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JMIR Preprints
A preprint server for pre-publication/pre-peer-review preprints intended for community review as well as ahead-of-print (accepted) manuscripts
Background: Limited public understanding of randomized controlled trials (RCTs) hinders recruitment, retention, and confidence in research. Early exposure to trial concepts may strengthen health literacy and research engagement. The Kid’s Trial was a global, decentralized, child-led study that co-created and conducted an RCT to help children understand trials, their importance, and improve critical thinking. Objective: This paper presents its design, outcomes, and methodological reflections. Methods: The Kid’s Trial employed a dedicated website with study materials guiding children through each step of designing and conducting an RCT. Each step was linked to an online survey. Materials were co-developed with two patient and public involvement groups of children and parents. Any child, aged 7 to 12 years, could take part in as many or as few steps as desired. Recruitment combined online and offline strategies, and engagement and self-reported learning were descriptively analyzed.
The co-created REST (Randomized Evaluation of Sleeping with a Toy or Comfort Item) trial was a two-arm, pragmatic RCT comparing one week of sleeping with versus without a comfort item. The primary outcome was sleep-related impairment, and the secondary outcome was overall sleep quality. Analyses followed an intention-to-treat approach using mixed-effects models adjusted for baseline measures. Results: Overall, 224 children from 15 countries participated in at least one step. Participation varied: 37% (n = 82) completed one step and 21% (n = 48) completed six. The REST trial randomized 139 children, with 73% (n = 101) completing outcome surveys. Adjusted mean differences (intervention–control) were −0.53 for sleep-related impairment (95% CI −3.40 to 2.34; P=.71) and 0.28 for sleep quality (95% CI 0.01 to 0.55; P=.04), a small, uncertain difference not supported with sensitivity analyses. Post-study responses (n = 20) indicated improved understanding of RCT concepts. Conclusions: The Kid’s Trial demonstrates the feasibility of a decentralized, child-led RCT co-created through participatory citizen-science methods. Children can meaningfully contribute to trial design and conduct, and experiential participation may foster early trial literacy and critical thinking. Future studies should enhance engagement through community partnerships, shorter intervals between steps, and embedded learning assessments to improve inclusivity and retention.
Journal Description
JMIR Preprintscontains pre-publication/pre-peer-review preprints intended for community review (FAQ: What are Preprints?). For a list of all preprints under public review click here. The NIH and other organizations and societies encourage investigators to use interim research products, such as preprints, to speed the dissemination and enhance the rigor of their work. JMIR Publications facilitates this by allowing its authors to expose submitted manuscripts on its preprint server with a simple checkbox when submitting an article, and the preprint server is also open for non-JMIR authors.
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Background: Limited public understanding of randomized controlled trials (RCTs) hinders recruitment, retention, and confidence in research. Early exposure to trial concepts may strengthen health liter...
Background: Limited public understanding of randomized controlled trials (RCTs) hinders recruitment, retention, and confidence in research. Early exposure to trial concepts may strengthen health literacy and research engagement. The Kid’s Trial was a global, decentralized, child-led study that co-created and conducted an RCT to help children understand trials, their importance, and improve critical thinking. Objective: This paper presents its design, outcomes, and methodological reflections. Methods: The Kid’s Trial employed a dedicated website with study materials guiding children through each step of designing and conducting an RCT. Each step was linked to an online survey. Materials were co-developed with two patient and public involvement groups of children and parents. Any child, aged 7 to 12 years, could take part in as many or as few steps as desired. Recruitment combined online and offline strategies, and engagement and self-reported learning were descriptively analyzed.
The co-created REST (Randomized Evaluation of Sleeping with a Toy or Comfort Item) trial was a two-arm, pragmatic RCT comparing one week of sleeping with versus without a comfort item. The primary outcome was sleep-related impairment, and the secondary outcome was overall sleep quality. Analyses followed an intention-to-treat approach using mixed-effects models adjusted for baseline measures. Results: Overall, 224 children from 15 countries participated in at least one step. Participation varied: 37% (n = 82) completed one step and 21% (n = 48) completed six. The REST trial randomized 139 children, with 73% (n = 101) completing outcome surveys. Adjusted mean differences (intervention–control) were −0.53 for sleep-related impairment (95% CI −3.40 to 2.34; P=.71) and 0.28 for sleep quality (95% CI 0.01 to 0.55; P=.04), a small, uncertain difference not supported with sensitivity analyses. Post-study responses (n = 20) indicated improved understanding of RCT concepts. Conclusions: The Kid’s Trial demonstrates the feasibility of a decentralized, child-led RCT co-created through participatory citizen-science methods. Children can meaningfully contribute to trial design and conduct, and experiential participation may foster early trial literacy and critical thinking. Future studies should enhance engagement through community partnerships, shorter intervals between steps, and embedded learning assessments to improve inclusivity and retention.
Background: The health benefits of breastfeeding for both infants and parents are well-established, yet global breastfeeding rates remain below recommended levels. Parent-targeted Digital Health Inter...
Background: The health benefits of breastfeeding for both infants and parents are well-established, yet global breastfeeding rates remain below recommended levels. Parent-targeted Digital Health Interventions (DHIs), including mobile health (mHealth) and electronic health (eHealth) strategies, offer a scalable way to support breastfeeding, but their effectiveness remains uncertain. Objective: To explore the effectiveness of parent-targeted DHIs for improving breastfeeding outcomes. Methods: Seven databases (CENTRAL, CINAHL, Education Research Complete, Embase, MEDLINE, PsycINFO and Scopus) were searched on April 15, 2024, for randomised controlled trials (RCTs) involving parents of children aged under five years. Eligible interventions aimed to promote breastfeeding and were primarily delivered via digital platforms (e.g. mobile apps, text messaging and websites). Studies were excluded if the DHI exclusively targeted breastfeeding within clinical settings or focused on non-digital content. Outcomes of interest included exclusive breastfeeding, any breastfeeding, breastfeeding duration, breastfeeding self-efficacy, cost-effectiveness and adverse events. Risk of bias of the primary outcome was assessed using the Cochrane Risk of Bias 2 (RoB2) tool. Meta-analyses were conducted in accordance with Cochrane methods and result are reported following PRISMA guidelines. Results: Thirty-one (29 RCTs and 2 cluster-RCT) studies, including 14776 participants from 17 diverse countries were included. Nineteen of the interventions focused on mHealth strategies, nine were delivered online and five were telecommunication interventions. Risk of bias was indicated with ‘some concerns’ or ‘high risk’ for 26 (84%) studies. Pooled results indicated that DHIs can significantly improve the odds of exclusive breastfeeding (OR: 2.35, 95% CI: 1.71 to 3.23, I2=81%; 26 trials, 9884 participants), however considerable heterogeneity was present. Pooled results also indicated DHIs may improve breastfeeding duration (SMD: 0.50, 95% CI: 0.30 to 0.69, I2=15%, 5 trials, 601 participants), and ‘any’ breastfeeding (OR: 1.16, 95% CI: 0.99 to 1.35, I2=7%, 14 trials, 7974 participants). Conclusions: Improvements to exclusive breastfeeding rates and breastfeeding duration are linked to major societal and health benefits for infants and mothers. Our results indicate that parent-targeted DHIs are effective for improving key breastfeeding behaviours, with evidence of their impact spanning diverse populations and contexts. Clinical Trial: PROSPERO (CRD42023492644)
Background: Randomized controlled trials (RCTs) comparing video-based psychotherapy (VBT) and face-to-face therapy (F2F) show considerable methodological heterogeneity, limiting the interpretability o...
Background: Randomized controlled trials (RCTs) comparing video-based psychotherapy (VBT) and face-to-face therapy (F2F) show considerable methodological heterogeneity, limiting the interpretability of findings regarding comparative efficacy. Objective: The objective of this systematic review is to compare VBT and F2F in terms of symptom reduction with strict methodological inclusion criteria, especially regarding the therapeutic setting and the duration of psychotherapy. Methods: PubMed, Embase and PsycInfo were systematically searched for RCTs comparing synchronous VBT and F2F exceeding 500 minutes in total. Primary outcome was post-treatment symptom severity. PRISMA criteria were followed. A three-level meta-analysis was conducted to analyze multiple outcomes per study. Risk of bias was assessed following Metapsy guidelines for psychological intervention trials. Results: Out of 9,446 records screened, 86 articles underwent full-text review; 11 RCTs (n = 858; mean age = 38.47 years; 49.3% female) met the inclusion criteria. Diagnoses included post-traumatic stress disorder, depression, obsessive-compulsive disorder, bulimia nervosa, generalized anxiety disorder, and somatoform pain. Across 36 outcomes, no significant differences in symptom reduction emerged between VBT and F2F (Hedges’ g = -0.07; 95% CI [-0.53, 0.40]; SE = 0.21; p = .76). No moderating effects were detected. Information criteria favored the three-level model over conventional approaches. Conclusions: The findings indicated that there were no significant differences between VBT and F2F. These results suggest that VBT is a viable method for delivering psychotherapy for symptom reduction. Future research should focus on the effectiveness of VBT in long-term treatment and the contextual and cultural factors that may influence it. Clinical Trial: DOI: 10.17605/OSF.IO/ZN8Q5
Background: Physical activity, sleep, and sedentary behaviour are essential components within the 24-h time frame. A scientific understanding of 24-hour movement behaviours is a crucial prerequisite f...
Background: Physical activity, sleep, and sedentary behaviour are essential components within the 24-h time frame. A scientific understanding of 24-hour movement behaviours is a crucial prerequisite for formulating targeted intervention guidelines and programs. Objective: The objective of this study was to map the current state of research and fill the gaps in 24-h movement behaviours among Chinese children and adolescents. Methods: Web of Science, PubMed, EBSCO, and CNKI (China National Knowledge Infrastructure) were systematically searched for relevant studies published between January 2019 and October 2025. The study followed the PRISMA guidelines, and the literature screening process involved three rounds: duplicate removal, title and abstract screening, and full-text screening. Inclusion criteria: Targeting Chinese children and/or adolescents aged 3–18 years; Focusing on 24-h movement behaviours; Published in Chinese or English; Published between January 2019 and November 2025. Data extraction included: title, author, year, country, study type, research design, adherence to guideline, sample characteristics, and research results. Results: 92 studies were included in this scoping review. All the included studies were published between 2019 and 2025, showing a generally increasing trend over the years. The review included 817,482 participants aged 3–18 years, predominantly from the general population. Geographically, Shanghai and Guangdong were core regions, while the underdeveloped central and western regions had extremely low representation. Most studies used a cross-sectional design (80), with few longitudinal or intervention ones. For monitoring, device-based tools dominated sedentary behaviours/screen time assessment (31), questionnaires were the primary tool for physical activity (40), and public data utilization was low. Research variables centered on mental health, covering physical fitness, social interaction, body composition, etc. 52 studies showed average compliance rates of 22.34% (Moderate-to-Vigorous Physical Activity, MVPA), 45.35% (screen time), 37.77% (sleep), and only 8.39% for all three. Conclusions: Research on 24-h movement behaviours among Chinese children and adolescents faces multiple challenges, including an uneven geographic distribution of samples, limited diversity in monitoring methods, and low compliance with relevant guidelines. Most studies adopt a cross-sectional design, with few prospective cohort studies and intervention experimental studies. Additionally, there is no guidelines for 24-h movement behaviours tailored to Chinese children and adolescents.
Background: Stroke is a leading cause of death and disability worldwide, with particularly high prevalence and mortality in Chinese populations. While biomedical approaches to stroke risk reduction ar...
