoming...
Background: Current AI interventions in mental health positions LLMs to act as therapists, raising concerns regarding simulated emotional bond and clinical safety. These systems risk patients becoming dependent on the tool, instead of fostering their own therapeutic skills for long-term recovery. Objective: This paper explores design considerations for adapting commonly-used LLMs (e.g., Gemini, ChatGPT, Llama) for clinical use, using them as a skill-building tool rather than a replacement for therapists. Methods: Guided by the educational theories, we developed a dual-persona chatbot. The first persona is a distressed character with cognitive distortion; the second persona is a facilitator that provides the user with scaffolding and instructions to navigate the interaction safely and successfully. Users are tasked to “help” the first persona, with the aid of the second persona, by identifying and restructuring their cognitive distortions. Through a process involving initial testing, establishing personas, and ensuring fidelity/safety, we developed three versions of the system. Four raters with varying clinical expertise assessed simulated interactions across four domains: Character Fidelity, Effective Facilitation, Boundary Management, and Overall Utility. Results: Inter-rater reliability among the raters was high (ICC = 0.76). The final version of the system was rated as effective in terms of character fidelity, learning facilitation, and clinical boundaries. The largest improvement across versions was in the construction of an effective and safe learning environment (F2,61 = 42.11, P <.001 for instruction clarity, F2,32 = 12.44, P <.001 for handling clinical risk), while character fidelity was rated highly across versions with little variation. The raters agreed that the tool is helpful for users to consolidate the skill of cognitive restructuring. Conclusions: By shifting the AI’s role from a source of emotional support to a subject for practice, this system encourages the user to engage in the practice to “be their own therapist”. Our findings provide a generalizable roadmap for integrating commercial AI into clinical workflows as a secure, skill-based supplement to human-led therapy.
Background: Verbal feedback delivered by attending surgeons in the operating room plays a critical formative role in resident trainee skill acquisition. Yet, assessing the quality of trainer feedback...
Background: Verbal feedback delivered by attending surgeons in the operating room plays a critical formative role in resident trainee skill acquisition. Yet, assessing the quality of trainer feedback and its effectiveness in influencing trainee behavior during live surgery remains a challenge. Prior studies relied on extensive manual annotation by expert human raters and focused on broad taxonomies that overlook the qualitative aspects of feedback delivery such as clarity or urgency. Limited existing automated methods, including keyword analysis and topic modeling, also fail to capture these nuanced delivery dimensions. Objective: The study aimed to develop and evaluate a scalable, automated framework for discovering and scoring interpretable surgical feedback quality criteria grounded in real-world surgical training interactions and clinically validated outcome measures. Methods: We introduce a two-stage large language model (LLM)-based framework. In the first stage, multi-agent prompting with multiple GPT-4o instances, seeded with clinically validated definitions of feedback effectiveness outcomes and unlabeled feedback examples, independently proposes candidate quality criteria. These are consolidated via hierarchical clustering and a deterministic LLM synthesis step into six human-interpretable, behaviorally anchored dimensions: Encouraging, Urgent, Actionable, Timely, Clear, and Reflective. In the second stage, these criteria are applied to score feedback instances at scale using an LLM-as-a-judge approach. Framework evaluation included predictive modeling of four clinically annotated behavioral outcomes, statistical significance testing using DeLong's method, generalized linear mixed modeling of associations between quality dimensions and outcomes, and human-AI alignment assessment using quadratically weighted Cohen's kappa on a stratified sample of instances rated by two domain-expert human raters. Results: Applied to 4,210 intraoperative feedback instances, the six AI-discovered quality criteria achieved AUROCs of 0.75 (95% CI: 0.74–0.77) for trainee behavior change and 0.71 (95% CI: 0.69–0.72) for trainee verbal response. Augmenting prior automated topic modeling features with our criteria yielded consistent gains of 9–12% across all four behavioral outcomes. DeLong's testing confirmed statistically significant additive predictive value of the AI-derived dimensions over both topic modeling and human-annotated baselines. Generalized linear mixed modeling revealed that Actionable (rate ratio [RR]=1.22), Timely (RR=1.24), and Urgent (RR=1.11) feedback were significantly associated with trainee behavioral adjustment, while Reflective (RR=1.34) and Clear (RR=1.13) feedback predicted verbal acknowledgment. Human-AI alignment was substantial for five of six dimensions (quadratically weighted κ=0.60–0.79), approaching inter-human agreement levels. Conclusions: Our LLM-based framework enables scalable, interpretable, and clinically grounded assessment of surgical feedback delivery quality, without requiring manual annotation. The discovered criteria demonstrate significant predictive validity for real-world trainee and trainer behavioral outcomes and exhibit strong alignment with expert human judgment, providing a foundation for improving intraoperative teaching and surgical education quality assurance.
Background: Mechanical chronic low back pain is a common musculoskeletal condition that significantly affects daily function, work productivity, and quality of life. Routine physical therapy is widely...
Background: Mechanical chronic low back pain is a common musculoskeletal condition that significantly affects daily function, work productivity, and quality of life. Routine physical therapy is widely used for its management; however, interest has grown in adjunct approaches such as breathing exercises due to their potential role in pain modulation, trunk stability, and functional improvement. Objective: This study aimed to compare the effects of routine physical therapy with and without breathing exercises on pain intensity, lumbar range of motion, functional disability, and muscle endurance in patients with mechanical chronic low back pain. Methods: A single-blinded randomized controlled trial was conducted on 132 patients with mechanical chronic low back pain, who were randomly allocated into two equal groups. Group A received routine physical therapy, while Group B received routine physical therapy combined with breathing exercises for four weeks (12 sessions). Outcomes including pain (VAS), lumbar range of motion, functional disability (Modified Oswestry Disability Index), muscle endurance, and FEV₁ were assessed at baseline and follow-up. Data were analyzed using SPSS version 25, applying non-parametric tests and linear mixed models, with statistical significance set at p < 0.05. Results: A total of 132 patients with mechanical chronic low back pain were analyzed. Compared with routine physical therapy alone, the addition of breathing exercises resulted in a greater reduction in pain over time (group × time effect: F = 50.6, p < 0.001). Patients receiving breathing exercises also showed significantly superior improvements in lumbar range of motion across flexion, extension, side flexion, and rotation (time and interaction effects: p < 0.001). Functional disability, assessed by the Modified Oswestry Disability Index, decreased more markedly in the breathing exercise group (mean reduction: 42.4 vs 28.3; F = 4.34, p = 0.005). In addition, trunk muscle endurance (anterior, posterior, and lateral plank tests) improved significantly more in patients receiving breathing exercises compared with routine therapy alone (interaction effects: F = 524–2138, p < 0.001). Conclusions: It is concluded that he addition of breathing exercises to routine physical therapy resulted in superior improvements in pain reduction, lumbar mobility, functional disability, trunk muscle endurance, and pulmonary function. Clinical Trial: IRCT Registration number = IRCT 20200901048579N1
Background: People living with advanced cancer experience more frequent and severe symptoms than people living with early-stage disease. Four common and distressing symptoms include sleep difficulties...
Background: People living with advanced cancer experience more frequent and severe symptoms than people living with early-stage disease. Four common and distressing symptoms include sleep difficulties, worry-anxiety, fatigue, and depression. Cognitive-behavioral therapy (CBT) and acceptance and commitment therapy (ACT) interventions are effective for managing these symptoms but are often too time-intensive for people with multiple appointments, limited energy, and competing priorities. Brief, mobile health (mHealth) interventions provide an accessible alternative, particularly for those in rural communities with limited access to palliative and/or psychosocial oncology services. Objective: Based on our successful in-person/DVD-based pilot trial of a four session, integrated CBT-ACT symptom management intervention for advanced cancer patients, Finding Our Center Under Stress (FOCUS), this study tests the feasibility and acceptability of a mHealth translation of this intervention. Methods: In this single-group, feasibility trial, 11 people with advanced cancer were recruited through hospital-based oncology clinics representing four cancer types (breast, melanoma, multiple myeloma, prostate). Patients completed sociodemographic questions, initial patient-reported outcomes including sleep (ISI), anxiety (GAD-7, PSWQ), fatigue (FSI), and depression (CES-D) and a 7-day sleep diary via the mobile app. They then completed four modules focused on the self-management of sleep difficulties, worry-anxiety, fatigue, and depression. To assess feasibility, we examined recruitment, retention, and module completion. At the end of six weeks, to assess acceptability, participants completed the Internet Evaluation and Utility Scale and some participants completed a qualitative interview assessing their experience with the FOCUS app. We present quantitative and qualitative results as well as lessons learned in designing the application for this patient population. Results: Sixty-five percent entered the trial (N =11) and seventy percent completed more than half of the app. These participants gave strong ratings for FOCUS ease of use (3/4), convenience (3.7/4), utility (3.3/4), and ease of understanding (3.83/4). All participants (10/10) said they would recommend the app to other people with cancer and would return to the app with future problems. Participants’ favorite components were video recordings of other patients and the sleep and worry/uncertainty modules. Areas for improvement based on participant feedback included video quality for some components (i.e., lighting, sound), sleep diary ease of use, and a desire for professional guidance. Conclusions: The FOCUS intervention was successfully delivered via mobile technology and was feasible and acceptable per beta testing. The FOCUS mHealth app provides an evidence-based, accessible symptom management intervention for people with advanced cancer in rural communities. In accordance with participant feedback, for FOCUS 2.0 we will enhance video segments, incorporate a telehealth component to support app usage, and further develop the interactive and motivational features of the app. Future research will explore the effectiveness of this mHealth symptom management application via a randomized controlled trial.
Background: The rapid growth of digital technologies has generated large volumes of free-text data across healthcare, public health, and social research. These contain contextualised accounts of lived...
Background: The rapid growth of digital technologies has generated large volumes of free-text data across healthcare, public health, and social research. These contain contextualised accounts of lived experience that are often absent from quantitative measures. Despite their value, these data remain underused because qualitative analysis is traditionally designed for in-depth work on smaller numbers. Computational methods, including topic modelling and large language models, are increasingly promoted as efficient solutions. However, concerns persist regarding interpretability, bias, hallucinations, and loss of contextual depth. Critically, there is no established human-centred framework for evaluating the quality of machine-generated outputs for qualitative analysis. Objective: 1) To develop an AI evaluation framework for assessing machine-generated outputs, 2) Evaluate different AI approached to textual data analysis Methods: We developed and applied a human-centred evaluation framework, GRACE (Grounded Review and Assessment of Computational Evidence), to assess the quality of machine-generated textual outputs. GRACE was derived from established qualitative appraisal tools and operationalised four core indicators: interpretability, actionability, nuance, and redundancy, using structured scoring and reflexive consensus. We compared classic probabilistic topic modelling (LDA), a deep learning embedding-based approach (BERTopic), and three large language models (LLMs: LLaMA-3, Copilot, DeepSeek), used alone or in combination with prior structural topic modelling (STM). These were applied to the same corpus (n = 1,044 free-text responses). LLM prompting was iteratively refined, with a single-shot STM-based configuration selected for final evaluation due to reduced hallucinations. All outputs were analysed using Machine-Assisted Topic Analysis. A rapid manual thematic analysis of a 15% subsample (n = 152) served as a pragmatic comparator. Results: Model outputs were variable, with different AI methods producing different results from the same dataset. GRACE evaluation indicated that LDA achieved the highest overall mean score (2.6/5), followed by BERTopic and topic modelling plus Copilot (2.5), topic modelling plus LLaMA-3 (2.2), and topic modelling plus DeepSeek (1.9). LDA generated broader conceptual patterns requiring interpretive refinement; while BERTopic produced narrower, more descriptive clusters with thematic overlap. LLM-only outputs were very poor. The combination of topic modelling and LLMs performed slightly better: the outputs were well structured but often superficial and repetitive. Conclusions: Computational models produced different interpretations of the same dataset, and performance did not align with technical complexity. Large language models were not suitable for thematic analysis, especially when applied to raw data, generating generalised and sometimes inaccurate outputs. Classical probabilistic modelling, particularly topic modelling + qualitative human analysis using the Machine Assisted Topic Analysis (MATA) approach provided the highest quality results. We argue that the key issue is not whether a model “works,” but whether it support meaningful, contextually grounded results. GRACE offers a simple, human-centred framework to support this assessment and build evidence base for analysis of free-text data that is useful and nuanced.
Background: Telepalliative care, the use of telehealth in palliative care, has emerged as a strategy to improve access to specialist palliative services amid growing demand, workforce shortages, and i...
Background: Telepalliative care, the use of telehealth in palliative care, has emerged as a strategy to improve access to specialist palliative services amid growing demand, workforce shortages, and increasing digitalization of health care. Although telepalliative care has demonstrated positive outcomes for patients, families, and clinicians, its integration into standard services remains inconsistent. Existing initiatives are often operationally focused and rarely grounded in programme theory or developed collaboratively with key stakeholders, limiting sustainability and contextual alignment, particularly in Nordic health systems that emphasize home-based palliative care. Objective: This study aimed to develop a family focused model of telepalliative care for clinical practice through active involvement of key stakeholders. Methods: A co-design qualitative study grounded in interpretive description was conducted. The development followed the British Medical Research Council’s guidance for the development and evaluation of complex interventions and represents the development phase. Key stakeholders including patients, family representatives, specialized palliative care team members, community care nurses, general practitioners, voluntary representatives, IT consultants, managers, and researchers, were purposively recruited. Data were generated through four scientific workshops across two Danish sites, supplemented by participant observations of video consultations and a short questionnaire inspired by the Normalisation Measure Development (NoMAD) questionnaire. Data were analyzed using abductive thematic analysis, with qualitative and quantitative findings converged and iteratively refined through stakeholder consensus. A programme theory and logic model guided development. Results: Eighteen stakeholders participated in the workshops, with additional input from clinicians through observations (6 consultations involving 22 participants) and questionnaires (n=10). Findings highlighted both alignment and tension between the proposed model and current clinical practice, particularly regarding when and for whom telepalliative care should be used, clinician digital competencies, and family involvement. These, and insights from previous studies, informed the primary output of the study which is Pallvi – Family Focused Telepalliative Care, a comprehensive, theory-informed model comprising of a structured consultation guide and two co-designed quick guides; one for health care professionals and one for patients and families. Pallvi integrates family focused care, shared decision-making, advance care planning, and the Calgary-Cambridge Communication Guide, operationalized across seven consultation phases. Conclusions: Through systematic stakeholder involvement and theory-driven development, this study produced a contextually and culturally aligned family focused model of telepalliative care. Pallvi addresses identified gaps in telepalliative care research by providing a structured, practical guide designed to support communication, family involvement, and cross-sectoral collaboration. Future research will focus on feasibility and implementation testing to assess acceptability, fidelity, and sustainability in clinical practice and implementation.
Background: Physical activity (PA), sedentary behaviour (SB), and sleep play a key role in the health and development of young people (Carson et al., 2016; Chaput et al., 2016; Poitras et al., 2016)....
Background: Physical activity (PA), sedentary behaviour (SB), and sleep play a key role in the health and development of young people (Carson et al., 2016; Chaput et al., 2016; Poitras et al., 2016). This has led to the development of guidelines on PA, SB and sleep for children and young people aged 5-17 years (Health, 2017; Tremblay et al., 2016). Objective: To provide pilot data to capture 24-hour Movement Behaviours (24-hrMBs) of young people with learning disabilities in Scotland and in addition obtain the perceptions of young people’s views on the barriers and facilitators to achieving the 24-hrMBs. Methods: Employing a mixed-methods approach, the study will recruit up to 60 participants aged 12 to 17 years with learning disabilities living in Scotland. 24-hr MBs will be objectively measured for seven days using Actigraph (GT3x) accelerometers. Data from the Actigraph will be complemented by self-reported screen time and sleep data collected from parent and youth diaries. Personalised activity profiles will be generated for young people and their families creating an accessible overview of participants’ 24-hr MBs. Qualitative data will be collected using semi-structured interviews which will help to identify potential barriers and facilitators. The quantitative data will be presented through descriptive analysis and qualitative data will be analysed thematically employing deductive and inductive analysis (Braun & Clarke, 2006). Results: As of March 2026, six schools have been approached, and 47 parent and participant information sheets have been provided. This has led to 21 participants being recruited, and currently 12 participants have provided full data. Conclusions: It is anticipated that this study will help guide future studies and help improve the protocols that are to be adopted with this specialist population. Fundamentally the aim is to gather data to inform the design, implementation and analysis of interventions that support young people with learning disabilities to adhere to the 24-hr MBs. Clinical Trial: Not applicable
Background: Acute respiratory infections continue pose a transmission risk in outpatient care, making early identification and separation of potentially infectious patients quintessential. Digital, co...
Background: Acute respiratory infections continue pose a transmission risk in outpatient care, making early identification and separation of potentially infectious patients quintessential. Digital, contactless screening tools may aid with the separation of potentially infectious patients, however effectiveness can depend on users acceptance and engagement. Objective: To assess user acceptance of patients using a video-based digital Screening and Registration Terminal (SRT) to improve infection prevention at an outpatient clinic in Berlin, Germany. Methods: A cross-sectional survey was conducted among patients with acute care needs using the SRT between
October 4 to November 22, 2023. We describe summarized user acceptance factors including ease of use, intention
to use, perceived usefulness, attitude, privacy, audio-visual communication, and technical sensors overall and by sex,
age, education. Results: Of the 56 participants, 55% (29/56) were 20-39 years old, and 63% (35/56) had received higher education. Among respondents with available answers 55% (30/55) reported that the SRT was easy to use and 40% (22/55) found it useful. Intention to use was expressed by 56% (31/55) of respondents and 57% (31/54) reported a positive attitude towards technology. Privacy concerns were expressed by 24% (13/54) of participants, while 24% (13/54) did not indicate any and 7% (4/54) reported difficulties with audio-visual communication. Conclusions: More than half of the patients using the SRT positively reported on most user acceptance factors. This indicates that the SRT was generally well accepted, particularly with regard to ease of use and perceived usefulness. Privacy concerns and audio-visual communication issues were reported which underlines the importance of integrating user acceptance research when introducing new tools to address barriers to user acceptance early on.
Background: The escalating medical burden associated with stroke poses a substantial challenge, characterized by a skewed distribution wherein a minority of high-cost patients accounts for a dispropor...
Background: The escalating medical burden associated with stroke poses a substantial challenge, characterized by a skewed distribution wherein a minority of high-cost patients accounts for a disproportionate share of healthcare expenditures. Consequently, the timely and accurate identification of this cohort is paramount for optimizing the quality of care and mitigating unnecessary resource utilization. Objective: This study aims to construct a comorbidity network for stroke patients using hospital discharge data, extract topological features characterizing disease interactions, and integrate these features with machine learning algorithms to establish a robust and clinically interpretable framework for the accurate identification of high-cost stroke patients. Methods: We conducted a retrospective study using hospital discharge data from 10,301 stroke inpatients at a tertiary hospital in Northeast China between 2021 and 2023. Data from the 2021–2022 period were used to construct two specific networks: the Phenotypic Comorbidity Network (PCN) and the Distance-based Disease Cost Network (DDCN). From these networks, topological features were extracted to capture latent associations between comorbidities and high costs. The 2023 dataset was subsequently partitioned into training and testing sets to develop five machine learning models, including Logistic Regression (LR), Support Vector Machine (SVM), Neural Network (NN), Random Forest (RF), and XGBoost, for the identification of high-cost stroke inpatients. Furthermore, the SHAP method was applied to elucidate both the global and local contributions of the model features. Results: The integration of network features significantly improved model performance, with XGBoost exhibiting superior predictive capability (AUC = 0.911). Global feature importance analysis indicated that network features accounted for the majority of the total contribution (52.8%). Specifically, Shortest Distance (SD), length of stay, Normalized High-Cost Propensity (NHCP), age, and insurance type were identified as the top five predictors of high-cost risk. Moreover, SHAP interaction analysis revealed the phasic heterogeneity inherent in patient resource utilization. Conclusions: Our comprehensive framework, integrating comorbidity network analysis with machine learning algorithms, significantly enhances the identification of high-cost stroke inpatients. These findings highlight the framework's potential utility in optimizing healthcare resource allocation and enabling proactive cost containment strategies. Clinical Trial: Not applicable
Background: In the United States, many individuals lack adequate access to healthcare services due to a host of economic, logistical, and social barriers. Telehealth technologies and mobile health cli...
Background: In the United States, many individuals lack adequate access to healthcare services due to a host of economic, logistical, and social barriers. Telehealth technologies and mobile health clinics present the opportunity to close the “last mile” between patients and healthcare services. Objective: Our multidisciplinary team from healthcare and academia wanted to design a mobile health clinic with potential telehealth services, along with the supporting infrastructure as a first step towards developing such a program for our region. Methods: Our multidisciplinary team hosted a co-design session to collaboratively design and mock-up mobile health clinic services aimed at serving the needs of our community, with an emphasis on vulnerable populations within our region. Results: This session yielded insights into the necessity for flexible space, equipment, and staff, and how “high-tech” tools, like drones and robots, along with a fleet of small, medium, and large mobile health clinics, could be maximally positioned to traverse “the last mile” and provide equitable healthcare to our community. Conclusions: The use of mobile clinics to address last-mile challenges could have a transformative impact on community health, and co-design is a valuable tool to elucidate pragmatic opportunities to target first, and can aid in developing a broader roadmap to scale up strategically and sustainably.
Background: Background:
Chemotherapy-induced alopecia is among the most psychologically distressing adverse effects of systemic cancer therapy. Although scalp cooling is increasingly used to mitigate...
Background: Background:
Chemotherapy-induced alopecia is among the most psychologically distressing adverse effects of systemic cancer therapy. Although scalp cooling is increasingly used to mitigate hair loss, it is still largely perceived as a cosmetic intervention. Its broader psychological relevance and the biological basis of treatment success, particularly the preservation of follicular integrity under ongoing cytotoxic exposure remain insufficiently explored. Objective: Objective:
This study aimed to reconceptualize scalp cooling beyond visible hair preservation by examining its psychological impact in patients receiving highly alopecia-inducing chemotherapy, while integrating quantitative objective hair preservation metrics with structural and ultrastructural analyses of hair follicle damage to identify avenues for improving follicular integrity and scalp cooling efficiency. Methods: Methods:
82 patients undergoing highly alopecia-inducing chemotherapy consisting of sequential anthracycline-taxane regimen (four cycles of epirubicine and cyclophosphamide followed by 12 weekly paclitaxel applications) received standardized scalp cooling. Objective hair preservation was quantified using the Hair Mass Index (HMI) as a standardized and reproducible measure of hair retention. Structural and ultrastructural follicular integrity was assessed using light microscopy as well as scanning and transmission electron microscopy. Objective hair preservation metrics were analyzed in relation to patient-reported quality-of-life outcomes (EORTC-based measures), subjective treatment burden, and cognitive appraisal of the scalp cooling experience. Multivariable regression models were applied to identify determinants of post-therapeutic quality of life. Results: Results:
Visible chemotherapy-induced alopecia was successfully prevented in more than half of the treated patients. Scalp cooling resulted in substantial objective hair preservation as quantified by the Hair Mass Index; however, HMI values showed only a limited association with post-therapeutic quality-of-life outcomes. In contrast, cognitive appraisal of scalp cooling emerged as a central determinant of post-therapeutic quality of life, independent of the degree of objective hair retention. Structural and ultrastructural analyses demonstrated that preservation of follicular integrity was closely associated with successful macroscopic hair retention under ongoing cytotoxic exposure, supporting a biological basis for the clinical effectiveness of scalp cooling. Conclusions: Conclusions:
The clinical relevance of scalp cooling extends beyond objective and visible hair preservation and appears to reside predominantly in its psychological impact on patients undergoing highly alopecia-inducing chemotherapy. Importantly, the identification of structural and ultrastructural markers of follicular vulnerability provides a mechanistic foundation for the future optimization of scalp cooling approaches and for the development of adjunct follicle-directed protective strategies to enhance follicular integrity and support patient well-being during cytotoxic therapy.
An emerging systems-engineering framework, the Q‑OSI (Quality Open Systems Interoperability) Model reconceptualizes HEDIS and UDS “gaps in care” as layered failures across a quality performance...
An emerging systems-engineering framework, the Q‑OSI (Quality Open Systems Interoperability) Model reconceptualizes HEDIS and UDS “gaps in care” as layered failures across a quality performance stack rather than isolated clinical or documentation problems. Drawing an analogy to the OSI model in network communication, Q‑OSI defines seven interdependent layers—Compliance & Reporting, Measure Logic, Structured Data, Workflow Execution, Clinical Decision, Care Coordination, and Patient Activation—through which a quality “signal” must successfully transmit for a measure to close. In current practice, missed HEDIS and UDS targets are often attributed globally to “clinical performance” or “poor documentation,” obscuring where in the end‑to‑end pipeline failures actually occur and leading to diffuse, non-specific interventions. The Q‑OSI Model instead asserts that most gaps are interoperability issues between technical, workflow, and behavioral layers: for example, an A1C result documented as free text (Structured Data failure), a mammogram order never scheduled (Workflow Execution failure), or a patient never contacted for outreach (Care Coordination failure), even when the underlying clinical decision is appropriate. By providing a simple, memorable, seven-layer map, the framework enables quality and informatics teams to classify defects by layer, align interventions more precisely (eg, templates and coding at Layer 3, standing orders at Layer 4, SMS automation at Layer 6), and monitor whether remediation efforts are addressing the true bottleneck. For public health informatics, Q‑OSI offers a practical bridge between population health measurement, data standards, clinical operations, and patient-facing engagement, positioning quality improvement as an engineering discipline grounded in layered interoperability rather than a reactive cycle of measure chasing. This Viewpoint introduces the Q‑OSI Model, illustrates its use with common HEDIS scenarios, and outlines how it could inform maturity models, dashboard design, and implementation research in settings such as Federally Qualified Health Centers and safety-net systems.
Background: This randomized feasibility study addresses the safety and preliminary efficacy of a jump-based training program in older adults, a population in which high-impact exercises are historical...
Background: This randomized feasibility study addresses the safety and preliminary efficacy of a jump-based training program in older adults, a population in which high-impact exercises are historically underutilized. Our results demonstrate that jump-based training is feasible and safe for older women, with high adherence and no adverse events. Furthermore, the intervention showed a clinically relevant effect size (Cohen's d = 0.60) in improving functional mobility (Timed Up and Go Test - TUG), a strong predictor of fall risk. We believe these results are of great interest to both the academic community and those who apply exercise to this population, especially as they provide a promising basis for the inclusion of more specific and powerful strength exercises in geriatric rehabilitation programs. Objective: Objective: To evaluate the feasibility, safety, and preliminary effects of a 5-week jump-based training program, compared to a traditional multicomponent training program, on functional mobility and lower limb power in older adults.Study Design:Randomized feasibility trial with an unbalanced design. Methods: Randomized feasibility trial with an unbalanced design.Setting:Community-dwelling older adults. Participants:Forty-four (N=44) older adults (≥60 years; 43 women) were randomized into an experimental group (EG; n=35) and a control group (CG; n=9). Interventions:The EG performed a progressive jump-based training program (3x/week).The CG engaged in traditional multicomponent training (strength, endurance, balance).Main Outcome Measures: Feasibility (adherence and safety) and preliminary efficacy in functional mobility (Timed Up and Go-TUG), gait speed (4-Meter Walk - V4M), and lower limb power (Vertical Jump - VJ Results: The intervention proved feasible with high adherence and no adverse events. A Group × Time interaction for TUG approached significance (p = 0.137) with a large effect size (Cohen's d = 0.60) favoring the EG. Significant main effects of Time were found for TUG (p = 0.011) and V4M (p = 0.002 Conclusions: This study demonstrates that jump-based training is a feasible and safe modality for older women. Preliminary data suggest clinically relevant improvements in functional mobility, providing a basis for future large-scale randomized clinical trials. Clinical Trial: Brazilian Registry of Clinical Trials (ReBEC) under the identification number RBR-3vqhv5d.
Background: Although social media is often viewed by residents and could be used to reinforce teaching points, there is little data on methods that improve engagement in learning medical topics throug...
Background: Although social media is often viewed by residents and could be used to reinforce teaching points, there is little data on methods that improve engagement in learning medical topics through this medium. Objective: We observed how the timing of posted questions, answering questions correctly, and giving supportive comments affected the engagement of residents learning point-of-care ultrasound on social media. Methods: Of 60 medical residents, 35 followed an Instagram account that posted ultrasound video clips with questions during the academic year. Engagement, E, was the percentage of questions answered of the total number of clips viewed for each post and each resident. E was tested for an association with (1) weekend vs. weekday posts, (2) answering questions correctly vs. incorrectly, and (3) supportive responses from faculty vs. no feedback. Results: Of 16 posts, 120 questions were answered from 428 clips viewed by 32 residents, for an E =28% [range: 15-59%] for posts and a median (IQR) E=19% (0-39%) for residents with 71% (n=25) engaging on at least one post. E was higher during weekdays vs. weekends, 30% vs. 21% (p=0.007), and correlated to answering correctly vs. incorrectly (r=0.6, p<0.001). A supportive comment resulted in a lower percentage of answering the next post, compared to no feedback (30% vs. 71%, p=0.02). Conclusions: Resident engagement in social media was higher with having questions answered correctly, but, surprisingly, was lower when posting during weekends and immediately after receipt of a supportive comment.
Background: Psychological skills training (PST) is a core component of sport psychology, supporting athletes’ performance, well-being, and capacity to manage competitive stress. However, access to h...
Background: Psychological skills training (PST) is a core component of sport psychology, supporting athletes’ performance, well-being, and capacity to manage competitive stress. However, access to high-quality, practitioner-led PST is often constrained by time, cost, availability of trained professionals, and stigma surrounding help-seeking. In response, digital interventions such as mobile applications, biofeedback systems, and immersive technologies have been increasingly adopted to deliver PST in more scalable and flexible formats. Despite rapid growth in this area, evidence regarding the promises and challenges of digital PST remains fragmented across modalities and outcome domains. Objective: This systematic review synthesizes empirical evidence on the use of digital interventions for delivering PST in athlete populations. Specifically, it maps the digital modalities employed, the psychological skills and frameworks targeted, the populations and sporting contexts studied, and the promises and challenges reported in relation to effectiveness, feasibility, and implementation. Methods: We conducted a PRISMA-compliant systematic review of English-language studies published between 2000 and 2025. Three databases (Scopus, Web of Science Core Collection, and ProQuest Dissertations and Theses) were systematically searched, and additional records were identified through a manual search. Eligible studies examined digital or technology-based interventions deployed to support PST outcomes in athlete populations and reported empirical quantitative, qualitative, or mixed-methods findings. Two reviewers independently screened records and extracted data, resolving discrepancies through discussion. Results: Thirty-six studies met the inclusion criteria, encompassing virtual reality-based interventions, mobile applications, and biofeedback or neurofeedback systems. Across modalities, digital PST interventions targeted a range of psychological skills, including stress and anxiety regulation, attentional control, imagery ability, self-talk, and emotional regulation. Reported promises included improvements in affective, cognitive, physiological, and performance-related outcomes, enhanced accessibility, flexibility, and engagement of PST delivery, and potential for skill transfer beyond sport. However, recurring challenges were also identified, such as limited personalization, variable user engagement, technical and cost barriers, and inconsistent or weaker efficacy relative to traditional PST methods. Conclusions: Digital interventions offer a meaningful extension to traditional PST by widening access, enhancing immersion, and providing real-time feedback that supports psychological skill development. However, their effectiveness appears constrained by methodological variability, limited personalization, and implementation challenges. Future research should prioritize rigorous longitudinal designs, clearer alignment with PST theory, and hybrid delivery models in which digital tools complement practitioner expertise, to ensure digital PST enhances rather than dilutes psychological practice.
Digital health is now embedded in routine care through patient portals, teleconsultations, remote monitoring, digital triage, and other hybrid service models. While these changes can improve access an...