Background: Stroke is a leading cause of death and disability worldwide, with particularly high prevalence and mortality in Chinese populations. While biomedical approaches to stroke risk reduction are well established, culturally grounded practices such as Shi Liao (Chinese food therapy) remain understudied despite their longstanding use in Traditional Chinese Medicine (TCM). Objective: The purpose of this scoping review was to map existing evidence of Shi Liao and its relationship to biomedical and lifestyle stroke risk factors among Chinese populations. Methods: Following Joanna Briggs Institute guidelines, we searched MEDLINE, CINAHL, and Web of Science (1966-2025), supplemented with citation chasing and Google Scholar searches. Eligible studies included quantitative, qualitative, or mixed methods, which examined adults of Chinese descent who practiced Shi Liao and its relationship to diet and stroke risks. Data were extracted and synthesized using a scoping review methodology. Results: Six studies published between 2010 and 2024 were included, comprising randomized signs included randomized controlled trials (n=3), mixed methods (n=1), cross-sectional (n=1), and a quality improvement project (n=1). Across these studies, Shi Liao served either as the primary intervention or as the guiding framework for dietary education and self-practice. Its application varied from structured clinical programs to culturally tailored nutrition curricula and constitution-based self-management, but each incorporated core TCM components such as body constitution assessment, thermal and moisture energies, and seasonal food selection. Collectively, the studies reported preliminary evidence of reduced blood pressure, improved glycemic control, healthier dietary behaviors, and high cultural acceptability. However, all were limited by small sample sizes, inconsistent operational definitions of Shi Liao, and sparse reporting of stroke-specific outcomes. Conclusions: Shi Liao represents a culturally congruent dietary practice with potential to reduce stroke risks and improve health behaviors among Chinese populations. While preliminary findings suggest Shi Liao may support stroke risk reduction, the available evidence remains methodologically limited, characterized by small sample sizes, short intervention durations, and inconsistent operationalization of Shi Liao across studies. Future research should standardize definitions, conduct larger clinical trials, and examine long-term impacts to inform integration of Shi Liao into culturally tailored stroke prevention strategies. Clinical Trial: N/A
Background: Postoperative follow-up after brain tumor surgery is typically limited to intermittent clinic visits, leaving subtle neurological or general deterioration between visits underrecognized. D...
Background: Postoperative follow-up after brain tumor surgery is typically limited to intermittent clinic visits, leaving subtle neurological or general deterioration between visits underrecognized. Digital self-monitoring platforms may help fill this gap, but evidence in neuro-oncology is scarce, particularly regarding how patient-reported symptom trajectories can feed into future artificial intelligence (AI)–driven early warning systems. Objective: To evaluate the feasibility, usage patterns, and preliminary usability of a smartphone/web-based self-monitoring system for patients after brain tumor surgery, and to explore simple rule-based digital alerts as a first step toward an AI-based early warning framework. Methods: We conducted a single-center prospective pilot study including adults discharged after brain tumor surgery who had access to a smartphone and could use a web app. Participants completed brief symptom surveys consisting of 51 binary items across seven symptom domains, with an automatically calculated daily total score and score-history visualization. Feasibility was assessed by enrollment, retention, submission counts, and submission rates. Four interpretable alert rules based on current score, short-term worsening, new-onset symptom combinations, and persistence across domains were evaluated using each patient’s last three submissions as the analytic unit. Clinical deterioration was defined a priori as objective decline in performance status, new neurological deficit, radiologic progression, or clinically significant laboratory changes. Rule performance metrics and bootstrap confidence intervals were computed. Usability and acceptability were evaluated using the System Usability Scale (SUS) and additional adherence-related items. Results: Of 64 enrolled patients, 30 with ≥3 submissions formed the analysis cohort (median age 57 years; 43% malignant tumors); six (20%) experienced clinical deterioration during follow-up. Patients contributed a median of 8.5 submissions (mean 19.0) at 1.7 surveys/week on average, indicating sustained but heterogeneous engagement. The best-performing rule, based on net short-term score increase, achieved an AUROC of 0.88 with sensitivity 0.83, specificity 0.92, and accuracy 0.90 on the last-window dataset, outperforming rules based solely on current score or multi-domain persistence. Among 23 app users who completed the SUS, the mean score was 84.0, reflecting high perceived usability; higher-frequency users reported stronger perceived usefulness and habit-driven use. Conclusions: This pilot study demonstrates that a smartphone/web-based self-monitoring platform for brain tumor patients is feasible and well accepted, and that simple, transparent rules applied to longitudinal symptom scores can capture early signals of clinical deterioration. These findings support further development of integrated, AI-assisted digital early warning systems that combine patient-reported trajectories with clinical and physiological data to enhance postoperative neurosurgical care.
Background: Managing Type 1 Diabetes (T1D) requires regular glucose monitoring and appropriate insulin dose adjustments. Although the use of continuous glucose monitoring (CGM) sensors has been benefi...
Background: Managing Type 1 Diabetes (T1D) requires regular glucose monitoring and appropriate insulin dose adjustments. Although the use of continuous glucose monitoring (CGM) sensors has been beneficial, there are still inherent delays in CGM measurements and insulin onset of action, making accurate glucose prediction essential. Smartphones can collect Global Positioning System (GPS) data that can be converted into location categories (e.g., “gym,” “cafe,” “restaurant”), which provide information about a person’s location and offer insight into their behavior, and both location and behavior may influence blood glucose levels. Objective: This systematic review aims to evaluate existing research on the use of location category data as a predictor of blood glucose fluctuations in individuals with diabetes. It explores whether such data have been used to identify location categories where people with diabetes are more likely to be out of range, potentially supporting timely corrective actions. Methods: The systematic review was conducted following PRISMA guidelines, identifying studies examining the use of semantic or geographic location category data for blood glucose prediction in individuals with diabetes. Eligible studies were analyzed for the location category data used, predictive modeling approaches, and outcome measures. Results: 665 screened studies, only three met the inclusion criteria. All were from a single research project involving 40 individuals with Type 2 Diabetes (T2D), monitored over a period of 3 days. These studies utilized geographic and temporal data but did not classify places by location category. No studies investigated the use of the location category in the context of T1D. Conclusions: No studies have used location categories to predict blood glucose levels in individuals with T1D. Limited research in T2D has incorporated GPS data, but without identifying specific place types such as restaurants, gyms, or workplaces. In contrast, mental health research has effectively applied location-based methods to predict stress, anxiety, and depression, showing that the places people visit and the time they spend there reflect important behavioral patterns. Because diabetes management also relies on daily behaviors such as eating, physical activity, and routine, applying these methods from mental health research may provide new insights into how specific locations influence blood glucose variability and support more timely, personalized diabetes management strategies.
Background: Digital healthcare technologies, including mobile applications and telemedicine platforms, have transformed how medical professionals communicate and deliver care. Remote consultation betw...
Background: Digital healthcare technologies, including mobile applications and telemedicine platforms, have transformed how medical professionals communicate and deliver care. Remote consultation between medical providers and specialists plays a vital role in ensuring access to appropriate expertise, particularly in medically underserved or geographically remote areas. However, the diversity in technological modalities, devices, and patterns of use across specialties and regions has not been systematically mapped. Objective: This scoping review aimed to explore the current status and characteristics of teleconsultations among medical providers and specialists, focusing on device use, consultation modalities, clinical specialties, and regional differences. Through this approach, we aimed to provide a comprehensive overview of technological and practical trends in mobile health (mHealth) and telemedicine. Methods: A systematic scoping review was conducted in accordance with PRISMA-ScR guidelines using the MEDLINE and Embase databases. The search covered studies published up to March 2024, with no restrictions on the publication year. Studies meeting the predefined inclusion criteria were also included. Results: In total, 113 citations were screened, of which 79 articles were included. Studies were analyzed according to consultation method, target, and regional characteristics. Of these, 83.5% were in the doctor-to-doctor category. E-mail, videoconference, and app-based consultations were the most common, with videoconference use decreasing and app use increasing annually. Approximately 90% of the studies used medical images, most frequently photographs. Orthopedics and dermatology were the most frequently involved specialties, followed by internal medicine. Regarding regions, 58.2% of consultations were domestic and 41.8% were international. Rural-to-urban domestic consultations comprised 45.7%, whereas consultations from low- and middle-income countries (LMICs) and high-income countries (HICs) accounted for 30.3%. Conclusions: This review examined doctor-to-doctor and doctor-to-patient consultations with doctor involvement. Specialties in which medical images are central, such as orthopedics and dermatology, were more frequently represented than in other fields. This highlights disparities in the use of teleconsultation across clinical disciplines and suggests that addressing these imbalances is essential for broader adoption. Furthermore, the findings indicated a progressive shift from videoconference-based interactions to mobile and app-based platforms, reflecting ongoing technological advancements. Optimizing the integration of these digital tools and promoting equitable access are critical for enhancing the quality and reach of teleconsultation practices in future digital health systems. Clinical Trial: Not applicable. This study is a scoping review and does not involve a clinical trial. This review has been registered in the Open Science Framework Registry (https://doi.org/10.17605/OSF.IO/K4TVU).
Using 2022-2025 data for Japan’s 47 prefectures, we test whether Google Trends indicators improves stacked-ensemble predictions of prefectural subjective well-being....
Using 2022-2025 data for Japan’s 47 prefectures, we test whether Google Trends indicators improves stacked-ensemble predictions of prefectural subjective well-being.
Background: Artificial Intelligence (AI) is increasingly integrated into healthcare, with potential to enhance disease diagnosis, treatment, and patient outcomes. However, successful adoption relies o...
Background: Artificial Intelligence (AI) is increasingly integrated into healthcare, with potential to enhance disease diagnosis, treatment, and patient outcomes. However, successful adoption relies on healthcare providers’ preparedness and trust. Objective: To evaluate French healthcare professionals’ and students’ use, concerns, and perceptions of AI, and to assess their interest in AI-related training. Methods: We conducted a cross-sectional national survey distributed via PulseLife between December 2023 and March 2025. The 12-item questionnaire assessed demographics, AI usage, confidence, perceived benefits, concerns, and training needs. Reliability and validity of the instrument were assessed using Cronbach α, exploratory and confirmatory factor analysis. Descriptive statistics and chi-squared test were performed using R (version 4.3.1). Results: A total of 1625 healthcare respondents participated, including 1212 professionals (52.9% physicians, 19.1% nurses) and 413 students. Only 6.6% reported prior AI training, while 78.3% expressed interest in receiving training. Physicians showed the highest confidence in AI (P = .003). Main concerns included algorithmic bias (48.2%), data transparency (40.9%), and deterioration of the doctor–patient relationship (38.6%). Anticipated benefits included improved diagnosis (47.6%), time saving (42.1%), reduced medical errors (39%). Conclusions: French healthcare providers and students remain insufficiently trained in AI, despite strong interest in acquiring such skills. Structured AI training programs and transparent regulatory frameworks are urgently needed to facilitate responsible adoption of AI in healthcare.
Background: Smart home technologies integrated with Technology-Enhanced Healthcare (TEH) systems are transforming residential care by supporting independent living, continuous health monitoring, and r...
Background: Smart home technologies integrated with Technology-Enhanced Healthcare (TEH) systems are transforming residential care by supporting independent living, continuous health monitoring, and remote clinical interventions. The Internet of Medical Things (IoMT), wearable biosensors, and AI-driven analytics enable proactive healthcare delivery and personalized interventions, particularly for older adults and individuals with chronic conditions. Objective: This review synthesizes current literature on TEH integration within smart homes, examining global deployment patterns, technological maturity, biomedical sensor integration, machine learning applications and health outcomes. It also identifies implementation challenges and disparities to improve digital healthcare strategies. Methods: A systematic search was conducted across PubMed, Scopus, Web of Science, ScienceDirect, Google Scholar, and IEEE Xplore for peer-reviewed studies published between January 2005 and February 2025. Following screening of 6,047 records, 119 studies were included, covering experimental, qualitative, and system design methodologies. Data were extracted on geographic deployment, sensor types, TEH architectures, machine learning algorithms, clinical outcomes, and adoption barriers. Results: TEH adoption is concentrated in Europe, East and Southeast Asia, and higher income countries, with potential emerging initiatives in West Asia in lower income regions. Smart home maturity ranges from foundational systems with basic automation to connected ecosystems with centralized IoT coordination and intelligent systems with data driven adaptive monitoring). Integration of biomedical sensors—wearable ECG, SpO₂, EEG, glucose monitors, smart rings, environmental sensors, and radar-based devices—enables continuous monitoring of cardiovascular, respiratory, neurological, metabolic, and mobility parameters. Machine learning and AI algorithms can support early disease detection, predictive health analytics, activity recognition, and personalized interventions. Evidence indicates remote monitoring improves early detection of health issues, chronic disease management, medication adherence, and psychological wellbeing. A representative case study in Australia demonstrated that remote TEH monitoring of 100 patients over 276 days led to a 46.3% reduction in predicted healthcare expenditure, 53.2% reduction in predicted hospital admissions, and 67.9% reduction in length of stay compared with 137 matched controls. Adoption barriers include interoperability challenges, data privacy, digital literacy gaps, social and economic disparities, and long-term sustainability concerns. Conclusions: TEH integration in smart homes enhances independent living, preventive care, and personalized health management while reducing hospitalizations and healthcare costs. Widespread implementation requires standardized evaluation frameworks, robust interoperability, adaptable design, equitable access, and clinical friendly. By addressing technical, social, and regulatory challenges, TEH-smart home systems can achieve scalable, sustainable, and effective digital healthcare delivery.