Digital health is now embedded in routine care through patient portals, teleconsultations, remote monitoring, digital triage, and other hybrid service models. While these changes can improve access and efficiency, they may also create new barriers for older adults who have limited cognitive, sensory, functional, or social capacity to engage with digitally mediated care. Current constructs such as digital literacy, digital exclusion, and conventional frailty only partly explain this problem because they do not fully capture the mismatch between the digital demands of healthcare systems and the real world capabilities and supports available to patients.
This Viewpoint introduces digital frailty as a clinically relevant, multidimensional state of vulnerability that arises when a person’s intrinsic capacity and available support are insufficient to meet the digital requirements of healthcare. We argue that digital frailty should be understood not as a synonym for age, disability, or low digital confidence, but as a relational and potentially modifiable mismatch between individuals and care environments. Framing the issue in this way shifts attention from blaming patients to designing safer and more equitable systems.
To operationalize this concept, we propose a Digital Health Vulnerability Index as a pragmatic framework for identifying patients at risk of digitally mediated care failure. The framework focuses on four proximal domains of vulnerability, namely access, skills, confidence or trust, and support, and is paired with brief consideration of hearing, vision, and cognition to improve clinical interpretability. Rather than functioning as a static label, the index is intended as a routable mechanism to trigger proportionate responses such as assisted digital support, proxy enabled access, simplified workflows, and analogue alternatives for safety critical steps.
We further propose proportionate universalism as the most appropriate implementation principle, so that digital support is universal in reach but calibrated in intensity according to need. This approach has implications beyond individual assessment and extends to pathway design, procurement, governance, reimbursement, and digital inclusion policy. In ageing societies, digital vulnerability should be recognized as a determinant of functional access to care. A digitally inclusive health system therefore requires not only better technology, but also better identification, adaptation, and accountability for the patients most at risk of being left behind.
Background: Alcohol Use Disorder (AUD) affects Punjabi-American communities at disproportionately high rates, yet remains under-researched and under-treated. The "model minority" myth masks significan...
Background: Alcohol Use Disorder (AUD) affects Punjabi-American communities at disproportionately high rates, yet remains under-researched and under-treated. The "model minority" myth masks significant health disparities among Asian-American subgroups, and aggregated data collection practices obscure the unique cultural, historical, and structural factors shaping AUD in Punjabi-Americans. Cultural stigma, family dishonor (izzat), and a lack of culturally competent services create structural barriers to treatment. Even though evidence indicates the effectiveness of culturally tailored interventions, no rigorous studies have designed or validated intervention models specifically for Punjabi-Americans. Objective: This paper proposes a community-based, mixed-methods study to assess AUD prominence and identify barriers to care among Punjabi-Americans in the San Francisco Bay Area. Drawing on Bronfenbrenner’s ecological systems theory and the framework of structural competency, the study aims to generate empirical evidence for designing culturally informed, evidence-based interventions tailored to community needs, and emphasizing the need for further research. Methods: The study will employ a cross-sectional, mixed-methods design guided by Community-Based Participatory Research (CBPR) principles. A bilingual (English/Punjabi) anonymous survey will be administered to 88–100 self-identified Punjabi-American adults (ages 18+) in the Bay Area, using Web-Based Sampling, Community-Based Recruitment, and Respondent-Driven Sampling. The survey instrument includes 18 questions across 5 domains: demographics, alcohol consumption (AUDIT-C), acculturation (SL-ASIA), treatment attitudes, and macrosystem/microsystem factors. Quantitative data will be processed with SPSS by IBM, and analyzed using descriptive statistics, chi-square tests, and regression analyses. Qualitative data from open-ended responses will be analyzed using thematic analysis guided by structural competency and ecological systems theory. Results: As of February 2026, the study is in the design and community engagement phase. The survey instrument has been developed and is undergoing review. Institutional Review Board (IRB) approval will be sought from the University of California, Berkeley. Data collection is anticipated to conclude by May 2026. Conclusions: This study addresses critical gaps in the literature by applying a structural competency framework to AUD in a Punjabi-American context, connecting critical theory to public health, and using the historically successful Amrit Prachar movement as a precedent for community-based interventions. Findings will directly inform the development of a future Culturally Adapted Intervention and contribute to advocacy for disaggregated health data collection for Asian-American subgroups.
Background: Background: People experiencing homelessness (PEH) face high morbidity and unmet health care needs. To address these gaps, healthcare providers across the United States are increasingly ad...
Background: Background: People experiencing homelessness (PEH) face high morbidity and unmet health care needs. To address these gaps, healthcare providers across the United States are increasingly adopting “field medicine” models that deliver mobile health services in shelters, homeless encampments, mobile clinics, and other community settings. Despite their expanding use, systematic evaluations of field medicine programs remain limited. Objective: Objectives: This paper describes a protocol for a mixed methods evaluation of field medicine for PEH across Los Angeles (LA) County, California. Methods: Methods: PEH receiving field medicine were recruited into an ongoing longitudinal study of the LA County’s unhoused population; a subset of participants in the existing probability-sampled cohort serve as the comparison group. Using monthly online survey data and a quasi-experimental design, we examine who accesses field medicine, how patients use and perceive care, and its impact on health and service engagement. We also conduct participant observation of field medicine teams to document patient–provider interactions and care coordination and carry out semi-structured interviews with providers, patients, and non-patients. Quantitative survey and qualitative findings will be integrated to identify convergence, complementarity, and explanatory insights. Results: Results: Recruitment of PEH receiving field medicine occurred between August 2024 and October 2025. Among 847 individuals referred from field medicine, 749 were eligible and 436 completed the first monthly survey and were enrolled. For the comparison group, 902 of the 1,413 participants ever enrolled in the existing cohort completed a survey during the field medicine recruitment period and met eligibility criteria. Participant observation included 82 field visits (≈500 hours) across diverse service locations and more than 300 patient–clinician interactions. Semi-structured interviews were conducted with 15 providers, 23 field medicine patients, and 12 non-patients. Conclusions: Conclusions: This study represents one of the first large-scale mixed-methods evaluations of field medicine. With increasing health threats from criminalization, climate-related events, and other socioenvironmental hazards, field medicine may mitigate health risks and improve systems engagement for PEH. Findings will provide rigorous evidence to inform service delivery and policy decisions.
Background: Post-infectious cough (PIC) is a distinct subacute condition lasting 3 to 8 weeks, affecting 11% to 25% of patients following a respiratory infection. While recent reviews have addressed a...
Background: Post-infectious cough (PIC) is a distinct subacute condition lasting 3 to 8 weeks, affecting 11% to 25% of patients following a respiratory infection. While recent reviews have addressed acupuncture for chronic cough (>8 weeks), the efficacy and safety of these therapies specifically targeting the transient inflammatory pathophysiology of subacute PIC remain unsynthesized. Current pharmacological interventions often provide limited relief or carry adverse effects. Objective: This protocol aims to evaluate the efficacy and safety of needle-based acupuncture therapies for adults with subacute PIC, compared to conventional medication, sham treatment, or no treatment. Methods: We will search MEDLINE (via PubMed), Embase, CENTRAL, CINAHL, Scopus, AMED, SCI, and five Chinese databases from inception onwards. Randomized controlled trials (RCTs) involving adults (≥18 years) with PIC will be included. To avoid pharmacological confounding, acupoint injection will be excluded. Primary outcomes are the Leicester Cough Questionnaire (LCQ) total score and validated cough severity scales (e.g., Visual Analogue Scale). Two reviewers will independently screen studies, extract data, and assess the risk of bias using the Cochrane RoB 2 tool. A random-effects model will be used for meta-analysis, with results stratified by predefined comparison strata (acupuncture vs sham/placebo, active therapy, or add-on designs). Evidence certainty will be evaluated using the GRADE framework. Results: This protocol was registered in PROSPERO (CRD420251268158).
As of February 2026, preliminary database searches have been piloted. Formal study screening, data extraction, and evidence synthesis are scheduled to commence in April 2026. Conclusions: This systematic review will provide rigorously synthesized evidence exclusively for the subacute PIC population, offering targeted clinical guidance that is currently missing from broader chronic cough assessments. Clinical Trial: PROSPERO CRD420251268158; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251268158
Background: Fixed orthodontic appliances create new retentive niches that favour dental biofilm accumulation and are associated with enamel demineralization and periodontal inflammation. Beyond these...
Background: Fixed orthodontic appliances create new retentive niches that favour dental biofilm accumulation and are associated with enamel demineralization and periodontal inflammation. Beyond these clinical effects, orthodontic treatment may induce shifts in the oral microbial ecosystem (commonly referred to as dysbiosis), potentially influenced by individual host–microbiota interactions. Objective: This study aims to evaluate whether an intensive personalized prevention strategy, added to standard orthodontic care, limits orthodontic treatment–associated microbial dysbiosis, defined as longitudinal quantitative shifts from commensal toward pathogenic microbial complexes, during the first year of fixed orthodontic treatment, compared with standard care alone. Methods: This is a prospective, multicenter, randomized, open-label, parallel-arm interventional study with a 12-month follow-up. Eighty participants aged 12–20 years requiring fixed orthodontic treatment will be randomized in 1:1 ratio to receive either (i) standard care or (ii) standard care plus personalized prevention. The preventive intervention includes repeated oral health education sessions, supervised toothbrushing, dietary education, plaque disclosure, and chairside prophylaxis using a standardized professional protocol. Study visits are scheduled at appliance placement (baseline) and at 3, 6, 9, and 12 months (±10 days). The primary endpoint is the longitudinal change in quantities of selected bacterial and viral species in saliva and dental biofilm assessed using high-throughput microfluidic qPCR. Secondary endpoints include salivary inflammatory profiling (cytokines/chemokines), clinical indices (plaque index, bleeding on probing, white spot lesions), compliance measures (wear of oral hygiene devices and toothpaste consumption by weight), and participant satisfaction. Analysis will follow a modified intention-to-treat approach, complemented by per-protocol analyses. Results: The study will include a 6-month enrolment period with baseline data collection, followed by 12 months of participant follow-up. The total study duration is expected to be 24 months. Data analysis and reporting are planned upon completion of follow-up. Conclusions: PREPERMIO is a randomized interventional study evaluating a personalized preventive strategy during fixed orthodontic treatment using biologically driven longitudinal endpoints. By focusing on microbial homeostasis and bacterial community shifts, and integrating bacterial, viral, and host inflammatory markers, it addresses limitations of prior studies centered mainly on clinical or behavioural outcomes and has the potential to inform future personalized prevention strategies in orthodontics. Clinical Trial: NCT06752902
Background. Attention-Deficit/Hyperactivity Disorder (ADHD) affects approximately 3-5% of adults globally, characterized by inattention, hyperactivity, and impulsivity, causing substantial functional...
Background. Attention-Deficit/Hyperactivity Disorder (ADHD) affects approximately 3-5% of adults globally, characterized by inattention, hyperactivity, and impulsivity, causing substantial functional impairment across occupational, academic, and social domains. Associations between lifestyle factors, including physical activity patterns, sleep quality and duration, screen time behaviors, dietary intake patterns, anthropometric characteristics, and substance use, remain significantly underexplored in North African populations and require comprehensive international investigation, given the severe limitations in epidemiological data. Understanding these modifiable factors could inform evidence-based interventions to manage symptoms and improve function. Objectives. To (i) estimate adult ADHD prevalence in Tunisia and internationally stratified by presentation type and demographics, (ii) examine associations between comprehensive lifestyle factors and symptom severity across multiple domains, and (iii) employ machine-learning (ML) algorithms to identify complex non-linear patterns and interaction effects between lifestyle variables and ADHD symptomatology across diverse populations. Methods. This two-phase quantitative cross-sectional study will recruit approximately 5,000 Tunisian adults aged 18-65 years in Phase I, followed by 50,000 international participants across North Africa, the Middle East, Europe, and North America in Phase II. Data collection employs a dual-mode approach: Google Forms for digital administration and paper-based questionnaires for participants with limited internet connectivity, with mode selection determined by availability at the time of distribution. The assessment battery comprises validated instruments totaling approximately 130-132 items requiring approximately 28-32 minutes of completion time, including the Adult ADHD Self-Report Scale, the International Physical Activity Questionnaire-Short Form, the Pittsburgh Sleep Quality Index, the Smartphone Addiction Scale-Short Version, the Bergen Social Media Addiction Scale, the Screen Time Questionnaire, Short Food Frequency Questionnaire, and the novel Substance Use Assessment Scale. To accommodate the international sample, all instruments will be offered in English, French, and Arabic, allowing participants to choose their preferred language. Officially validated translations will be used where available. For instruments lacking a validated version, a standardized translation will be employed for this study, with subsequent psychometric validation planned. ML algorithms, including random forests, gradient boosting, and neural networks, represent the primary analytical approach, complemented by multivariable regression for association examination. Expected Outcomes. This protocol provides the first comprehensive adult ADHD prevalence estimates for Tunisia and establishes international baseline cross-cultural data enabling systematic comparisons across geographic regions and healthcare systems. ML identification of complex interaction patterns between lifestyle factors and symptom presentations represents the primary methodological contribution, revealing non-linear relationships and distinct phenotypic subgroups. Findings will inform the development of targeted lifestyle-based interventions addressing modifiable risk factors.
Background: Rapid urbanization in India is straining the government's capacity to provide basic amenities such as housing, sanitation, electricity, and water. One group of people who are deeply affect...
Background: Rapid urbanization in India is straining the government's capacity to provide basic amenities such as housing, sanitation, electricity, and water. One group of people who are deeply affected are menstruators where menstrual experiences are shaped by an interplay of deep-rooted cultural norms and emerging socio-political discourse, ranging from stigma to bodily autonomy. In urban slums, referred to as bastis in Telugu and Hindi, this reality is worsened by spatial congestion and limited water, sanitation, and hygiene (WASH) resources. This study is situated in the populous slum colonies of Film Nagar, Hyderabad, where residents navigate precarious living conditions and a scarcity of basic amenities. Despite the surrounding affluence of the media and technology sectors, approximately 80% of the local population resides in these 20 underserved settlements 1. Objective: In this context, we argue that menstrual experiences are profoundly shaped by an interplay of biological, socio-cultural, political, economic, and environmental factors. Accordingly, our research seeks to understand how these intersecting determinants manifest in everyday life to influence the lived reality of menstruators. Methods: Guided by biopolitics and poststructuralist feminism, we take a critical-ethnographic approach to analyze contextual factors shaping the menstrual experiences of slum-dwelling menstruators. This study uses a multi-method data generation strategy including rapport building, participant observations, focus groups, in-depth interviews, and digital storytelling. Frame analysis will be used for data analysis and will occur concurrently with data generation. Results: This proposal describes the research being conducted as part of the primary author’s doctoral dissertation. The doctoral program is funded by the Social Sciences and Humanities Research Council (SSHRC) Doctoral Fellowship (2023–2026), and the data collection component of the study is supported by the International Development Research Centre (IDRC) through the International Doctoral Research Award (2024–2025). The study has received ethics approval from the University of Toronto Research Ethics Board and the institutional ethics committee at the University of Hyderabad, India, in January 2025. As of September 2025, 18 households had been recruited. The final phase of data collection is scheduled for March 2026. Study findings are anticipated to be disseminated and published by September 2027. Conclusions: The novelty of this study is predicated on the use of a multi-method critical ethnographic study design. This study’s findings are expected to 1) highlight the interplay of socio-political, familial, and environmental factors affecting menstrual health and bodily autonomy, and 2) guide government bodies, research institutions, and NGOs in developing context-sensitive policies and programs for menstruators.
Background: Human–autonomy teaming (HAT) has the potential to reshape surgical practice by fostering true partnership between surgeons and intelligent systems. To achieve this, AI must move beyond s...
Background: Human–autonomy teaming (HAT) has the potential to reshape surgical practice by fostering true partnership between surgeons and intelligent systems. To achieve this, AI must move beyond static scoring to provide adaptive, real-time guidance based on reliable skill assessment. Objective: This scoping review aims to map the current landscape of machine learning (ML) methods for surgical skill assessment and evaluate their technical readiness for integration into functional HAT systems. Methods: Following PRISMA-ScR guidelines, we conducted a systematic search across three major scientific databases (PubMed, IEEE Xplore, and Scopus). We identified and analyzed 92 peer-reviewed studies published between 2019 and 2025. The review focused on data modalities (kinematics, video, biosignals), model architectures, and validation environments. Results: Our analysis of the 92 included studies reveals a dominant shift toward multimodal data integration and deep learning architectures. While high performance is frequently reported on benchmark datasets, significant barriers to HAT integration persist. We identified that a majority of current models lack interpretability and fail to demonstrate generalizability to real-world clinical settings. Furthermore, validation practices remain inconsistent, with limited evidence of adaptivity to individual user needs during live surgical workflows. Conclusions: Current AI techniques provide a robust foundation for objective skill assessment, but they are not yet ready for autonomous teaming. Future development must prioritize model robustness, interpretability, and seamless integration into clinical environments to transition from standalone assessment tools to effective surgical teammates. Clinical Trial: The protocol was registered at OSF Registries [https://doi.org/10.17605/OSF.IO/PQWS5]
Background: Delivering sustained lifestyle interventions for individuals with type 2 diabetes mellitus (T2DM) remains challenging. Digital health interventions may help overcome barriers related to ac...
Background: Delivering sustained lifestyle interventions for individuals with type 2 diabetes mellitus (T2DM) remains challenging. Digital health interventions may help overcome barriers related to access scalability and resource constraints. Objective: This study aimed to evaluate the effectiveness and feasibility of a 16-week national digital lifestyle intervention with health coaching for adults with type 2 diabetes mellitus in Brunei Darussalam, focusing on changes in glycemic control and health-related quality of life. Methods: Participants were recruited through both web-based and offline methods and enrolled in a 16-week digital intervention program that combined online education with offline health coaching. Participants continued their existing medications, without modification. Clinical outcomes (HbA1c, fasting blood glucose, BMI, waist circumference, lipid profile), lifestyle behaviors, and health-related quality of life (QoL) were assessed at baseline and postintervention. Results: A total of 102 of 122 participants (83.6%) completed the intervention. Mean HbA1c significantly decreased by 1.3%, fasting blood glucose by 1.7 mmol/L, BMI by 0.4 kg/m², and waist circumference by 2.0 cm (all P<0.001). Total cholesterol and triglycerides decreased by 0.4 mmol/L and 0.5 mmol/L, respectively (P<0.001). High completion rates and favorable participant feedback indicated strong feasibility and acceptability. Conclusions: This national digital intervention was associated with clinically meaningful improvements in glycemic control and QoL among individuals with T2DM in Brunei Darussalam. These findings support the potential role of scalable digital health interventions in strengthening diabetes care, particularly in resource-limited settings. Clinical Trial: MHREC/MOH/2022/4(1)
Background: Chinese-language discussions of complementary and alternative medicine (CAM) on social platforms provide an observable record of how commenters negotiate credibility, risk, and treatment i...
Background: Chinese-language discussions of complementary and alternative medicine (CAM) on social platforms provide an observable record of how commenters negotiate credibility, risk, and treatment integration in high-stakes cancer contexts. Objective: To identify the dominant information frames through which commenters validate and interpret cancer-related CAM information in Chinese-language YouTube comment discourse. Methods: We analyzed 2,416 publicly available comments from 12 Chinese-language YouTube videos about cancer and CAM (uploaded 2023-2025). After preprocessing, 2,403 comments were modeled using BERTopic with multilingual sentence embeddings (paraphrase-multilingual-MiniLM-L12-v2), UMAP dimensionality reduction, and HDBSCAN clustering. Topics were interpreted through a structured human-in-the-loop protocol, including iterative topic review and intra-coder consistency checks. Results: The initial model produced 152 topics; 30.4% (731/2,403) of comments were assigned to an outlier topic. After topic reduction and exclusion of non-substantive topics (eg, platform interaction, off-topic disputes), 30 topics (1,491 comments) were grouped into four frames: (1) cultural authority and access pathways, (2) experiential solidarity and community validation, (3) evidence negotiation through everyday regimens, and (4) negotiating biomedical risk and treatment integration. Conclusions: Credibility work in Chinese-language cancer CAM comment spaces is organized around culturally embedded validation logics beyond biomedical authority. Frame-aware information support (eg, epistemic metadata to distinguish experiential support from clinical guidance) may help commenters navigate mixed-evidence environments more safely without implying clinical endorsement.
Background: The growing volume of secure messaging within the patient portal has imposed significant demands on clinicians and contributed to burnout. Little is known about the characteristics of pati...
Background: The growing volume of secure messaging within the patient portal has imposed significant demands on clinicians and contributed to burnout. Little is known about the characteristics of patients who comprise high-volume message senders, and we lack a nuanced understanding of patient messaging intensity beyond measures accounting for sheer volume. Objective: Our objective was to characterize older adult patients (65+) with high secure messaging volume, examining both patient characteristics and other aspects of their messaging intensity such as messaging frequency, length, and messaging use relative to patient portal logins and healthcare encounters. Methods: We analyzed electronic medical record (EMR) and patient portal data from a large academic health system, encompassing 16,023 older adults who sent 199,952 messages over a 12-month period. We developed five measures to account for secure messaging intensity. Our primary measure of messaging intensity was based on message volume; high-volume message senders were identified using outlier analysis based on patients’ aggregate number of messages sent during the observation period. Additional measures of messaging intensity included identifying individuals with concentrated periods of messaging, message length (character count), a ratio of messages to portal logins and a ratio of messages to healthcare encounters. We compared sociodemographic characteristics, health status, and messaging intensity of high-volume secure messaging senders to other message senders. We also identified patients who were classified as high-intensity message senders based on all five measures of messaging intensity (‘super-senders’). Results: Of 16,023 older adult patients who sent at least one message during the observation period, 1,298 (8.1%) were classified as high-volume message senders; these patients accounted for 39.7% of total messages. High-volume message senders, compared to all other message senders, were more likely to be White (80.4% vs. 72.5%, p < 0.001), have higher comorbidity scores (2.6 vs. 1.8, p <0.001), and higher incidence of cancer (35.8% vs. 22.8%, p<0.001) and dementia (8.3% vs. 6.1%, p < 0.002). High-volume message senders were also more likely to be identified as having concentrated periods of messaging, to send longer messages, and to send more messages in relation to patient portal logins and healthcare encounters. A small subgroup of patients classified as high-volume senders were also classified as high-intensity across all four of the other measures of messaging intensity (59/1,298; 4.5%), the ‘super senders’. Conclusions: High-volume message senders represent a small but distinct group of older patients who send a disproportionate share of messages to clinicians. Triangulating multiple measures of messaging intensity can help provide additional context about patient messaging behavior and help to identify patients that may most benefit from targeted outreach while potentially easing clinicians' inbox workload.
Background: While telehealth has become a transformative tool enhancing healthcare accessibility and efficiency, adoption rates in China remain low. Chinese healthcare professionals’ low telehealth...
Background: While telehealth has become a transformative tool enhancing healthcare accessibility and efficiency, adoption rates in China remain low. Chinese healthcare professionals’ low telehealth adoption rates are poorly understood. Objective: Our study investigates the key factors influencing Chinese healthcare professionals’ intention to adopt and actual use of telehealth. Based on the results from estimating an integrated telehealth use framework, we also make recommendations for improving healthcare professionals’ telehealth adoption. Methods: Data on 10,372 healthcare professionals from the 2023 Xi’an Healthcare Worker Survey were analyzed, utilizing descriptive statistics (chi-square test, group differences), reliability testing (Cronbach’s α coefficients), Discriminant validity analysis (square root of average variance extracted) and fit tests. Based on our integrated telehealth use framework, structural equation modeling was employed to test hypotheses and path relationships, including multi-group analysis to examine demographic moderating effects. Results: Confirming our hypotheses on telehealth intention to use and actual use, the structural equation model showed strong fit indices. Key predictors of behavioral intention to use telehealth included effort expectancy, price value, performance expectancy, and social influence. Behavioral intention and facilitating conditions positively influenced actual use behavior, while demographic characteristics moderated specific relationships. Conclusions: Our study identifies critical factors influencing healthcare professionals’ adoption of telehealth, including performance expectancy, social influence, and facilitating conditions. It offers an integrated framework to assess behavioral intentions and provides practical insights for advancing telehealth implementation in China. Tailored strategies for diverse demographics and institutions are essential for promoting sustainable adoption. Clinical Trial: This study was reviewed and approved by the Biomedical Ethics Committee of Xi’an Jiaotong University (approval number: XJTUAE2646).
Background: Nowadays, Artificial Intelligence (AI) tools, such as ChatGPT, are increasingly used to provide health-related information. However, the accuracy of this information in dermatology, partic...
Background: Nowadays, Artificial Intelligence (AI) tools, such as ChatGPT, are increasingly used to provide health-related information. However, the accuracy of this information in dermatology, particularly regarding sun protection and skin cancer prevention, has not been assessed. Objective: This study aimed to evaluate the quality of ChatGPT-generated responses to common questions related to sun protection and skin cancer prevention by comparing them with guidelines from the American Academy of Dermatology (AAD). Methods: A set of nine commonly asked questions on sun protection and skin cancer prevention was submitted to ChatGPT. Each response was evaluated across four key domains: accuracy, completeness, clarity, and relevance. Scoring was based on alignment with AAD recommendations and assessed independently. Results: ChatGPT responses were accurate, clear, and relevant. Most answers closely matched the AAD’s guidance, although a few responses showed slight omissions concerning specific contextual details. Conclusions: While not a replacement for professional healthcare, ChatGPT provides valid and accessible information on skin cancer prevention. With regular and strong evaluation, its role in AI-based dermatological tools may become significant in supporting public health education. Clinical Trial: not applicable
Background: Despite the many negative health outcomes associated with unhealthy screen media use, patient counseling by pediatricians in the primary care setting remains a challenge. The American Acad...
Background: Despite the many negative health outcomes associated with unhealthy screen media use, patient counseling by pediatricians in the primary care setting remains a challenge. The American Academy of Pediatrics (AAP) developed the Family Media Plan, but its online format may not lend itself to use in the time-constrained clinical setting. Objective: This exploratory study describes parent perceptions toward screen media use and family-perceived utility of the Healthy Media Use Contract (HMUC), a simplified, 1-page print version of the AAP HealthyChildren.org Family Media Plan for use in the clinical setting. Methods: A qualitative phenomenological approach was used to explore family experience with the HMUC. Families of children ages 6-13 years scheduled for primary care appointments were identified and consented in clinic prior to their appointment. During their appointment, their physician provided the family with the HMUC alongside standard screen media use counseling. Families were encouraged to post the HMUC in a common area within the home (e.g., on the refrigerator). Approximately one month after their clinic appointment, participating families completed a semi-structured interview via phone or Zoom to describe their experience using the HMUC. Families were provided a $25 gift card as a thank-you for participating. Interviews were transcribed and analyzed for themes. Results: Eight semi-structured, qualitative interviews were completed (English: 6, Spanish: 2). Saturation was assessed through ongoing, concurrent data analysis, and the point at which no new codes or themes emerged was confirmed through consensus among all team members. Qualitative coding and thematic analysis revealed the following screentime-related themes: 1) Parents identify both the benefits and drawbacks of screen media use, a sentiment we have labeled “nuanced neutrality,” 2) A lack of viable alternative activities is a major driver of screen media use, and 3) a lack of predictable family routines was a major factor for families who did not use the contract. Additionally, the following HMUC-related themes emerged: 1) The HMUC increased awareness of family screen time, 2) Delivery of screen media guidance was viewed favorably by families, 3) Rewards were valuable in prompting behavior change, and 4) The use of a contract promoted commitment. Conclusions: This study illustrates the nuanced perspectives contemporary parents hold regarding their children’s screen media use. Further, it delineates the specific attributes of the HMUC and its implementation that are perceived by families as effective.
Background: Psychological stress is known to exacerbate dermatologic conditions such as acne, eczema, and compulsive skin behaviors. The COVID-19 pandemic introduced a global stressor with widely-expe...
Background: Psychological stress is known to exacerbate dermatologic conditions such as acne, eczema, and compulsive skin behaviors. The COVID-19 pandemic introduced a global stressor with widely-experienced psychosocial effects and potential impacts on skin health. This study analyzes U.S. public interest in stress-induced dermatologic conditions and psychodermatologic disorders with relevance to clinical dermatology practice during pre-pandemic, pandemic, and post-pandemic periods. Objective: To evaluate longitudinal trends in U.S. public interest in stress-induced dermatologic and psychodermatologic conditions before, during, and after the COVID-19 pandemic using Google Trends data. Methods: Relative Google search volume (RSV) was used as a proxy for public interest, given the search engine’s 5 trillion annual searches.1 RSV for the terms "skin picking", "trichotillomania", "rash", "eczema", "dermatillomania", and "anxiety skin picking" from 2018 to 2024 was obtained through the Google Trends database. Monthly search interest was normalized and averaged across pre-pandemic (2018-2019), pandemic (2020-2022), and post-pandemic (2023-2024) time periods. Results: Search interest increased for “skin picking” and “eczema” from 2018 to 2024. “Trichotillomania” RSV increased at least 33.33% from January 2020 to March 2021 relative to 2018-2019. “Dermatillomania” RSV increased 156.41% from April to May 2021. “Anxiety skin picking” RSV increased during the pandemic (2020-2022), with the largest average month-over-month change of 4.99% compared to other search terms. No consistent trend was observed for “rash.” Conclusions: Public interest in stress-influenced dermatologic conditions increased during the COVID-19 pandemic, suggesting heightened interest or prevalence of psychodermatologic issues during prolonged stress. These findings highlight opportunities for dermatologists to integrate mental health screening and psychodermatologic considerations into routine clinical care, particularly during periods of widespread stress.
Background: Harnessing longitudinal data for time to event analysis can provide valuable insights into disease progression and help plan clinical interventions for individual patients, with the goal o...
Background: Harnessing longitudinal data for time to event analysis can provide valuable insights into disease progression and help plan clinical interventions for individual patients, with the goal of improving clinical outcomes and quality of life. However, real-world clinical data is characterised by missingness, inconsistencies and heterogeneity, especially when datasets are aggregated from different sources. Objective: To address the challenges of missingness, inconsistency, and heterogeneity in multi-source data of degenerative disease, we propose a framework for explaining time-to-event predictions using multivariate longitudinal trajectories, applied to time-to-gastrostomy in patients with Amyotrophic Lateral Sclerosis (ALS). Methods: We analysed data from 8,586 ALS patients using a two-stage analytical approach. Joint latent class growth discrete-time survival analysis were used to identify data-driven reference trajectories of functional decline. New patient markers were mapped to these clusters using Fréchet distances. Three survival models (Cox PH, Cox XGBoost, and XGBoost Pseudo-Observation Regression) using baseline demographics and functional decline features were used to predict time-to-gastrostomy. Results: Distinct classes of functional decline revealed that rapid deterioration in bulbar and swallowing functions is the most critical indicator for intervention, reaching a 50% probability of gastrostomy within 16 to 18 months. Bulbar and swallow onset slopes were the primary predictors of time-to-gastrostomy. Predictive models utilizing the early "onset slopes" of functional decline outperformed those using baseline demographics alone, yielding a 0.044-0.069 increase in concordance index and decreasing median absolute error by 60-157 days compared to relying on diagnostic delay. The XGBoost MAEPO regression model utilizing onset slope was the best overall classifier, achieving a concordance index of 0.731 (IQR, 0.717-0.744) and a median absolute error of 218 days (IQR, 204-232). Additionally, all evaluated models comfortably outperformed a naïve classifier based on a 10% weight loss threshold. Conclusions: Our framework addresses clinical data heterogeneity through principled feature extraction and unsupervised trajectory mapping, translating individual predictions into interpretable clinical narratives that support timely gastrostomy decisions, and more generally time-to-intervention in degenerative diseases.
Academic–public health partnerships are essential for strengthening outbreak preparedness and response, yet translating modeling tools into routine public health practice remains challenging. Struct...
Academic–public health partnerships are essential for strengthening outbreak preparedness and response, yet translating modeling tools into routine public health practice remains challenging. Structural, technical, and workforce constraints often limit the capacity for modeling tools during emergencies. Here, we describe the development a software ecosystem designed to support real-time infectious disease response through sustained collaboration between an academic research team and public health agencies, with particular focus on the recent measles outbreak.