Background: Child maltreatment is a major public health concern with long-term neurobiological and psychosocial consequences. The detection and reporting of suspected cases often remain fragmented, wi...
Background: Child maltreatment is a major public health concern with long-term neurobiological and psychosocial consequences. The detection and reporting of suspected cases often remain fragmented, with significant variability across services and the absence of a unified surveillance system. Pediatricians also lack adequate digital tools and specialized training to support timely recognition and documentation. Although international evidence shows that integrated digital registries and structured educational programs enhance early identification and interprofessional coordination, no comparable model has yet been systematically implemented in Italy. The Sentinel project was developed to address these gaps through the combined introduction of a REDCap-based digital registry and a structured training program for pediatric healthcare professionals. Objective: This study aims to evaluate the usability, feasibility, and preliminary impact of an integrated surveillance and training system designed to improve the early detection, documentation, and reporting of suspected child maltreatment by pediatricians and healthcare professionals. Methods: This observational, exploratory, monocentric study will span 24 months and involve hospital and community pediatricians who voluntarily enroll and provide informed consent. The project includes two interconnected components: (1) the development and implementation of a secure, anonymized digital registry for standardized data collection on suspected maltreatment, and (2) a theoretical–practical training program delivered through lectures, e-learning modules, webinars, and hands-on sessions. Usability will be assessed using the System Usability Scale (SUS). Training effectiveness will be evaluated through pre–post knowledge tests, competency assessments, and qualitative feedback. Statistical analyses will include descriptive statistics, paired-sample tests, Poisson or negative binomial regression for changes in reporting rates, and multivariable models to identify predictors of training outcomes and registry usability. Results: We expect high usability of the digital registry, with mean SUS scores exceeding 80. Reporting rates of suspected maltreatment are anticipated to increase markedly following implementation. Training is expected to result in substantial improvements in knowledge, competencies, and satisfaction, enhancing professionals’ capacity to recognize and manage suspected maltreatment. The integrated system is expected to improve reporting completeness, timeliness, and interprofessional coordination. Conclusions: The Sentinel project is expected to validate an innovative, scalable model that integrates digital surveillance with structured training to enhance early detection and management of child maltreatment. By standardizing data collection, strengthening professional competencies, and fostering collaboration across hospital and community settings, the project aims to support the development of a regional or national observatory and promote an evidence-based, system-wide cultural shift in child protection. Clinical Trial: ClinicalTrials.gov Identifier: NCT07250074
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.
Background: Residents in subspecialty units, such as pediatric endocrinology, are provided with several learning opportunities that encompass theoretical and practical knowledge, skills, and attitudes...
Background: Residents in subspecialty units, such as pediatric endocrinology, are provided with several learning opportunities that encompass theoretical and practical knowledge, skills, and attitudes. However, due to the often short-term residencies, not all clinical situations are experienced. Medical education research indicates that e-learning presents an excellent complementary opportunity to address this gap. We developed a unique blended learning teaching approach that includes structured case-based e-learning courses based on Kolb’s learning cycle model. Objective: To evaluate the utility and usability of blended learning with a novel e-learning tool. Methods: We employed a problem-solving approach and utilized the physical separation of case-based e-learning and theoretical content delivery as the educational model for residents in an pediatric endocrinology and diabetology unit. Residents worked asynchronously on clinical scenarios and complete formative assessments with immediate feedback (flipped class teaching method). In addition, all cases could be discussed with the specialists during the face-to-face learning opportunities (blended learning). We evaluated Kirkpatrick’s level 1 (Reaction) and level 2 (Learning) outcomes using the postgraduate Medical E-learning Evaluation Survey (MEES) User Experience Questionnaire (UEQ). Results: Questionnaires from 12 pediatric residents and one questionnaire from a 4th-year medical student were evaluated. The main strengths in MEES were the ability to apply translatable content to daily real-world work (12/13 users), providing summaries when needed (9/13 users), offering accessible sources of information (9/13 users), and feedback on answers (8/13 users). The main weaknesses concerned the devices used for e-learning (5/13 users), the personalization of the e-learning (4/13 users), and the presence of an instrument to help navigate the e-learning (4/13 users). No significant problems or defaults were reported. According to the UEQ results, dependability is the least well-evaluated category, whereas attractiveness and stimulation are the best-evaluated categories. Conclusions: Our e-learning proposal provides a practical approach to applying theoretical knowledge through interactive clinical cases. The evaluations show that users are highly motivated for e-learning, highlighting the adaptability and effectiveness of our tool for medical postgraduate education in pediatric endocrinology. The identification of strengths and weaknesses in our e-learning will guide us in adapting and improving the tool. We believe that evaluating the various aspects of e-learning is crucial, as these elements can significantly impact learning outcomes.
Background: While technology can widen access to mental health treatments, digital mental health interventions (DMHIs) frequently have low engagement and high dropout rates. Better understanding user...
Background: While technology can widen access to mental health treatments, digital mental health interventions (DMHIs) frequently have low engagement and high dropout rates. Better understanding user engagement with DMHIs can help researchers design technologies that users are more likely to benefit from. However, a major challenge is that the term “engagement” is very broad, not well-understood, and operationalized differently in different projects. Different communities, such as Behavioral Science and Human-Computer Interaction, have different perspectives on user engagement for DMHIs, which have led to challenges when designing for engagement. Objective: This study investigated clinical researchers’ views of user engagement when designing DMHIs. Methods: We conducted qualitative semi-structured interviews with 12 clinical mental health researchers who have developed DMHIs using Human-Centered Design (HCD) methods. Results: We identified different user engagement dimensions for DMHIs: digital mental health components (i.e., intervention, technology, and human support); levels of engagement (micro and macro); and visibility of the engagement (visible and invisible). We also describe the challenges of designing DMHIs for engagement. Conclusions: Our study highlights how clinical researchers operationalize engagement by focusing on macro-engagement activities but, when measuring engagement, primarily measure micro-engagement activities. Furthermore, to appropriately capture engagement, we need to include more qualitative methods to complement other measurement methods.
Background: Black youth with HIV (BYWH) endure higher rates of Post-Traumatic Stress Disorder, compared with White youth or youth without HIV. While trauma-informed approaches have been associated wit...
Background: Black youth with HIV (BYWH) endure higher rates of Post-Traumatic Stress Disorder, compared with White youth or youth without HIV. While trauma-informed approaches have been associated with improvements in health outcomes among marginalized communities, research infrequently includes cross-cutting practices for disrupting trauma driving care disengagement among BYWH. Greater attention is needed on methods for promoting inclusivity in HIV care spaces—such as building trust and safety by honoring patients’ values, beliefs, customs, and preferences—to mitigate psychological harm. Objective: In this study, we sought to assess a youth-focused HIV clinic’s capacity for providing trauma-sensitive, inclusive practices. Methods: A semi-structured interview guide was prepared via community-engaged discussions to evince implementation determinants relative to trauma-informed care with a specific focus on cross-cutting practices for disrupting trauma and promoting inclusivity. Personnel of the HIV clinic were invited to participate in one-on-one interviews, which were audio recorded, transcribed verbatim, and coded inductively via de novo themes and thematically via the Consolidated Framework for Implementation Research (CFIR). Results: Twenty clinic personnel participated, with 90% cisgender female, 40% (8) Black, and 40% (8) White, with a mean age of 46.58 (SD= 11.40) and length of employment 12 years (SD= 11.82). Three themes emerged: 1) Current efforts to promote inclusivity, which included staff attitudes (e.g., desires/ commitments), behaviors (e.g., practice changes to assure anonymity of diagnosis), conditions (e.g., human-equity centered culture) and practices (e.g., promotion of patient choice, demonstrated allyship, prioritization of hiring staff attuned to intersectional stigma, removal of HIV visibility, and normalization of HIV. Organizational-level identity campaigns and ally-ship based groups were also discussed. 2) Efforts needed to address barriers to inclusivity, which included general barriers and those related to perceived stigma and bias, and 3) Facilitators to implementing further inclusive practices, which included CFIR domains of staff retention, collaboration, and communication and external resources. Conclusions: Findings indicated that while many conditions and practices exist, additional efforts are needed to promote inclusivity. Results contribute to the growing literature demonstrating explicitly how inclusivity is crucial to fostering a trauma-informed culture. Future interventions should address stigma and bias as barriers to the promotion and practice of inclusivity.
Background: Axia is a digital therapeutic (DTx) that provides a disease-specific and patient-tailored home-based exercise program for axial spondyloarthritis patients (axSpA). The app combines structu...
Background: Axia is a digital therapeutic (DTx) that provides a disease-specific and patient-tailored home-based exercise program for axial spondyloarthritis patients (axSpA). The app combines structured exercise with patient education and disease-management features and has a strong emphasis on gamification and behavioral conditioning to maximize long-term adherence. However, it has remained unclear how Axia is perceived and evaluated by patients Objective: This study aimed to describe the specific gamification elements implemented in Axia and evaluate their impact on perceived app quality, user experience, and likelihood of recommending the app to others after 12 weeks of use. Methods: This single-center study was conducted at an outpatient rheumatology clinic. After 12 weeks of ad libitum app use, 27 participants with axSpA completed paper-based assessments of the user version of the Mobile Application Rating Scale (uMARS, range 0-5) and the Net Promoter Score (NPS, range -100% - 100%). Results: The mean overall uMARS app quality score was 4.44 (SD 0.43). Functionality received the highest mean score (4.54, SD 0.53), while information received the lowest (4.34, SD 0.49). The mean NPS rating was 9.52 (SD 0.75), corresponding to an overall NPS of 92.6%. The uMARS overall quality score correlated strongly with the NPS (ρ=0.57, p=0.002). No associations were found between age or sex and either uMARS scores or NPS. Conclusions: The mean overall uMARS app quality score was 4.44 (SD 0.43). Functionality received the highest mean score (4.54, SD 0.53), while information received the lowest (4.34, SD 0.49). The mean NPS rating was 9.52 (SD 0.75), corresponding to an overall NPS of 92.6%. The uMARS overall quality score correlated strongly with the NPS (ρ=0.57, p=0.002). No associations were found between age or sex and either uMARS scores or NPS. Clinical Trial: DRKS-ID: DRKS00038067
Background: Generative artificial intelligence (GenAI) is rapidly entering mental health care, supporting both client-facing tools (e.g., chatbots for support and self-management) and clinician-facing...
Background: Generative artificial intelligence (GenAI) is rapidly entering mental health care, supporting both client-facing tools (e.g., chatbots for support and self-management) and clinician-facing systems (e.g., documentation and assessment aids). Whether these tools ultimately help or harm psychotherapists and their clients depends not only on their technical performance but on how psychotherapists trust and distrust them in practice—that is, when they are willing to rely on GenAI, when they withhold reliance, and how they manage clients’ own GenAI use. Understanding how psychotherapists negotiate their trust and distrust is essential for future responsible and ethical integration of GenAI in mental health care, where GenAI’s promising benefits, such as reducing administration burden or enhancing client’ accessibility, must be balanced against risk that requires professional judgement rather than blanket adoption or rejection. Yet little empirical work has examined how practicing psychotherapists actively calibrate trust and distrust in GenAI across tasks and contexts, or how these judgments shape the evolving psychotherapist–client–GenAI relationship. Objective: This study aims to examine (1) what are psychotherapists' experiences with, perceptions of, and trust/distrust in GenAI in therapeutic contexts? and (2) how do psychotherapists perceive the role of GenAI within the therapeutic relationship, and how do their perceptions shape their trust and distrust in GenAI? Methods: Between January 2025 and May 2025, we conducted an interview study with 18 psychotherapists in the United States. Psychotherapists were recruited. Results: Our findings show that psychotherapists' adoption of GenAI was highly individualized and underpinned by “conditional'' trust—confidence that depended on maintaining professional control, aligning GenAI use with specific tasks, and considering who was using the GenAI tools. Trust was sustained when GenAI operated in clinician-supervised, supportive roles, but diminished when control shifted, tasks became high-stakes, or GenAI appeared to encroach on the therapeutic relationship (e.g., forming emotional bonds with clients or replacing core psychotherapist functions). Additionally, participants also voiced distrust towards the broader sociotechnical ecosystem, including developers, commercial incentives, and the absence of clear organizational guidelines. Conclusions: Psychotherapists’ perspectives offer critical insights into GenAI's current usages in their professional practices and the conditions under which they are willing to trust and distrust GenAI tools. Their experiences highlight the importance of maintaining clinician control, ensuring contextual appropriateness, and preserving the human connection central to psychotherapy. Future work should further examine how therapeutic orientation, professional experience, and client characteristics shape trust and distrust in GenAI. As GenAI becomes more embedded in mental-health care, research is also needed to explore how specific GenAI system features can be responsibly designed to support clinical workflows and enhance therapeutic relationships. Organizational and policy frameworks will be essential to ensure responsible, ethically aligned, and human-centered GenAI deployment in psychotherapy.