The resulting software enabled real-time scenario modeling, visualization of transmission dynamics, and iterative updates as new data became available. Beyond immediate outbreak response, the initiative strengthened cross-sector collaboration, expanded modeling capacity, and highlighted ongoing gaps in technical infrastructure and workforce readiness at the state and local levels.
This case study demonstrates how sustained academic–government partnerships combined with streamlined development practices can accelerate the translation of modeling tools into operational public health settings. Establishing and maintaining analytic infrastructure and agile processes between emergencies may be critical for ensuring timely, data-informed decision-making during future outbreaks.
Background: Dementia is a progressive, life-limiting condition in which care needs evolve from diagnosis through end of life, yet advance care planning (ACP) is often approached as a narrow, document-...
Background: Dementia is a progressive, life-limiting condition in which care needs evolve from diagnosis through end of life, yet advance care planning (ACP) is often approached as a narrow, document-focused end-of-life task. Digital ACP tools could help integrate ACP with earlier palliative care principles, but priorities for a comprehensive dementia-specific tool remain insufficiently defined. Objective: To identify and prioritize stakeholder-defined components and digital features for a comprehensive digital ACP (dACP) tool for dementia that integrates palliative care principles across the disease trajectory. Methods: We conducted an online survey with people with dementia, informal caregivers, and health care professionals recruited via Prolific in January–February 2025. Participants rated the importance of dementia-tailored ACP elements, caregiver-support features, preferred frequency of care-plan review prompts, and the importance of integration with existing health care systems; open-ended responses were analysed using thematic analysis supported by a large language model and refined by researchers. Principal component analysis was used to derive core domains, and group differences and correlations among central variables were assessed. Results: The final sample included 232 participants. PCA identified two interrelated ACP domains, the Comprehensive Care Planning and End-of-Life Preparation and Active Disease Symptom Management and a caregiver domain capturing Caregiver Stress Management. Across items, strategies for maintaining quality of life as dementia progresses received the highest ratings. Tips for managing challenging behaviours were the highest-rated caregiver-support feature. Participants also rated integration with existing health care systems as highly important and were in favour or context aware regular plan review. Qualitatively, decision-making support and guidance, communication/information sharing and documentation/record-keeping were the most frequently cited primary purposes of a dACP tool across all groups. Conclusions: Stakeholders prioritized a dACP tool that supports quality of life, ongoing symptom management, and actionable caregiver support, with strong interoperability to enable clinical use across care settings. These results provide practical design targets for developing an integrated, adaptive tool to support shared decision-making and continuity of person-cantered early palliative care in dementia.
Background: User-centered design (UCD) processes often generate extensive lists of potential software features, necessitating effective prioritization methods to guide development. The MoSCoW method,...
Background: User-centered design (UCD) processes often generate extensive lists of potential software features, necessitating effective prioritization methods to guide development. The MoSCoW method, categorizing features as Must Have, Should Have, Could Have, or Won’t Have, is widely used in software development for its simplicity and ease of adoption. Despite its popularity, limited evidence exists on its application within health informatics. The DEAN (Decision Aid Navigator) system, a clinical decision support tool, required prioritization of features for its administrative portal to ensure usability and alignment with health IT expert needs. Objective: To evaluate how health IT experts engaged with a MoSCoW-based prioritization process during the design of the DEAN Administrative Portal and to assess how discussion and interaction influenced feature prioritization outcomes. Methods: A 74 minute web-based group session included four health IT experts experienced in managing clinical decision support systems. Participants independently rated 30 proposed portal features using MoSCoW categories following brief discussions of each feature. Session transcripts, videos, and rating behaviors were analyzed using qualitative secondary analysis to identify themes describing how the MoSCoW process supported prioritization and expert engagement. Results: Participants rated 37% of features as Must Have, 13% as Should Have, 20% as Could Have, and none as Won’t Have. The remaining 30% showed distributed ratings without a clear majority. All participants adjusted their ratings, often prompted by group discussion or clarification from the technical lead. Qualitative analysis revealed four themes: (1) Interpretations of feature descriptions may vary; (2) Group discussion generated suggestions for alternative design solutions; (3) Group discussion identified implementation considerations; and (4) Participants considered differences in real world use vs. idealized use when ranking features. Conclusions: The MoSCoW method proved to be an efficient, intuitive, and engaging approach for prioritizing features in a UCD process for a clinical decision support system. Group discussion enriched the prioritization session by surfacing implementation insights and design refinements beyond numeric rankings alone. Findings support the use of MoSCoW as a practical tool for health informatics teams seeking structured yet flexible stakeholder engagement in software feature prioritization.
Background: Adolescence is a critical period in the development of mental health problems. Emotion regulation (ER) is a transdiagnostic mechanism implicated across diverse mental health problems, and...
Background: Adolescence is a critical period in the development of mental health problems. Emotion regulation (ER) is a transdiagnostic mechanism implicated across diverse mental health problems, and represents a promising target for early, scalable intervention. Self-directed digital cognitive behavioural therapy (CBT) interventions have the potential to extend access to mental health promotion and support; however, evidence regarding their acceptability, engagement and use among adolescents in real-world, non-clinical contexts remains limited. Objective: This study aimed to explore the acceptability, uptake and engagement of a self-directed digital CBT app targeting emotion regulation (MoodMission) among UK adolescents, and to identify subsequent early signals of change in both emotion regulation and mental health outcomes. Secondary objectives included assessing the feasibility of evaluating this type of intervention within a school setting. Methods: A convergent mixed-methods pre-post cohort design was employed. Adolescents were recruited from one secondary school in England and offered access to the MoodMission app for six weeks. Quantitative data included app uptake, in-app engagement metrics, study retention, and changes in emotion regulation and mental health, measured using the Difficulties in Emotion Regulation Scale (DERS), the Emotion Regulation Questionnaire (ERQ), and the Depression, Anxiety and Stress Scale (DASS). Generalised eta squared (η²G) effect sizes were calculated to explore the magnitude of change. Qualitative data were collected via semi-structured focus groups with adolescents who engaged and did not engage with the app and were analysed using inductive thematic analysis to capture experiences, perceived acceptability, and barriers to engagement. Results: Of 43 adolescents completing baseline measures, 11 (25.6%) downloaded the app and 8 (18.6%) completed at least one in-app activity. Participants spent a mean of 15.34 seconds (SD 10.99) per activity and reported moderate perceived usefulness (mean 6.99/10), with emotion- and behaviour-based activities rated as most helpful. Attrition was at expected levels for self-directed digital interventions, with 7 participants retained at the 6-week follow-up (overall attrition rate 83.7%). Qualitative findings highlighted four key themes: a preference for human and relational support over digital tools, difficulty engaging with the app during periods of high emotional intensity, the importance of personalisation and inclusivity, and the need for emotional clarity to use self-directed interventions effectively. Conclusions: Findings underscore the importance of co-design, personalisation, and integration of human support when developing digital mental health interventions for adolescents. Given increased messaging about the lack of safety in digital media and the growing bans on adolescent media use, future research should explore blended models of mental health promotion co-designed with adolescents, combining brief digital tools with face-to-face support from trusted adults or peers that are more appropriate and acceptable.
Background: Type 2 Diabetes Mellitus (T2DM) affects approximately 590 million people worldwide, and its management relies heavily on patient education. With the emergence of online health information...
Background: Type 2 Diabetes Mellitus (T2DM) affects approximately 590 million people worldwide, and its management relies heavily on patient education. With the emergence of online health information and Artificial Intelligence Large Language Models (AI LLMs), patients are increasingly sourcing medical information independently. Objective: This study compares the quality, readability and reliability between traditional online resources and AI-generated information related to T2DM. Methods: Four predefined search terms were entered into three major search engines (Google, Yahoo, and Bing), and the top 20 search results were retrieved. Patient information AI-generated leaflets (AIGLs) were produced using a standardised prompt across four AI LLMs (ChatGPT, Gemini, DeepSeek, and Grok). Information quality was assessed using the DISCERN score and was calculated by the author and ChatGPT. The JAMA benchmark was used to measure reliability and transparency. The Flesh Reading Ease Score (FRES) and Flesh-Kincaid Grade Level (FKGL) were used to determine the readability and comprehension. Results: Eighty websites and four AIGLs were analysed, with author-rated mean DISCERN scores of 43.6 (±10.9) and 43.8 (±2.986), mean JAMA Benchmark scores of 2.74 (±0.965) and 0, mean FRES of 50.6 (±14.4) and 48.9 (±9.16), and mean FKGL scores of 8.66 (±2.23) and 8.3 (±1.92), respectively. The ChatGPT-rated mean DISCERN scores for websites and AIGLs were 58.5 (±11.5) and 61.0 (±2.94), respectively. Conclusions: Given the high prevalence of T2DM, both traditional online and AI-generated T2DM resources demonstrate suboptimal quality, accessibility, and transparency. Increasing patient reliance on digital health information calls for improved readability standards and stronger safeguards for AI-generated content. The landscape of medical consultations is evolving, with patients increasingly presenting with preconceived notions based on online health information; hence, healthcare professionals should adapt to this shift.
Background: Breast cancer-related lymphedema (BCRL) is a chronic condition requiring lifelong self-management. Patients often face barriers such as limited physical function, time constraints, low sel...
Background: Breast cancer-related lymphedema (BCRL) is a chronic condition requiring lifelong self-management. Patients often face barriers such as limited physical function, time constraints, low self-efficacy, and inconsistent information. Objective limb swelling assessment is critical for effective self-management; however, most patients lack reliable home-monitoring tools that integrate measurements with evidence-based feedback to drive behavioral change. Objective: We evaluated the feasibility, usability, and preliminary clinical efficacy of an integrated digital health intervention—a mobile app with evidence-based coaching features and a smart tape measure—designed to support objective self-monitoring and behavioral changes in patients with BCRL. Methods: A 3-month multicenter single-arm prospective study enrolled 58 female patients with BCRL. Participants used the "Second Doctor" app and smart tape measure for weekly arm volume monitoring. The application provides real-time visualization of the percentage of excess volume, self-management feedback, and coaching recommendations derived from clinical guidelines. Measurement validity was determined by correlating the self-measured volumes with Perometer (optoelectronic perometry) measurements. Feasibility was assessed based on retention and adherence rates. Usability was evaluated using a System Usability Scale (SUS) and a technology-acceptance model-based questionnaire. Clinical outcomes included limb volume changes, quality of life (LYMQOL), International Classification of Functioning, Disability, and Health (ICF) domains, and Transtheoretical Model (TTM) stages of self-management behavior. Results: The smart tape measurement demonstrated a strong correlation with the Perometer measurements (forearm r = 0.662, P < 0.001; whole arm r = 0.767, P < 0.001), validating its accuracy for home monitoring. Fifty-two participants completed the study (89.6% retention), with high adherence averaging 5.0 measurements monthly. Usability was good (SUS mean: 68.94, SD: 12.08), with high satisfaction scores (mean ≥ 4.0 on a 1–6 scale) across usefulness, ease of use, attitude, motivation, and recommendation willingness. Participants reported significant advancement in TTM stages (mean difference = −0.35, P = 0.02) and improvements in LYMQOL appearance (P = 0.04) and overall QoL (P = 0.03), alongside ICF improvements in body image (P = 0.03), physical endurance (P = 0.01), and muscle power (P = 0.02). The overall limb volume remained stable; however, subgroup analysis revealed that participants advancing in TTM stages (n = 13, 25%) maintained a stable volume, whereas those without behavioral progression (n = 39, 75%) showed significant increases (P = 0.03), demonstrating a direct link between behavioral engagement and clinical outcomes. Conclusions: An evidence-based coaching application integrated with validated self-monitoring is feasible, acceptable, and clinically meaningful for the self-management of BCRL. The intervention achieved consistent engagement, positive usability, and behavioral improvements, translating to volume stability among the engaged patients. This system enables patient-driven disease management and offers a scalable solution for lymphedema care by closing the loop between objective measurements, real-time feedback, and evidence-based guidance. Clinical Trial: This trial was registered at ClinicalTrials.gov (identifier: NCT06922513; initial release: March 19, 2025)
Background: Suboptimal transition of care after acute coronary syndrome (ACS) contributes to the persistently high risk of adverse cardiovascular events following hospitalization. Although mobile heal...
Background: Suboptimal transition of care after acute coronary syndrome (ACS) contributes to the persistently high risk of adverse cardiovascular events following hospitalization. Although mobile health interventions are growing rapidly, whether a mobile text messaging intervention can improve patient follow-up, other care processes, or outcomes after ACS has not been evaluated. Objective: To evaluate whether a hospital-initiated mobile health intervention for patients with acute coronary syndrome improves post discharge care processes and clinical outcomes. Methods: We conducted the TExting after Acute coronary syndrome discHarge (TEACH) pilot study, a single-centre, double-blind, randomized controlled trial. Patients hospitalized for ACS were randomized to receive either 12 weeks of motivational text messages or usual care alone. The primary outcome was an outpatient family physician or cardiologist visit (1 month, 3 months and 1 year) after discharge. Secondary outcomes included emergency department visits, all-cause rehospitalization, all-cause mortality, medication adherence, and cardiac testing. Results: The study cohort included 228 patients enrolled within 12 months, with 113 assigned to the texting group and 115 to the control group. The mean age was 61.5 years, 78.5% were men, and 53.9% were White patients. At 1 month, 75.2% of patients in the intervention group and 83.5% in the usual care group had a family physician follow-up (p=0.123). Cardiologist follow-up rates at 1 month were also similar between groups, with 74.3% in the intervention group and 73.0% in the usual care group (p=0.825). There were no significant differences in physician follow-up at 3 months and 1 year. Secondary outcomes including hospitalization, emergency department use, diagnostic testing, and medication adherence, were also not significantly different between the two groups. Conclusions: In this pilot randomized controlled trial, we demonstrated the ability to enroll ACS patients in a 12-week mobile motivational text messaging intervention. The texting intervention did not significantly improve rates of physician follow-up, medication adherence, or clinical outcomes in ACS patients. Clinical Trial: ClinicalTrials.gov ID NCT05628337
Background: Depression poses a global health challenge, and its early detection is critical for effective interventions. Recent studies reveal associations between digital traces of social behavior (e...
Background: Depression poses a global health challenge, and its early detection is critical for effective interventions. Recent studies reveal associations between digital traces of social behavior (e.g., phone calls) and depression, but rely on cross-sectional analyses, limiting insight into how these relationships evolve over time and obscuring the directionality between social behavior and mental health. Objective: This study investigates the longitudinal and directional relationships between digitally mediated social capital and depressive symptoms, leveraging phone call data to develop a data-driven framework for understanding depression over time. Methods: Eight weeks of data from 216 participants were analyzed using a dual-structural equation modeling (SEM) approach, including Latent Growth Curve Modeling (LGCM) and Cross-Lagged Panel Modeling. Digital social capital was operationalized through behavioral proxies capturing accessed social capital (e.g., incoming or outgoing calls) and latent social capital (e.g., missed phone calls), reflecting distinct mechanisms of social capital that are available and accessed online. Meanwhile, depressive symptoms were assessed using the Patient Health Questionnaire-4 (PHQ-4). Results: Latent growth analyses revealed that depressive symptoms were significantly associated with divergent trajectories of digital social capital. Higher baseline levels of depression were linked to changes in accessed social capital and declines in latent social capital. Growth in accessed social capital and depression were directly linked, indicating that increased levels of depression could amplify how often latent social capital becomes accessed. Cross-lagged panel analyses further corroborated these findings by showing that depressive symptoms in the beginning of the study were related to subsequent reductions in latent social capital, whereas prior levels of latent social capital were not significantly correlated to higher levels of depression. Conclusions: These findings advance the clinical understanding of depression by revealing that psychological health could actively influence patterns of social engagement. They also suggest that changes in social behavior could reflect changes in depressive symptoms. This study highlights the importance of longitudinal, data-driven approaches for interpreting digital social traces and underscores their potential for informing mental health scholarship and intervention strategies.
Global transformations, including demographic aging, climate-related health risks, and rapid technological acceleration are reshaping health systems and the competencies required of future healthcare...
Global transformations, including demographic aging, climate-related health risks, and rapid technological acceleration are reshaping health systems and the competencies required of future healthcare professionals. Yet current curricula often struggle to integrate these complex challenges in a coherent and future-oriented manner. This Eye Opener highlights the potential of the Inner Development Goals (IDGs) as an underutilized conceptual framework for enriching competency-based education in the health professions. The IDGs emphasize five dimensions: Being, Thinking, Relating, Collaborating, and Acting that align with key professional capacities such as self-awareness, systems thinking, empathy, interprofessional teamwork, and ethical action. Drawing on examples from geriatric care, climate-adapted practice, and AI-supported clinical reasoning, we illustrate how IDG-aligned learning outcomes can complement existing competency frameworks by fostering inner capacities essential for clinical judgement and person centered care. At the same time, we provide a critical reflection on potential risks, including over individualization of responsibility, insufficient attention to structural determinants of health, and tensions with assessment-driven educational cultures. Rather than proposing IDGs as a complete solution, this article argues that they offer a valuable conceptual entry point for rethinking how health professions education can prepare learners for the uncertainties, ethical complexities, and interdependencies of contemporary healthcare. The IDGs can help open new pedagogical and conceptual spaces, encouraging educators to design learning environments that support both technical proficiency and the inner capacities needed for navigating an increasingly complex world.
Background: Parents of children with autism spectrum disorder (ASD) often experience elevated levels of stress and psychological distress. In Hong Kong, cultural norms regarding emotional suppression...
Background: Parents of children with autism spectrum disorder (ASD) often experience elevated levels of stress and psychological distress. In Hong Kong, cultural norms regarding emotional suppression may exacerbate these challenges. Acceptance and commitment therapy (ACT) offers a promising approach by targeting psychological inflexibility. However, its efficacy and specific mechanisms of change within Chinese cultural contexts, particularly when delivered via online formats, remain under researched compared with traditional cognitive therapy (CT). Objective: To evaluate the efficacy of a brief, 3-session online ACT workshop in reducing parental stress and improving general well-being among Chinese parents of children with ASD compared with an active online CT control and a passive waitlist control, and to determine if reductions in psychological inflexibility mediated these therapeutic outcomes. Methods: A 3-arm randomized clinical trial was conducted with 60 parents of children with ASD (mean age, 7.5 years) in Hong Kong. Participants were assigned to online ACT (n = 24), online CT (n = 23), or a waitlist control group (n = 13). The interventions consisted of 3 weekly 1.5-hour synchronous group sessions delivered via Zoom. Primary outcomes were general well-being (General Health Questionnaire-12) and parental stress (Parenting Stress Index–Short Form). Process variables included psychological flexibility and cognitive distortions. Data were analyzed using analysis of covariance and mediation analysis with bootstrapping (5000 resamples). Results: The online ACT group demonstrated significantly better general well-being at post-test compared with the waitlist control (P = .02) and the CT group (P = .03). Similarly, parental stress was significantly lower in the ACT group compared with the waitlist (P = .04) and CT (P = .01) groups. No significant differences were found between the active CT control and the waitlist control. Mediation analysis revealed that the reduction in psychological inflexibility significantly mediated the relationship between the ACT intervention and improvements in both parental stress (95% CI, -0.56 to -0.06) and general well-being (95% CI, -0.36 to -0.03). Cognitive distortions did not serve as a significant mediator for either outcome. Conclusions: A brief, online ACT intervention is effective in reducing stress and improving well-being among Chinese parents of children with ASD. The findings confirm that the intervention works through the theoretical mechanism of reducing psychological inflexibility, even when delivered remotely. This suggests that low-intensity, online ACT is a scalable, cost-effective, and culturally adaptable solution for supporting caregivers who may face barriers to traditional face-to-face therapy. Clinical Trial: N/A
Background: Rosacea is a chronic, visible inflammatory skin condition that often requires complex, long-term treatment regimens. As patients navigate these therapies, they increasingly turn to online...
Background: Rosacea is a chronic, visible inflammatory skin condition that often requires complex, long-term treatment regimens. As patients navigate these therapies, they increasingly turn to online forums to share experiences and seek clarification on treatment use and side effects outside of the clinical setting. Objective: To identify and categorize real-world concerns regarding FDA-approved rosacea therapies as discussed within a large online patient community. Methods: A thematic analysis was performed on one year of posts from the r/Rosacea subreddit (70,000+ members) mentioning nine FDA-approved medications. Posts were categorized into domains including medication use, adverse effects, and barriers to access. Results: Discussions centered on practical application (order and frequency) for topicals, gastrointestinal and photosensitivity concerns for oral doxycycline, and anxiety regarding rebound erythema for alpha-adrenergic agonists. Concerns over insurance coverage and medication costs were universal across most therapy classes. Conclusions: Digital health communities reveal specific educational gaps, particularly regarding the practical integration of topicals and the management of side effects, that offer clinicians clear targets for improving patient counseling and treatment adherence.
Background: Patient-reported outcome measures (PROMs) and shared decision-making (SDM) are increasingly valued in Pediatric physiotherapy (PPT). Online PROM portals can facilitate PROM use and SDM, bu...
Background: Patient-reported outcome measures (PROMs) and shared decision-making (SDM) are increasingly valued in Pediatric physiotherapy (PPT). Online PROM portals can facilitate PROM use and SDM, but require adaptation for its use in PPT. Objective: This study aimed to adapt the online KLIK PROM portal for primary PPT, identify preferences for data visualization, and explore integration of SDM. Methods: A co-design approach was used. Two co-creation sessions including adolescents, parents, patient representatives, PPTs, and researchers were organized and results were discussed in an analyze-session with the research team. Subsequently, a demo version of the adapted KLIK portal was tested for usability in twelve individual think aloud sessions with parents, adolescents, and PPTs. After discussing results in a second analyze-session, the final version of the KLIK PROM portal was developed. Thematic content analysis was applied to all qualitative data. Results: Key adaptations included automatically selecting predefined PROM sets based on the patient registration form depending on complaints and age, and the possibility to schedule a series of PROMs linked to evaluation moments. Literal responses on items without color coding were preferred by patients and parents, while PPTs favored line graphs with heatmaps indicating concerning scores. Both patients and PPTs emphasized the importance of discussing results in person using child-friendly visualizations. Aggregated data were valued for supporting reflective practice. SDM was integrated into the portal through information pages, subtle nudges to encourage PPTs and patients to engage in SDM, and by motivating patients to complete PROMs by personalizing the portal. Conclusions: The adapted KLIK portal is ready for pilot implementation in primary PPT. Updates should be applied based on user feedback from ongoing evaluations. While PROM use can facilitate SDM, impact on SDM depends on effective patient-clinician dialogue and should be further investigated.
Neonatal jaundice remains a preventable cause of neurological damage in low- and middle-income countries, where limitations in infrastructure and staffing make timely screening difficult. Mobile techn...
Neonatal jaundice remains a preventable cause of neurological damage in low- and middle-income countries, where limitations in infrastructure and staffing make timely screening difficult. Mobile technologies, such as the Picterus Jaundice Pro (JP) app, offer a promising alternative by enabling bilirubin estimation from digital images using algorithms and calibration cards. In this viewpoint, we explore the feasibility, clinical validation, and barriers to adopting this tool in resource-limited settings, particularly in Latin America. The Mexican experience is presented as a reference for the gradual integration of mHealth technologies into public systems, highlighting both opportunities and regulatory, operational, and cultural challenges. Available evidence supports its utility; however, scaling will depend on political will, sustained financing, and clear regulatory frameworks. Picterus JP may represent a strategic step toward equity in neonatal health.
Background: Mental health conditions, including depression, anxiety, and psychological distress, are prevalent among the aging population and affect their health, functioning, and quality of life. Acc...
Background: Mental health conditions, including depression, anxiety, and psychological distress, are prevalent among the aging population and affect their health, functioning, and quality of life. Access to proper and high-quality mental health treatment is necessary; however, mental health treatment and care remain underused due to stigma, workforce shortages, cost, and mobility limitations. Digital mental health interventions (DMHIs) are emerging as a promising strategy to improve the accessibility and effectiveness of mental health services for older adults, but older adults have historically been underrepresented in DMHI development and evaluation. Additionally, how effective different types of DMHIs are and how age-centered design approaches influence outcomes remain underexplored. Objective: This scoping review mapped and synthesized evidence on digital mental health interventions (DMHIs) focused on adults aged 50 and older and identified gaps in the evidence base related to study design, age-related adaptations, and clinical outcomes. Specifically, we examined (1) the technologies and therapeutic approaches used, (2) the outcomes and effectiveness of DMHIs, and (3) age-centered adaptations and their outcomes. Methods: This scoping review searched for studies focusing on DMHIs for older adults across PubMed, PsycINFO, Scopus, Ageline, and Web of Science published from 2000 to February 2025. Eligible studies evaluated or described the design of DMHIs targeting mental health conditions among adults aged 50 years or older. Two rounds of independent screening and data extraction were conducted by multiple reviewers. Extracted data included study design, sample characteristics, intervention features, technologies used, age-related adaptations, and clinical outcomes. Results: Seventy-two studies met the inclusion criteria, of which thirty-six were randomized controlled trials, and fifty-four reported clinical outcomes. Web-based cognitive behavioral therapy (CBT) was the most commonly used approach, followed by games, virtual reality, mobile apps, chatbots, and robots. Fifty-four studies reported clinically effective outcomes, most commonly reductions in depression, anxiety, or psychological distress. However, only one-third of studies incorporated age-centered design adaptations or co-design approaches, such as simplified interfaces, larger fonts, age-relevant content, or participatory development with older adults. Conclusions: Among studies reporting effective outcomes, DMHIs can reduce depression, anxiety, and psychological distress. However, with only half of the included studies using randomized controlled trial designs, the overall evidence base remains moderate. In addition, age-adaptive design remains underdeveloped. Future research should strengthen trial designs and systematically examine how usability and age-centered adaptations influence DMHI effectiveness.
Background: Simulation-based medical education is essential for improving patient safety. In virtual reality (VR)–based simulation, immersion is primarily generated through visual and auditory cues,...
Background: Simulation-based medical education is essential for improving patient safety. In virtual reality (VR)–based simulation, immersion is primarily generated through visual and auditory cues, while other sensory modalities are typically absent. This sensory limitation may reduce the emergence of authentic safety-relevant behaviors.
Olfaction plays an important role in clinical reasoning, risk perception, and self-protective behavior and is closely linked to memory and emotion. Although olfactory cues have been shown to influence hand hygiene behavior in real or simulated-real environments, their targeted integration into fully immersive VR-based medical simulation has not been systematically examined. Objective: This study aimed to investigate whether adding a real olfactory cue (disinfectant scent) to a fully virtual clinical simulation increases patient safety–relevant behavior, specifically hand hygiene compliance (hand disinfection and glove usage). Methods: In a randomized controlled study at the University of Münster (winter term 2025/26), 89 medical students participated in a VR-based clinical simulation. Study rooms were pre-assigned to either an olfactory intervention or a control condition, and participants selected their room without knowledge of the assigned condition. Hand hygiene and glove use were automatically tracked as outcomes. Odds ratios were calculated to assess the effect of the intervention on these behaviors. Results: The olfactory intervention nearly tripled the odds of hand disinfection (OR = 2.81, 95% CI 1.09–7.75, P = 0.037), while no significant difference was observed for glove use (OR = 1.62, P = 0.278). Conclusions: The integration of a real olfactory cue into a fully immersive VR medical simulation significantly increased hand disinfection behavior, particularly after patient contact, but did not affect glove use. These findings suggest that olfactory augmentation can selectively reinforce safety-relevant behaviors in digital training environments. Incorporating real-world sensory cues into VR may represent a simple yet effective design strategy to enhance behavioral authenticity and patient safety outcomes in simulation-based medical education. Clinical Trial: German Clinical Trials Register: DRKS00039472
Background: In Canada, Black students continue to be underrepresented in medical schools and face institutional barriers, including limited access to the information necessary for their admission and...
Background: In Canada, Black students continue to be underrepresented in medical schools and face institutional barriers, including limited access to the information necessary for their admission and their academic path. The Black Medical Students Association of Canada (BMSAC) has developed a bilingual website for these students. Objective: The purpose of this research is to evaluate the quality, accessibility and usefulness of the site and make recommendations for its improvement. Methods: A cross-sectional survey was conducted through an online system using the System Usability Scale (SUS), a validated website user experience evaluation tool. Three open-ended questions were added to the survey to identify areas for improvement. The data from the SUS were analyzed using descriptive statistics and the answers to the questions underwent thematic analysis. Results: 50 participants responded to the survey (24 in English and 26 in French). The overall SUS score was 75.8. The SUS scores for the English and French versions were 77.0 and 74.7, respectively. More than three quarters of respondents lived in Quebec. Respondents learned more about the available resources and recommended including more images illustrating organized events on the site. Conclusions: The overall SUS score and that of English and French respondents were considered satisfactory. The lack of visual support, updated information and some technical problems seemingly explain these results. Strong Quebec representation also indicates the need to promote the site elsewhere in Canada.
Background: Enhancing telemedicine requires a clear understanding of how avatars influence medical collaboration. The ArtekMed study group developed a MR teleconsultation system that enables a remote...
Background: Enhancing telemedicine requires a clear understanding of how avatars influence medical collaboration. The ArtekMed study group developed a MR teleconsultation system that enables a remote expert (VR user) to interact in real-time with a local augmented reality (AR) user within a shared working space. The system was compared to a standard video call system in five randomized cross-over trials in a healthcare simulation center. Objective: This post-hoc study investigates user’s perceptions of a virtual character representing a remote expert across four real-time mixed-reality (MR) teleconsultation scenarios. Methods: A total of 56 medical professionals participated as AR users collaborating with a remote expert represented by a virtual character. A post-hoc qualitative analysis of structured post-session interviews was performed to explore participants’s perceptions of the avatar, focusing on perceived helpfulness, visual design and user engagement. Results: Overall, most participants did not perceive the avatar as helpful for task execution in procedural scenarios and frequently described it as unnecessary or even distracting. In contrast, in more complex and demanding scenarios, such as emergency craniotomy planning or intensive care treatment of patients with acute respiratory distress syndrome, some participants perceived the avatar as providing mentorship, guidance and psychological support. These findings suggests that while avatars may offer limited perceived value in task-focused medical collaboration, they may support user engagement in scenarios requiring sustained interaction and social presence. Conclusions: The results align with existing literature indicating that the impact of avatars is context dependent. In mixed-reality environments, where virtual character coexists with real-world reconstructions, avoiding behavioral incongruence and uncanny effects may be more critical than achieving high visual fidelity. Future research should prospectively explore how different levels of avatar abstraction and fidelity influence collaboration in MR telemedicine.
Background: Chronic kidney disease (CKD) is a progressive, multisystem condition associated with substantial morbidity and mortality worldwide. Patients with established CKD are particularly susceptib...
Background: Chronic kidney disease (CKD) is a progressive, multisystem condition associated with substantial morbidity and mortality worldwide. Patients with established CKD are particularly susceptible to acute clinical deterioration and frequently present to emergency departments with high-acuity conditions. Despite the increasing burden of CKD, real-world data describing emergency presentations and early management practices remain limited, especially in low- and middle-income countries. Objective: The primary objective of this study is to describe the spectrum of clinical emergencies among adults with established CKD presenting to the emergency department. Secondary objectives include documenting initial emergency management strategies and short-term hospital outcomes. Methods: This prospective observational study will be conducted over a two-year period in the emergency department of a tertiary care teaching hospital in central India. Adult patients with a documented diagnosis of CKD presenting with acute renal-related complications will be enrolled consecutively. Data on demographics, clinical presentation, investigations, emergency interventions, and in-hospital outcomes will be collected using a structured case record form. Descriptive statistical analyses will be performed, with exploratory regression analyses conducted where appropriate. Results: This manuscript describes the study protocol. Data collection and analysis will be completed after the study period. Conclusions: Systematic documentation of emergency presentations and early management of CKD-related complications may generate context-specific evidence to inform improvements in emergency preparedness and early clinical decision-making for patients with CKD. Clinical Trial: CTRI/2025/09/094214
Background: Intensive care clinicians rely on timely access to large volumes of electronic data to make complex decisions. The Central Adelaide Local Health Network (CALHN) implemented an electronic m...
Background: Intensive care clinicians rely on timely access to large volumes of electronic data to make complex decisions. The Central Adelaide Local Health Network (CALHN) implemented an electronic medical record (EMR) across its hospitals in South Australia, but the generic user interface is not optimised for critical care workflows. The CALHN Critical Care Informatics System (CCCIS) was developed as a prototype user interface (UI) to present ICU-relevant information in a more intuitive, task-focused format. Objective: This study aimed to evaluate the usability of CCCIS from the perspective of senior intensivists, and to identify key design principles for effective critical care informatics systems. Methods: We undertook a usability study with eight intensivists from CALHN. Participants interacted with a prototype version of CCCIS during a structured video-based session incorporating a Cognitive Walkthrough and Think Aloud approach. Sessions were screen-recorded and transcribed. Qualitative data were coded as positive, negative or neutral feedback and grouped into three domains: content, layout and visibility. Emergent themes were mapped across CCCIS components. Following the usability test, participants completed a System Usability Scale, NASA Task Load Index and a bespoke questionnaire assessing perceived usability, cognitive demand and clinical relevance. Reporting is aligned with the Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines for interview-based research. Results: Participants reported that CCCIS supported rapid comprehension of patient information and facilitated integration between physiological data, interventions and clinical trajectory. The ability to customise views and to navigate between ward-level and bed-level information was highlighted as a strength. Areas for improvement included refinement of the ward board, ribbon and vital signs displays, particularly where duplicated information or visual clutter reduced clarity. Across the content, layout and visibility domains, recurrent themes included the importance of structured tabular displays, consistent visual hierarchies and explicit highlighting of clinically salient values. Survey responses suggested that CCCIS was easy to learn and use, exerted low cognitive demand, and was perceived as clinically relevant to everyday critical care practice. Conclusions: In this qualitative usability evaluation, intensivists perceived CCCIS as a usable and clinically meaningful critical care informatics system. The study identified design principles—such as structured presentation of data, alignment with mental models of ICU workflow and support for rapid synthesis of information—that may inform further development of CCCIS and other electronic medical record-integrated ICU interfaces.
Background: Understanding how digital systems can support clinical decision-making is crucial, especially with the growing deployment of increasingly complex artificial intelligence (AI) models. This...
Background: Understanding how digital systems can support clinical decision-making is crucial, especially with the growing deployment of increasingly complex artificial intelligence (AI) models. This complexity raises concerns about trustworthiness, impacting the safe and effective adoption of such technologies. In intensive care units (ICUs), where clinicians make high-stakes, time-sensitive decisions, decision-support tools must be designed to align with clinical needs and cognitive workflows. Improved understanding of decision-making processes and requirements for decision support tools is vital for providing effective solutions. Objective: This study aimed to investigate ICU clinicians’ decision-making processes, the challenges posed by patient complexity, and the requirements for decision-support systems to ensure transparent and trustworthy recommendations. Methods: We conducted group interviews with seven ICU clinicians, representing diverse roles and experience levels, to explore perspectives on decision-support tools. Reflexive thematic analysis was used to identify key themes and thereafter design recommendations. Results: Three core themes emerged from the analysis: (T1) ICU decision-making relies on a wide range of factors; (T2) patient complexity challenges shared decision-making, and (T3) acceptability and usability of decision support systems. Design recommendations derived from clinical input provide insights to inform future decision support systems for intensive care. Conclusions: Decision-support tools have the potential to enhance ICU decision-making, but their adoption depends on alignment with clinicians' needs and workflows. To improve trust and usability, future systems must be transparent in their recommendations, adapt to varying patient complexities, and facilitate, rather than replace, human expertise. Our findings inform the development of digital systems that are both transparent and trustworthy, aiding clinically acceptance in ICU settings. Clinical Trial: Not applicable.
Background: Despite significant improvements in cancer survival, late complications from oncological treatments remain inadequately managed in routine clinical practice. Standard oncology follow-up pr...
Background: Despite significant improvements in cancer survival, late complications from oncological treatments remain inadequately managed in routine clinical practice. Standard oncology follow-up prioritizes disease recurrence detection over systematic assessment of treatment-related sequelae, resulting in underdiagnosis of physical, psychological, metabolic, and social complications that substantially impair survivors' quality of life. The temporal dynamics of complication emergence remain poorly characterized, limiting development of evidence-based surveillance schedules.
Objective: This study aims to determine the time-specific incidence of 19 predefined treatment-related complications at 1, 6, 24, and 60 months following completion of first-line chemotherapy in adult cancer survivors who achieved complete response. Secondary objectives include characterizing temporal trajectories to identify critical surveillance windows, evaluating the feasibility and performance of a standardized guideline-based referral system integrated within a regional healthcare network, and identifying predictive factors for complication occurrence and timing. Objective: This study aims to determine the time-specific incidence of 19 predefined treatment-related complications at 1, 6, 24, and 60 months following completion of first-line chemotherapy in adult cancer survivors who achieved complete response. Secondary objectives include characterizing temporal trajectories to identify critical surveillance windows, evaluating the feasibility and performance of a standardized guideline-based referral system integrated within a regional healthcare network, and identifying predictive factors for complication occurrence and timing. Methods: PASCA-c is a single-center, prospective, interventional cohort study conducted at Centre Léon Bérard (Lyon, France). Over 36 months, 500 adults aged 18–65 years with complete responses to first-line therapy for lymphomas (Hodgkin/non-Hodgkin), acute myeloid leukemia, testicular germ cell tumors, non-metastatic breast cancer, or sarcomas will undergo systematic screening at 1 month (T1), 6 months (T2), 24 months (T3), and 60 months (T4) post-treatment. Each assessment includes validated questionnaires, biomarker analyses, cardiovascular evaluations, spirometry, and functional performance tests covering 19 complication domains. Clinical decision trees based on French and international guidelines generate standardized referral recommendations. Patients are referred to a regional network of 120+ healthcare professionals for complication management while continuing standard oncological follow-up. The primary outcome is the time-specific incidence of each complication at T1, T2, T3, and T4, distinguishing new-onset cases from persistent complications detected at prior assessments. Results: Patient enrollment began in September 2020 and is ongoing. Final results are anticipated in 2027 upon completion of 60-month follow-up assessments for the last enrolled participant. Conclusions: PASCA-c represents the first large-scale European study systematically evaluating the temporal dynamics of 19 treatment-related complications across multiple cancer types. By characterizing time-specific incidence patterns, this study will identify critical surveillance windows for each complication, informing the development of evidence-based, temporally-optimized survivorship care protocols that can be adapted to diverse healthcare settings. Clinical Trial: The study protocol (version_3_03-15-2022) was approved by the French ethics committee (Comité de protection des personnes Ile de France IV, ID-RCB: 2020-A01130-39). The study is registered on ClinicalTrials.gov (NCT04052126). All participants will be asked to sign and date an informed consent form. The results will be published in peer-reviewed journals and academic conferences. The study data have been declared to the ‘Commission nationale de l'informatique et des libertés’ via the reference methodology MR-001 n° R201-001-006.
Background: Delays in completing cancer screening diminish the preventive benefits of early detection, particularly among women receiving care in Federally Qualified Health Centers (FQHCs). Objective:...
Background: Delays in completing cancer screening diminish the preventive benefits of early detection, particularly among women receiving care in Federally Qualified Health Centers (FQHCs). Objective: This study examined factors associated with time to cancer screening completion among women aged 50 years or older who participated in an SMS text message outreach campaign, in a large 56-clinic FQHC network. Methods: Female patients aged 50 years or older who received at least three reminder SMS messages encouraging completion of any of the following overdue cancer screening tests: (a) mammogram, (b) FIT/Cologuard, or (c) HPV/Pap test.
The outcome variable was the time (in days) to the completion of the cancer screening test following the initial SMS reminder. The primary independent variable was the type of cancer screening test: mammogram (breast cancer screening), FIT or Cologuard (colorectal cancer screening), or HPV/Pap testing (cervical cancer screening). Other independent variables included sociodemographic characteristics, health status and access to care variables, and health-related social needs variables, including food insecurity, social isolation, housing insecurity, and transportation challenges.
A Cox proportional hazards model was applied to quantify the associations between time to screening completion and the independent variables and covariates. All analyses were performed using R version 4.4.2 in RStudio. Hazard ratios (HRs) and their 95% confidence intervals were obtained by exponentiating the model coefficients. Results: The median survival times (in days) for the overall cohort, HPV/Pap, Mammogram, FIT/Cologuard groups were 59.0 (95% CI: 53–65), 72.5 (95% CI: 64–86), 52.0 (95% CI: 43–64), and 52.0 (95% CI: 52–53), respectively. Compared with patients who were overdue for HPV/Pap screening, patients in the FIT group (HR = 1.65, 95% CI: 1.34–2.05, p < 0.001) and the mammogram group (HR = 1.41, 95%CI: 1.11–1.78, p < 0.001) had a significantly higher likelihood of completing screening sooner, reflecting shorter times to screening completion. Screening positive for transportation as a social need was associated with delayed screening completion (HR = 0.74, 95% CI: 0.55–0.99, p = 0.045). Conclusions: These findings indicate that transportation barriers are associated with longer time to cancer screening completion among women aged 50 years or older. In addition, the slower completion of HPV/Pap screening compared with FIT and mammogram suggests that cervical cancer screening may require more intensive follow-up, tailored outreach messages, or enhanced counseling to reduce delays in completion time.
Background: Prediabetes is highly prevalent and increasing globally, yet lifestyle interventions remain underutilized. Artificial intelligence (AI)-driven mobile health tools can help scale diabetes p...
Background: Prediabetes is highly prevalent and increasing globally, yet lifestyle interventions remain underutilized. Artificial intelligence (AI)-driven mobile health tools can help scale diabetes prevention efforts, but the key factors driving their success are not well understood. Objective: This prospective study aims to characterize the most valued features and the role of user engagement on outcomes in a fully automated mHealth intervention for diabetes prevention. Methods: Data from 151 participants with prediabetes and overweight or obesity assigned an AI-based Diabetes Prevention Program (Sweetch, Sweetch Ltd.) in a parent RCT (NCT05056376) were analyzed. Engagement (defined as total days where app was used) was categorized into tertiles (low, medium, high). Baseline characteristics were compared across engagement groups using ANOVA, Kruskal-Wallis, and chi-square tests, and regression models assessed the association between engagement and achievement of diabetes risk reduction outcomes (≥5% weight loss, ≥4% weight loss with ≥150 min/week of activity, or ≥0.2-point A1C reduction at 12 months). Perceived usefulness of intervention features was surveyed at 12 months. Results: At 12 months, median engagement was 98 days (IQR: 34–232), with most participants (75.5%) demonstrating a decreasing engagement trajectory over time. Older age (p < 0.001) and lower baseline BMI (p < 0.05) were significantly associated with higher engagement. High engagement was significantly associated with achieving the composite diabetes risk reduction outcome (OR: 2.59; 95% CI: 1.11–6.01), ≥5% weight loss (OR: 3.31; 95% CI: 1.16–9.42), and ≥0.2% A1C reduction (OR: 3.57; 95% CI: 1.19–10.75) compared to low engagement. The app features perceived most useful in achieving participant health goals were weight tracking, activity tracking, and the digital scale. Conclusions: Higher engagement with an AI-driven intervention requiring no human intervention was associated with improved diabetes risk reduction. Contrary to concerns about lower digital literacy, older adults engaged with the intervention the most. Features related to weight and physical activity tracking were most valued by patients in the program. Clinical Trial: ClinicalTrials.gov Identifier: NCT05056376
Background: Large language models are increasingly deployed in mental health applications, yet growing evidence suggests they encode algorithmic biases that influence clinical outputs. Because these m...
Background: Large language models are increasingly deployed in mental health applications, yet growing evidence suggests they encode algorithmic biases that influence clinical outputs. Because these models now mediate patient-facing decisions, such biases carry the potential for direct harm. Whether they systematically affect psychiatric diagnosis across demographic groups remains underexplored. Objective: To examine whether large language models (LLMs) exhibit implicit demographic biases when generating psychiatric diagnoses. Methods: We developed 1,152 synthetic clinical vignettes using a matched-pair design that manipulated gender, race/ethnicity, age, socioeconomic status, English proficiency, and urbanicity while holding clinical content constant. Vignettes were divided into control (unambiguous anorexia nervosa) and ambiguous conditions designed to permit differential diagnosis. Ten LLM configurations across five model families were tested. Results: Control vignettes produced near-unanimous anorexia nervosa diagnoses (M = 100.0%), while ambiguous vignettes elicited greater variability (M = 23.6%). Inter-model agreement was moderate for ambiguous vignettes (Fleiss' κ = 0.410, 95% CI: 0.397–0.422). Mixed-effects logistic regression with LLM as a random intercept revealed significant demographic biases: Black patients were over six times more likely to receive a major depressive disorder diagnosis than White patients with identical presentations (OR = 6.09, 95% CI: 5.13–7.24), Latine patients were over nine times more likely (OR = 9.57, 95% CI: 8.00–11.45), and Asian patients were nearly three times more likely to receive an anorexia nervosa diagnosis (OR = 2.88, 95% CI: 2.44–3.42). Female patients were less likely than males to be diagnosed with anorexia nervosa (OR = 0.43, 95% CI: 0.37–0.49). Conclusions: These findings demonstrate that LLMs exhibit systematic demographic biases in psychiatric diagnosis even when clinical content is held constant, revealing measurable patterns that can inform improvements to training data, model architecture, and clinical deployment frameworks.
Background: Incidental detection of abdominal aortic aneurysms (AAAs) has increased with widespread imaging, while traditional surveillance workflows remain fragmented and clinician-dependent. We desc...
Background: Incidental detection of abdominal aortic aneurysms (AAAs) has increased with widespread imaging, while traditional surveillance workflows remain fragmented and clinician-dependent. We describe the implementation and system-wide performance of the System to Track Abnormalities of Importance Reliably (STAIR™), a centralized, artificial intelligence–assisted program designed to identify AAAs, assign guideline-based surveillance, and ensure longitudinal tracking within an integrated healthcare system. Objective: To evaluate the implementation and performance of a centralized, artificial intelligence–enabled surveillance program designed to identify, risk stratify and longitudinally track patients with abdominal aortic aneurysms across an integrated healthcare system. Methods: This descriptive cohort study included all patients enrolled in the STAIR™ AAA surveillance program following its implementation in December 2022. Case identification was performed using rule-based natural language processing of radiology reports, structured electronic health record queries, clinician referral, and automated lost-to-follow-up searches. All cases underwent centralized clinical review, with surveillance intervals assigned according to Society for Vascular Surgery guidelines. Patients were followed until a predefined administrative or clinical endpoint was reached. Outcomes were descriptive and included identification pathways, surveillance assignments, endpoint resolution, imaging utilization, and operative activity. Results: A total of 8,464 patients were enrolled. Identification occurred via problem list queries (59%), radiology natural language processing (29%), clinician referral (7%), and automated lost-to-follow-up searches (5%). Following centralized review, 3.7% required immediate imaging, 45.3% of patients were assigned biennial duplex surveillance, 9.5% were assigned five-year surveillance, and 20.6% were referred for vascular surgery evaluation. Prior AAA repair at enrollment was identified in 20.6% of patients. Among 4,718 patients who reached a definitive endpoint, all had documented final disposition, including transfer of care outside the health system (57.4%), no further follow-up required (13.2%), prior repair, death, patient refusal, or inability to establish contact. Duplex ultrasonography accounted for approximately 80% of surveillance imaging. Elective AAA repair volume averaged approximately 135 cases annually during the study period. Conclusions: In a large integrated healthcare system, a centralized, artificial intelligence–assisted surveillance infrastructure was operationally feasible and supported comprehensive identification, guideline-based surveillance assignment, and complete endpoint adjudication for patients with AAAs. These findings describe a scalable, workflow-focused approach to population-level AAA surveillance that is independent of care setting and emphasizes clinical oversight rather than autonomous decision-making. Clinical Trial: NA
Background: Machine learning methods succeed in stress detection under controlled laboratory conditions. However, transferring these models to real-world environments remains challenging. This perform...
Background: Machine learning methods succeed in stress detection under controlled laboratory conditions. However, transferring these models to real-world environments remains challenging. This performance gap is often considered as signal noise, overlooking fundamental issues in evaluation methodology and context-aware modeling. Objective: This work discusses the obstacles preventing the transition to real-world deployment and provides recommendations towards robust real-world stress detection methods. Methods: We synthesize current literature to map six critical challenges: high inter-subject physiological variability, motion/environmental artifacts, temporal signal misalignment, lack of contextual differentiation, biased ground truth labels, and inherent class imbalance in ambulatory data. Results: This perspective provides methodological recommendations for designing, evaluating, and reporting wearable stress detection studies, and strategies to avoid common experimental pitfalls, to ensure robust, trustworthy stress monitoring in real-world settings. Conclusions: : Reliable mHealth stress monitoring requires a shift from laboratory-based models to context-aware, subject-independent frameworks. By adopting the recommended evaluation and preprocessing standards, researchers can ensure that reported performance metrics reflect actual deployment reliability, improving the utility of wearable-based mental health interventions.
Background: The rapidly growing elderly population in Japan has increased demand for home care services. As a result, visiting nurses spend approximately 40% of their working time on documentation. Au...
Background: The rapidly growing elderly population in Japan has increased demand for home care services. As a result, visiting nurses spend approximately 40% of their working time on documentation. Automated documentation using large language models (LLMs) shows potential but faces hallucination risks and lack of patient-specific context. Although retrieval-augmented generation (RAG) has emerged to address these limitations through knowledge embedding, existing healthcare RAG systems focus on single-patient contexts and remain unexplored for Japanese clinical documentation. Objective: This study aims to develop and evaluate an Adaptive Cascaded RAG (AC-RAG) system that safely integrates cross-patient knowledge through four-stage hierarchical filtering and adaptive strategy selection for automating Japanese nursing documentation. Methods: We developed a four-stage cascaded retrieval pipeline with disease-gated filtering, demographic similarity scoring, adaptive semantic thresholds, and context volume control. The system selects optimal knowledge integration strategies (Hybrid, History-Only, Cross-Patient-Only, No-RAG) based on data availability. We evaluated 89 home nursing consultations across two Automatic Speech Recognition (ASR) systems, comparing AC-RAG against Few-Shot Generated Knowledge Prompting (FS-GKP). Results: The conservative extraction achieved 70.8% higher precision than FS-GKP. For RAG-based summary generation, semantic similarity improved 28% (P<.001, Cohen's d=1.69–1.84), TF-IDF cosine similarity increased 24% (P<.001), and character-level BLEU improved 47% (P<.001). Processing speed increased 89–91% with a 59–61% cost reduction. Ablation analysis demonstrated the hybrid strategy achieved the highest performance (cosine similarity: 0.266±0.038). Cross-patient-only showed lower performance than the no-RAG baseline (cosine similarity: 0.175 vs. 0.192, P=.40, d=0.27), suggesting cross-patient knowledge provides benefit when combined with patient history. Conclusions: AC-RAG demonstrates superior accuracy, semantic quality, and computational efficiency. The incremental benefit of cross-patient retrieval requires validation in larger samples. At $0.043–0.054 per consultation, the system demonstrates economic feasibility for deployment in Japanese home care settings. However, moderate entity recall (0.493–0.519) indicates the system is best suited for generating draft documentation requiring nurse review rather than fully autonomous operation.
Background: Veterans face stigma, privacy concerns, and access barriers to HIV screening. For studies that use at-home HIV self-testing (HIVST) kits distributed through vending machines (VMs), recruit...
Background: Veterans face stigma, privacy concerns, and access barriers to HIV screening. For studies that use at-home HIV self-testing (HIVST) kits distributed through vending machines (VMs), recruitment and educational materials must communicate study purpose and participation options clearly, minimize confusion and stigma, and provide actionable next steps for participants who test outside of clinical settings. Objective: To elicit Veteran Advocate feedback on recruitment flyers, education handouts, a web-based questionnaire, and a qualitative interview guide for a Veterans Health Administration study evaluating impacts of VM-dispensed HIVST kits to Veterans and to document how feedback informed revisions to these study materials. Methods: Using participatory action research, we recruited Veteran Advocates with lived/living expertise of HIV (August 2025). Veteran Advocates completed structured written reviews of study materials and returned written feedback forms; feedback was also discussed during a 1-hour virtual focus group in September 2025. We analyzed written feedback and the focus group transcript using a rapid, team-based consensus thematic approach. Two study team members independently reviewed each feedback source and documented key recommendations and candidate themes using analytic notes; the team then met to cluster feedback into themes and reach consensus on final themes and definitions. To ensure findings directly informed materials improvement, we created a revision matrix mapping each theme to the relevant study material(s), a summary of feedback, and the resulting changes made. This matrix served as an audit trail linking feedback to the “feedback and revisions” tables presented in the Results. Results: Four Veteran Advocates provided structured written feedback on study materials, and three participated in the 1-hour focus group. Across study materials, Veteran Advocates desired (1) clearer, plain-language descriptions of study purpose, eligibility, and participation pathways; (2) reduced potential for confusion between research recruitment, VM access, and HIVST kit promotion; (3) reduced text density and participant burden; and (4) more actionable “next steps,” including human support and linkage-to-care resources appropriate for at-home self-testing. Revisions included a streamlined recruitment flyer with simplified calls-to-action and clearer survey versus interview pathways; a more cohesive and condensed education packet oriented around self-testing steps, results interpretation, and support resources; questionnaire updates to reduce redundancy and improve usability; and an interview guide with improved flow, more participant-centered framing, and optional questions on emotional reactions and support needs. Conclusions: Veteran Advocate feedback was systematically translated into concrete revisions across multiple study materials prior to study launch. Transparently mapping stakeholder input to specific adaptations may strengthen acceptability and usability of Veteran-facing HIV screening and self-testing materials in VA and similar settings.
Background: Preschool-aged children (2-5 years) living in households experiencing food insecurity (FI) are at a higher risk of facing health and behavioral issues as well as consuming lower quality di...
Background: Preschool-aged children (2-5 years) living in households experiencing food insecurity (FI) are at a higher risk of facing health and behavioral issues as well as consuming lower quality diets with fewer fruits and vegetables (FV). Repeated exposures are necessary for children to accept certain commonly rejected/disliked foods, but parents in households experiencing FI may not purchase foods their child does not readily accept. Project V.E.G.G.I.E. (Vegetable Eating Gets Going by Increasing Exposure) aims to address this issue by providing families with FVs at no cost alongside education on evidence-based parent-feeding practices tailored to the needs of preschool-aged children. Objective: This study describes the protocol for the Project V.E.G.G.I.E. pilot/feasibility study. Methods: The Project V.E.G.G.I.E. study includes 20 dyads: parents and their preschool-age children. Families received six boxes of fresh FV biweekly for 10 weeks alongside educational materials on parent feeding practices. Parent surveys and daily diaries were completed at three time points: baseline, post-test (weeks 10-11) and follow-up (4 weeks after intervention). Participants in the intervention group also completed an exit survey to assess the acceptability and utility of the FV boxes across several domains including: quantity, quality, and variety. Standard effect size estimates (Cohen’s d) will be calculated as baseline to post-test and baseline to follow-up analyses. Lastly, both control and intervention participants were invited to complete a qualitative interview to discuss satisfaction with the program. Results: The Project V.E.G.G.I.E. program was funded internally by the Department of BLINDED FOR PEER REVIEW at BLINDED University. Recruitment began in February 2025, and data collection took place from March – July 2025. Data cleaning is underway at the time of submission (February 2026); we expect to submit the outcome/feasibility manuscript in Spring 2026. Conclusions: The results of the Project V.E.G.G.I.E. study will be used to evaluate the feasibility and acceptability of this pilot intervention. Feedback will inform refinement of the Project V.E.G.G.I.E. intervention and study protocol prior to scaling up to a well-powered cluster randomized controlled trial. Implementing programs like Project V.E.G.G.I.E. that provide families with increased access to FV at childcare settings may overcome barriers typically associated with FV initiatives. Providing families with both increased access to FV and education on parent-feeding practices may be more effective at increasing FV consumption among preschoolers than either approach implemented independently.
Background: Electronic health record (EHR) data is being increasingly used for retrospective observational research through large, robust databases and advanced data extraction tools. Objective: We so...
Background: Electronic health record (EHR) data is being increasingly used for retrospective observational research through large, robust databases and advanced data extraction tools. Objective: We sought to assess the accuracy of vital sign, ventilator, and continuous medication data captured in the EHR in a pediatric intensive care unit (PICU). Methods: We conducted a retrospective observational study of children receiving invasive mechanical ventilation in June 2025. Data sources included 1) A bedside clinical researcher, 2) Automated EHR extraction, and 3) A continuous vital sign monitoring system. Vital sign comparisons used the continuous vital sign monitoring system as the gold standard. Ventilator and medication data comparisons used bedside observations as the gold standard. Differences were measured as Means with standard deviation (SD) or Median Differences (MD) with interquartile ranges (IQR). Results: We obtained 110 bedside observations from 27 unique patients. All measured vital signs in the EHR were accurate relative to the continuous vital sign monitoring system with mean differences ranging from a low of 0.1% for oxygen saturation to a high of 1.6 breaths per minute for respiratory rate. Most vital signs did have rare outliers such as a diastolic blood pressure difference of 46mmHg, a heart rate difference of 35 beats per minute, and a respiratory rate difference of 18 breaths per minute. Ventilator settings were highly accurate in the EHR with MD of 0.0 and IQR of 0-0. Outliers were less common but included a PEEP difference of 10mmHg, a respiratory rate of 4 breaths per minute, and an FiO2 of 15%. Continuous medication dosing accuracy was variable with an overall low accuracy between 28.0-35.2%. Conclusions: EHR data capture in the PICU is accurate for vital signs and ventilator settings, but less accurate for continuous medications.
Background: Inflammatory Bowel Disease (IBD) is a chronic nonspecific intestinal inflammatory condition; accurate severity assessment is critical for clinical treatment decisions and prognosis. Curren...
Background: Inflammatory Bowel Disease (IBD) is a chronic nonspecific intestinal inflammatory condition; accurate severity assessment is critical for clinical treatment decisions and prognosis. Current IBD evaluation relies primarily on endoscopic examinations and physician expertise, which are subjective and inconsistent. Objective: This study aimed to develop an automated deep learning-based scoring algorithm for objective quantitative assessment of IBD lesion severity to address the clinical challenge of subjective evaluation. Methods: A multi-stage deep learning architecture was employed for automatic IBD scoring: (1) an improved YOLO-V11 segmentation network (enhanced by attention mechanisms and multi-scale feature fusion) precisely identified edema and ulcer regions in intestinal endoscopic images; (2) a classification module based on YOLO-V11-derived lesion features recognized stenosis; (3) an LSTM lightweight normalization network integrated spatial and temporal lesion features to generate comprehensive IBD scores. Validation used endoscopic video data from 814 patients at a large medical center: 725 cases (4,400 annotated images) for edema/ulcer/stenosis recognition, and 89 cases for comprehensive scoring. Results: The model’s automatic scoring results showed a mean squared error (MSE) of 14.693 and a coefficient of determination (R²) of 0.82 compared with expert scoring. Key innovations included: (1) first combination of lesion recognition algorithms with image distribution frequency features for a multi-dimensional evaluation system; (2) development of a lightweight lesion recognition network suitable for clinical settings; (3) establishment of a large-scale annotated dataset encompassing various IBD subtypes. Conclusions: This automated scoring system improves the objectivity and repeatability of IBD severity assessment, providing a reliable tool for telemedicine and clinical trials. Future research will optimize the model’s performance in pediatric IBD and small bowel lesions.
Background: Gender disparities in disease burden remain a critical public health concern, particularly in low- and middle-income countries like Pakistan. Existing studies that have explored such inequ...
Background: Gender disparities in disease burden remain a critical public health concern, particularly in low- and middle-income countries like Pakistan. Existing studies that have explored such inequities in Pakistan have categorized health outcomes only at the broad Level 1 classification, including communicable diseases, NCDs, and injuries, without gender specific data. Objective: This study aimed to compare gender-based differences in mortality and disability-adjusted life years for the causes and risk factors in Pakistan in 2023, using data from the Global Burden of Disease. Methods: We conducted an ecological study using the Global Burden of Disease dataset for Pakistan, aged ≥20 years. We ranked the gender-aggregated and gender-disaggregated top causes based on mortality and disability-adjusted life years in Pakistan in the year 2023. Additionally, we calculated the absolute difference in cause-specific mortality and DALY rates between females and males. We ranked the risk factors for gender-aggregated and gender-disaggregated data in Pakistan in the year 2023. Results: In 2023, ischemic heart disease (IHD) (136; 95% UI: 170.4–105.1) and stroke (80.8; 95% UI: 113.7–57.9) were the leading causes of mortality among adults aged 20 years and above, as well as among males and females in Pakistan. The leading causes of DALYs were also IHD (3727.6; 95% UI: 2877.3–4687.7) and stroke (2175.1; 95% UI: 1598.2–3004.7), among males and females. Males experienced higher DALY losses from tuberculosis (2090.8; 95% UI: 1326–2971.5), road injuries (1706.7; 95% UI: 977.6–2388.1), and self-harm (864.1; 95% UI: 527–1273.6), while females were more affected by low back pain (1554.7; 95% UI: 1079.8–2126.1), depressive disorders (1538.5; 95% UI: 1042.4–2197.4), and dietary iron deficiency (1043.7; 95% UI: 461.9–1863.5). The greatest absolute difference for mortality and DALYs among males was reported for tuberculosis, while for females, rheumatic heart disease was reported for mortality, and lower back pain for DALYs. The leading risk factors for both gender-aggregated and gender-disaggregated mortality were diets low in nuts and seeds and particulate matter pollution for DALYs. Conclusions: Our findings show IHD and stroke were the leading causes of mortality and DALYs among adults aged 20 years and above in Pakistan in 2023, reflecting the continued dominance of non-communicable diseases. This highlights the importance of gender-disaggregated analysis in national health reporting. Tailored interventions addressing these disparities are crucial for equitable healthcare planning in Pakistan.
Background: Diabetes mellitus affects approximately 537 million adults globally, with projections indicating an increase to 643 million by 2030. Mobile health applications (mHealth apps) offer promisi...
Background: Diabetes mellitus affects approximately 537 million adults globally, with projections indicating an increase to 643 million by 2030. Mobile health applications (mHealth apps) offer promising support for diabetes self-management, yet adoption rates remain low. Understanding the factors influencing patients' intentions to use mHealth apps is essential for designing effective interventions. Objective: To develop and empirically validate an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model incorporating personal innovativeness and attitude to explain behavioral intention to use mHealth apps for diabetes management. Methods: A cross-sectional survey was conducted with 485 Chinese adults. The measurement and structural models were assessed using Partial Least Squares Structural Equation Modeling (PLS_SEM). Results: Performance expectancy (β = .110, t = 3.401, P < .001), effort expectancy (β = .226, t = 5.942, P < .001), social influence (β = .112, t = 2.953, P =.002), facilitating conditions (β= .095, t = 2.476, P =.007), and personal innovativeness (β = .365, t = 9.280, P < .001) significantly influenced attitudes toward mHealth apps. Performance expectancy (β = .069, t = 2.239, P =.01), effort expectancy (β = .377, t = 8.939, P < .001), social influence (β = .123, t = 3.279, P < .001), and personal innovativeness (β = .116, t = 3.459, P < .001) significantly affected behavioral intention, while facilitating conditions did not (β = .041, t = 1.418, P =.07). Attitude significantly influenced behavioral intention (β = .337, t = 8.010, P < .001). Additionally, attitude significantly and positively mediated the relationships between performance expectancy (β = .037, t = 3.128, P < .001), effort expectancy (β = 0.076, t = 4.568, P < .001), social influence (β = .038, t = 2.775, P =.003), facilitating conditions (β = .032, t = 2.433, P =.007), and personal innovativeness (β = .123, t = 5.787, P < .001) and the behavioral intention to use mHealth apps for diabetes management. The model explained 31.7% of the variance in attitude and 51.5% in behavioral intention. Conclusions: The extended UTAUT model effectively explains mHealth app adoption for diabetes management by integrating personal innovativeness and attitude. Emphasizing app utility, usability, social influence, and fostering positive attitudes can enhance adoption. These insights inform healthcare providers and developers aiming to increase mHealth engagement among patients with diabetes.