Background: Liver cirrhosis leads to an impaired liver function with reduced metabolisation capacity which affects the pharmacokinetics of several drugs requiring dose adjustments. Although limited li...
Background: Liver cirrhosis leads to an impaired liver function with reduced metabolisation capacity which affects the pharmacokinetics of several drugs requiring dose adjustments. Although limited literature provides guidance on appropriate administration of drugs in cirrhosis, no guidelines currently existfor dose selection or adjustment. Objective: The objective of this study is to provide guidance on the selection, dosing and appropriate use of drugs in patients with liver cirrhosis and to evaluate the clinical application of these recommendations. Methods: Three steps are planned to establish dose recommendations for patients with liver cirrhosis: (1) A systematic literature search will be conducted to identify specific recommendations for drug selection and dosing in cirrhosis and assessed for reporting quality and evidence level. Subsequently, the resulting recommendations will be undergo an internal pre-assessment procedure for relevance of the covered drugs with regard to availability, the clinical impact of adverse drug reactions, the frequency of use and the expected benefit of dose adjustment. (2) A modified Delphi procedure will be conducted to (a) analyze the clinical handling of the identified drugs by experts in clinical practice in a first round and (b) harmonize differing dose recommendations in a second round. (3) Finally, the adopted dose recommendations will be implemented in a clinical study involving patients with liver cirrhosis to analyze their impact on the patients´ safety. Results: The study has been registered in the German Registry of Clinical Trials (DRKS00033779), and the planned clinical validation phase is currently underway. Conclusions: This protocol outlines a structured approach combining a systematic literature review of specific dose recommendations in patients with cirrhosis integrating a quality assessment to ensure the inclusion of only high-quality evidence, expert opinions by a Delphi consensus aligning differing recommendations and a clinical validation to support safer drug therapy in patients with liver cirrhosis. Clinical Trial: This study was registered in the German Registry of Clinical Trials (DRKS) on 2024-12-27 with code number DRKS00033779 (https://www.drks.de/search/de/trial/DRKS00033779 ).
Background: Large Language Models (LLMs) have shown broad applicability in medicine, including the generation of clinical documents. Beyond content creation, LLMs can also be used to evaluate the qual...
Background: Large Language Models (LLMs) have shown broad applicability in medicine, including the generation of clinical documents. Beyond content creation, LLMs can also be used to evaluate the quality of medical documents. Because of LLMs' ability to simulate (or impersonate) specific personas, they can offer diverse perspectives (such as those of healthcare professionals versus patients with lower health literacy) on the clarity of medical texts. Objective: The primary objective of this research was to evaluate the ability of LLMs to simulate diverse user personas, varying by demographic profiles including educational background, gender, visit frequency, for the task of interpreting ICU discharge summaries. The study aimed to benchmark the clarity assessments generated by these LLM personas against a baseline established by human participants with corresponding backgrounds, in order to highlight the potential and limitations of using current LLMs to create personalized health information. Methods: We evaluated the ability of LLMs to simulate diverse user personas for the task of interpreting ICU discharge summaries. LLMs were prompted to adopt personas with varied demographic profiles, including different educational backgrounds. The resulting LLM-generated assessments of the summaries’ clarity were then benchmarked against a baseline established by human participants with corresponding backgrounds. Results: LLMs demonstrated a strong ability to simulate personas based on educational attainment, accurately interpreting key medical information in 88% of cases. However, the models’ performance varied widely when other demographic variables were introduced. For instance, persona performance was highly erratic based on gender, with simulated male personas achieving 97% accuracy while female personas achieved only 44%. The inclusion of additional details, such as the frequency of prior emergency room visits, further degraded the models' performance. Conclusions: This research highlights both the potential and the significant limitations of using LLMs to create personalized health information. While LLMs are promising for simulating user perspectives based on education, the current models exhibit unpredictable performance when tasked with incorporating other fundamental demographic traits like gender.
Background: There are specific challenges in identifying and delivering effective treatments which can protect and improve oral health in residential aged care facilities (RACFs). This is especially t...
Background: There are specific challenges in identifying and delivering effective treatments which can protect and improve oral health in residential aged care facilities (RACFs). This is especially the case in those living in regional and rural areas. Given the consequences of poor oral health for older people living in RACFs, there is an urgent need for high-quality evidence on oral health interventions that are appropriate to context and need, accessible, and cost-effective for aged care residents. Applying aqueous forms of silver fluoride (AgF) can be effective and suitable for improving this population’s oral health and wellbeing. Objective: This paper outlines a research protocol which aims to test the effectiveness of an AgF intervention package in reducing tooth sensitivity, tooth pain, arresting caries, and improving oral health and wellbeing in older adults living in regional and rural RACFs. Methods: This study protocol describes a cluster randomised controlled trial with two parallel arms. The control arm will receive delayed intervention after 3-months. This approach allows for all participants to receive an oral examination and access to AgF treatment by the end of the study. Study sites include RACFs in public and private sectors across rural and regional Queensland and New South Wales, Australia. Oral assessments will be undertaken for RACF residents who provided consent, with at least one natural tooth. Teeth will be assessed for eligibility to receive AgF treatment. Outcomes at the 3-month follow-up will be collected through survey and clinical examination and include tooth sensitivity and pain, dental caries and oral health-related quality of life. Results: This clinical study is part of an overarching project funded by the MRFF Dementia Ageing and Aged Care Grant #2024439. Data collection commenced in May 2025 for the cluster randomized controlled trial and is anticipated to continue until March 2026. Conclusions: This research protocol will provide a rigorous test of the efficacy of a minimally invasive intervention package of AgF to improve the oral health and wellbeing of older adults in RACFs. Clinical Trial: This clinical trial has been registered with the Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12625000072415: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12625000072415
Background: The paradigm of Deep Learning (DL) has fundamentally transformed Medical Image Analysis (MIA), offering automated, objective, and highly accurate solutions for intricate diagnostic challen...
Background: The paradigm of Deep Learning (DL) has fundamentally transformed Medical Image Analysis (MIA), offering automated, objective, and highly accurate solutions for intricate diagnostic challenges. Traditional computer vision methods are often limited by manual feature engineering, a constraint overcome by DL techniques, specifically Convolutional Neural Networks (CNNs), which automatically learn hierarchical features from raw image data. Objective: This comprehensive review aims to systematically cover the current state of DL in MIA. The primary objectives are to detail foundational model architectures, review their applications across various imaging modalities, analyze the critical challenge of data scarcity in medical contexts, and discuss advanced techniques and future directions intended to overcome these limitations. Methods: This paper is a narrative review. It systematically examines foundational DL model architectures (including CNNs, U-Net, and Transformers) and key applications (classification, segmentation, detection, and reconstruction) in MIA. It synthesizes literature concerning the practical solutions deployed to address the challenge of limited labeled medical data, such as Transfer Learning (TL), semi-supervised learning, and the use of Generative Adversarial Networks (GANs). The review concludes by analyzing contemporary challenges and emerging trends. Results: DL models have demonstrated superior performance across core MIA tasks compared to traditional methods. Practical solutions like Transfer Learning and model specialization (e.g., U-Net for segmentation) are essential for boosting diagnostic accuracy in data-limited settings. Furthermore, advanced approaches like synthetic data generation (GANs) and collaborative learning methods are key to improving robustness and efficiency in real-world clinical environments. Conclusions: While DL has significantly advanced MIA, key challenges remain, notably the interpretability (the "black box" problem) of model decisions, the necessity for better generalization across diverse hospital settings, and ethical considerations surrounding data privacy. Future research is focused on developing Explainable AI (XAI) methods, personalized medicine applications, and privacy-friendly collaborative learning via techniques like Federated Learning. Clinical Trial: N/A
Background: Background: About 30% of adults who are eligible for colorectal cancer (CRC) screening have never been screened. Adherence to CRC screening is particularly poor among medically underserved...
Background: Background: About 30% of adults who are eligible for colorectal cancer (CRC) screening have never been screened. Adherence to CRC screening is particularly poor among medically underserved populations, including those with low income and racial/ethnic minority populations. Blood-based screening offers a less invasive alternative to stool-based tests and colonoscopy, potentially increasing adherence. Objective: This pre-defined interim analysis of a clinical study examines sociodemographic barriers and facilitators for implementing a trial using blood-based CRC screening, as well as patient satisfaction with the CRC screening blood test. Methods: Methods: We partnered with two Federally Qualified Health Centers (FQHCs) in the Midwest to conduct the clinical study. Eligible participants were 45-75 years of age at average risk for CRC, and were never screened or not up to date with screening. Participants were identified from electronic health records (EHR) and were invited to participate in the study, including a post-study satisfaction survey. Results: Results: From 482 eligible individuals, 198 expressed interest in participating in the study, and 79 were successfully enrolled. Although Hispanic/Latino individuals (OR: 2.51, 95% CI 1.35-4.66) were more likely to show interest in the blood-based test, there was a higher participation rate among Non-Hispanic White individuals (65%). Forty participants completed a satisfaction survey, and those who responded reported a positive experience with the blood test, indicating a high mean score (on a scale of 1-10) in terms of convenience (mean=9.25) and comfort (mean=9.23). Conclusions: Conclusion: Preliminary results from the first year of this study suggest that the blood-based screening option has a promising uptake among medically underserved populations with historically low adherence of CRC screening, such as racial/ethnic minorities. Clinical Trial: NCT05536713
Background: The population of adults aged 65 and older is rapidly increasing, while the availability of caregivers is declining. Smart homes that provide unobtrusive, continuous monitoring and alertin...
Background: The population of adults aged 65 and older is rapidly increasing, while the availability of caregivers is declining. Smart homes that provide unobtrusive, continuous monitoring and alerting on clinically relevant changes in daily activity patterns offer a potential innovative solution for aging in place. Objective: To prototype and evaluate a low-cost, community-based smart health system for monitoring the health of older adults with multiple chronic conditions and experiencing poverty, and to identify barriers and facilitators to adoption. Methods: Using a prospective, mixed-methods design and iterative community co-design, 46 older adults from 7 different language groups were continuously monitored for 6 months with ambient sensors installed in their homes. Two older adults were monitored for 4 and 5 months respectively resulting in a total sample of N=48. The system generated alerts based on movement pattern changes and escalated notifications to participants, support persons, community health workers (CHWs), and nurses. Sensor data were analyzed descriptively to quantify alert patterns and response rates, while text-based data from in-the-moment surveys, CHW and nurse notes, and semi-structured interviews underwent qualitative descriptive analysis and reflexive thematic coding. Results: The system generated 37 million sensor readings condensed into 1.2 million high-level events and 8,927 novel alerts. Qualitative data comprised of 34,086 words of text. Participants responded to 1.57% of initial email alerts and 7.8% of follow-up SMS alerts sent when no email response was received. CHWs responded to 83.6% of escalated alerts, resulting in 1,060 contacts with participants in response to alerts. Clinical contacts resulted in 72 interventions. Three major qualitative themes emerged: (1) mitigating aloneness, (2) building trust in technology and people, and (3) maintaining a human connection. Subthemes included safety, personalization, and digital distress. Participants rated the system highly (Net Promoter Score = 8.48/10) but expressed a strong preference for phone calls over automated alerts. Cultural expectations influenced adoption, particularly in multi-generational households. Conclusions: Communities can effectively engage in technology-delivered healthcare. Future research is needed to improve technical aspects of smart health systems, including accurate alerting utilizing machine learning, data visualizations for older adults and healthcare workers, and culturally sensitive features. Additional work should address how and when to communicate automated messaging, engage older adults with their own data, and integrate sensor-based monitoring into healthcare workflows. Research should also explore personalization through advanced computer models such as machine learning and strategies to reduce digital distress. Clinical Trial: None
Background: Health professions education faces increasing challenges from rising healthcare complexity, pedagogical shifts, and constrained curricular space, alongside rapidly expanding knowledge and...