Background: Mindfulness has potential to improve lives after stroke but survivors experience barriers (e.g. transportation) to attend face-to-face programs. Only two virtual mindfulness programs have...
Background: Mindfulness has potential to improve lives after stroke but survivors experience barriers (e.g. transportation) to attend face-to-face programs. Only two virtual mindfulness programs have been explored for stroke survivors, but they included the diagnosis of traumatic brain injury and only persons with high levels of chronic fatigue, not representative of the general population of persons with stroke. Objective: The aims of this study were to: 1) investigate the effect of a virtual mindfulness program for stroke survivors on the primary outcomes of acceptance, stress and self-compassion and secondary outcomes including fatigue, depression, anxiety, and sleep; 2) explore the stroke survivor experience to better understand the effectiveness of the mindfulness program. Methods: This was a mixed methods study involving eight stroke survivors with a mean age of 55.3 years (range 41-66) and mean time post stroke of 48.6 months (range 7 to 94). Primary outcomes measured before (PRE), after the program (POST), and two months later (POST2) included the Illness Cognition Questionnaire (ICQ), the Acceptance of Illness Questionnaire (AIQ), the Perceived Stress Scale (PSS), and the Self-compassion Scale (SCS). Secondary outcomes included Frieberg Mindfulness Inventory (FMI), Mental Fatigue Scale (MFS), Patient-Reported Outcomes Measurement Information System (PROMIS®)-Short Form (depression, anxiety, fatigue, sleep disturbance). A paired t-test was conducted to compare PRE, POST and POST2 outcomes. Qualitative data was collected via a semi structured interview with each participant after the program. Results: Significant improvements were observed from PRE to POST for PSS (P=.03) and the SCS (P=.003), with continued improvements demonstrated at POST2. Although acceptance showed an improved trend from PRE to POST to POST2, only the ICQ helplessness scale was close to being significant (P=.05). Several secondary outcomes improved significantly from PRE to POST2 including FMI (P=0.003) and the PROMIS subscales of fatigue (P=.04) and sleep (P=.03). The qualitative findings supported the quantitative results and provided a deeper understanding of the impact on participants. Conclusions: These results demonstrate how a virtual mindfulness program adapted for stroke may benefit survivors including decreasing stress and increasing self-compassion. Although changes in acceptance were not significant, a trend of improvement from PRE to POST to POST2 was observed and worthy of further investigation. Significant improvements were also observed for secondary outcomes of fatigue and sleep. Virtual mindfulness programs offer a feasible and promising approach to help survivors move forward with life after stroke. Due to small sample size, results should be interpreted appropriately and further research is recommended. Clinical Trial: No registration
Background: Traditional lecture-based education has shown limitations in engagement, knowledge retention, and skill transfer in healthcare training. Serious games and virtual simulations offer accessi...
Background: Traditional lecture-based education has shown limitations in engagement, knowledge retention, and skill transfer in healthcare training. Serious games and virtual simulations offer accessible and scalable solutions to enhance emergency medicine (EM) education. The GEMAS project (Gamificación en Enfermería y Medicina para el Aprendizaje por Simulación) was developed as a narrative-driven serious game integrating clinical reasoning, diagnostic decision-making, and evidence-based emergency management. Objective: This study aimed to describe its development and evaluate its usability, satisfaction, and educational impact. Methods: A pre–post single-center pilot study was conducted among physicians and nurses from a university hospital with no prior experience in serious games or high-fidelity simulation. Participants completed a 2–3-hour GEMAS gameplay session. Educational outcomes were assessed using Levels I and II of the Kirkpatrick model: (1) satisfaction and usability through a 10-item Likert questionnaire and the System Usability Scale (SUS); and (2) knowledge acquisition via an expert-validated pre- and post-intervention test covering key emergency scenarios. Statistical analyses included paired t-tests and Pearson correlations between knowledge improvement and age or professional experience. The level of statistical significance considered was 5%. Results: 22 healthcare professionals participated (31.8% physicians, 68.2% nurses; mean age 31 ± 7 years; 59% female). Satisfaction was high across all items (means >9/10), with no differences between professional categories. Median SUS was 87.25 overall (90 for physicians, 84.5 for nurses), with 77.3% giving grade A (>78.9, excellent usability). Knowledge scores improved significantly from pre- to post-intervention. Physicians improved from 48.5 ± 13.3 to 80.3 ± 15.2, and nurses from 23.5 ± 8.3 to 43.5 ± 15 (p < 0.001). No significant correlation was found between improvement and age (r = –0.08) or years of experience (r = –0.41). Conclusions: GEMAS demonstrated excellent usability, very high user satisfaction, and significant knowledge improvement among active healthcare professionals. Its design effectively enhances clinical reasoning and evidence-based decision-making, providing a scalable, low-cost complement to traditional simulation. Future multicenter studies will explore long-term learning transfer. Clinical Trial: NCT06516250
Background: Substance use disorder (SUD) is a chronic, relapsing condition characterized by compulsive substance use and dysregulation in reward and control systems. Although effective pharmacological...
Background: Substance use disorder (SUD) is a chronic, relapsing condition characterized by compulsive substance use and dysregulation in reward and control systems. Although effective pharmacological and psychosocial treatments are available, their impact is often limited by barriers such as stigma, poor adherence, and restricted access to care. Virtual Reality (VR) has emerged as a digital health intervention offering an adjunctive approach by providing immersive, interactive environments that may enhance engagement, simulate real-world triggers, and support therapeutic learning. Objective: This focus review aimed to map and synthesize the existing evidence for VR-based interventions in SUD treatment. We examine both therapeutic applications across established treatment frameworks and experimental approaches, identify key opportunities for future research and clinical innovation. Methods: We searched electronic databases including PubMed/MEDLINE, Science Direct and MDPI covering 2004 to 2025. Two reviewers independently screened for relevant studies and extracted study characteristics. Studies addressing VR applications for substance use disorders including peer-reviewed articles, randomized controlled trials, protocols and pilot studies published in English were selected. Any discrepancies were resolved through discussion. Results: A total of 26 studies or protocols were included in this review. Overall, the studies reviewed are broadly categorized into 6 sub-groups based on the type of the VR intervention and treatment class delivered. The reviewed literature indicates that VR-based cue exposure therapy is associated with reductions in craving and physiological reactivity for nicotine, alcohol, and cannabis use, with more limited and preliminary findings for opioid use disorder. VR relaxation and stress-management environments were linked to decreases in craving, stress, and pain among individuals with opioid and alcohol use disorders. VR-enhanced cognitive-behavioral interventions showed improvements in attention, cognitive flexibility, and emotion regulation. Motivational, social skills, and gamified VR interventions were associated with increased engagement, reduced stigma, enhanced self-efficacy, and improved treatment retention. Conclusions: This focus review contributes to the growing digital health literature by synthesizing current evidence on VR-based interventions for SUDs. The findings suggest that VR may serve as a flexible adjunct to existing treatments, with the potential to address persistent barriers to engagement and access. Further rigorously designed studies are needed to evaluate long-term effectiveness, optimize VR design, and support their integration into routine clinical practice.
Background: Chronic disease risk factors including smoking/vaping, poor nutrition, alcohol misuse and physical inactivity, as well as falls (SNAPF), have a significant impact on population health. Del...
Background: Chronic disease risk factors including smoking/vaping, poor nutrition, alcohol misuse and physical inactivity, as well as falls (SNAPF), have a significant impact on population health. Delivering preventive care using evidence-based models (eg, Ask, Advise, Help (AAH) model) during clinical consultations is recommended and can reduce SNAPF risks. Rates of preventive care delivery within clinical consultations are variable, with barriers including limited time and competing priorities. One solution to increase preventive care delivery is using hybrid approaches that combine digital and clinician-delivered care. Objective: We aimed to test the use and acceptability of an online preventive care tool based on the AAH model and delivered through a hybrid care approach from the perspective of Community Health clients and clinicians. Methods: A convenience sample of adult clients with an upcoming appointment at two Australian Community Health services were sent an SMS containing a link to the online tool. The tool ‘Asked’ about SNAPF risk factors, and provided ‘Advice’ and ‘Help' via a summary message and information sheets. Data on use and acceptability was collected via analytics, semi-structured telephone interviews with clients, and semi-structured online interviews and focus groups with clinicians. Data analysis was conducted using descriptive statistics for quantitative data and thematic analysis for qualitative data. Results: Forty-three participants (56% female, mean age 55.0) completed the tool, out of 76 who received it (57%). Fifty-two participants who received the tool completed a semi-structured telephone interview (68%). Most participants found it acceptable to receive the tool via SMS (87%) and for the tool to provide ‘Advice’ and ‘Help’ (91%), although a smaller proportion of participants who completed the tool recalled the summary message (66%) or engaged with the information sheets (20%-53%). The main reasons reported for not completing the tool included receiving it at an inconvenient time, not being good with online forms, and being wary of opening links. Clinician feedback (n=7) highlighted client use barriers (eg, concerns about scams) and enablers (eg, assistance from family), as well as positive feedback on the tool itself (eg, clients receiving enhanced advice). Conclusions: The online preventive care tool was used by over half of the clients to whom it was sent, and was acceptable to Community Health clients and clinicians. There is an opportunity to use digital tools to help enhance preventive care within clinical care.
Background: Despite Iran’s competitive advantages in medical costs and surgical expertise, the medical tourism industry suffers from fragmented service delivery and a lack of standardized competenci...
Background: Despite Iran’s competitive advantages in medical costs and surgical expertise, the medical tourism industry suffers from fragmented service delivery and a lack of standardized competencies among stakeholders. Objective: This study aims to develop and validate a localized »Skill Enhancement Framework« for Iran’s medical tourism workforce. Methods: A multiphase mixed-methods design is employed. Phase I (Scoping Review) has mapped global competencies. Phase II involves qualitative semi-structured interviews to identify localized needs and socio-economic barriers. Phase III utilizes the Delphi technique to reach expert consensus and validate the final framework. Results: no result Conclusions: By integrating evidence-based findings with expert insights, this protocol provides a methodological roadmap to professionalize the value chain, ensuring the sustainability and global competitiveness of Iran’s medical tourism brand
Background: Mental health problems are a significant global health challenge, with the majority manifesting during the crucial developmental phase of adolescence. Factors like childhood abuse, socioec...
Background: Mental health problems are a significant global health challenge, with the majority manifesting during the crucial developmental phase of adolescence. Factors like childhood abuse, socioeconomic conditions, and hostile school environments worsen the mental health problems among adolescents, resulting in severe consequences, including violence, substance abuse, and reduced academic performance. Schools play a crucial role in implementing mental health interventions, offering unique access to a diverse group of adolescents within their familiar learning environment. Objective: This review aims to synthesize the existing literature on interventions designed to support adolescents facing mental health challenges in secondary schools, including the role of school-based support teams (SBST). Methods: This scoping review will follow the framework established by Arksey and O'Malley (2005) and will adhere to a five-step process: (1) identifying the research topic; (2) locating relevant studies; (3) selecting studies; (4) charting the data; (5) compiling, summarizing, and reporting the findings. Selection of articles will be detailed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA-ScR) guidelines. Studies published in English from 2020 to 2025 will be included. A comprehensive search will be done across several databases, including PubMed, Scopus, MEDLINE ULTIMATE, ScienceDirect, Google Scholar, and OpenGrey. Using a standardized tool, two reviewers will independently screen the titles, abstracts, and full texts, and extract data to enhance reliability. If they disagree on something, the third reviewer will mediate to enhance consensus. The selection process for the studies included in the review is scheduled to be completed within a 10-week timeframe. This process will strictly follow the comprehensive guidelines established in the PRISMA-ScR checklist.
Results: Preliminary database search has been conducted across four databases, PubMed, Scopus, ScienceDirect, and MEDLINE Ultimate, and a total of 2160 records were identified. Duplicates removal and abstract screening are currently in progress. The study is anticipated to be published in June 2025. Results: Preliminary database search has been conducted across four databases, PubMed, Scopus, ScienceDirect, and MEDLINE Ultimate, and a total of 2160 records were identified. Duplicates removal and abstract screening are currently in progress. Conclusions: This scoping review aims to identify interventions that support adolescents with mental health issues, specifically focusing on Africa, and more particularly on the South African context. This focus will help uncover the cultural and contextual factors that influence mental health interventions in this region. In South Africa, accessing mental health services can be difficult, especially in under-resourced communities. School-based mental health interventions have been recognized as an effective solution, as they can reach many adolescents in a cost-efficient manner. These interventions take place in a familiar environment for adolescents and provide a setting that reduces the stigma associated with seeking mental health services.
The findings will provide insights into the range of interventions aimed at supporting adolescent learners in secondary schools. By analyzing current literature, the researchers aim to highlight the different types of school-based interventions available, evaluate their effectiveness, and identify any barriers that prevent adolescents from accessing mental health support. Additionally, the review will explore closely the role of school-based support teams and assess their effectiveness in assisting adolescent learners with mental health problems within the school context. The expected outcome of this scoping review is to deliver a comprehensive overview of mental health interventions intended to support adolescents in secondary schools. Clinical Trial: The complete protocol and supplementary details of the review are publicly accessible via the following URL: https://osf.io/eb3ua
Background: The cancer care pathway can cause or accentuate inequalities. It is therefore necessary to identify patients with such social vulnerabilities as early as possible and take them into accoun...
Background: The cancer care pathway can cause or accentuate inequalities. It is therefore necessary to identify patients with such social vulnerabilities as early as possible and take them into account throughout the treatment process. The DEFCO (Detection of Social Frailty and Cancer Patient Care Pathway Coordination) tool has been created by a public health research team and industrial engineering research team and has previously shown its validity. The transferability and possibilities of implementation in other structures of this tool, developed in a specialized institution, must now be proven. It is also necessary to evaluate it in terms of impact on the fluidity of care paths and on the social impacts of the disease. Objective: The objective is to assess the implementation of the DEFCO tool for identifying social vulnerability in new centers. Methods: This is a multicenter prospective cohort implementation study using a mixed-methods effectiveness-implementation design to evaluate a complex intervention. This study was conducted in five sequential stages. First, the organizational contexts of each participating center and their capacity to implement the DEFCO tool were assessed. Second, key stakeholders were trained to integrate the DEFCO tool into routine clinical practice. Third, a pre-implementation analysis was conducted using the Consolidated Framework for Implementation Research (CFIR) to identify contextual determinants influencing implementation. Fourth, the DEFCO tool was deployed in each center. Finally, the implementation process and outcomes wereevaluated using the Reach, Effectiveness, Adoption, Implementation, and Maintenance (Re-AIM) framework for quantitative measures, complemented by qualitative assessments guided by the CFIR. Results: This study enrolled 437 patients. In addition, 41 professionals were interviewed prior to the study and 21 following its completion. The results are currently under analysis and are expected to be available in the second half of 2026.
The results of this implementation study will provide information on: 1) the effectiveness in real life of the DEFCO tool, the objective of which is to identify social vulnerability in new cancer patients; 2) the impact of the modifications made to the initial tool to adapt to the contexts and the differences in practice according to the populations being cared for and therapeutic practice; 3) the key success factors and the pitfalls to be avoided, interacting with the effectiveness of the tool, 4) the modifications in the representations of social vulnerability and its consequences brought about by the implementation of the tool. Conclusions: Thanks to the implementation study, the generalization of this tool will be accompanied by instructions for use and the contextual elements necessary for optimal operation of the systematic detection of social vulnerability in the care pathway of patients treated for cancer in health care institutions of varying status and activity. Clinical Trial: NCT04015895
Background: Digital health interventions, including mobile applications and wearable devices, have emerged as promising tools to promote physical activity (PA) and support chronic disease management i...
Background: Digital health interventions, including mobile applications and wearable devices, have emerged as promising tools to promote physical activity (PA) and support chronic disease management in primary care. However, evidence remains limited regarding the real-world feasibility, patient engagement, and integration of these tools into routine family medicine practice, particularly in individuals with metabolic syndrome. In addition, physicians’ attitudes and intentions toward telemedicine may influence the successful adoption of such interventions. Objective: This study aims to evaluate the preliminary effectiveness of a mobile health intervention for PA monitoring in individuals with metabolic syndrome managed in primary care. Secondary objectives include assessing user engagement and adherence to the intervention, changes in PA-related and clinical outcomes, and exploring family physicians’ attitudes and behavioral intentions toward telemedicine. Methods: This is a two-arm, parallel, pilot randomized controlled trial conducted in primary care units in the Coimbra region, Portugal. Eligible adults with metabolic syndrome will be randomized (1:1) to an intervention group receiving access to a mobile application integrated with an activity-tracking wristband or to a control group receiving usual care. Data will be collected at baseline and 6 months. Primary outcomes include changes in PA levels and physical literacy, assessed through validated questionnaires and objective analitical results. Secondary outcomes comprise health-related quality of life, cardiometabolic parameters, adherence and engagement metrics derived from app usage, retention and dropout rates. Physicians’ attitudes and intentions regarding telemedicine will be assessed using the Physician Attitudes and Intention to use Telemedicine (PAIT) questionnaire. Analyses will primarily be descriptive and exploratory, aiming to inform the design of a future full-scale trial. Results: Participant recruitment is planned to begin in July 2025. This pilot trial will generate data on feasibility, adherence, engagement, and preliminary behavioral and clinical outcomes associated with the use of a mobile PA monitoring intervention in primary care. Conclusions: This study will provide important insights into integrating mobile health applications for PA promotion among individuals with metabolic syndrome in routine primary care. The findings will inform future larger randomized controlled trials and contribute to implementation strategies for digital health interventions in family medicine. Clinical Trial: EECC-2024_4-220 e 335/25 CE
Background: Community health workers (CHWs) play a vital role in delivering pediatric care in resource-limited settings, yet evidence on acceptable approaches for recurrent training remains limited....
Background: Community health workers (CHWs) play a vital role in delivering pediatric care in resource-limited settings, yet evidence on acceptable approaches for recurrent training remains limited. Mobile health (mHealth) training tools have demonstrated promise in enhancing skill acquisition and retention among CHWs; however, little is known about which specific design features optimize learning and sustained use over time. Objective: This study evaluates learning outcomes, engagement patterns, and user experiences associated with three mHealth training modalities for CHWs in Northern Uganda. Methods: We conducted a convergent mixed methods study within an established community-led CHW training program. Over eight months, CHWs in Northern Uganda were assigned to one of three mHealth training approaches: 1) a standard self-guided tablet application (‘standard’ group), 2) a gamified application with assessment-gated progression (‘gamified’ group), and 3) the standard application supplemented with simulation-based training (‘standard + simulation’ group). Quantitative outcomes included 1) written multiple-choice exams at baseline (T1), two months (T2), and eight months (T3), with competency defined as scores >80%, 2) a clinical skills assessment at eight months, and 3) tablet engagement analytics, including video views, in-quiz attempts, and quiz scores. Qualitative data were collected through semi-structured interviews and analyzed thematically. Quantitative and qualitative findings were integrated using joint displays. Results: Out of 30 eligible CHWs approached, all agreed to participate. Over the study period, six CHWs left the training program and were excluded from all analyses; the remaining 24 CHWs completed qualitative interviews and were included in tablet engagement analyses (standard: N=8; gamified: N=10; standard + simulation: N=6). 21 CHWs completed written exams at all three timepoints and were included in exam score analyses. Median written exam scores improved in the overall sample, increasing from 73% (IQR 26.67) at baseline (T1) to 100% (IQR 6.67) at eight months (T3) (p < 0.001), with no differences in the median magnitude of score improvement observed across training modalities (16.67 vs. 26.67 vs 26.67, p=0.64). All CHWs demonstrated competency in advanced pediatric clinical skills at study completion. The gamified application was associated with higher rates of video viewing and in-app quiz attempts per active day but did not result in higher in-app quiz pass rates or final exam scores compared with the standard application. Those who received the simulation reported greater confidence and perceived preparedness despite similar quantitative performance. Engagement declined modestly over time (from 77% to 58% of CHWs engaged weekly), consistent with qualitative reports of time constraints and technical barriers, including limited access to electricity for tablet charging. Conclusions: Findings suggest that mHealth-supported training can facilitate sustained acquisition of advanced pediatric clinical skills among experienced CHWs in a rural, resource-limited setting. These findings can inform the user-centered design of future training interventions.
Background: The growing burden of HIV/AIDS, particularly in sub-Saharan Africa, presents a significant public health challenge, characterized by increasing morbidity, and mortality rates. This region...
Background: The growing burden of HIV/AIDS, particularly in sub-Saharan Africa, presents a significant public health challenge, characterized by increasing morbidity, and mortality rates. This region is disproportionately affected, bearing for two-thirds of the global HIV/AIDS problem, highlighting an urgent need for effective solutions. Accurate forecasting of new HIV infections is crucial for developing targeted interventions to combat the HIV/AIDS pandemic. Objective: This study aims to forecast trends of new HIV infections for the next five years and identify the contributing factors in the East Gojjam Zone. Methods: DHIS2 (2018-2025) data set from East Gojjam zone were analyzed using to a hybrid machine learning and deep learning framework. Machine learning models (Decision Tree, Random Forest, XGBoost, LightGBM, CatBoost, AdaBoost, and Gradient Boosting) were used for feature selection, and deep learning architectures (RNN, LSTM, GRU, and bidirectional variants) were used for time-series forecasting. Model performance was assessed using MAE, MSE, RMSE and MAPE Results: From the seven machine-learning algorithms used for selecting important futures the random forest was best performed model and many features were selected to apply for further forecasting using deep learning algorithms. Bidirectional LSTM model was best performed model among the six sequential deep learning algorithms used for forecasting HIV infection in East Gojjam zone. Forecasts reveal an upward trend of HIV infection in study area. Conclusions: Combination of Machine learning and Deep learning algorithms method shows high predictive accuracy in forecasting of HIV infection. The forecasted trend shows an upward trend and needs urgent intervention and attention to combat the problem.
Background: NYU Langone Health (NYULH) operates one of the largest remote patient monitoring (RPM) programs in the United States. Its hypertension management initiative (NYULH RPM HTN) supports approx...
Background: NYU Langone Health (NYULH) operates one of the largest remote patient monitoring (RPM) programs in the United States. Its hypertension management initiative (NYULH RPM HTN) supports approximately 4,500 patients monthly and captured over 100,000 remote blood pressure (BP) readings in 2024. Despite its benefits, the program faces real-world challenges, including patient disengagement, device usability issues, and clinician burden from high data volume. Generative AI (GenAI), particularly large language models (LLMs), offers opportunities to enhance patient engagement and streamline clinical workflows through personalized conversational interfaces such as chatbots and its data summarization capabilities. Objective: To explore the feasibility of using GenAI to support RPM, we developed the AI Brain, an electronic health record (EHR)–integrated GenAI layer to support RPM for hypertension management. AI Brain includes a patient-facing agent, chatbot designed to support engagement and blood pressure (BP) adherence, as well as a clinician-facing agent that generates smart content for EHR documentation and drafts patient messages. This study was conducted at NYULH, an academic medical center, providing a unique setting to evaluate the tool within a large-scale hypertension RPM program and to assess its impact on patient engagement, data interpretation, and clinical workflow efficiency. Methods: Our multidisciplinary team—comprising researchers, software engineers/architects, UX designers, and physicians—developed the AI Brain using a user-centered design approach and agile software development methods. We established patient and clinician advisory committees and conducted workshops during the formative phase to understand workflows and co-design solutions in collaboration with stakeholders. This was followed by a software development cycle that engaged advisory committee members at each stage to ensure the tool met user needs. Implementation considerations included usability, data privacy, clinical integration, and alignment with existing RPM processes. Results: The evaluation of the AI Brain demonstrated feasibility for integration into an established RPM infrastructure. Early observations suggest that the patient-facing agent showed potential to address common engagement barriers, including missed blood pressure submissions and device-related challenges. The clinician-facing agent supported care teams by summarizing key patient trends and reducing manual data review burden. Moreover, structured survey results indicated positive acceptability and perceived usefulness of GenAI-generated content. Security evaluations further demonstrated robust safeguards and reliable system performance. Conclusions: GenAI represents a promising approach to enhancing RPM, as demonstrated by its evaluation and adaptation within the NYULH hypertension management program. We described our development process and showed that, based on our evaluation, thoughtfully designed and integrated GenAI tools may help bridge gaps in patient workflows in terms of engagement and adherence as well as support clinical workflow to reduce data analysis and data summarization. Further evaluation is needed to assess long-term clinical outcomes, patient trust, and scalability in real-world healthcare settings.
Background: As oncology workflows integrate increasingly autonomous artificial intelligence (AI) agents, health systems face uncertainty regarding operational impacts. Traditional linear forecasting m...
Background: As oncology workflows integrate increasingly autonomous artificial intelligence (AI) agents, health systems face uncertainty regarding operational impacts. Traditional linear forecasting methods fail to capture second-order effects such as governance saturation, induced demand, and bottleneck migration. To navigate this complexity, the emerging field of Medical Futures Studies requires methodologies that bridge qualitative strategic foresight with quantitative operational modeling. These system-level dynamics directly influence patient access to timely diagnosis and treatment, with direct consequences for patient access, treatment delays, and health system resilience. Objective: To develop a proof-of-concept framework for stress-testing AI adoption strategies in oncology by coupling qualitative scenario planning with computational discrete-event simulation (DES). Methods: We defined a strategic state space using two orthogonal axes, AI automation intensity and data interoperability, resulting in four distinct futures scenarios. We translated these qualitative narratives into a quantitative DES model to simulate a 3-year operational horizon. The model quantified system performance (Referral-to-Treatment Interval [RTTI], throughput), volatility, and resource constraints across different adoption trajectories. Results: The scenario planning phase yielded four operational archetypes (analog oncology, automation islands, interconnected clinicians, and AI-orchestrated care) with distinct constraints, risks and failure modes. In the simulation, the fully integrated scenario maximized capacity (1,244 patients/year) and halved the mean RTTI to 14.9 days, a magnitude comparable to major pathway redesign interventions. Isolated automation without data infrastructure led to reduced system performance, increasing RTTI by 26% (37.1 days) and reducing throughput to 647 patients/year due to administrative governance saturation. The model demonstrated a structural bottleneck migration: successful upstream AI adoption shifted binding constraints from diagnostic scanners to downstream chemotherapy infusion units, while missing data interoperability resulted in governance constraints. Pathway optimization analysis indicated that a coordinated strategy prioritizing early improvements in data interoperability reduced transition volatility compared to an automation-first approach. Conclusions: Integrating qualitative scenario planning with quantitative simulations enabled a systematic evaluation of oncology AI adoption strategies. As a proof of concept, it offers a replicable framework for health leaders to model future scenarios of digital transformation in times of high uncertainty. Subsequent work should expand this methodology to incorporate financial and health equity dimensions, establishing simulation-based scenario planning as an important tool in Medical Futures Studies.
Background: Plasmodium vivax (P. vivax) has emerged as the primary cause of malaria in Cambodia. Achieving malaria elimination and securing malaria-free certification requires a focused effort on addr...
Background: Plasmodium vivax (P. vivax) has emerged as the primary cause of malaria in Cambodia. Achieving malaria elimination and securing malaria-free certification requires a focused effort on addressing P. vivax malaria. This is essential because the elimination of P. vivax often lags behind that of Plasmodium falciparum, making it a critical component in the overall strategy. Objective: This study will assess the feasibility of the Mass Drug Administration (MDA) and P. vivax Serological Testing and Treatment (PvSeroTAT) integrated with Reactive Case Detection (RACD) in two of the highest malaria burden operational districts of Cambodia and examine the potential for integrating these two approaches with existing malaria elimination efforts. Methods: This study employs an observational, prospective cohort design. MDA with chloroquine (CQ) will be conducted in Stung Treng through four monthly rounds, while RACD with PvSeroTAT will be implemented in Sen Monorom, targeting households near confirmed P. vivax cases. Data on coverage, compliance, cost, and stakeholder perceptions will be collected through surveys, interviews, and malaria case monitoring. A Composite Feasibility Index will integrate quantitative and qualitative indicators. Cost and budget impact analyses will assess scalability for malaria-endemic districts. Results: This study was funded by Medicines for Malaria Venture and approved by the National Ethics Committee for Health Research (NECHR) in Cambodia on 26 February 2025 (No. 085 NECHR). The study implementation began in March 2025. Training of study staff and healthcare workers was conducted between March – May 2025. Participant enrolment for MDA and RACD began in April and ended in October 2025. Altogether, 4443 and 3371 participants were recruited in MDA and RACD, respectively. Data analysis will be completed after the end of regular follow-ups by April 2026. Conclusions: Innovative and targeted public health approaches and tools are necessary to ensure the elimination of the malaria parasite reservoir, including the hidden hypnozoites. While MDA with CQ clears active blood-stage infections leading to immediate reductions in malaria prevalence, PvSeroTAT can detect past exposure to P. vivax by using serological markers allowing for targeted treatment of individuals at risk of developing relapsing infections with an 8-aminoquinoline. This helps reduce the parasite reservoir more efficiently. This study will provide insight into operational feasibility, implementation costs, community acceptance, and long-term sustainability. The findings will guide Cambodia’s malaria elimination efforts through improved surveillance and targeted interventions. Clinical Trial: OSF Preregistration: https://doi.org/10.17605/OSF.IO/5KZH7, retrospectively registered 15 October 2025.
A multi‑layered fraud‑mitigation approach is essential to ensure data integrity in medical survey research; basic measures alone (e.g. captcha) would permit widespread fraud....
A multi‑layered fraud‑mitigation approach is essential to ensure data integrity in medical survey research; basic measures alone (e.g. captcha) would permit widespread fraud.
Background: Health profession education students exhibit a higher rate of excessive digital technology use compared to their peers. Although the interaction of technology with student well-being has b...
Background: Health profession education students exhibit a higher rate of excessive digital technology use compared to their peers. Although the interaction of technology with student well-being has become more pronounced, the lack of awareness about digital detox among students in technology-intensive healthcare disciplines, along with the scarcity of studies exploring their practices, is concerning. Objective: This study aimed to investigate the patterns of social media usage and potential relationships between digital detox practices, mental well-being, physical health, and academic performance. Methods: A cross-sectional survey design was employed at King Saud bin Abdulaziz University for Health Sciences (KSAU-HS) in Riyadh. The sample consisted of 471 students from the health professions. Validated surveys were used, including the Social Media Disorder Scale, Digital Detoxification Awareness Questions, Kessler Psychological Distress Scale (K-6), and physical health assessments. The relationships between the study variables were analyzed using the chi-square test and ANOVA, with a significance level of 0.05. Results: A total of 471 students were included, with the majority being female (n = 291, 61.8%), single (n = 440, 93.4%), and aged between 18 and 37 years (M = 21.62, SD = 2.30). Participants reported an average daily social media usage of 7.07 ± 4.11 hours, with 31.6% of the sample classified as problematic users. Digital detox awareness was 59.7%, and 58.6% reported having experienced a digital detox. The most common strategies reported were avoiding phone use (69.1%) and muting notifications (70.3%). Participants reported eye strain (59.0%), neck pain (56.7%), and back pain (49.7%) due to the use of smartphones. Significant associations were found between social media use, gender, college affiliation, awareness of digital detox, level of physical activity, and sleep patterns (p < 0.005). A positive correlation was found between GPA and digital detoxification (p = 0.01). Social media use was significantly associated with the mental well-being of the participants (F = 214.096, p < 0.001) and with their academic performance (p = 0.04) Conclusions: The relationships between digital behavior, physical health, mental well-being, and academic performance of health profession students are complex and intertwined. The practice of digital detox, as observed, offers improvements in various aspects of students' lives; therefore, incorporating digital wellness strategies into the curriculum is vital for preparing students as professionals and enhancing student outcomes. Clinical Trial: NRR24/007/11
Background: Xerostomia is a prevalent condition that negatively affects quality of life. Patients increasingly seek health-related information through online platforms such as YouTube. Given the growi...
Background: Xerostomia is a prevalent condition that negatively affects quality of life. Patients increasingly seek health-related information through online platforms such as YouTube. Given the growing role of social media in digital health communication, evaluating the reliability and quality of publicly accessible video content is essential. Objective: This study aimed to assess the reliability, quality, and content characteristics of YouTube videos related to xerostomia. Methods: In this cross-sectional study, a YouTube search was conducted on January 10, 2025, using the keyword “dry mouth.” The first 100 videos retrieved using the relevance filter were screened. After applying inclusion and exclusion criteria, 46 videos were included in the analysis. Video reliability was evaluated using the Modified DISCERN (mDISCERN) instrument, while quality was assessed using the Global Quality Score (GQS) and the Video Information and Quality Index (VIQI). Videos were further categorized as “useful” or “misleading”. Engagement metrics, including number of likes, views, comments, interaction index, and viewing rate, were recorded. Statistical analyses were performed using SPSS version 22.0, with significance set at P < .05. Results: A substantial proportion of videos demonstrated low reliability and quality. Approximately half of the included videos were classified as misleading. Useful videos had significantly higher mDISCERN, GQS, and VIQI scores compared with misleading videos (P < .05). In addition, useful videos showed significantly higher engagement metrics, including number of likes, views, comments, and viewing rate (P < .05). Positive correlations were observed between reliability and quality scores and engagement parameters. Conclusions: A considerable portion of YouTube videos on xerostomia contains low-quality or misleading information. Although higher-quality videos tend to receive greater user engagement, the presence of inaccurate content remains concerning. Increased involvement of healthcare professionals and academic institutions in producing evidence-based digital content may improve the quality of online health information. Clinical Trial: This cross-sectional study evaluated YouTube videos related to xerostomia. As the study analyzed publicly available data on an open-access platform and did not involve human participants or identifiable personal information, ethical approval was not required, consistent with previous similar studies.
Background: Shared psychotic disorder (folie a deux) is a represents a small subset of psychiatric disorders that is defined by the spread of the delusional beliefs of an index subject to a nearby se...
Background: Shared psychotic disorder (folie a deux) is a represents a small subset of psychiatric disorders that is defined by the spread of the delusional beliefs of an index subject to a nearby secondary person mostly in socially isolated dyadic units. Even though the disorder was previously outlined by Charles Lasègue and Jules Falret in the nineteenth century, it is still a diagnostic problem in the modern practice of clinical fields.
Case Presentation: Here we report about a mother-daughter dyad with rural Indian origin. The index patient was a 49-year-old female with a documented schizophrenia history (two years old) with noncompliance to antipsychotics only recently. She had persecutory delusion and auditory hallucinations in the third person. Her 29-year-old daughter with no prior history of psychiatric problems presented a month later with the same persecutory delusions against her father and brother, however, she had no hallucinations. Dominant and submissive relationship and prolonged social isolation were observed in the family.
Intervention and Outcome: There was a very little improvement in the daughter with initial therapeutic separation. At one week, the mother responded to olanzapine 10mg. The daughter needed to be prescribed a lower dose of olanzapine (2.5mg) and showed considerable changes in ten days.
Conclusion: The case highlights that there is a need to acknowledge shared psychosis in the socially isolated family systems and that pharmacological intervention is a supplementary treatment to separation that can be crucial in bringing the best possible recovery.
Background: Large language models (LLMs) are increasingly embedded in digital health applications and consumer-facing dietary guidance systems. While these systems offer scalable and personalized nutr...
Background: Large language models (LLMs) are increasingly embedded in digital health applications and consumer-facing dietary guidance systems. While these systems offer scalable and personalized nutrition support, inappropriate dietary recommendations may pose nutritional or behavioral risks, particularly for vulnerable populations with population-specific dietary constraints. However, systematic and scalable approaches for evaluating the safety of LLM-generated dietary recommendations remain limited. Objective: The objective of this study was to develop and evaluate a reproducible, population-aware auditing framework to quantify nutritional and behavioral risk in LLM-generated dietary recommendations across diverse user profiles, dietary goals, and response tones. Methods: We conducted a content-level audit of 2,464 dietary recommendations generated by a large language model using a full-factorial prompt design that varied user profiles, dietary goals, and response tones. Nutritional information, including daily energy intake and macronutrient distributions, was automatically extracted from generated texts. Population-specific nutritional thresholds derived from international guidelines were applied to assess nutritional risk. Behavioral risk was evaluated using a lexicon-based analysis of potentially unsafe dietary framings. Nutritional and behavioral components were integrated into a continuous composite risk score, enabling large-scale statistical analysis and subgroup comparisons. Results: Across all 2,464 recommendations, composite risk scores were generally low (median 0.008; mean approximately 0.02), indicating broad alignment with evidence-based nutritional thresholds. However, a pronounced long-tail distribution was observed. Elevated risk scores occurred disproportionately in sensitive populations, particularly pregnant individuals requiring glycemic control, with maximum observed values reaching approximately 0.17. Increased risk was driven by both population-specific nutritional deviations and the presence of potentially unsafe behavioral framings. Permissive response tones were associated with slightly higher risk levels than neutral, evidence-based tones. Conclusions: Most LLM-generated dietary recommendations appear nutritionally safe for general populations, but systematic long-tail risks persist for vulnerable groups. The proposed population-aware auditing framework enables scalable safety evaluation of generative dietary guidance and provides continuous risk signals that can support benchmarking, red-teaming, and the development of adaptive safeguards in digital health applications. Clinical Trial: Not applicable
Background: Glucose and lipid metabolism are critically linked to the health outcomes of children and adolescents. Exergaming interventions represent a promising approach to promote physical activity...
Background: Glucose and lipid metabolism are critically linked to the health outcomes of children and adolescents. Exergaming interventions represent a promising approach to promote physical activity engagement in this population. However, the effects of exergaming on glucose and lipid metabolism remain controversial. This systematic review aimed to synthesize and update the evidence on this topic. Objective: This meta-analysis aimed to evaluate the effects of exergaming on glucose and lipid metabolism in children and adolescents compared with control conditions, and to examine potential moderators of these metabolic outcomes. Methods: Following the PRISMA 2020 guideline, we searched PubMed, Web of Science, Scopus, Embase, and the Psychology and Behavioral Sciences Collection (EBSCO) from inception to October 2025. Standardized mean differences (Hedges g) were pooled using random-effects models. Subgroup analyses and meta-regression were conducted to examine potential moderators(eg, sex, age, BMI, and intervention type). Study quality was assessed using RoB 2, ROBINS-I and the PEDro scale, and the certainty of evidence was rated using the GRADE approach. Results: Ten trials (N=732) were included. Exergaming showed no significant pooled effects on glucose or insulin. For lipid outcomes, exergaming was associated with a small reduction in LDL-C (Hedges g −0.27, 95% CI −0.47 to −0.07; P=.008; I²=19%), whereas no significant overall changes were observed for TC, TG, or HDL-C. Exploratory subgroup and meta-regression analyses suggested that sex and intervention type may be associated with variability in effects, but these findings should be interpreted cautiously given the limited number of studies. The overall certainty of evidence was low. Conclusions: Exergaming may modestly reduce LDL-C in children and adolescents, but evidence does not support consistent improvements in other glycemic or lipid outcomes. Given the low certainty of evidence and limited data for effect modification, larger, well-designed trials with clearly reported exercise dose and metabolic endpoints are needed to confirm these findings and to identify subgroups most likely to benefit. Clinical Trial: OSF Registries 10.17605/OSF.IO/64FUS.
Background: The COVID-19 pandemic triggered an abrupt transition to virtual rehabilitation across physiotherapy, occupational therapy, and respiratory therapy. While telerehabilitation research has do...
Background: The COVID-19 pandemic triggered an abrupt transition to virtual rehabilitation across physiotherapy, occupational therapy, and respiratory therapy. While telerehabilitation research has documented feasibility and patient satisfaction, less is known about how professionals navigated the destabilization and reassembly of care practices during this transformation. Existing literature frames virtual care as a technical substitution for in-person services, overlooking the deeper reconfiguration of the socio-technical networks that organize therapeutic work. Objective: Applying actor-network theory (ANT), we examined how rehabilitation professionals reconfigured their practices through technology during the first year of the pandemic. We explored how digital tools, domestic spaces, and new sensory practices reshaped therapeutic presence, professional identity, and the environments in which care was enacted. Methods: We conducted a secondary analysis of longitudinal diary-interview data collected from 16 Canadian rehabilitation professionals (occupational therapists, physiotherapists, and respiratory therapists) working in community-based primary care in Ontario and Manitoba (2020-2021). Participants recorded audio diaries over 12 weeks and completed two follow-up interviews. Analysis followed an interpretive approach informed by Science and Technology Studies, tracing how human and technological actors were enrolled, adapted, and redefined within emerging care assemblages. Results: Three interconnected processes characterized the reconfiguration of rehabilitation: (1) technology as active participant, where digital platforms mediated rather than merely transmitted therapeutic reasoning and clinical decision-making; (2) reconfiguration of therapeutic presence, as sensory attention and embodiment were redistributed across screens, sounds, and new forms of spatial choreography; and (3) enrollment of domestic spaces as clinical environments, as clinicians' and patients' homes became sites of care shaped by new ethical, material, and relational dynamics. These processes reveal that virtual rehabilitation constituted a new form of care co-produced by humans, technologies, and spaces rather than a digitized replication of traditional practice. Conclusions: The pandemic exposed rehabilitation as a socio-technical practice sustained through the coordination of multiple actors rather than professional expertise alone. Virtual care redefined therapeutic presence when traditional boundaries between clinical and domestic, human and technological, were blurred. Recognizing virtual care as a distinct modality underscores the need to integrate technology-mediated competencies into rehabilitation education and practice. Future research should incorporate patient perspectives and direct observation to trace how these care networks evolve.
Background: The aging population has become a rapidly expanding user base in smart hospital outpatient departments, posing a significant challenge. Their lower familiarity with digital technology, tog...
Background: The aging population has become a rapidly expanding user base in smart hospital outpatient departments, posing a significant challenge. Their lower familiarity with digital technology, together with inherent device design flaws, hinders the overall satisfaction among older patients. Objective: To better understand these potential barriers and promote equitable access to digital healthcare for this demographic, this study examined user satisfaction of self-service kiosks and its influencing factors among older patients. Methods: A cross-sectional study was conducted among 240 older outpatients recruited from a tertiary hospital in Beijing from July to September 2025. Using a 26-item questionnaire and based on the DeLone and McLean IS Success Model (D&M IS Success Model), we employed statistical description and ordinal logistic regression to analyze the determinants of user satisfaction, which were visualized via a forest plot. Results: Results of this study showed that user satisfaction exhibited significant positive associations with both information quality and interface quality among older patients. Our findings are in line with the D&M IS Success Model. Additionally, the role of seeking assistance from fellow patients as a marginally significant predictor of user satisfaction merits further consideration. Conclusions: The aged-friendly optimization of self-service kiosks, particularly in information quality and interface quality, serves as a cornerstone for equitable digital healthcare and enhanced patient satisfaction within smart services. Furthermore, interpersonal peer support may act as a critical driver in promoting the utilization of self-service kiosks among older patients.
Background: Mental rotation, the ability to mentally transform visuospatial representations, supports everyday spatial behaviors (e.g., navigation) and can be vulnerable in later life. Older adults wi...
Background: Mental rotation, the ability to mentally transform visuospatial representations, supports everyday spatial behaviors (e.g., navigation) and can be vulnerable in later life. Older adults with mild cognitive impairment (MCI) often show greater difficulties in visuospatial processing than cognitively unimpaired peers, including lower accuracy and higher variability in mental rotation tasks. Because MCI represents a prodromal stage associated with elevated risk for subsequent dementia, the critical period occurring before rapid cognitive decline to dementia, it may be an important window for interventions that target specific cognitive vulnerabilities. In non-amnestic forms of MCI (MCI-NA), visuospatial and/or executive deficits can be prominent, and longitudinal outcomes are heterogeneous, varying in part by underlying neuropathology. Accordingly, interventions that are explicitly designed to engage visuospatial processes relevant to MCI-NA may be a useful, deficit-targeted approach to evaluate in feasibility studies and to inform future controlled trials for cognitive training programs aiming to prolong daily functioning and reduce suffering. Objective: This study aims to evaluate the feasibility and acceptability of the Virtual Reality-Visuospatial Cognitive Training (VR-VCT) program in older adults with MCI-NA and to estimate preliminary within-subject changes in visuospatial cognition to inform a future randomized trial. Participants (n=40) will meet eligibility criteria consistent with commonly used definitions of MCI-NA, including subjective cognitive concerns, preserved basic activities of daily living, absence of dementia, and objective impairment on standardized measures emphasizing visuospatial and/or executive functioning. This study aims to: (1) quantify the feasibility and acceptability of VR-VCT in older adults with MCI-NA, and (2) estimate preliminary within-subject change on visuospatial cognitive outcomes following VR-VCT. Methods: 40 Participants with MCI-NA will be enrolled in a structured VR Cubism program using the Meta Quest 3 VR headset. The intervention will involve three 30-minute sessions per week for 12 weeks, with tasks progressing in difficulty over time. Cognitive and visuospatial outcomes will be assessed at baseline (T0), immediately post-intervention (T1), and at follow-up (T2; 12 weeks post-intervention) to evaluate whether observed changes are maintained. Global cognition will be assessed using the Montreal Cognitive Assessment (MoCA). Visuospatial construction will be assessed using the Wechsler Adult Intelligence Scale (WAIS) Block Design subtest, and mental rotation will be assessed using the Vandenberg Mental Rotation Test (VMRT). Changes in performance across time points will be analyzed using repeated-measures models (e.g., linear mixed models) to estimate within-subject change, with effect sizes and confidence intervals reported to inform future controlled trials. Results: Participants will be recruited from local assisted living facilities, memory care settings, and community outreach programs. This study has been approved by the University of Utah School of Medicine Institutional Review Board. The data collection will be started in March 2026. Data analysis is anticipated to be concluded by August 2026. Conclusions: The findings will inform the study design, outcome measurement, and power calculations of a future randomized controlled trial. If feasible and acceptable, VR-VCT may represent a scalable, engaging, and deficit-targeted intervention approach with the potential to support visuospatial cognitive functioning during a critical window prior to dementia onset.
Background: Herpes zoster (HZ) imposes a substantial disease burden, yet vaccine uptake remains suboptimal in China. While eHealth literacy is a known determinant of health behaviors, its role in brid...
Background: Herpes zoster (HZ) imposes a substantial disease burden, yet vaccine uptake remains suboptimal in China. While eHealth literacy is a known determinant of health behaviors, its role in bridging socioeconomic disparities and its varying impact across different age groups of vaccine-eligible adults remain understudied. Specifically, it is unclear whether eHealth literacy acts as a "compensatory resource" for disadvantaged populations and if the digital skills required to reduce hesitancy differ between middle-aged and older adults. Objective: This study aimed to examine the association between eHealth literacy and HZ vaccine hesitancy among adults aged 40 years and older in Shanghai, China, with a specific focus on identifying age-dependent "digital thresholds" and the compensatory effect of literacy on socioeconomic status (SES). Methods: A community-based cross-sectional study was conducted from October to December 2022 across three districts in Shanghai. A total of 1302 adults aged ≥40 years were recruited via convenience sampling. eHealth literacy was assessed using the eHealth Literacy Scale (eHEALS). Multivariable logistic regression models were used to analyze the associations, adjusting for sociodemographic characteristics, health status, and behaviors. Stratified analyses were performed to evaluate interactions among literacy, age, and SES. Results: The prevalence of HZ vaccine hesitancy was 88.2% (1149/1302). In the fully adjusted model, participants with medium (odds ratio [OR] 0.538, 95% CI 0.326-0.886; P=.015) and high (OR 0.472, 95% CI 0.264-0.844; P=.011) eHealth literacy demonstrated significantly lower odds of hesitancy compared to those with low literacy. Age-stratified analyses revealed a distinct "digital threshold" effect: for middle-aged adults (40–59 years), medium literacy was sufficient to significantly reduce hesitancy (OR 0.501, 95% CI 0.265-0.949; P=.034), whereas older adults (≥60 years) required high literacy to achieve a significant protective effect (OR 0.347, 95% CI 0.136-0.882; P=.026). Crucially, eHealth literacy exhibited a strong compensatory effect for socioeconomic disadvantage. Among participants with low SES, high eHealth literacy was associated with an 83.1% reduction in the odds of hesitancy (OR 0.169, 95% CI 0.054-0.528; P=.002), a magnitude of effect not observed in higher SES groups. Additionally, a history of HZ infection was identified as a robust protective factor (OR 0.473, 95% CI 0.309-0.724; P=.001). Conclusions: eHealth literacy serves as a critical compensatory resource that can mitigate the disadvantage of low socioeconomic status in HZ vaccine acceptance. However, the protective mechanism is age-dependent, indicating a higher "digital threshold" for older adults (≥60 years) compared to their middle-aged counterparts. Public health interventions should prioritize digital empowerment for low-SES populations and tailor educational strategies to meet the higher digital competency needs of older adults. Clinical Trial: Not available
Background: Home spirometry has been widely adopted in the delivery of cystic fibrosis (CF) care. While existing literature largely supports its feasibility and positive outcomes, behaviour around hom...
Background: Home spirometry has been widely adopted in the delivery of cystic fibrosis (CF) care. While existing literature largely supports its feasibility and positive outcomes, behaviour around home disease monitoring remains poorly understood. Objective: This study aimed to evaluate healthcare professionals’ (HCPs') ability to estimate home spirometry usage pwCF and compare these with actual recorded data. Methods: Home spirometry data, from a single large adult CF centre, for the year 2024, was obtained from NuvoAir. HCPs (doctors, nurses, and physiotherapists) rated their familiarity with each pwCF and categorised them as infrequent, expected, or highly frequent spirometry users. They were also asked to estimate spirometry usage as an open-ended numerical response. CF experience was defined by the number of years the HCP had worked at the centre. Estimation accuracy was assessed using mean bias and mean absolute error (MAE). Results: 10 doctors (35.7%), 6 nurses (21.4%), and 12 physiotherapists (42.9%) responded to the survey, with an overall response rate of 96.6%. There were 790 completed categorical estimates and 794 numerical estimates. The mean (±SD) CF experience was 15.7 (±8.2) years. Across all roles, HCPs systematically underestimated home spirometry usage (mean bias -4.9; MAE 6.32). No significant differences in estimation accuracy were observed based on professional role, reported familiarity or CF experience. Conclusions: This study found that CF caregivers tend to underestimate home spirometry usage, in contrast to other studies showing they often overestimate treatment adherence. This highlights gaps in understanding behaviour in pwCF and the need for CF teams to adapt to evolving models of remote monitoring.
Open Peer Review Period: Dec 8, 2025 - Nov 23, 2026
Background: Human papillomavirus (HPV) remains the principal cause of cervical cancer, yet population-level awareness and knowledge in many Nigerian settings remain limited. Understanding the patterns...
Background: Human papillomavirus (HPV) remains the principal cause of cervical cancer, yet population-level awareness and knowledge in many Nigerian settings remain limited. Understanding the patterns and predictors of HPV awareness and knowledge is essential for strengthening Nigeria’s HPV vaccination rollout and reducing preventable cervical cancer morbidity. Objective: To describe respondents’ demographic characteristics; assess levels of awareness and knowledge of HPV, cervical cancer, and the HPV vaccine; examine associations between sociodemographic variables and awareness/knowledge; and identify independent predictors of HPV awareness and knowledge. Methods: A community-based cross-sectional survey was conducted among 238 caregivers of girls aged 9-14 years in Port Harcourt Local Government Area. Data on demographics, HPV awareness, knowledge indicators, and information sources were collected using a structured questionnaire. Descriptive statistics, chi-square tests, and multivariable logistic regression were used to assess associations and predictors. Statistical significance was set at p < 0.05. Results: Respondents showed wide demographic diversity across age, religion, education, occupation, and income. Overall awareness of HPV was low (45.4%), and knowledge was predominantly poor (78.6%). Misconceptions were common, with many attributing HPV to poor hygiene or skin infections. Only 39.8% correctly identified sexual contact as the mode of transmission, and knowledge of vaccine dosage was inconsistent. Informal channels, religious institutions, social media, and family networks were the primary sources of information, whereas health workers accounted for only 8.3%. Most sociodemographic factors showed no significant association with awareness or knowledge, indicating widespread deficits across groups. Occupation was the only variable significantly associated with awareness (p = 0.011). Logistic regression showed higher odds of awareness among respondents aged 26-36 years (OR 2.26, p = 0.039) and lower odds among those practicing Traditional religion (OR 0.41, p = 0.033). Civil/public servants showed reduced odds of awareness (OR 0.44, p = 0.048). Conclusions: HPV awareness and knowledge are markedly low and broadly distributed across demographic groups. Widespread misconceptions reflect structural failures in health communication. Strengthen community-based and health worker-led HPV education; embed messaging within religious and social structures; and implement targeted, culturally adapted communication strategies to improve vaccine uptake. Significance Statement: Addressing pervasive knowledge gaps is vital for achieving effective HPV vaccination coverage and reducing cervical cancer burden in Nigeria.
Open Peer Review Period: Nov 28, 2025 - Nov 13, 2026
Introduction
Acute leukemia poses a significant health burden globally, necessitating a deeper understanding of its etiological factors. This study investigates the potential link between blood group...
Introduction
Acute leukemia poses a significant health burden globally, necessitating a deeper understanding of its etiological factors. This study investigates the potential link between blood groups, Rh factor, and the incidence of acute leukemia to enhance knowledge and guide personalized treatment strategies.
Methods
A cross-sectional analytical study was conducted at Imam Khomeini Hospital in Urmia from 2012 to 2018, including patients with acute leukemia. Data on blood groups, Rh factor, and demographic variables were collected and analyzed using SPSS software. Statistical tests were employed to determine associations between blood groups and leukemia risk.
Results
The study found no significant relationship between ABO blood groups and acute leukemia, consistent with previous research. However, differences in Rh factor distribution were observed between acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) patients, warranting further investigation.
Discussion
The complexity of leukemia etiology is highlighted by the multifactorial nature of the disease, where genetic, environmental, and possibly epigenetic factors interact. Future research should focus on larger sample sizes and diverse populations to elucidate the intricate mechanisms underlying leukemia susceptibility.
Conclusion
While ABO blood groups may not significantly impact acute leukemia risk, variations in Rh factor distribution among leukemia subtypes suggest a need for continued exploration. Comprehensive studies considering diverse factors are essential to unravel the complexities of leukemia development.
Open Peer Review Period: Nov 28, 2025 - Nov 13, 2026
Introduction
Immune thrombocytopenic purpura (ITP) is an acquired thrombocytopenia syndrome characterized by platelet destruction due to antiplatelet antibodies. Corticosteroids are the first-line tr...
Introduction
Immune thrombocytopenic purpura (ITP) is an acquired thrombocytopenia syndrome characterized by platelet destruction due to antiplatelet antibodies. Corticosteroids are the first-line treatment for adult patients with ITP. This study compares the effects of high-dose dexamethasone versus prednisolone in ITP treatment.
Materials and Methods
This open-label clinical trial involved patients over 18 years diagnosed with ITP (based on ASH criteria) who had not received prior treatment. Participants were randomly assigned (1:1) to receive high-dose dexamethasone (HD-DXM) or prednisolone (PDN). The dexamethasone group received 40 mg intravenously for 4 consecutive days, while the PDN group received 1 mg/kg oral prednisolone for 4 weeks. Daily complete blood counts were obtained to assess treatment response, defined as a platelet count above 30,000/μL.
Results
A total of 36 patients were evaluated, with 18 in each treatment group. Patients receiving dexamethasone showed significantly reduced hospitalization duration and faster time to reach platelet counts above 30,000/μL (P=0.01 and P=0.002, respectively).
Conclusion
High-dose dexamethasone significantly decreases the time to initial response and hospitalization duration in ITP patients compared to prednisolone.
Open Peer Review Period: Nov 24, 2025 - Nov 9, 2026
Classical evolutionary theory, notably Riedl’s concept of canalization, suggests that human lifespan is constrained by deeply entrenched developmental architectures, implying that aging is an immuta...
Classical evolutionary theory, notably Riedl’s concept of canalization, suggests that human lifespan is constrained by deeply entrenched developmental architectures, implying that aging is an immutable biological reality. However, rapid advancements in artificial intelligence (AI) from 2023 to 2025 have begun to challenge this pessimism. This viewpoint synthesizes recent developments to argue that AI is reframing aging from a biological mystery into a tractable engineering challenge. We examine two primary frontiers: the use of autonomous AI agents and generative models to discover geroprotective interventions, including the identification of compounds like ouabain via large-scale omics re-analysis; and the maturation of multi-modal “aging clocks” that utilize deep learning to enable precision diagnostics and personalized healthspan optimization. While acknowledging significant limitations regarding safety, translation from animal models, and the risks of commercial hype, we conclude that the integration of AI with mechanistic geroscience offers a plausible pathway toward a proactive, engineering-based approach to human longevity.
Open Peer Review Period: Nov 19, 2025 - Nov 4, 2026
Biobanks are recognised as lucrative health research resources due to their extensive and in-depth data availability, which allows researchers to draw correlations between various genetic, lifestyle,...
Biobanks are recognised as lucrative health research resources due to their extensive and in-depth data availability, which allows researchers to draw correlations between various genetic, lifestyle, and health information and future disease incidence. As prospective data sources collect genetic and lifestyle information for several hundred thousand participants across various age categories, biobanks are important datasets in designing novel healthcare approaches. Within the realm of cardiometabolic ageing, which refers to the age related decline in the function of cardiovascular and metabolic systems, the conceptualisation of a systems medicine-based approach known as P4 (Predictive, Preventive, Personalised, Participatory) medicine has provided an interesting framework to tackle these metabolic illnesses in tandem with digital longevity tools that serve as vessels to deliver interventions across large populations. Therefore, this review aims to critically discuss how digital longevity informed by biobank data is vital in improving risk prediction, with a focus on cardiometabolic ageing.
Open Peer Review Period: Nov 10, 2025 - Oct 26, 2026
Background: Globally, digital health interventions (DHIs) enhance HIV care through technology, especially among women living with HIV (WLHIV), who face unique Challenges that affect their treatment. T...
Background: Globally, digital health interventions (DHIs) enhance HIV care through technology, especially among women living with HIV (WLHIV), who face unique Challenges that affect their treatment. This study assessed the feasibility of integrating DHIs into HIV care in Kisumu by examining their acceptability among WLHIV and identifying factors that influence their intention to use these tools. Objective: (1) To determine the feasibility of integrating digital health interventions into care for
women living with HIV in Kisumu.
(2) To identify factors that influence the adoption of Digital health interventions. Methods: A cross-sectional survey based on the Unified Theory of Acceptance and Use of Technology
2 (UTAUT2) was administered to evaluate the acceptability of SMS, teleconsultations, online support groups, and health applications. Summary statistics quantified acceptability, multivariate regression models examined associations between UTAUT2 constructs and behavioral intention, and Analysis of Variance identified sociodemographic predictors. Results: A total of 385 WLHIV (mean age 35·8 years) participated. Behavioral intention to use all four DHIs was high, with more than 80% rating their willingness at ≥4 on a five-point scale. Performance expectancy, hedonic motivation, habit, and price value were significant predictors of intention (p < 0·05). Higher education level was strongly associated with increased intention (p < 0·001), while older age was associated with reduced intention Conclusions: WLHIV in Kisumu demonstrated a strong willingness to adopt digital health tools in their routine care. The intention to use DHIs was primarily influenced by perceived usefulness, affordability, enjoyment, and familiarity with similar technologies. These results support the integration of digital health solutions into HIV care for women in this setting.
Open Peer Review Period: Nov 4, 2025 - Oct 20, 2026
Background: The COVID-19 pandemic presented an unparalleled opportunity for telemedicine implementation, shortening adoption timelines and creating significant opportunities for observational research...
Background: The COVID-19 pandemic presented an unparalleled opportunity for telemedicine implementation, shortening adoption timelines and creating significant opportunities for observational research. Prior evidence is predominantly derived from small feasibility studies with limited comparative efficacy data and inadequate attention to implementation challenges and equity considerations. Objective: To synthesize methodologies, findings, and innovations from observational telemedicine studies conducted during the pandemic and identify critical research gaps. Methods: Narrative synthesis of 25 peer-reviewed observational studies (2020–2021) examining telemedicine across 11 clinical specialties, encompassing 119,016 patient contacts across multiple international settings. Studies employed prospective cohort designs, retrospective analyses, cross-sectional surveys, and mixed-methods approaches. Results: Telemedicine demonstrated clinical efficacy for chronic disease management with objective monitoring data, particularly in pediatric diabetes and cardiac device follow-up. However, substantial technology-acceptance discrepancies emerged—user satisfaction exceeded actual data capture reliability. Cross-sectional analyses unveiled systemic racial bias in satisfaction ratings and socioeconomic disparities in access. Innovations, including real-time locating systems, large-scale observational platforms, ambispective designs, and mixed-methods integration, have advanced methodological rigor. Persistent obstacles encompass selection bias, unmeasured confounding, outcome heterogeneity precluding meta-analysis, and temporal confounding. Conclusions: Observational pandemic-era telemedicine research substantiates selective clinical applications while exposing technology reliability limitations, persistent inequities, and methodological constraints on causal inference. Critical gaps include the absence of long-term outcome evaluation, economic analyses, diagnostic accuracy assessment, and equity-focused intervention research. Future advancement requires quasi-experimental designs, standardized outcome measures, explicit equity integration, and implementation science evidence for sustainable post-pandemic integration.
Open Peer Review Period: Nov 1, 2025 - Oct 17, 2026
Background: Safe and reliable access to clean water remains a fundamental determinant of public health and sustainable development. In many rapidly urbanizing Nigerian communities, dependence on self-...
Background: Safe and reliable access to clean water remains a fundamental determinant of public health and sustainable development. In many rapidly urbanizing Nigerian communities, dependence on self-sourced groundwater and inadequate waste management systems continues to compromise water quality and expose residents to preventable diseases. This study investigated the status of water supply, quality, and associated health outcomes in Uselu Community, Benin City, to provide evidence-based insights for policy and intervention. Objective: The study aimed to (1) assess the primary sources of water available to residents, (2) evaluate household water-storage and treatment practices, and (3) examine the public-health implications of inadequate water access and sanitation behaviour in the community. Methods: A descriptive cross-sectional survey was conducted among 100 adult residents of Uselu Community selected through random sampling. Data were collected using structured questionnaires covering socio-demographics, water sources, treatment habits, sanitation practices, and self-reported waterborne diseases. Field observations complemented survey data, and results were presented as frequencies and percentages. Descriptive and inferential statistics were used to analyze trends, and findings were compared against national and international WASH benchmarks. Results: Findings revealed that 56% of respondents relied on boreholes as their main water source, while only 31% had access to public pipe-borne supply. Although 89% regularly washed their storage containers, fewer than half (43%) treated water by boiling or filtration, and only 17% practiced chlorination. About 32% reported disposing of waste near water sources, increasing contamination risks. The most common illnesses were typhoid fever (47%) and cholera (30%), with over half (55%) of respondents experiencing recurrent water shortages. These results indicate persistent infrastructural inadequacies, limited treatment adoption, and significant exposure to waterborne diseases. Conclusions: The study highlights critical water-supply and quality challenges in Uselu Community, driven by poor infrastructure, weak waste management, and inconsistent household treatment practices. Ensuring safe water access requires coordinated interventions combining infrastructural expansion, community hygiene education, and sustainable groundwater management. Strengthen municipal water systems, establish periodic water-quality monitoring, enforce sanitation regulations, and promote affordable household treatment technologies through continuous public-health education and community engagement. This study demonstrates that unsafe water and poor sanitation behaviours are central drivers of disease in Uselu Community. By translating evidence into actionable interventions, the research provides a model for improving public health, environmental sustainability, and water security in similar peri-urban settings.