Background: Health professions education faces increasing challenges from rising healthcare complexity, pedagogical shifts, and constrained curricular space, alongside rapidly expanding knowledge and technological advances. While artificial intelligence (AI) holds immense promise for transforming health professions education, evidence of its effectiveness remains unclear. Objective: We synthesized evidence from randomized controlled trials (RCTs) on the effectiveness of AI in undergraduate health professions education in improving learning outcomes. Methods: We searched PubMed and Cochrane (which covered PubMed, Embase, CINAHL and trial registries) from database inception till 19 November 2025 for RCTs that compared AI against standard educational interventions. We categorized outcomes according to Kirkpatrick’s levels (reaction, knowledge, behavior and results), assessed risk-of-bias using the ROBUST-RCT tool, performed random-effects meta-analysis (RevMan 5.4) and rated certainty-of-evidence using the GRADE approach. Results: Of 19303 unique records identified, 50 RCTs (n=3,746 participants) published between 2020 and 2025 were included. The overall risk of bias was high in majority of the studies due to poor allocation concealment and blinding, and certainty of evidence ranged from low to very low. Students who received AI-assisted learning appeared to perform better in theoretical knowledge (standardized mean difference [SMD] 0.65, 95% CI 0.37–0.93, 20 studies, 1647 participants, I2=86%, low-certainty) and may have a positive effects on practical and personal skills (Practical: SMD 0.45, 95% CI -0.20–1.09, 6 studies, 449 participants, I2=89%; Personal: SMD 0.54, 95% CI 0.28–0.81, 5 studies, 420 participants, I2=36%; low-certainty), but effects on other learning outcomes are uncertain (very-low-certainty-evidence), including self-efficacy (SMD 0.94, 95% CI 0.56–1.33, 13 studies, 1020 participants, I2=87%), satisfaction (SMD 0.69, 95% CI 0.35–1.03, 17 studies, 1409 participants, I2=88%), clinical skills (SMD 0.78, 95% CI 0.35–1.21, 17 studies, 1235 participants, I2=92%) and task efficiency (SMD -0.10, 95% CI -1.89–1.68, 4 studies, 243 participants, I2=96%). Conclusions: In undergraduate health professions education, low-certainty evidence suggests that AI may improve some learning outcomes, including knowledge, personal and practical skills with unclear effects on others. However, substantial variation in study findings lowered our confidence on the estimates and no studies assessed higher-level outcomes of behavior and health outcomes. With the rising interest in AI, further RCTs are expected to provide updated results and strengthen the evidence base to inform educational practice. Clinical Trial: PROSPERO (CRD42021243832).
Background: Crown preparation is a technically demanding psychomotor skill in undergraduate dental education. While traditional typodont training is the gold standard, it is resource-intensive and is...
Background: Crown preparation is a technically demanding psychomotor skill in undergraduate dental education. While traditional typodont training is the gold standard, it is resource-intensive and is difficult to individualize. Haptic Virtual Reality Simulation (HVRS) offers a sustainable pedagogical alternative, but its efficacy in transferring skills to the physical environment remains a subject of critical investigation. Objective: This study aimed to evaluate whether short-term training on the HVRS improves crown preparation performance on typodont teeth, and to examine manual dexterity and self-confidence outcomes. Methods: The study was conducted with 44 fifth-semester dental students at Karolinska Institutet, allocated to a simulator group (SIM; n = 22) or a control group (n = 22). The SIM group completed three hours of haptic virtual reality simulator (HVRS) training over one week, while the control group received no simulator exposure. Both groups prepared a maxillary first molar for a monolithic zirconia crown on a phantom head. Crown preparation quality was assessed using PrepCheck® and a blinded examiner. Manual dexterity was measured using the Grooved Pegboard Test (GPT), and self-confidence and simulator perceptions were evaluated through surveys. Results: The SIM group achieved a higher mean total preparation score than the control group (11.91 vs. 10.91), though the difference was not statistically significant (p = 0.235). Significantly better scores were observed in Total Occlusal Convergence (TOC) angle for the SIM group (p = 0.04). Manual dexterity improved in both groups, but the control group remained significantly faster at baseline and post-intervention (p = 0.044 and p = 0.001). While overall self-confidence levels were comparable, the SIM group reported fewer instances of "very low" confidence (5%) compared to the Control group (18%). Participants perceived the simulator’s tactile realism as low, though they valued its ability to demonstrate procedural workflows. Conclusions: Three hours of self-directed HVRS training do not significantly improve overall crown preparation quality or general self-confidence, but improve performance in TOC angle for crown preparations. HVRS may serve as an effective "Partial Task Trainer" within a blended curriculum.
Background: Digital assessment technologies, such as optical motion capture and inertial measurement units, enable detailed kinematic analysis and continuous monitoring of upper-limb activity in perso...
Background: Digital assessment technologies, such as optical motion capture and inertial measurement units, enable detailed kinematic analysis and continuous monitoring of upper-limb activity in persons with neurologic diseases. While such digital metrics of functioning are increasingly recognized in research, their uptake in clinical neurorehabilitation is limited. It remains unclear which digital metrics of functioning clinicians perceive as most meaningful and how these are integrated into patient-centered care. Understanding clinicians’ information needs and reasoning processes is a prerequisite for targeted education and competency development to support the implementation of digital assessments. Objective: To characterize how rehabilitation professionals perceive, prioritize, and integrate digital metrics of functioning into clinical reasoning and to identify features that would support their routine use. Methods: Three 90-minute focus groups were conducted in 3 Swiss neurorehabilitation centers, involving 11 clinicians with diverse professional backgrounds (5 physiotherapists, 4 occupational therapists, 1 movement scientist, and 1 medical practitioner). Participants discussed essential parameter domains and individually indicated the relevance and meaningfulness of 17 kinematic metrics related to the well-studied drinking task and 10 established arm use performance metrics. Verbatim transcripts were analyzed using reflexive thematic analysis, and rating data were summarized descriptively. Results: Five main themes were identified. (1) Functional requirements to interpret movement quality and performance (active/passive range of motion (ROM), strength, selective muscle control, grasp) form the basis for interpreting movement. (2) Essential aspects of movement quality (smoothness, efficiency, compensatory movement) are valued when aligned with observable task execution. (3) Added value of real-world performance (hourly activity profiles, arm-use symmetry, functional workspace) represents the reference for patient-centered reasoning. (4) Individualizing what matters, including diagnosis-specific preferences, shapes assessment selection. (5) Blending clinical eye and reference data reflects clinicians’ reliance on visual judgment complemented by normative values. Intuitive metrics such as task duration, number of movement units, and ROM were favored, whereas confidence was lower in more complex metrics (e.g., jerk, inter-joint coordination). Conclusions: Clinicians value intuitive digital metrics of functioning when they are clearly linked to patient-centered outcomes and supported by normative references. The findings highlight the need for targeted educational strategies and digital competency training that help clinicians interpret digital metrics and integrate them with contextual information and clinical reasoning.
Background: Work ability decline among working-age adults is a major public health concern, requiring effective prevention strategies. While conventional in-person prevention programs show benefits, f...
Background: Work ability decline among working-age adults is a major public health concern, requiring effective prevention strategies. While conventional in-person prevention programs show benefits, fully digital formats offer scalability and accessibility advantages. However, evidence on the effectiveness, sustainability, and behavioral mechanisms of digital-only prevention programs remains limited. Objective: This study aims to evaluate the effectiveness and sustainability of a fully digital multicomponent prevention program on work ability, health, and health behaviors, and to examine behavioral mechanisms through ecological momentary assessment (EMA). Methods: This quasi-experimental mixed-methods evaluation will compare three intervention formats: fully digital prevention program, digitally supported program (N=147), and conventional in-person program (N=98). Participants are working-age adults (18-65 years) eligible for German pension insurance prevention programs. The digital intervention consists of a 6-months app-based program including coaching sessions, webinars, and health modules targeting physical activity, stress management, and nutrition. Assessments occur at five timepoints: baseline (T0), mid-intervention (T1, 3 weeks), post-intervention (T2, 6 weeks), and follow-ups at 3 months (T3) and 6 months (T4). Primary outcomes include the Work Ability Index and SF-12 Physical and Mental Component Summaries. Secondary outcomes include physical activity, motivation, stress, and eating behavior. Daily EMA captures intention, habit, affect, and perceived behavioral control. Data will be analyzed using repeated measures ANOVA/ANCOVA for effectiveness, multilevel modeling for EMA data, and thematic analysis for qualitative interviews. Results: Data collection for the fully digital prevention program is ongoing, while comparative baseline data from the digitally supported (N=147) and conventional in-person (N=98) programs are already available. Analyses will include repeated-measures ANOVA and ANCOVA to assess changes in work ability, physical and mental health, and related behaviors. EMA data will be analyzed using multilevel modeling to examine daily fluctuations in intention, affect, perceived behavioral control, and habit strength, providing insights into behavioral mechanisms, while qualitative interviews will be analyzed using thematic analysis to identify recurring themes related to user experiences, perceived effectiveness, and contextual factors influencing engagement and sustained behavior change. Conclusions: This study will provide evidence on the sustainability of digital prevention effects through 6-month follow-up, address gaps in understanding mechanisms of behavior change through dual process theory tested with real-time EMA data, and contribute comparative effectiveness evidence for digital versus blended versus conventional intervention formats. Clinical Trial: German Clinical Trials Register (DRKS): DRKS00036417
https://www.drks.de/search/de
Background: Osteoporosis and diabetes are both prevalent chronic diseases. The complex pathophysiological interactions between glucose metabolism and bone health contribute to an elevated osteoporosis...
Background: Osteoporosis and diabetes are both prevalent chronic diseases. The complex pathophysiological interactions between glucose metabolism and bone health contribute to an elevated osteoporosis risk in diabetic patients. However, some glucose-lowering medications adversely affect bone metabolism. Herbal formulations have been proposed as complementary interventions, although systematic evidence supporting their use remains limited. Previous studies by our research group indicated that Gushuling (GSL) may improve osteoporotic conditions. Nevertheless, high-quality randomized controlled trials are lacking to clarify the efficacy of GSL for diabetes complicated by osteoporosis. Objective: This study evaluates the effectiveness and safety of GSL in managing patients with both diabetes and osteoporosis. Methods: This prospective, randomized, single-center clinical trial enrolled 60 participants, who were centrally allocated in a 1:1 ratio to receive either Gushuling (GSL) combined with Caltrate D3 Tablets and Alendronate Sodium Tablets, or Caltrate D3 Tablets and Alendronate Sodium Tablets alone. A 24-week treatment period was followed by a final assessment at week 36, which occurred 12 weeks after treatment discontinuation. The primary outcome was bone mass, measured by bone mineral density (BMD). Secondary outcomes included serum levels of Ca, P, ALP, plasma 25-hydroxyvitamin D3 [25(OH)D3], β-CrossLaps (β-CTx), osteoprotegerin (OPG), MiR-135a-5p, Foxo1, and PTGS2, in addition to blood glucose levels. All statistical analyses were conducted using SPSS 28.0, with no interim analysis performed. Results: Data collection will commence in August 2024 and conclude in June 2025, with analysis scheduled to begin in the summer of 2026. Final results are anticipated by the end of 2026.This study provides evidence to advance the clinical understanding of Traditional Chinese Medicine for managing diabetes mellitus complicated by osteoporosis. Conclusions: This trial establishes a methodological framework for evaluating the clinical efficacy, safety, and potential mechanisms of GSL in patients with diabetes and osteoporosis. It also explores expanded avenues for integrating Traditional Chinese Medicine into the comprehensive management of diabetes complicated by osteoporosis. Clinical Trial: This study has been registered with the Chinese Clinical Trial Registry under registration number: ChiCTR2400087572.https://www.chictr.org.cn/searchproj.html.Registration date: July 30, 2024.