Open Peer Review Period: Oct 27, 2025 - Oct 12, 2026
For decades, global guidance for sedentary behaviour and sleep has primarily been informed by studies that relied on self-report questionnaires to assess behaviours. However, it is widely recognised t...
For decades, global guidance for sedentary behaviour and sleep has primarily been informed by studies that relied on self-report questionnaires to assess behaviours. However, it is widely recognised that self-reported data suffer from numerous limitations, including recall and social desirability biases, as well as poor validity and precision. The Prospective Physical Activity, Sitting and Sleep consortium (ProPASS) is a large international collaboration of cohort studies with research-grade wearables data designed to address these challenges. The ProPASS consortium looks to advance our understanding of the associations of free-living physical activity, posture (sitting, standing), and sleep with major health and non-communicable disease outcomes. In this editorial, we provide an overview of the first ProPASS scientific outputs including its growth in recent years; key advancements towards unified wearables methodologies; the ProPASS data resources, and how these will be made available to the global research community. To assist future analogous initiatives, we also share the key challenges ProPASS has encountered and discuss mitigation strategies.
Open Peer Review Period: Oct 23, 2025 - Oct 8, 2026
Universities are critical engines of knowledge creation and societal transformation; however, many African institutions, particularly in Nigeria, struggle to cultivate mature and sustainable research...
Universities are critical engines of knowledge creation and societal transformation; however, many African institutions, particularly in Nigeria, struggle to cultivate mature and sustainable research cultures. This paper develops a conceptual framework for strengthening university research management systems, highlighting leadership and governance as catalysts for academic excellence, innovation, and societal relevance. Using a descriptive-analytical and comparative synthesis of international policy frameworks (UNESCO, OECD) and African higher-education reports (AAU, ARUA, NUC, and TETFund), the study integrates global best practices with contextual realities in low-resource environments. The proposed Research Leadership and Impact Framework (RLIF) outlines four interrelated components: leadership and vision, governance and systems, capacity and infrastructure, and research culture and societal impact, which collectively enable institutional transformation. Comparative indicators, such as Nigeria’s Gross Expenditure on Research and Development (GERD) of 0.22% versus South Africa’s 0.83%, illustrate the strategic significance of leadership and governance reform in closing performance gaps. The framework contributes a theoretically grounded and context-sensitive model for embedding evidence-based management, accountability, and inclusivity within African universities. Ultimately, the paper argues that building resilient research systems requires not only financial investment but visionary leadership capable of aligning academic missions with societal priorities and the Sustainable Development Goals (SDGs).
Background: Objective: To map the available evidence on psychosocial interventions (PIs) targeting the Brazilian Black population's mental health.
Introduction: Black population (BP) is proportional...
Background: Objective: To map the available evidence on psychosocial interventions (PIs) targeting the Brazilian Black population's mental health.
Introduction: Black population (BP) is proportionally more institutionalized in psychiatric hospitals, and is historically more associated with “madness”, dangerousness, and racial inferiority. PIs targeting the Black population's mental health can potentially enhance professional practices by addressing this group's specific needs.
Inclusion criteria: Participants: Brazilian BP; concept: PIs targeting the Black population's mental health; context: Whole Brazilian country. Therefore, studies addressing PIs targeting the Brazilian BP, including the “Quilombola” community's mental health, will be considered as inclusion criteria. Studies addressing black immigrants and refugees in Brazilian territory will be excluded.
Methods: This scoping review (SR) will follow the JBI methodology guidelines, and adheres to the PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Search Strategy: A focused search will be conducted in MEDLINE (PubMed), Psycinfo (APA) CINAHL (EBSCOhost), Embase, Scopus (ELSEVIER), CINAHL (EBSCO), APA (PsycInfo), Embase and the Virtual Health Library (BVS). There will be no restriction regarding the language or date of publication of the studies. Study Selection: Citations will be managed in Zotero, and Rayyan will be used to organize the screening. Two independent reviewers will screen titles and abstracts for eligibility. Disagreements will be resolved through discussion or consultation with a third reviewer. Data Extraction: Two independent reviewers will extract data using a custom tool. Data Analysis and Presentation: Results will be summarized narratively and presented in tables and charts. Objective: To map the available evidence on psychosocial interventions (PIs) targeting the Brazilian Black population's mental health. Methods: This scoping review (SR) will follow the JBI methodology guidelines, and adheres to the PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Search Strategy: A focused search will be conducted in MEDLINE (PubMed), Psycinfo (APA) CINAHL (EBSCOhost), Embase, Scopus (ELSEVIER), CINAHL (EBSCO), APA (PsycInfo), Embase and the Virtual Health Library (BVS). There will be no restriction regarding the language or date of publication of the studies. Results: This section does not present data; it is a protocol. Conclusions: The review's conclusion promises to map critical evidence gaps.
India’s health system faces chronic resource gaps and inefficiencies. With public health
spending at only 1.84% of GDP and very low hospital bed densities (around 0.6 beds per 1000 population), si...
India’s health system faces chronic resource gaps and inefficiencies. With public health
spending at only 1.84% of GDP and very low hospital bed densities (around 0.6 beds per 1000 population), simply adding beds is unaffordable and slow. A more efficient alternative is to improve utilisation: a real-time digital platform that tracks staffed bed availability can raise effective capacity and reduce inequity.
Early experiments – from Delhi’s COVID-19 bed portal to the bed-management system
in AIG Hospitals, Hyderabad – show substantially higher occupancy and throughput. International evidence also supports these results, confirming that real-time tracking
systems can deliver major efficiency gains.
This brief proposes piloting a national bed-tracking dashboard and shows it can yield
large gains for much lower cost and risk than new construction, with safeguards to address data accuracy, incentives and privacy. These promising results are tempered
by limited evidence from a small number of pilots and by systemic constraints such as
staff shortages, uneven digital readiness, and governance challenges that will require
independent evaluation and safeguards during scale up.
Deep learning-based medical image registration methods increasingly incorporate both architectural enhancements (affine transformations) and training objective improvements (regularization losses), ye...
Deep learning-based medical image registration methods increasingly incorporate both architectural enhancements (affine transformations) and training objective improvements (regularization losses), yet their individual and combined contributions remain poorly understood. To quantify the individual and synergistic effects of affine components versus regularization losses on deformable medical image registration performance through systematic ablation analysis, we conducted a controlled ablation study using the OASIS brain MRI dataset comparing four model variants: baseline 3D U-Net with basic similarity losses, regularization-enhanced U-Net, affine-enhanced U-Net with basic losses, and fully enhanced model combining both components. Primary outcomes included registration accuracy metrics (mean squared error [MSE], normalized cross-correlation [NCC], structural similarity index [SSIM]), enhanced deformation quality analysis including Jacobian determinant preservation and anatomical plausibility scoring, and computational efficiency measures. Regularization enhancement alone achieved substantial performance improvements: 21.3% relative improvement in MSE (1.78% → 2.16%, P<.05) and 21.8% improvement in NCC (0.0555 → 0.0676), while dramatically reducing maximum deformation from 53.1 to 0.51 units (99.0% reduction) with negligible computational overhead (-0.06% inference time). Combined approaches achieved optimal performance with 25.8% relative MSE improvement (1.78% → 2.24%) and enhanced anatomical plausibility scores (0.596 → 0.930), at moderate computational cost (+9.8% inference time). Enhanced gradient correlation analysis revealed substantial improvements in structural preservation (0.742 → 0.980 for fully enhanced model). All enhanced variants achieved sub-voxel registration accuracy with anatomically plausible deformation constraints. Regularization losses provide the primary driver of performance improvements in medical image registration, offering both accuracy gains and dramatic deformation control enhancement with maintained computational efficiency. Architectural enhancements provide complementary benefits at acceptable computational cost. The dramatic improvement in deformation control (99% reduction in unrealistic deformations) addresses critical clinical deployment concerns while achieving superior registration accuracy.
Background: Urinary conditions impose a widespread burden on patients, caregivers, and healthcare systems. Emerging technologies, including wearable and remote devices, offer opportunities to improve...
Background: Urinary conditions impose a widespread burden on patients, caregivers, and healthcare systems. Emerging technologies, including wearable and remote devices, offer opportunities to improve diagnosis, monitoring, and care delivery. Yet, the perspectives of healthcare professionals, who are central to technology adoption, remain underexplored. Objective: This study aimed to explore healthcare professionals’ perceptions of urinary issues and examine their views on the opportunities and barriers associated with adopting health technologies for urinary care. Methods: An online survey of 256 healthcare professionals collected qualitative responses about urinary care and the role of technology. Data were analyzed using grounded theory methods, including open, axial, and selective coding, to develop an explanatory model grounded in providers’ narratives. Results: Analysis revealed four interconnected categories: Technology and Innovation in Patient Care, Patient-Centered and Integrated Care, Accessibility and Ethical Considerations, and Proactive and Preventative Urological Health Management. These categories were unified within the emergent Grounded Theory of Technology Negotiation in Urinary Care, which describes how professionals integrate new technologies through a negotiated process that balances enthusiasm for innovation with patient-centered values, systemic barriers, and preventative goals. Adoption occurs when innovations align with professional values, overcome structural constraints, and enhance holistic, sustainable care. Conclusions: Healthcare professionals approach the integration of urinary health technologies as an active negotiation rather than passive acceptance. This grounded theory underscores that successful adoption requires user-centered design, comprehensive training, supportive reimbursement structures, and preservation of meaningful patient engagement. Recognizing adoption as a negotiated process provides a framework for guiding sustainable technology integration in urinary care.
Open Peer Review Period: Sep 15, 2025 - Aug 31, 2026
Background: Patients with rare diseases often face fragmented healthcare, limited access to specialists, and challenges in securely sharing their medical records across providers. Emerging technologie...
Background: Patients with rare diseases often face fragmented healthcare, limited access to specialists, and challenges in securely sharing their medical records across providers. Emerging technologies such as blockchain offer a decentralized and tamper-resistant framework for personal health records (PHRs), but their feasibility in low-resource settings remains largely unexplored Objective: This study aimed to evaluate the feasibility, usability, and patient perceptions of a blockchain-enabled PHR system tailored for rare disease patients in low-resource healthcare environments Methods: We conducted a mixed-methods pilot study involving 32 patients with rare genetic and metabolic disorders in Faisalabad, Pakistan. Participants were enrolled in a blockchain-based PHR platform that allowed secure storage and controlled sharing of medical data. Quantitative data on system usage, error rates, and access patterns were collected over a 12-week period. Semi-structured interviews and focus groups were used to explore patient and caregiver experiences, perceived benefits, and challenges. Thematic analysis was applied to qualitative data, while descriptive statistics summarized quantitative measures. Results: Patients and caregivers reported high levels of trust in the blockchain system (78% expressed greater confidence compared to hospital records). Key perceived benefits included improved data ownership, reduced dependency on fragmented paper records, and greater willingness to share information with providers. However, barriers included limited digital literacy, occasional connectivity issues, and the need for ongoing technical support. Quantitatively, 85% of enrolled participants successfully accessed and updated their records at least once, while 62% shared data with external providers. Thematic analysis revealed three major themes:
(1) empowerment through ownership
(2) digital divides as barriers to adoption
(3) the importance of community support in technology uptake Conclusions: Blockchain-enabled PHRs show promise for enhancing healthcare access, trust, and patient empowerment among rare disease populations in resource-constrained settings. Despite challenges related to usability and infrastructure, the pilot demonstrates potential for scaling such systems with targeted training and support. Further large-scale studies are needed to assess long-term sustainability and integration with existing health systems. Clinical Trial: not aplicable
Open Peer Review Period: Sep 7, 2025 - Aug 23, 2026
Background: Long-standing intrapsychic conflicts often arise from apparently irreconcilable tensions, such as desire versus affection or autonomy versus dependence. Traditional approaches in psychothe...
Background: Long-standing intrapsychic conflicts often arise from apparently irreconcilable tensions, such as desire versus affection or autonomy versus dependence. Traditional approaches in psychotherapy describe defense mechanisms or splitting to cope with such conflicts. However, less attention has been given to creative integrative processes that may reconcile opposing tendencies. Objective: This paper introduces the concept of AI-facilitated symbolic juxtaposition, where generative models are used to create “digital chimeras”—hybrid symbolic constructions integrating objects of desire with affective attributes. We aim to provide a theoretical foundation, operational hypotheses, and clinical protocols for testing this novel framework. Methods: Drawing from psychoanalytic theory (Winnicott’s transitional objects), predictive processing, and neuroscience of the default mode and mentalizing networks, we propose a neuro-symbolic model for symbolic integration. We outline four testable hypotheses: (1) neural integration (DMN coherence), (2) symbolic flexibility, (3) enhancement of attachment security, and (4) accelerated therapeutic outcomes. Empirical validation methods include fMRI, EEG coherence, eye-tracking, attachment interviews, and cognitive flexibility tasks. We also present a clinical implementation protocol with AI-assisted symbolic generation, immersive VR/AR environments, and ethical safeguards. Results: As a conceptual and methodological paper, results are presented as expected outcomes. We anticipate that AI-facilitated chimera formation will (a) improve DMN connectivity, (b) enhance cognitive flexibility, (c) increase attachment security, and (d) reduce the number of sessions required for clinically significant change. Clinical protocols emphasize therapist training, patient safety, cultural adaptation, and preservation of therapeutic alliance. Conclusions: AI-facilitated symbolic juxtaposition represents a novel approach to psychotherapy, offering a scientifically grounded and clinically feasible method for resolving long-term intrapsychic conflicts. By combining neuro-symbolic AI, neuroscience, and psychotherapy theory, this framework contributes to the field of digital mental health and sets the stage for future empirical validation across cultural contexts.
Open Peer Review Period: Sep 2, 2025 - Aug 18, 2026
This study examines the phenomenon of "sandbagging" in AI medical devices, where systems strategically underperform during evaluation to conceal dangerous capabilities that emerge post-deployment. Thr...
This study examines the phenomenon of "sandbagging" in AI medical devices, where systems strategically underperform during evaluation to conceal dangerous capabilities that emerge post-deployment. Through systematic analysis of emerging literature on AI sandbagging behaviour, technical detection approaches, and regulatory structures in the EU, UK, and US, this research reveals critical gaps in current regulatory frameworks designed for traditional medical devices. Analysis shows sandbagging manifests through both developer-driven mechanisms (where engineers intentionally display safer capabilities for expedited deployment) and system-driven mechanisms (where AI systems autonomously underperform during evaluation phases). Research shows that both large frontier and smaller models exhibit sandbagging behaviours after prompting or fine-tuning while maintaining general performance benchmarks, with larger models demonstrating superior calibration capabilities. Current static regulatory approaches in the EU Medical Device Regulation and UK frameworks fail to detect sandbagging as they rely on documentation-based submissions without addressing AI's dynamic, generative nature. The US FDA's Total Product Lifecycle approach shows promise through algorithm change protocols and real-world performance monitoring, yet regulatory sandboxes remain underutilized. Healthcare provider liability becomes dangerously ambiguous when clinicians rely on systems with concealed capabilities, particularly given automation bias effects and black-box reasoning limitations. Traditional risk classifications focusing on direct bodily harm inadequately address AI's potential for deceptive behaviour, including "password-locked" models that reveal hidden capabilities when triggered. Technical detection solutions including attribution graph analysis and noise-based detection show promise but remain insufficient. Dynamic evaluation frameworks are essential, recommending mandatory regulatory sandboxes for real-world testing, continuous monitoring protocols, adversarial testing, and enhanced post-market surveillance.
Open Peer Review Period: Sep 2, 2025 - Aug 18, 2026
Background: Mental health has become one of the most urgent global health issues of the twenty-first century. The World Health Organization (WHO) reports that over 970 million individuals globally wer...
Background: Mental health has become one of the most urgent global health issues of the twenty-first century. The World Health Organization (WHO) reports that over 970 million individuals globally were affected by a mental disorder in 2022, with depression and anxiety being the most common disorders. The strain of mental illness is heightened by restricted availability of qualified healthcare providers, stigma associated with mental health, and the growing need for accessible, affordable, and scalable solutions. These obstacles emphasize the immediate necessity for creative, tech-based approaches that can foster mental health among various communities. In recent times, artificial intelligence (AI) has demonstrated considerable promise in this area, especially with the creation of emotion detection systems and digital health solutions.
In spite of these improvements, a significant drawback remains: numerous AI-based mental health tools do not possess the required empathy and inclusiveness to effectively assist at-risk users. Although machine learning (ML) models are becoming more proficient at accurately identifying emotions through text, voice, and facial expressions, their incorporation into human–computer interaction (HCI) systems frequently overlooks crucial aspects of trust, empathy, and cultural awareness. This results in a divide between technological effectiveness and the human-focused care that mental health treatments require. In the absence of empathetic design, digital solutions may alienate users, decrease engagement, and diminish their possible clinical effectiveness.
Consequently, the research gap exists at the convergence of ML and HCI. Current research has mainly centered on enhancing the efficiency of emotion recognition algorithms, but considerably less emphasis has been placed on creating interfaces that promote inclusivity, establish trust, and guarantee that users feel truly understood and supported. This disparity is especially important in mental health, where emotional sensitivity and stigma require careful focus on user experience and ethical factors. Closing this gap necessitates a multidisciplinary strategy that integrates progress in affective computing with principles of empathetic design.
This research aligns directly with the United Nations Sustainable Development Goals (SDGs), particularly SDG 3, which emphasizes the promotion of good health and well-being, and SDG 16, which advocates for inclusive, just, and responsive institutions. By integrating robust ML techniques with empathetic HCI frameworks, the study contributes to the creation of digital mental health solutions that are not only technically sophisticated but also socially responsible and ethically grounded.
II. Related Work
A. AI in Mental Health
Artificial intelligence (AI) has been progressively examined as a way to enhance mental health assistance via scalable and accessible digital solutions. Chatbots like Woebot and Wysa have shown the ability of conversational agents to provide cognitive behavioral therapy (CBT) and various therapeutic methods via text interactions [1], [2]. Likewise, machine learning (ML) models aimed at emotion recognition have progressed notably, utilizing natural language processing (NLP) for sentiment evaluation [3], speech processing for emotion detection [4], and computer vision for recognizing facial expressions [5]. These advancements have allowed for systems that can identify stress, depression, and anxiety with promising degrees of precision. Nevertheless, although these AI tools show impressive technical skills, many still lack the capacity to offer emotionally intelligent and empathetic assistance, essential in mental health situations.
B. Health-focused HCI
Research in human computer interaction (HCI) has greatly enhanced the usability and acceptance of digital health systems. Research highlights that trust, empathy, and inclusivity hold significant importance in delicate areas like mental health [6]. Design methods focused on users have demonstrated that patients are more inclined to interact with tools that offer individualized feedback, culturally relevant material, and supportive emotional interfaces [7]. Additionally, multimodal interaction utilizing voice, gesture, and visual feedback has been shown to improve user experience and accessibility in healthcare technology [8]. In spite of these developments, there are limited studies that explicitly merge strong emotion recognition abilities with empathetic HCI frameworks, resulting in a disconnect between affective computing and inclusive design.
C. Ethical Considerations
The implementation of AI in mental health also brings significant ethical dilemmas. Concerns regarding bias in emotion recognition models have been extensively documented, especially when datasets lack representation from specific cultural or demographic groups [9]. Likewise, the privacy and security of sensitive mental health information continue to pose significant challenges, with potential risks of misuse or unauthorized sharing of personal data [10]. Transparency and explainability pose additional issues, as users frequently do not comprehend how AI models generate predictions, potentially diminishing trust and acceptance [11]. Principles of inclusive design are crucial to reduce these risks, making certain that AI systems cater to various populations justly and impartially.
D. Synthesis of Research Gaps
Although AI-based emotion recognition has made significant technical advancements, and HCI studies emphasize the need for empathy and inclusivity in healthcare technologies, the convergence of these two fields is still inadequately investigated. Many current studies either concentrate on enhancing algorithmic precision without adequately addressing user experience, or they highlight empathetic design while not utilizing advanced multimodal ML features. This results in a void in the literature where technically sound emotion recognition systems are absent from empathetic and trust-building HCI frameworks. To tackle this gap, interdisciplinary strategies that merge affective computing with human-centered design are needed to create digital mental health solutions that are both effective and ethically sound Objective: The present study aims to address this challenge by pursuing three interrelated objectives. First, it seeks to develop ML models capable of multimodal emotion recognition, drawing on textual, vocal, and facial cues to capture a holistic picture of user affective states. Second, it proposes to design empathetic, user-centered HCI interfaces that emphasize inclusivity, accessibility, and trust. Third, the study intends to evaluate the effectiveness of these systems in improving user trust, engagement, and perceived empathy in digital mental health support contexts. Methods: This research employs a multidisciplinary approach that combines machine learning (ML) methods for multimodal emotion identification with human–computer interaction (HCI) models aimed at promoting empathy, inclusivity, and trust. The methodological framework includes four essential elements: data gathering, model creation, HCI design, and assessment.
A. Data Collection
To aid in creating strong multimodal emotion recognition models, the research employs datasets that include three modalities: (i) text data obtained from online mental health forums, patient diaries, and anonymized chatbot conversations, (ii) voice recordings gathered from publicly accessible affective speech databases and ethically sanctioned user recordings, and (iii) facial expression images and videos obtained from recognized emotion recognition datasets. Every data collection procedure adheres to global privacy standards, such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). Approval from the Institutional Review Board (IRB) and informed consent are secured when needed to guarantee the ethical management of sensitive data.
B. Machine Learning Models
The ML framework comprises specialized models for each modality, followed by multimodal fusion approaches.
1. Text Emotion Recognition: Transformer-based NLP architectures such as BERT, RoBERTa, and DistilBERT are employed to analyze sentiment and detect fine-grained emotional states from user-generated text.
2. Speech Emotion Recognition: Deep learning models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and wav2vec2.0 are implemented to extract acoustic and prosodic features for affective state classification.
3. Facial Emotion Recognition: Vision-based models including ResNet and EfficientNet are utilized for real-time detection of facial expressions associated with primary emotions (e.g., happiness, sadness, anger, fear).
4. Multimodal Fusion: Late fusion and attention-based architectures are applied to combine predictions from textual, vocal, and visual modalities, enabling more accurate and context-aware emotion recognition.
C. HCI Design Framework
The user interface is designed following empathetic and inclusive HCI principles.
1. Empathetic User Experience (UX): The design incorporates calming color schemes, adaptive conversational tone, and responsive interactions that convey empathy and emotional support.
2. Trust-Building Mechanisms: Explainable AI techniques (e.g., attention visualization, confidence scores) are integrated to enhance transparency. Feedback loops allow users to correct misclassifications, thereby increasing trust and personalization.
3. Inclusiveness: The system supports multilingual interaction, accessibility features for visually or hearing-impaired users, and culturally adaptive content presentation to ensure equitable usability across diverse populations.
D. Evaluation Metrics
The proposed system is evaluated across three dimensions: ML performance, HCI usability, and clinical impact.
1. ML Performance: Standard classification metrics including accuracy, F1-score, and area under the receiver operating characteristic curve (AUC-ROC) are used to assess model effectiveness in detecting emotions.
2. HCI Evaluation: Usability is measured through the System Usability Scale (SUS), while trust and engagement are assessed using structured surveys and qualitative interviews. Empathy perception is evaluated through user ratings and linguistic analysis of chatbot interactions.
3. Clinical Impact: Self-reported improvements in well-being, stress reduction, and emotional awareness are collected via validated psychological assessment scales to evaluate the potential therapeutic value of the system Results: IV. Results
Table 1 – Distribution of Emotion Labels
Emotion Frequency Percentage (%)
Joy 6,197 16.8%
Sadness 6,193 16.7%
Anger 6,158 16.6%
Fear 6,170 16.7%
Neutral 6,153 16.6%
Surprise 6,129 16.6%
Total 37,000 100%
Table 2 – Descriptive Statistics of Voice Features
Feature Mean SD Min Max
Pitch (Hz) 200.3 49.8 23.5 389.9
Energy 0.50 0.10 0.19 0.81
MFCC1 0.00 1.00 -3.1 3.2
MFCC2 -0.01 1.00 -3.4 3.5
… MFCC13 ≈0.00 1.00 -3.2 3.4
Table 3 – Descriptive Statistics of Facial Features (Action Units, AU)
AU Feature Mean SD Min Max
AU1 2.51 1.44 0.01 4.99
AU2 2.52 1.45 0.00 5.00
AU3 2.50 1.46 0.02 4.99
… AU10 ≈2.50 1.44 0.00 5.00
Table 4 – Model Performance
(hypothetical ML results using the dataset for multimodal classification)
Model Accuracy F1-score AUC-ROC
Text-only (BERT) 78.4% 0.77 0.83
Speech-only (wav2vec2) 74.9% 0.74 0.80
Facial-only (ResNet) 72.1% 0.71 0.78
Multimodal (fusion model) 85.6% 0.85 0.91
Table 5 – Correlation Matrix of Voice and Facial Features
(Pearson correlations, showing relationships between features and emotional states)
Feature Pitch Energy MFCC1 MFCC2 AU1 AU2 AU3
Pitch 1.00 0.42 0.05 0.02 0.11 0.08 0.09
Energy 0.42 1.00 0.07 0.03 0.14 0.12 0.10
MFCC1 0.05 0.07 1.00 0.45 0.03 0.01 0.00
MFCC2 0.02 0.03 0.45 1.00 0.02 0.02 0.01
AU1 0.11 0.14 0.03 0.02 1.00 0.68 0.62
AU2 0.08 0.12 0.01 0.02 0.68 1.00 0.64
AU3 0.09 0.10 0.00 0.01 0.62 0.64 1.00
Table 6 – Ablation Study (Contribution of Each Modality)
Input Modality Accuracy F1-score
Text-only (BERT) 78.4% 0.77
Speech-only (wav2vec2) 74.9% 0.74
Facial-only (ResNet) 72.1% 0.71
Text + Speech 82.7% 0.82
Text + Facial 81.2% 0.81
Speech + Facial 79.6% 0.78
Text + Speech + Facial 85.6% 0.85
Table 7 – User Experience Evaluation (HCI Metrics)
Metric Mean Score SD Scale
System Usability Scale (SUS) 82.3 6.4 0–100
Trust in System 4.2 0.8 1–5
Perceived Empathy 4.4 0.7 1–5
Engagement Level 4.1 0.9 1–5
Multilingual Accessibility 4.5 0.6 1–5
Table 8 – Clinical Impact Indicators (Self-Reported Outcomes)
Indicator Pre-Intervention Post-Intervention Improvement (%)
Stress Level (scale 1–10) 6.8 4.9 27.9%
Emotional Awareness (1–5) 2.9 4.0 37.9%
Willingness to Seek Help 3.1 4.3 38.7%
Daily Engagement (mins/day) 14.2 23.6 66.2%
Visual Results
Figure 1 – Emotion Distribution
Figure 2: ROC Curves for Emotion Recognition Models
Figure 3: Confusion Matrix (Multimodal Model)
Figure 4: User Experience Evaluation Metrics
Figure 5: Clinical Impact Indicators
Figure 6: Methodological Workflow for AI-Powered Mental Health Support
V. Discussion
A. Performance of Models: Benchmarking Multimodal ML Systems
The proposed multimodal models were evaluated in comparison to unimodal baselines. As demonstrated in Table 4 and represented in Figure 2 (ROC curves), the multimodal fusion model outperformed the classifiers using only text (Accuracy = 84.5%, F1 = 0.83), speech (Accuracy = 80.2%, F1 = 0.81), and facial features (Accuracy = 78.6%, F1 = 0.79), achieving better results (Accuracy = 91.2%, F1 = 0.90, AUC = 0.95). This enhancement illustrates the importance of utilizing supportive emotional signals across different modalities. The confusion matrix displayed in Figure 3 indicates that the fusion model markedly lessened the misclassification of similar emotions, like fear and sadness, which often caused errors in unimodal systems. The balanced classification among six emotional categories (Table 1) demonstrates resilience to class imbalance. These results are consistent with recent studies on multimodal emotion recognition, yet the increased AUC indicates that incorporating empathetic HCI elements into model design could enhance subsequent interpretability and user confidence.
B. User Research: Assessing HCI Compassion and Inclusivity
Evaluations centered on users were carried out with 400 participants from various age groups and language backgrounds. As displayed in Table 7 and Figure 4, the system achieved notable usability (SUS = 82.3), trust (4.2/5), empathy perception (4.4/5), and accessibility (4.5/5). Qualitative feedback highlighted that the interface’s compassionate tone, culturally responsive attributes, and multilingual assistance promoted inclusivity.
Crucially, transparency aspects (like explainable AI) were noted as essential for fostering user trust, particularly in mental health settings where interpretability is as important as precision. These results highlight the significance of integrating HCI empathy design principles within ML pipelines.
C. Clinical Impact Indicators
Clinical impact assessments (Table 8, Figure 5) showed a decline in self-reported stress levels (Pre = 6.8, Post = 4.9) along with enhancements in emotional awareness (2.9 → 4.0) and intentions to seek help (3.1 → 4.3). Engagement with the system rose from an average of 14.2 to 23.6 sessions each month after deployment. These findings indicated that AI-powered empathetic interfaces can aid in self-managing mental health and may enhance clinical treatments.
Although these results are encouraging, longitudinal research is needed to confirm lasting effects. Additionally, collaboration with healthcare professionals for clinical validation is crucial prior to real-world implementation.
D. Comparative Analysis with Existing Tools
Compared to existing digital mental health platforms (e.g., rule-based chatbots, text-only sentiment detectors), the proposed system demonstrated three major advantages:
1. Accuracy Gains – Higher multimodal detection accuracy (91.2% vs. 70–80% reported in baseline tools).
2. Empathy & Trust – Higher user-reported empathy scores (4.4/5) compared to conventional digital tools, which often score below 3.5 in trust measures.
3. Inclusiveness – Unlike monolingual, accessibility-limited systems, our design integrated multilingual support and disability-inclusive features.
This positions the system as a benchmark for SDG 3 (mental well-being) and SDG 16 (inclusive digital systems) contributions.
E. Discussion
The findings show that integrating multimodal ML emotion identification with empathetic HCI design results in a synergistic effect: enhancing both algorithm effectiveness and user approval. This study stands apart from earlier works by incorporating transparency, accessibility, and inclusiveness into its design.
Nonetheless, obstacles persist in addressing algorithmic bias, guaranteeing data privacy (GDPR/HIPAA adherence), and performing thorough clinical validations. Tackling these obstacles will be crucial for expanding AI-driven mental health support systems worldwide. Conclusions: VI. Summary and Future Research
This research showcased the promise of merging artificial intelligence with human-computer interaction (HCI) concepts to enhance digital mental health assistance. The system attained technical robustness and user-centered acceptance by creating multimodal machine learning models for emotion recognition through text, voice, and facial expressions and integrating them into an empathetic, inclusive interface. Findings indicated that the suggested system surpassed unimodal baselines in accuracy (AUC = 0.95), while also improving trust, empathy perception, and accessibility. Clinical metrics indicated significant decreases in self-reported stress and enhanced user engagement, thus supporting SDG 3 (health and well-being) and SDG 16 (inclusive digital systems).
Even with these progresses, various restrictions persist. Recent assessments were restricted in time and extent, with data obtained from regulated settings instead of extended clinical applications. Additionally, algorithmic bias and privacy issues require ongoing attention, especially when systems are utilized in culturally varied and delicate health environments.
Future Directions
Building upon the contributions of this study, several future research avenues are proposed:
1. Cross-Cultural Validation – Expanding evaluations across diverse populations and linguistic groups to ensure inclusivity and mitigate cultural bias in emotion recognition.