Background: Inflammatory ocular diseases (IOD) are frequently associated with multi-system autoimmune conditions and can lead to significant visual morbidity, including visual impairment and blindness...
Background: Inflammatory ocular diseases (IOD) are frequently associated with multi-system autoimmune conditions and can lead to significant visual morbidity, including visual impairment and blindness. Emerging evidence suggests that inflammation contributes to the development of depression and other mental health disorders. Individuals with childhood-onset IOD may be at increased risk of poor well-being and mental health outcomes. However, the prevalence of mental health conditions in this population remains unclear. Objective: To review the evidence regarding the prevalence of mental health conditions amongst children and adults with childhood onset inflammatory ocular disease. Methods: This systematic review will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Eligible studies will report mental health disorder prevalence and outcomes in individuals with childhood-onset IOD, regardless of age at assessment of outcome. Studies evaluating interventions or focusing primarily on mental health effects secondary to visual impairment or blindness will be excluded. Searches will be conducted in PubMed, the Cochrane Central Register of Controlled Trials, Embase, Ovid, and PsycArticles. Grey literature will be identified through Google searches. Two researchers will independently screen titles, abstracts, and full texts, extract data, and assess risk of bias using the ROBINS-E tool; disagreements will be resolved by a third reviewer. Data will be synthesised descriptively, with attention to study design, outcome measures, co-occurrence of multi-system disease, and methodological quality. Results: A preliminary scoping search has been completed to estimate the volume of relevant literature. Full searches will begin in November 2025. Data extraction, analysis, and synthesis will follow, using a narrative approach to summarise mental health outcomes across studies. The final review is expected to be completed by August 2026. Conclusions: The findings from this review will help to establish the prevalence of mental health conditions amongst people with childhood onset IOD. The results from this review will support recommendations for further research and policies to ensure the best health outcomes for children. Clinical Trial: PROSPERO registration: CRD420251182619
Background: Clinical decision support systems aim to reduce adverse drug events, particularly those arising from drug-drug interactions. However, poor user acceptance—often linked to alert fatigue...
Background: Clinical decision support systems aim to reduce adverse drug events, particularly those arising from drug-drug interactions. However, poor user acceptance—often linked to alert fatigue—limits their clinical effectiveness and reduces patient safety. Objective: This systematic review examined the effect of alert fatigue on clinician acceptance of drug-drug interaction alerts, explored strategies to mitigate alert fatigue, and assessed associated clinical consequences. Methods: Five electronic databases were searched for studies reporting original data on clinician responses to drug-drug interaction alerts from clinical decision support systems. A meta-analysis estimated the pooled override rate, with subgroup analyses to explore heterogeneity. Results: Of 44 included studies, 25 (57%) reported on alert acceptance/override patterns and 18 (41%) on strategies to reduce alert fatigue. Only five studies (12%) assessed clinical outcomes. The overall drug-drug interaction alert override rate was 79.8% (95% CI: 73.0–86.7%), with individual study rates ranging from 55.4% to 95.7%. Inappropriately overridden alerts ranged from 3.2% to over 90%. The most common override reasons, such as "Will monitor", provided limited insight into alert fatigue. Common mitigation strategies included tailoring databases according to severity or clinical relevance and contextualising alerts based on patient-specific and/or laboratory data. Conclusions: Despite high override rates, consistent measures for alert fatigue were lacking. Inappropriate overrides without clinical justification pose patient safety risks, highlighting the need for standardised metrics. To meaningfully compare studies, a standardised definition for alert fatigue is required. We suggest a methodological framework to assist future studies reporting on alert fatigue. Clinical Trial: The protocol for this review was registered at PROSPERO (CRD42024541597).
Background: Medical graduates are commonly deficient in Health System Science (HSS) competencies to effectively work through the complex healthcare system. Yekatit 12 Hospital and Medical College refo...
Background: Medical graduates are commonly deficient in Health System Science (HSS) competencies to effectively work through the complex healthcare system. Yekatit 12 Hospital and Medical College reformed its medical residency curricula through integrating and implementing health system-oriented competencies. Objective: This study aimed to explore the experiences and challenges of implementing health system science-oriented medical residency curricula in Yekatit 12 Hospital Medical College, Ethiopia. Methods: Methods: A facility-based qualitative study was conducted at Yekatit 12 Hospital Medical College in Addis Ababa, Ethiopia from March to April 2024. The study employed an exploratory case study design among 15 participants purposively selected, academic and health sector leaders, faculty, residents, and graduated physicians with HSS integrated curriculum. Data were collected through in-depth interviews using a semi-structured questionnaire. Data analysis involved coding, thematic grouping, and narrative synthesis of the findings. Results: Result: The key informant interviewees reported that learning with HSS integrated curriculum help the graduates to develop comprehensive competences. The participants believed that a health system science integrated curriculum, fostering a comprehensive understanding of the healthcare system, encourages interprofessional collaboration, prioritizing patient-centered care. Implementation of HSS integrated curriculum requires capacity building training, commitment of the faculty and leadership. The major challenges reported to implement system oriented medical education in Yekatit 12 hospital medical college were lack of trained mentors and supervisors, a lack of budget for quality improvement project, lack of practical teaching methods. Conclusions: Conclusion: The implementation of the health system science integrated curriculum at Yekatit 12 Hospital Medical College has shown significant improvement in health system science competencies of the graduates. It has provided residents and graduates with a comprehensive understanding of healthcare systems, enhanced problem-solving skills, and a focus on patient-centered care. The implementation of the Health System integrated curriculum should be improved through staff capacity building and the allocation of additional resources.
The rapid integration of artificial intelligence (AI) into clinical practice necessitates urgent restructuring of medical education and physician assessment to ensure future doctors are proficient and...
The rapid integration of artificial intelligence (AI) into clinical practice necessitates urgent restructuring of medical education and physician assessment to ensure future doctors are proficient and responsible users of AI tools. Despite the existence of core AI competencies, the current state of AI education within Canadian Undergraduate Medical Education (UGME) is highly inconsistent and disjointed, and available data indicate that most medical students receive minimal to no formal AI training even as they anticipate AI profoundly shaping their future careers. National policy, specifically the Pan-Canadian AI for Health (AI4H) Guiding Principles, has advanced the agenda by calling for AI literacy among health professionals and emphasizing core values such as equity, robust data practices, and Indigenous-led data governance. However, these principles offer limited practical guidance on the educational and regulatory mechanisms required for effective implementation. We contend that this critical implementation deficit arises from a traditional, sequential reform model in which faculty development, curriculum change, and regulatory updates occur in isolation. This slow, siloed approach is fundamentally inadequate for addressing AI’s inherent speed, opacity, and significant equity risks. To overcome this, we propose a three-lever concurrent implementation framework. This model posits that AI competencies transition from abstract requirements to practical application only when three levers—clinician-educator capacity, digitally enabled learning environments, and regulatory and assessment reform—are activated simultaneously and in alignment. This Viewpoint extends existing AI competency frameworks by theorizing AI curriculum implementation as a problem of concurrent lever activation and by outlining minimum concurrent actions for deans and regulators that can be adapted to competency-based medical education systems. Although illustrated with Canadian examples, the framework is designed to be transferable beyond Canada and offers a testable, licensure-level blueprint for embedding AI competence in medical education.
Background: The COVID-19 pandemic imposed fundamental challenges on global healthcare systems and highlighted the need to reconsider traditional models of medical service delivery. In this context, di...
Background: The COVID-19 pandemic imposed fundamental challenges on global healthcare systems and highlighted the need to reconsider traditional models of medical service delivery. In this context, digital health technologies emerged as critical solutions for ensuring continuity of care. However, acceptance of these technologies appears to be shaped by how different stakeholders perceive power within care relationships. Objective: This review aimed to synthesize evidence on how power perception influences the acceptance of digital health systems among patients and healthcare professionals, to identify factors that shape stakeholders’ attitudes and behaviors toward telehealth adoption. Methods: A systematic review was conducted using two major scientific databases, supplemented by expert recommendations. The search initially identified 550 articles. After applying predefined inclusion and exclusion criteria and screening titles, abstracts and full texts, 25 studies were selected for the final analysis. Results: The findings indicated that digital health technologies enhance patients’ empowerment, increase their control over health conditions, improve self-care and foster participation in shared decision-making. In contrast, many physicians, nurses and other healthcare professionals viewed these developments with concern. The evidence suggests that such technologies influence their perception of professional power, particularly in relation to decision-making autonomy and symbolic status. Conclusions: This systematic review revealed two contrasting perspectives on digital health. Patients largely perceived telehealth technologies as empowering tools, whereas many healthcare providers regarded them as potential threats to their professional authority and autonomy. These divergent perceptions of power may critically shape the acceptance and long-term integration of telehealth systems into routine care.
Background: Community-based medical education (CBME) is valued as a vital component of preparing medical students for practice but is often problematic to deliver due to placement capacity. Virtual Re...
Background: Community-based medical education (CBME) is valued as a vital component of preparing medical students for practice but is often problematic to deliver due to placement capacity. Virtual Reality (VR) has the possibility to provide students with learning experiences that help them to understand community structures and health and social care needs in these settings. Objective: The aim of our study was to explore medical students’ engagement, presence, experiences and perceptions of a VR GP home visit, alongside the experiences and perceptions of educators. Methods: This mixed methods study designed and piloted a 360-degree VR simulation of a general practice (GP) home visit to support CBME in a respiratory case. Pre-questionnaires assessed all participants’ digital competence and VR experience, followed by post-tests for students measuring their engagement (User Engagement Scale) and presence (Multimodal Presence Scale). Focus groups were conducted to explore students’ and educators’ experiences and perceptions. Quantitative data were analysed using correlation, regression and mediation analysis, whilst reflexive thematic analysis was used to identify themes from focus groups. Results: Forty-five medical students and 14 educators participated in the VR intervention, with 31 students and 14 educators contributing to focus groups. Results showed that VR interest was significantly associated with participants’ engagement, mediated by presence. Physical presence scored highest, while social/self-presence was lower, reflecting limited interactivity. Qualitative findings highlighted VR’s immersive potential, and its use a complementary tool to support community-based medical education. Conclusions: VR CBME focussed scenarios have the potential to address placement capacity issues and complement existing simulation-based education. They should be carefully designed to balance immersion and interactivity, and reflect more complex social scenarios to capitalise on the safe, formative environment VR provides. They should also be cognisant that digital competency in students doesn’t correlate with engagement. Clinical Trial: NA
Background: Federated learning (FL) enables multi-institutional model training on clinical text without sharing raw data; however, gradient inversion methods can reconstruct sensitive information from...
Background: Federated learning (FL) enables multi-institutional model training on clinical text without sharing raw data; however, gradient inversion methods can reconstruct sensitive information from shared model updates. The extent of such privacy leakage in FL applied to radiology reports, and the role of tokenizer design, remains unclear. Objective: To quantify gradient-based reconstruction of radiology report text in an FL setting and to compare privacy risk across three transformer tokenization strategies in a controlled, tokenizer-aware evaluation. Methods: Six FL clients trained a GPT-2–style transformer (117M parameters; sequence length 32) on two public radiology corpora comprising 368,751 diagnostic reports, 98,206 discharge summaries, and 1,500 MIMIC-CXR free-text reports. Models were trained using three tokenizers (GPT-2, RadBERT, LLaMA-2) with batch sizes of 64, 128, and 256. A curious-server threat model was assumed, and analytic gradient inversion was applied to recover text. Reconstruction fidelity was measured over five runs using exact sentence accuracy, S-BLEU, and ROUGE-L. Results: Exact sentence reconstruction ranged from 33% to 42% across tokenizers. At batch size 64, accuracy was 42.1% (GPT-2), 42.3% (RadBERT), and 39.4% (LLaMA-2), decreasing to 37.3%, 37.2%, and 34.3% at batch size 256. S-BLEU scores declined with increasing batch size (e.g., GPT-2: 0.44→0.33; RadBERT: 0.48→0.35; LLaMA-2: 0.39→0.30). RadBERT yielded higher reconstruction fidelity and greater recovery of clinical terms, but no tokenizer prevented leakage. Conclusions: Substantial portions of radiology report text can be reconstructed from FL gradients even with larger batch sizes and domain-specific tokenizers. Tokenizer design influences leakage severity and should be incorporated into privacy evaluations for clinical language models. Integrating safeguards such as secure aggregation and differential privacy is necessary to meet HIPAA and GDPR requirements when deploying FL for radiology NLP. Clinical Trial: Not applicable.
Background: Artificial intelligence (AI)- based clinical decision support systems (CDSS) can improve diagnostics and treatment decisions, but they are rarely implemented in practice. Barriers include...
Background: Artificial intelligence (AI)- based clinical decision support systems (CDSS) can improve diagnostics and treatment decisions, but they are rarely implemented in practice. Barriers include limited integration into clinical workflows, lack of transparency, and insufficient involvement of end users in system design. Participatory and user-centered approaches offer ways to address these challenges by aligning development processes with the needs and routines of clinical staff. However, systematic evidence on how such approaches are applied in the development of AI-based CDSS remains limited. Objective: The objective of our study was to examine how participatory approaches are used in the development, piloting, and implementation of AI-based CDSS. We analyzed which user perspectives were included, which participatory methods were applied, how they contributed to technical design, and which ethical, legal, and social implications were addressed. Methods: This scoping review followed the PRISMA ScR guideline, with a protocol published in advance. A systematic search was conducted in MEDLINE, ACM Digital Library, CINAHL, and PsycInfo for studies published from 2012 onward, complemented by snowballing and manual searches. Primary studies in English or German were included if they involved clinical staff in the development, piloting, implementation, or evaluation of AI-based CDSS. Two independent reviewers conducted screening and data extraction, resolving disagreements by consensus. Data analysis followed JBI methodology and focused on the scope of participation, theoretical and methodological foundations, and reported impacts of participatory approaches. Results: Of 3,177 identified records, 17 met the inclusion criteria. The studies showed broad variation in terminology and methods, most often describing user-centered or iterative processes and less frequently co-design. Physicians were involved in nearly all studies, nurses frequently, and other professional groups only occasionally. Participation mainly supported requirements analysis, adaptation of models to clinical workflows, and the design of explainable interfaces. In several projects, it also influenced data selection, annotation, and visualization. Common barriers included time constraints, limited continuity of participation, and uncertainty toward AI. Ethical, legal, and social aspects mainly were addressed implicitly through themes such as autonomy, responsibility, and traceability, while fairness and bias were rarely discussed. Conclusions: Participatory processes in AI-based CDSS development should extend across all stages of system design and address not only usability but also data quality, bias, and broader ethical, legal, and social issues. Equal inclusion of nursing and therapeutic expertise is essential to reflect the diversity of clinical decision-making. Clear methodological standards are needed to ensure comparability and to strengthen participation as a genuine co-design process shaping data, models, and values in clinical AI.
The amount of data generated in clinical practice is increasing substantially, which has benefitted the use of real-world data (RWD) for real-world evidence (RWE) in biomedical research. While early R...
The amount of data generated in clinical practice is increasing substantially, which has benefitted the use of real-world data (RWD) for real-world evidence (RWE) in biomedical research. While early RWE efforts focused on structured EHR and claims data, advances in analytical methods, such as artificial intelligence, are expected to further enhance the ability to obtain deeper insights from RWD. Most recently, real-world imaging data (RWiD) has emerged as a novel and valuable resource, enabled by improvements in imaging infrastructure, data standardization, and de-identification technologies. Medical imaging is essential at multiple stages of clinical care, ranging from screening to post treatment assessment and surveillance. Medical imaging has become a key component of patient management, as it augments clinical decision-making across many medical specialties. RWiD is the retrospective collection of routinely gathered clinical imaging data. Using only radiology reports results in limited information compared to datasets that contain the actual images. Radiology reports primarily focus on clinical decision making rather than research purposes. Therefore, the actual images add value to real world datasets. However, using RWiD is challenging due to complex de-identification and harmonization, as well as requirements for file storage, file transfer, and computation. This article describes the background, challenges, and opportunities of real world imaging data with a focus on life sciences and biopharmaceutical applications.
Background: Implementing digital mental health interventions (DMHI) for those with psychosis is a persistent challenge. A process evaluation, or studies conducted alongside trials, is one research met...
Background: Implementing digital mental health interventions (DMHI) for those with psychosis is a persistent challenge. A process evaluation, or studies conducted alongside trials, is one research method that may address this issue. However, a synthesis of process evaluation data in this area is missing. Objective: To explore what is known about context, implementation, and mechanisms of impact by synthesizing process evaluation data from trials evaluating DMHIs used by people with psychosis. Methods: A mixed–method systematic review using a two–phase search strategy underpinned by the Medical Research Council (MRC) process evaluation framework was conducted. Database searches of CENTRAL and PsycInfo in 2024 and 2025 first identified an index sample of peer–reviewed trials predominantly conducted in the United Kingdom (50≥% United Kingdom sample in multi–country studies). Next papers linked to the index sample were retrieved and included if they reported process evaluation data as operationalized in the MRC framework. Two authors independently screened references, extracted summary data and assessed the quality of index trials. One author qualitatively synthesized process evaluation data using a deductive framework synthesis approach using the MRC framework. Findings were triangulated with senior authors and presented as a narrative synthesis. Results: Searches identified 14 DMHIs and 45 papers reporting process evaluation data, though only two were labelled as such. Qualitative syntheses of process evaluation data generated five themes aligned with the MRC framework: (1) variation in how DMHIs were used in RCTs (implementation); (2) enabling implementation: resource preparation and supporting users (implementation); (3) helping users to respond in more helpful ways (mechanisms); (4) addressing perceived and actual implementation factors (context); (5) limited impact of user characteristics on DMHI outcomes (context). Conclusions: Our findings highlight considerations for future DMHI development, delivery and evaluation. Practical delivery considerations include addressing actual contextual factors (users’ treatment needs and preferences, everyday and clinical factors, and staff availability for blended DMHIs), personalizing professional support and facilitating user access to necessary technology. Conceptual design considerations include embedding content personalization, flexible delivery and user–centered design in DMHIs. Research efforts could focus on validating how and for whom DMHIs work and embedding process evaluation in trials. Clinical Trial: PROSPERO: CRD42024439117.
Background: Type 2 diabetes (T2D) is a complex chronic condition that imposes a substantial burden on healthcare systems. Prevention and early detection are critical to mitigating its impact. Automate...
Background: Type 2 diabetes (T2D) is a complex chronic condition that imposes a substantial burden on healthcare systems. Prevention and early detection are critical to mitigating its impact. Automated machine learning (AutoML) models have the potential to predict individual risk and guide personalized interventions. However, their clinical deployment remains limited due to the retrospective nature of most datasets, lack of external validation, and heterogeneity in variable selection Objective: To map AutoML approaches applied to T2D risk prediction, with a specific focus on their ability to integrate clinical, behavioral, environmental, and genomic data. Methods: A PRISMA-guided rapid review was conducted across six databases (PubMed, Scopus, Web of Science, IEEE Xplore, Google Scholar, and EMBASE) to identify empirical studies (2015–2025) that used AutoML tools for T2D prediction based on at least two data types (e.g., clinical, behavioral, environmental, genomic). Screening, data extraction, and synthesis were performed systematically by two independent reviewers, with arbitration by a third AI reviewer (ChatGPT). Results: Thirteen studies met inclusion criteria. Methodological diversity ranged from conventional machine learning with manual feature selection to partially or fully automated pipelines using tools such as TPOT, H2O AutoML, or Azure ML. Reported performance varied (AUC 0.75–0.99), but external validation was uncommon. Behavioral and environmental data were only partially integrated, and no study incorporated genomic data despite its recognized potential. Most studies lacked transparency and reproducibility, with no public code or pipeline sharing Conclusions: AutoML holds significant promise for improving T2D risk prediction through automation and model explainability. Yet, to support clinical adoption and generalizability, future AutoML pipelines must be developed using prospective, multicenter datasets, integrate diverse and harmonized data types, including genomics, and adhere to open science principles of transparency, reproducibility, and interpretability
Background: Spirometry is the gold standard test for diagnosing chronic obstructive pulmonary disease (COPD) and other obstructive lung diseases, but it requires calibrated equipment and trained perso...
Background: Spirometry is the gold standard test for diagnosing chronic obstructive pulmonary disease (COPD) and other obstructive lung diseases, but it requires calibrated equipment and trained personnel and is often unavailable in low and middle income settings. Consequently, airflow limitation is under detected in many regions. Because chest radiography is widely available and inexpensive, recent studies have explored whether deep learning can estimate spirometric indices from chest radiographs. Objective: To build on prior work by evaluating a deep learning model that predicts the FEV₁/FVC ratio and classifies obstructive lung disease from chest radiographs, and to assess model fairness across demographic subgroups. Methods: We retrospectively assembled a cohort of 3,537 unique patients who underwent both pre bronchodilator spirometry and chest radiography at a single Canadian hospital. A convolutional neural network (ConvNeXt base) was trained to predict the continuous FEV₁/FVC ratio using 2,263 patients for training, 566 for validation and 708 for testing. By thresholding predictions at 0.70, examinations were also classified as obstructive or non obstructive. Performance was summarized overall and within age, sex and ethnicity strata. Results: On the held out test cohort (708 patients, 3,274 radiograph examinations), the model achieved a mean squared error of 0.07 for ratio prediction. For the binary obstruction task, sensitivity was 0.70, specificity 0.72, positive predictive value 0.71 and negative predictive value 0.71. These values exceeded those of a prevalence matched random classifier across all examined demographic groups. Subgroup analyses showed particularly large gains in specificity, and absolute accuracy improvements of 0.20–0.30 were observed in cohorts with higher obstruction prevalence. Fairness analyses revealed no clinically meaningful differences in performance across age, sex or ethnicity. Conclusions: This study extends earlier work on chest radiograph–based estimation of lung function by demonstrating comparable performance in a North American cohort and providing a comprehensive fairness assessment. Given the ubiquity of chest radiography and the under utilization of spirometry, such models could offer a practical screening tool for obstructive lung disease, especially in regions where access to spirometry is limited. Prospective validation is warranted to support clinical adoption. Clinical Trial: Not applicable.
Background: Repeated-measures datasets are common in biomechanics and digital health, where each participant contributes multiple correlated trials. If cross-validation (CV) ignores this structure, in...
Background: Repeated-measures datasets are common in biomechanics and digital health, where each participant contributes multiple correlated trials. If cross-validation (CV) ignores this structure, information can leak from training to test folds, inflating performance and undermining clinical credibility. Objective: To evaluate the impact of subject-aware validation strategies on model reliability in repeated-measures classification tasks, using fear of re-injury prediction post–anterior cruciate ligament reconstruction (ACLR) as a case study. Methods: We analyzed 623 hop trials from 72 individuals post-ACLR to classify fear of re-injury based on biomechanical features. Four cross-validation (CV) strategies were compared: stratified 10-fold CV, Leave-One-Participant-Out CV (LOPOCV), Group 3-Fold CV, and a nested framework combining LOPOCV (outer loop) with Group 3-fold CV (inner loop). Ten supervised classifiers were benchmarked across classification accuracy, train–test generalization gap, model ranking consistency, and computational efficiency. Results: Stratified 10-Fold CV systematically overestimated model performance (e.g., Extra Trees accuracy of 0.91 vs. 0.66 under LOPOCV) due to subject-level data leakage. Group and nested CV strategies yielded more conservative and stable estimates. The nested LOPOCV + Group CV framework achieved a good balance between generalization and participant-level independence, with reduced bias and overfitting compared to non-nested alternatives. Conclusions: Subject-aware validation strategies are essential for trustworthy ML evaluation in repeated-measures settings. Nested CV designs improve reproducibility, reduce selection bias, and align with regulatory expectations for clinical ML tools. These findings support best practices in model validation for biomechanics and digital health applications.