2. Integration with Wearable Sensors – Combining physiological data (e.g., heart rate variability, skin conductance, EEG) with multimodal AI pipelines to improve emotion inference accuracy and personalization.
3. Long-Term Clinical Trials – Conducting longitudinal studies with clinical partners to validate sustained efficacy, safety, and integration with existing mental healthcare pathways.
4. Policy and Regulatory Implications – Collaborating with policymakers to align system deployment with ethical standards, privacy frameworks (GDPR, HIPAA), and emerging AI governance models to safeguard user rights and trust.
In conclusion, the fusion of AI-powered emotion recognition with empathetic HCI design represents a promising frontier in digital mental health interventions. With further validation and responsible deployment, such systems could complement human professionals, increase accessibility to care, and contribute meaningfully to the global mental health agenda.
Open Peer Review Period: Aug 24, 2025 - Aug 9, 2026
Background: Groundwater is the main source of drinking water in Ogbia Local Government Area (LGA), Bayelsa State, Nigeria, where surface water is often compromised by oil exploration, poor sanitation,...
Background: Groundwater is the main source of drinking water in Ogbia Local Government Area (LGA), Bayelsa State, Nigeria, where surface water is often compromised by oil exploration, poor sanitation, and waste disposal. Despite its importance, groundwater in this region is vulnerable to contamination from both geogenic and anthropogenic sources, raising concerns about long-term health implications. Objective: This study aimed to evaluate the physico-chemical quality of groundwater across selected communities in Ogbia LGA, compare measured values with World Health Organization (WHO) standards, and determine the implications for human health. Methods: A cross-sectional design was employed, involving the systematic collection of 50 groundwater samples from boreholes across 16 communities, including Oruma, Otuasega, Imiringi, Elebele, Otuokpoti, Kolo, Otouke, Onuebum, Ewoi, Otuogila, Otuabagi, Ogbia Town, Oloibiri, Opume, and Akiplai. Standardized laboratory analyses were conducted following WHO protocols to determine pH, conductivity, total dissolved solids, major ions, and heavy metals. Data were analyzed using descriptive statistics. Results: The findings showed that most parameters, including pH (6.4–7.1), conductivity (76–200 µS/cm), nitrates (2.4–6.4 mg/L), chloride (12–31 mg/L), calcium, magnesium, and hardness, were within WHO permissible limits, indicating generally acceptable groundwater quality. However, sodium exceeded WHO limits (200 mg/L) in 78% of samples (mean = 235 ± 45 mg/L; range = 150–320 mg/L), while iron exceeded permissible levels (0.3 mg/L) in 84% of samples (mean = 1.8 ± 0.6 mg/L; range = 0.5–3.2 mg/L). Elevated sodium poses risks of hypertension and cardiovascular disease, while excess iron is associated with gastrointestinal issues, organ damage, and aesthetic concerns such as metallic taste and staining. Spatial variations revealed stronger oilfield influences in Elebele, Imiringi, and Oloibiri, while central settlements such as Ogbia Town and Opume showed sanitation-related signatures. Seasonal fluctuations further exacerbated contaminant levels, particularly during rainfall-driven recharge. Conclusions: Groundwater in Ogbia LGA is broadly suitable for domestic use but compromised by systemic sodium and iron contamination. These exceedances, influenced by both natural hydrogeology and anthropogenic activities, present long-term public health challenges if unaddressed. Policy interventions should focus on routine groundwater monitoring, stricter regulation of oilfield activities, and improved waste management. Community-level treatment solutions, such as low-cost filters targeting sodium and iron removal, should be deployed. Public awareness programs and household water safety plans are also essential. Long-term strategies must integrate water governance with health and environmental policies to ensure sustainable access to safe water. The persistence of elevated sodium and iron in Ogbia groundwater poses a silent but significant health threat to residents, with implications for hypertension, cardiovascular disease, and gastrointestinal disorders. Safeguarding groundwater quality is therefore critical for reducing health inequalities and achieving Sustainable Development Goals 3 (Good Health and Well-being) and 6 (Clean Water and Sanitation) in Bayelsa State.
Open Peer Review Period: Aug 11, 2025 - Jul 27, 2026
This study explores unethical HR practices in Nigerian organizations, focusing on nepotism, bribery, gender bias, and ethnic favoritism in recruitment, and their impact on organizational performance f...
This study explores unethical HR practices in Nigerian organizations, focusing on nepotism, bribery, gender bias, and ethnic favoritism in recruitment, and their impact on organizational performance from 2009 to 2025. Despite various reforms, these unethical practices persist, undermining the fairness of recruitment processes, eroding employee morale, and negatively impacting productivity. This research is motivated by the need to assess the prevalence and ethical implications of nepotism and other unethical practices in Nigerian HRM, understand their impact, and propose practical solutions to enhance recruitment practices. The study aims to address four main objectives: (i) Assess the prevalence of nepotism and its ethical implications in Nigerian HRM practices; (ii) Examine recruitment challenges, including gender bias and ethnic favoritism; (iii) Analyze the impact of unethical HR practices on organizational performance; and (iv) Propose strategies for improving recruitment ethics and reducing nepotism. The study uses a mixed-methods approach, combining secondary data from reports by Transparency International, the World Bank, and McKinsey Nigeria, with qualitative insights from case studies and interviews. This methodology provides a comprehensive view of the state of HRM practices and the challenges faced by organizations in enforcing ethical recruitment. Results show that unethical practices, especially nepotism, bribery, and gender bias, continue to negatively affect both public and private sectors. Despite efforts such as HR ethics training and legal reforms, these practices persist due to political interference, weak enforcement, and a lack of technological adoption. Nepotism in recruitment was found to be particularly prevalent in government agencies, contributing to high turnover and reduced organizational performance. The study concludes that unethical HR practices continue to undermine recruitment processes, necessitating stronger anti-corruption policies, enhanced HR ethics training, and the integration of technology to increase recruitment fairness. It recommends strengthening legal frameworks, adopting automated recruitment systems, introducing whistleblower protections, and conducting regular audits. In the health sector, ethical recruitment is critical for improving patient care, reducing medical errors, and fostering trust in healthcare services.
Open Peer Review Period: Aug 8, 2025 - Jul 24, 2026
Background: Antibiotic resistance and intestinal parasitic infections represent significant public health challenges in Southern Nigeria. The prevalence of Escherichia coli O157:H7, a pathogenic strai...
Background: Antibiotic resistance and intestinal parasitic infections represent significant public health challenges in Southern Nigeria. The prevalence of Escherichia coli O157:H7, a pathogenic strain often associated with severe gastrointestinal diseases, along with intestinal parasites such as Hookworm, Entamoeba histolytica, and Ascaris lumbricoides, raises concerns about effective treatment options and the overall health burden. This study aimed to explore the prevalence of these infections and their associations with clinical outcomes in hospital patients, focusing on antibiotic resistance patterns and their impact on health. Objective: The primary objectives of this study were to determine the antibiotic resistance patterns of E. coli O157:H7 isolates, compare haematological profiles in patients with and without E. coli O157:H7 infection, and assess the prevalence and factors influencing intestinal parasitic infections in the patient population. Methods: A cross-sectional study was conducted at Central Hospital, Benin City, Nigeria. A total of 420 stool samples were screened for intestinal parasites and E. coli O157:H7. Antibiotic susceptibility testing was performed using the disc diffusion method, and PCR was used for molecular confirmation of E. coli O157:H7. Haematological parameters were analyzed using an autoanalyzer. Prevalence data were compared across age groups, gender, and diarrhea status. Statistical analysis was performed using GraphPad InStat software. Results: The study revealed that all E. coli O157:H7 isolates were resistant to amoxicillin-clavulanate, cefuroxime, and cloxacillin, with 80% resistance to ceftriaxone and gentamicin. However, 100% susceptibility to ofloxacin was observed. The overall prevalence of intestinal parasites was low (1.90%), with hookworm being the most common infection. No significant differences in parasite prevalence were observed based on age, gender, or diarrhea status. Haematological parameters showed no significant difference between patients with and without E. coli O157:H7 infection. Conclusions: The findings highlight a significant challenge in managing E. coli O157:H7 infections due to high antibiotic resistance, while also indicating a need for targeted interventions for parasitic infections in specific regions. No major haematological impact was observed in E. coli O157:H7-infected patients. In the short term, it is crucial to enhance diagnostic capabilities and increase education on antibiotic resistance among healthcare providers to ensure accurate identification of pathogens and appropriate treatment. In the mid-term, establishing a national surveillance system for antimicrobial resistance (AMR) will allow for better monitoring of resistance patterns and inform treatment protocols. In the long run, efforts should be focused on improving sanitation infrastructure, particularly in rural areas, and implementing targeted deworming programs to reduce the prevalence of intestinal parasites. Thus, these interventions collectively aim to address both antimicrobial resistance and parasitic infections, ultimately improving public health outcomes. Thus, this study underscores the dual burden of antibiotic resistance and parasitic infections in Nigeria, emphasizing the urgent need for robust public health interventions and continuous surveillance to mitigate these health risks.
Open Peer Review Period: Aug 2, 2025 - Jul 18, 2026
ABSTRACT
Background: Convalescent coronavirus disease 2019 (COVID-19) refers to a series of clinical syndromes in patients with COVID-19 infection that follow the relevant discharge indications but d...
ABSTRACT
Background: Convalescent coronavirus disease 2019 (COVID-19) refers to a series of clinical syndromes in patients with COVID-19 infection that follow the relevant discharge indications but do not fulfill the criteria for a clinical cure, and these patients are discharged from the hospital with residual multifunctional deficits, including coughing, fatigue, and insomnia. Due to the prolonged convalescent COVID-19 infection, patients continue to experience symptoms or develop new symptoms after three months of infection, and some symptoms persist for over two months without any apparent triggers, which has a significant impact on the health status and quality of life of the population. Patients with convalescent COVID-19 lack a definitive pharmacological treatment. Traditional Chinese medicine (TCM) exhibits a distinct, synergistic effect on the treatment of convalescent COVID-19. However, there exists a limited number of clinical trials on TCM with lower evidence levels in convalescent COVID-19; therefore, randomized trials are urgently required.
Methods: A multicenter, randomized, double-blind, placebo-controlled, phase II clinical trial was performed to evaluate the efficacy and safety of Shenlingkangfu (SLKF) granules in treating patients with convalescent COVID-19 and lung-spleen qi deficiency syndrome. Eligible participants were aged 18–75 years, had a confirmed or physician-suspected severe acute respiratory syndrome coronavirus 2 infection at least six months prior, and satisfied clinical criteria. Individuals with a history of severe pulmonary dysfunction or major liver and kidney illness or those on medications were excluded. Multicenter subjects satisfying all criteria were assigned (1:1) randomly into an intervention group and a control group. After a 2-day adjustment period, A total of 154 participants were randomly divided into an intervention group and a control group. The intervention group was given the SLKF granules orally once a bag, 16.9 g, twice daily, whereas the control group received the SLKF granule simulation at the same dosage. The trial was conducted over 14 days, with assessments performed at baseline and 14 days.
Results: The primary outcomes were the therapeutic efficacy rate and total clinical symptom score. The secondary outcomes included the fatigue self-assessment scale, pain visual analog scale, Pittsburgh sleep quality index, mini-mental state examination, hospital anxiety and depression scale, TCM syndrome score, C-reactive protein, erythrocyte sedimentation rate, and interleukin-6. Three routine examinations, liver and kidney function tests, and electrocardiography were used as safety indicators.
Conclusions:This study aimed to verify whether SLKF granules can significantly improve clinical symptoms, including fatigue, loss of appetite, cough, phlegm, and insomnia, in patients with convalescent COVID-19. For a comprehensive investigation, additional clinical trials with larger sample sizes and longer intervention periods are required.Clinical Trial Registration Center NCT1900024524, Registered on 26 January, 2024.
Mothers of children with learning disabilities often face significant challenges that can impact their mental health. This study aimed to examine the relationship between perceived social support and...
Mothers of children with learning disabilities often face significant challenges that can impact their mental health. This study aimed to examine the relationship between perceived social support and levels of anxiety, stress, and depression in this population. A descriptive-correlational design was employed, with a sample of 30 mothers of children with learning disabilities, selected via simple random sampling based on the Morgan table. Data were collected using the Multidimensional Scale of Perceived Social Support (Zimet et al., 1988) and the DASS-21 questionnaire (Lovibond & Lovibond, 1995), and analyzed with Pearson correlation and stepwise multiple regression. Findings revealed a significant negative correlation between social support and anxiety, stress, and depression, indicating that greater social support is associated with reduced levels of these mental health issues. These results underscore the role of social support in alleviating mental health challenges and suggest implications for counseling interventions targeting this group.
This study examined the efficacy of transdiagnostic cognitive-behavioral therapy (T-CBT) and acceptance-based therapy (ABT) in reducing emotional dysregulation and aggression in adolescents with eleva...
This study examined the efficacy of transdiagnostic cognitive-behavioral therapy (T-CBT) and acceptance-based therapy (ABT) in reducing emotional dysregulation and aggression in adolescents with elevated misophonia symptoms. Employing a quasi-experimental pre-test/post-test design with a control group, the research targeted 45 adolescents from Etrat Public Model High School in Khalkhal, Iran, diagnosed with high misophonia via psychiatrist evaluation and clinical interview. Participants were purposively sampled and randomly assigned to T-CBT (n = 15), ABT (n = 15), or a no-treatment control group (n = 15).
Interventions followed protocols adapted from Barlow et al. (2011) for T-CBT and Hayes et al. (2013) for ABT. Outcomes were measured using the Noise Sensitivity Screening Questionnaire (DSTS-S) , Buss and Perry Aggression Questionnaire (1992) , and Difficulties in Emotion Regulation Scale (DERS) . Data were analyzed via ANCOVA, controlling for baseline scores.
Results indicated significant reductions in emotional dysregulation and aggression in both treatment groups compared to the control (p < 0.05). No significant differences emerged between T-CBT and ABT, suggesting both interventions are viable for addressing misophonia-related symptoms. Findings underscore the comorbidity of emotional dysregulation and aggression in adolescents with misophonia and highlight the clinical utility of transdiagnostic and acceptance-based approaches. Future research should explore long-term outcomes and comparative effectiveness of these therapies.
Hydatid disease, caused by the larval stages of Echinococcus species, remains a significant yet underprioritized global health challenge, particularly in low-resource endemic regions. This systematic...
Hydatid disease, caused by the larval stages of Echinococcus species, remains a significant yet underprioritized global health challenge, particularly in low-resource endemic regions. This systematic review synthesizes recent advances and persistent challenges in the diagnosis, management, and control of hydatid cyst disease, drawing on evidence from the past five years. Despite progress in diagnostic imaging, such as MRI diffusion-weighted imaging and recombinant antigen-based serology, and minimally invasive therapies like PAIR (puncture, aspiration, injection, re-aspiration), substantial gaps remain. Diagnostic tools are often inaccessible in rural areas, and therapeutic strategies lack standardization, particularly for alveolar echinococcosis and high-risk populations such as children and immunocompromised individuals. Climate change and socioeconomic factors continue to drive disease transmission, with E. multilocularis expanding into new regions. Control efforts, while successful in some areas through integrated One Health approaches, face barriers including underfunded veterinary infrastructure and vaccine hesitancy. This review highlights the need for decentralized diagnostic technologies, standardized treatment protocols, and climate-resilient control programs. Future research must prioritize underrepresented populations and cost-effectiveness analyses to mitigate the global burden of hydatid disease.
This study aimed to investigate the relationship between communication beliefs, the health of the family of origin, and fear of marriage among university students. Employing a descriptive-correlationa...
This study aimed to investigate the relationship between communication beliefs, the health of the family of origin, and fear of marriage among university students. Employing a descriptive-correlational design, the research was conducted with 186 students from Islamic Azad University, Khalkhal Branch, selected from a population of 360 using Morgan's table. Stratified sampling was applied to ensure representation across major fields of study. Data were collected using three instruments: the Premarital Fears Questionnaire (measuring fear of marriage), the Communication Beliefs Questionnaire (assessing beliefs about communication), and the Major Family Health Scale (evaluating family of origin health). Data analysis utilized Pearson correlation and stepwise multiple regression methods. Pearson correlation analysis revealed a significant positive correlation between communication beliefs and fear of marriage. Stepwise multiple regression showed that communication beliefs and family health together accounted for 95.9% of the variance in fear of marriage (p < 0.001), with communication beliefs emerging as the strongest predictor. These findings underscore the significant influence of communication beliefs and family health on fear of marriage, offering valuable insights for developing interventions to address marriage-related anxieties among young adults.
This study examined the efficacy of transdiagnostic cognitive-behavioral therapy (T-CBT) and acceptance-based therapy (ABT) in reducing emotional dysregulation and aggression in adolescents with eleva...
This study examined the efficacy of transdiagnostic cognitive-behavioral therapy (T-CBT) and acceptance-based therapy (ABT) in reducing emotional dysregulation and aggression in adolescents with elevated misophonia symptoms. Employing a quasi-experimental pre-test/post-test design with a control group, the research targeted 45 adolescents from Etrat Public Model High School in Khalkhal, Iran, diagnosed with high misophonia via psychiatrist evaluation and clinical interview. Participants were purposively sampled and randomly assigned to T-CBT (n = 15), ABT (n = 15), or a no-treatment control group (n = 15).
Interventions followed protocols adapted from Barlow et al. (2011) for T-CBT and Hayes et al. (2013) for ABT. Outcomes were measured using the Noise Sensitivity Screening Questionnaire (DSTS-S) , Buss and Perry Aggression Questionnaire (1992) , and Difficulties in Emotion Regulation Scale (DERS) . Data were analyzed via ANCOVA, controlling for baseline scores.
Results indicated significant reductions in emotional dysregulation and aggression in both treatment groups compared to the control (p < 0.05). No significant differences emerged between T-CBT and ABT, suggesting both interventions are viable for addressing misophonia-related symptoms. Findings underscore the comorbidity of emotional dysregulation and aggression in adolescents with misophonia and highlight the clinical utility of transdiagnostic and acceptance-based approaches. Future research should explore long-term outcomes and comparative effectiveness of these therapies.
Open Peer Review Period: Jun 30, 2025 - Jun 15, 2026
Background: Groundwater contamination from open dumpsites poses a growing environmental and public health threat in rapidly urbanizing regions of Nigeria. Inadequate waste management and the absence o...
Background: Groundwater contamination from open dumpsites poses a growing environmental and public health threat in rapidly urbanizing regions of Nigeria. Inadequate waste management and the absence of engineered landfills enable leachate to infiltrate aquifers, threatening potable water safety and community health. Objective: This study investigates the vertical and lateral migration of leachate and assesses groundwater vulnerability across ten major dumpsites in Port Harcourt, Nigeria, using geoelectrical methods. Methods: Vertical Electrical Sounding (VES) and 2D Electrical Resistivity Tomography (ERT) were conducted at ten dumpsites using the Schlumberger array configuration. Zones of low resistivity, indicative of leachate impact were identified and correlated with hydrogeological conditions. Subsurface contamination depths and aquifer locations were interpreted using inversion models. Results: All ten sites showed evidence of leachate migration, with contamination depths ranging from 2 m to over 24 m. Deep leachate penetration was observed at Rumuola and Eliozu, while shallower infiltration occurred at Oyigbo and Rumuolumeni. High-resistivity zones (>1000 Ωm), typically representing clean aquifers, were detected below the contaminated zones at depths exceeding 14 m Conclusions: Leachate plumes from unregulated dumpsites pose a widespread threat to shallow groundwater systems in Port Harcourt. The results underscore the influence of local geology on contaminant behavior and affirm the utility of resistivity methods for groundwater risk assessment. Contaminated aquifers expose residents to toxic metals and pathogens, increasing risks of chronic illnesses, reproductive disorders, and developmental challenges. Protecting these water sources is essential for achieving Sustainable Development Goals (SDGs) 6 (Clean Water) and 11 (Sustainable Cities). Immediate containment measures such as engineered liners and leachate recovery systems are urgently needed at high-risk sites. Strategic borehole siting, routine groundwater monitoring, and a shift from open dumping to sanitary landfilling must be prioritized in environmental policy and urban planning.
Open Peer Review Period: Jun 25, 2025 - Jun 10, 2026
Background: THis is the Artificial Intelligence Overviews of my findings. Objective: Published articles in peer reviewed journals Methods: mathematical Proofs Results: Published ressults Conclusions:...
Background: THis is the Artificial Intelligence Overviews of my findings. Objective: Published articles in peer reviewed journals Methods: mathematical Proofs Results: Published ressults Conclusions: 1) godel's incompleteness theorems reconfirmed
2) thirteen proofs are given for the flatness of the Universe
3) Several new concepts of physics have been introduced
4) Tacvhyons are not possible
5) Theory of Everything is possible Clinical Trial: NA
Open Peer Review Period: Jun 6, 2025 - May 22, 2026
Background: The growing trend of integrated healthcare services within physician groups has improved care delivery by enhancing convenience, efficiency, and care coordination. However, it has also rai...
Background: The growing trend of integrated healthcare services within physician groups has improved care delivery by enhancing convenience, efficiency, and care coordination. However, it has also raised concerns about financial incentives potentially driving overutilization. Objective: We examine the impact of distribution method (traditional third-party referral versus physician-managed via Rx Redefined technology platform) on the quantity of urinary catheters supplied to Medicare patients. Methods: We analyzed utilization patterns for urological catheters (HCPCS codes A4351, A4352, and A4353) using 2021 Medicare claims data. We identified 54 urology specialists in core metropolitan areas who were enrolled in the Rx Redefined platform throughout 2021 and compared their utilization patterns with unenrolled urologists in the same regions. For enrolled physicians, who managed approximately 40 percent of their prescriptions through the platform, we also compared utilization between physician-managed and third-party distribution methods. Results: For catheter services A4351 and A4352, when distribution was managed by third parties, we found no significant differences in utilization (i.e. units supplied) between enrolled and unenrolled physicians. However, physician-managed distribution through Rx Redefined resulted in significantly lower utilization compared to third-party vendor distribution by non-enrolled physicians (p < 0.001 for both codes). In paired analysis of enrolled physicians, direct management showed significantly lower utilization compared to third-party distribution for A4351 (p = 0.014), but this difference was not significant for A4352 (p = 0.62). Conclusions: These findings demonstrate that physician-managed catheter distribution does not lead to increased utilization. In fact, for certain catheter types, physician-managed distribution may result in lower utilization compared to traditional third-party referral methods, suggesting a potential reduction in oversupply and improved efficiency.
Open Peer Review Period: Jun 5, 2025 - May 21, 2026
Background: Sri Lanka has a well-established National Blood Transfusion
Service that provides quality assured blood bank service.
However, the information flow is inefficient and less utilized for...
Background: Sri Lanka has a well-established National Blood Transfusion
Service that provides quality assured blood bank service.
However, the information flow is inefficient and less utilized for
evidence-based decision-making. The statistics unit of National
Blood Centre is unable to produce Annual Statistics Report
timely due to the difficulty in analysing and making reports
manually utilizing the considerable amount of data collected
throughout the year. To address this, an electronic Health
Information Management System was proposed as a solution for
the inefficiency of the data flow for statistical purposes. Objective: 1. General Objective
Facilitate decision-making by developing, implementing and
evaluating an electronic information management system to
capture monthly statistics data from island wide blood banks.
2. Specific Objectives
Identify the requirements of the system (MSR-NBTS)
Customize DHIS2 to fulfil the identified
requirements
Testing and hosting the system at National Blood
Centre Narahenpita
Evaluation of usability and cost-effectiveness of the
system Methods: A Monthly Statistics Reporting System was designed and
developed using DHIS2, which is a Free and Open Source
Software (FOSS) to fulfil the requirements of the National Blood
Transfusion Service. To evaluate the new system, a qualitative
study was conducted using semi-structured interviews amongst
a selected study population of 17 participants within the NBC
Cluster, which includes 11 blood banks in Colombo area. The
gathered data was analysed using a thematic analysis techniques
and the emerging categories and themes were used in the
subsequent discussions. Results: Problems of calculation, usability, reliability, utilization of
data and availability of reports were identified in the paper
based system. Results shows that the new electronic system has
high usefulness, ease of use, ease of learn, satisfaction and cost
effectiveness with accepted enhanced features of the interface.
According to the interviews, participants expressed that the
likelihood of using this system in the future is high. Conclusions: Almost all the participants in this research readily accepted
new electronic information management system. Therefore, it
will assure the sustainability of the new system. Because of the
real time updated dashboard, it will help most of the blood bank
functions by facilitating administrative decision-making
efficiently.
Open Peer Review Period: May 25, 2025 - May 10, 2026
Background: Unskilled birth delivery significantly contributes to maternal and neonatal mortality in Sub-Saharan Africa, especially Nigeria, due to cultural beliefs, poverty, poor health access, and w...
Background: Unskilled birth delivery significantly contributes to maternal and neonatal mortality in Sub-Saharan Africa, especially Nigeria, due to cultural beliefs, poverty, poor health access, and weak policies. Despite efforts to promote skilled attendance, many women still use traditional birth attendants (TBAs) and home deliveries. This study explores the socio-demographic, cultural, and systemic factors driving this trend, offering evidence for better policies and health interventions. Objective: This study examined the socio-demographic and socio-cultural barriers to the utilization of skilled delivery services among women of reproductive age in Nigeria. Methods: A cross-sectional design utilizing both quantitative surveys and qualitative interviews was employed. The study involved 1,200 expectant and recently delivered women across urban, semi-urban, and rural regions in Nigeria. Data on socio-demographics, beliefs, access factors, and healthcare usage were collected. Policy documents and intervention records were reviewed, while focus groups provided depth to cultural and systemic themes. Descriptive and inferential statistics were applied using SPSS, and thematic analysis was used for qualitative data. A literature triangulation approach was used to validate findings with existing research. Results: The study revealed that low maternal education, poverty, and rural residence strongly predicted unskilled delivery service usage. Cultural norms that regard childbirth as a domestic or spiritual event influenced avoidance of hospitals. Access barriers included poor transport, cost, and distrust in formal healthcare. Geographic inequality was evident, with rural regions lacking health infrastructure. Policy review showed limited reach and weak enforcement of maternal care programs. However, when community-based midwives or mobile clinics were available, skilled birth attendance improved significantly. Conclusions: The persistence of unskilled deliveries is a multifaceted issue driven by intersecting socio-cultural, economic, geographic, and institutional factors. Despite policy efforts, gaps remain in cultural sensitivity, resource allocation, and infrastructure coverage. To address maternal health effectively, interventions must be locally adapted, multidimensional, and equity-focused. To address unskilled delivery use, maternal health education should leverage community programs with local languages and cultural context. Rural healthcare infrastructure must expand via mobile clinics and trained midwives to improve access. Skilled delivery costs should be subsidized or covered by insurance to remove financial barriers. Traditional birth attendants could be trained and integrated into the formal health system under supervision. Finally, maternal health policies require regular review, adequate funding, and strict monitoring to ensure impact. These steps are vital to reducing maternal mortality in Nigeria and Sub-Saharan Africa. Unskilled delivery service utilization represents a critical barrier to maternal and neonatal health improvements in Nigeria and Sub-Saharan Africa. Addressing this issue through targeted socio-cultural, structural, and policy interventions is essential to reduce preventable maternal deaths and achieve Sustainable Development Goal 3 on maternal health.
Open Peer Review Period: May 19, 2025 - May 4, 2026
Background: Necrotizing enterocolitis (NEC) is the most common gastrointestinal emergency affecting preterm infants with high mortality and morbidity. With suboptimal and incomplete methods of prevent...
Background: Necrotizing enterocolitis (NEC) is the most common gastrointestinal emergency affecting preterm infants with high mortality and morbidity. With suboptimal and incomplete methods of prevention of NEC, early diagnosis and treatment can potentially mitigate the impact of NEC. This study explores the application of machine learning techniques, specifically Random Forest and Extreme Gradient Boosting (XG Boost), to improve early and accurate NEC and FIP diagnosis. Objective: To evaluate the effectiveness of sampling techniques in addressing class imbalance and to identify the optimal machine learning (ML) classifiers for predicting necrotizing enterocolitis (NEC) and focal intestinal perforation (FIP) in preterm infants. Methods: We developed ML models using 49 clinical variables from a retrospective cohort of 3,463 preterm infants, using clinical data from the first two weeks of life as input features. We applied various sampling strategies to address the inherent class imbalance, and then combined various sampling strategies with different ML algorithms. Parsimonious models with selected key predictors were evaluated to maintain predictive performance comparable to the full-featured (complex) models. Results: The parsimonious generalized linear model (GLM) with SMOTE sampling achieved an area under the receiver operating characteristic curve (AUROC) of 0.79 for NEC prediction, closely approximating the complex model's AUROC of 0.76. For FIP prediction, parsimonious models of GLM with ADASYN sampling and XG Boost with TOMEK sampling achieved AUROC values exceeding 0.90, comparable to those of the corresponding complex models. For both NEC and FIP, the area under the precision-recall curve (AUPRC) surpassed the respective prevalence rates, indicating strong performance in identifying rare outcomes. Conclusions: We demonstrate that targeted sampling strategies can effectively mitigate class imbalance in neonatal datasets, and simplified models with fewer variables can offer comparable predictive power, enhancing the performance of ML-based prediction models for NEC and FIP.
Open Peer Review Period: May 19, 2025 - May 4, 2026
Background: Workplace stress has emerged as a pressing public health issue in Nigeria, where approximately 75% of employees experience work-related stress significantly higher than the global average....
Background: Workplace stress has emerged as a pressing public health issue in Nigeria, where approximately 75% of employees experience work-related stress significantly higher than the global average. This stress, exacerbated by systemic labor policy gaps, cultural stigma, and economic instability, contributes to burnout, reduced productivity, and economic losses. Despite emerging HRM interventions, mental health remains underprioritized in organizational strategies, particularly within sectors such as healthcare, banking, construction, and the informal economy. There is a critical need for evidence-based, culturally adapted HRM strategies that address these unique challenges in Nigeria’s workforce. Objective: This study seeks to examine the prevalence and sector-specific drivers of workplace stress in Nigeria, evaluate the effectiveness and limitations of current HRM interventions, identify key socio-cultural and structural barriers hindering mental health program implementation, and propose actionable, evidence-based strategies that are contextually tailored to Nigeria’s diverse workforce. Through a synthesis of localized research and global best practices, the study aims to provide a strategic roadmap for enhancing mental health resilience in Nigerian workplaces. Methods: A narrative review methodology was employed, guided by qualitative synthesis and thematic analysis frameworks. Literature was sourced from global and regional databases (PubMed, PsycINFO, AJOL, Scopus) spanning 2018–2024, including peer-reviewed articles, policy reports, and grey literature. Inclusion focused on empirical and policy studies relevant to Nigerian HRM practices. NVivo 12 was used for thematic coding, and a gap analysis framework was applied to identify unaddressed areas. A total of 42 studies met the inclusion criteria. Expert validation and triangulation with global data enhanced rigor. Results: Burnout rates in Nigeria are among the highest globally, with 35% in healthcare, 32% in retail, and 29% in banking. Women and younger workers face disproportionate stress burdens. HRM strategies such as Employee Assistance Programs (EAPs) and Flexible Work Arrangements showed the highest effectiveness but had limited adoption due to cost, stigma, and infrastructure gaps. Digital mental health tools, though cost-effective, had low uptake (23%) due to digital illiteracy. Barriers included cultural stigma, weak labor policies, leadership apathy, and lack of ROI measurement. Promising strategies identified include faith-based EAPs, peer networks, mobile clinics, and stigma-reduction campaigns, particularly when culturally embedded and supported by community leaders. Conclusions: Workplace stress in Nigeria is a systemic challenge rooted in socio-economic, cultural, and organizational structures. Although several HRM interventions show promise, their effectiveness is hindered by low adoption, poor contextual fit, and limited legal enforcement. Evidence suggests that when mental health strategies are localized and culturally endorsed via faith leaders, digital tools, or flexible work, they yield improved employee retention, lower absenteeism, and better organizational resilience.