Background: The accelerated aging of the global population, projected to include 1.5 billion people over 60 by 2050, poses significant challenges to conventional eldercare systems. While aging in plac...
Background: The accelerated aging of the global population, projected to include 1.5 billion people over 60 by 2050, poses significant challenges to conventional eldercare systems. While aging in place is widely advocated for preserving independence, traditional home care models often struggle to meet complex needs. This gap is exacerbated by the diminishing capacity of informal support networks. Smart home technologies offer a viable pathway to augment care; however, their widespread adoption is impeded by a confluence of usability challenges, privacy concerns, and socioeconomic barriers. Objective: This scoping review aimed to examine how smart home technologies could meet the diverse needs of older adults aging in place, identify the key determinants influencing their adoption and sustained use. Methods: This scoping review was conducted in accordance with the Joanna Briggs Institute (JBI) methodology and reported following the PRISMA-ScR guidelines. To ensure comprehensive coverage, a systematic search was performed across eleven databases (PubMed, Web of Science, Embase, CINAHL, Cochrane Library, APA PsycInfo, CNKI, Wanfang, VIP, SinoMed, and OpenGrey) targeting literature published between January 2008 and December 2024. Studies were included if they investigated smart home technologies used by older adults (aged 60 and above) living at home and examined relevant needs and adoption determinants. Study selection and data extraction were guided by the Population–Concept–Context (PCC) framework. Thematic analysis was applied to synthesize findings and identify core themes, adoption determinants, and existing gaps in the evidence base. Results: A total of 24 studies met the inclusion criteria, with most published after 2015 and primarily conducted in China, the United States, and European countries. The technologies examined ranged from basic sensor-based systems to integrated smart aging platforms. Older adults expressed three core priorities for technology use: health management, safety and emergency support, and support for independent living. Together, these accounted for more than 60 percent of the identified needs. Key facilitators of adoption included strong social support, better digital skills, and a clear perception of usefulness. In contrast, adoption was often limited by challenges such as complex user interfaces, affordability issues, privacy concerns, and cultural resistance. Notably, few studies addressed long-term usage patterns or sustainable implementation models, and the needs of socioeconomically vulnerable groups were insufficiently represented in the current evidence base. Conclusions: This review highlighted three primary demands among older adults: health management, safety and emergency support, and the ability to live independently. The adoption of smart aging technologies is influenced by supportive factors such as perceived usefulness and social support, as well as obstacles including usability difficulties, limited digital skills, and privacy concerns. Addressing existing evidence gaps call for a shift from technology-centered approaches to context-aware, user-driven systems grounded in older adults’ everyday experiences.
Open Science Framework Registration: https://doi.org/10.17605/OSF.IO/E27KH
Background: Telemedicine has transformed healthcare delivery by improving access to specialized care and reducing costs. While its efficacy has been established in critical care, emergency services, a...
Background: Telemedicine has transformed healthcare delivery by improving access to specialized care and reducing costs. While its efficacy has been established in critical care, emergency services, and chronic disease management, its application in highly critical anesthesia settings remains underexplored. In anesthesiology, telemedicine has shown promise in preoperative evaluations, intraoperative monitoring, and postoperative follow-up, yet a universal, standardized tele-supervision solution for the operating room (OR) is lacking. Objective: This study aimed to evaluate a novel telemedical supervision system for critical anesthetic scenarios in a simulated OR environment, comparing it to traditional on-site supervision. Specifically, adherence to standard operating procedures (SOPs), number and modality of senior physician contacts, workload, and user perceptions were assessed. Methods: In this randomized controlled simulation study, 16 anesthesiology residents (within their first two years of training) from the University Hospital Uniklinik RWTH Aachen (Germany) were randomized using block randomization into two groups. The intervention group received remote support via a tele-supervision system exclusively, while the control group used a conventional phone with on-site support. The telemedical system comprised an anesthesia workstation (AN-WS) that integrated data from patient monitor, anesthesia device, and syringe pumps using the IEEE 11073 SDC standard, and a mobile supervision workstation (SV-WS) that enabled the senior physician to monitor multiple ORs and communicate via text, audio, and video. The simulated scenario involved a 51-year-old male patient undergoing an appendectomy who developed an anaphylactic reaction three minutes after receiving Cefuroxime.
Primary outcomes focused on the completion rate of necessary SOP measures. Secondary outcomes included the workload measured using the NASA Task Load Index (TLX), and participants' post-scenario questionnaire responses. Statistical comparisons were performed using Welch’s t-test. Results: All participants in both groups contacted the senior physician at least once. The control group averaged 6.44 SOP measures that were supported, whereas the intervention group averaged 5.00. The mean SOP completion rate was 92.5% (IQR: 88.9%–94.7%) in the control group and 91.6% (IQR: 87.5%–93.5%) in the intervention group, with no significant difference (t = 0.439, df = 11.94, P = .67). NASA-TLX scores revealed lower mental demand in the tele-supervision group, but higher temporal demands compared to controls. Subjective evaluations indicated mixed preferences regarding on-site support; however, the majority of participants acknowledged the tele-supervision system as a viable alternative when on-site support is not feasible. Conclusions: This study demonstrated no statistically significant differences between the groups, indicating similar performance with high adherence to SOPs and comparable clinical decision-making in a simulated high-stakes environment. Despite increased temporal workload, user feedback was positive, underscoring the system’s potential to address staffing shortages and resource limitations. Further research in real clinical settings is needed to optimize usability and validate these findings. Clinical Trial: This study was conducted entirely in a simulated environment using a human patient simulator and did not involve real patient enrollment or collection of clinical outcomes. As such, it was classified as a simulation-based educational study and not registered in a WHO-accredited trial registry. We acknowledge the ICMJE policy and respectfully clarify that this study does not fall under its clinical trial definition.
Background: Artificial intelligence is increasingly used in scientific research to generate, refine, and summarize literature. Its ability to process large datasets promises greater efficiency in evid...
Background: Artificial intelligence is increasingly used in scientific research to generate, refine, and summarize literature. Its ability to process large datasets promises greater efficiency in evidence synthesis and review. However, generative AI tools often produce inaccurate results and may cite retracted or unreliable studies without warning, posing risks to research integrity. Whether these systems can reliably detect and exclude retracted publications remains unclear. Objective: In this pragmatic trial nine, freely available generative AI tools have been tested for their ability to answer question without citing retracted literature. Methods: Each generative AI was asked five standardized questions about 15 different retracted articles. The articles were chosen from the Retraction Watch-database, including most cited and most recent retracted articles. All questions were repeated twice to assess consistency, and answers were rated for accuracy and reliability. Results: None of the nine AI tools consistently identified or excluded retracted articles. ChatGPT-5 performed best (8/15, (53.3%) correct), while SciSpace, ScienceO S, and Consensus showed no fully correct results. Microsoft Copilot achieved the highest topic-overview accuracy (87%), and ChatGPT-4 showed the greatest consistency (97.2%). OpenEvidence performed reliably within medical literature but reached perfect accuracy in only 2 of 13 (15.4%) cases. Conclusions: No free generative AI tool can reliably detect or exclude retracted studies. Even the best systems missed a substantial proportion of retracted articles. Until retraction-aware verification is integrated, independent source checking remains essential to preserve research integrity. Clinical Trial: https://doi.org/10.17605/OSF.IO/B6J2W
Background: Due to the colonization of tobacco plants by European settlers and the subsequent intensive marketing of commercial tobacco products to American Indian and Alaska Native (AI/AN) communitie...
Background: Due to the colonization of tobacco plants by European settlers and the subsequent intensive marketing of commercial tobacco products to American Indian and Alaska Native (AI/AN) communities in the U.S., commercial cigarette smoking accounts for half of all deaths among AI/AN people. Lack of awareness, access to treatment, and the absence of culturally relevant, effective smoking cessation interventions contribute to these high death rates. Objective: To culturally adapt iCanQuit, a smartphone smoking cessation application (“app”) proven efficacious for the general population, for AI/AN people. Methods: A user-centered and community-based participatory research (CBPR) mixed-methods approach was applied to culturally adapt iCanQuit for AI/AN people in collaboration with a community advisory board (CAB) of AI/AN individuals using a three-step process between June/2024 and August/2025. Step 1 identified ways to culturally adapt the iCanQuit for AI/AN people through one-on-one qualitative interviews with eight prior iCanQuit AI/AN participants. Step 2 involved developing prototypes of cultural refinements identified in Step 1 through regular bi-weekly meetings of the CAB, research and app development teams. The prototypes were then evaluated with a separate group of four prior iCanQuit AI/AN participants through one-on-one qualitative interviews. Step 3 involved beta testing the app through a six-day diary study followed by one-on-one qualitative interviews with a nationally recruited group of seven AI/AN adults who smoke commercial cigarettes. The development work associated with Step 3 was further informed by the CAB and the research and app development teams. Results: Step 1 yielded five suggested cultural refinements to iCanQuit that were subsequently developed and tested in Steps 2 and 3: (1) modify the app’s stories to feature AI/AN adults and elders who quit smoking, emphasizing the values of culture, spirituality, family, and community as motivators; (2) add the value of honoring the Earth as a motivator to quit smoking; (3) make the appearance of the app’s “guide” character more representative of AI/AN people; (4) add information distinguishing ceremonial vs. commercial tobacco use; and (5) use earth tones in the app’s colors. In Step 3, 100% of diary study participants rated the beta version of the app as excellent or good/meets expectations (69% and 31%, respectively) and that it felt made for them. They suggested six modifications which were incorporated into the final version of the app: (1) include a vaping FAQ, (2) feature motivation icons more prominently, (3) increase notification frequency, (4) track today’s cigarettes rather than yesterday’s, (5) allow users to update how much they spend per pack of cigarettes; and (6) rename the medications tool to reflect the inclusion of AI/AN traditional healing modalities. Conclusions: A user-centered and CBPR development process yielded IndigeQuit—one of the first known apps developed specifically to help AI/AN adults quit commercial cigarette smoking. Clinical Trial: ClinicalTrials.gov Identifier, NCT06145763
Background: Telemedicine has rapidly transformed postoperative care, yet the influence of patients’ digital literacy on satisfaction remains underexplored in orthopedic surgery. The purpose of this...
Background: Telemedicine has rapidly transformed postoperative care, yet the influence of patients’ digital literacy on satisfaction remains underexplored in orthopedic surgery. The purpose of this study was to compare patient satisfaction between telemedicine and in-person (OPD) follow-up after upper-extremity surgery and to examine the association between digital literacy and satisfaction. Objective: This study aimed to compare patient satisfaction between telemedicine and traditional in-person outpatient follow-up after upper-extremity surgery, and to investigate the association between patients' digital literacy and satisfaction with their postoperative care. Methods: Seventy adults undergoing non-traumatic hand or upper-extremity surgery were randomized 1:1 to telemedicine or OPD follow-up. Satisfaction and digital literacy were assessed using standardized Likert-scale questionnaires at 2 and 6 weeks postoperatively. Between-group comparisons used t-tests, Mann–Whitney U, or chi-square tests as appropriate (p < 0.05). Results: Participants in the telemedicine group were younger (mean ± SD = 53.3 ± 11.5 vs. 59.7 ± 14.2 years, p = 0.018) and showed higher digital-literacy scores (mean ± SD = 8.9 ± 1.3 vs. 8.1 ± 1.5, p = 0.041). Overall satisfaction remained high in both groups at 2 and 6 weeks (median = 5 [IQR 5–5]), with no significant differences across any satisfaction domain (p > 0.15). Intra-group analyses revealed stable satisfaction over time (p > 0.3). Conclusions: Telemedicine follow-up provides patient satisfaction comparable to traditional visits and is well accepted among digitally literate urban patients. Differences in age and digital literacy suggest the need for targeted digital-skills support when implementing telemedicine at scale. Clinical Trial: Thai Clinical Trials Registry (TCTR20250528008), registered on May 28, 2025.
Background: Background: Multimodal large language models (MLLMs) capable of integrating visual and textual information represent a promising advancement for clinical applications requiring image inter...
Background: Background: Multimodal large language models (MLLMs) capable of integrating visual and textual information represent a promising advancement for clinical applications requiring image interpretation. Wound care assessment, which demands simultaneous analysis of wound photographs and clinical data, provides an ideal domain to evaluate multimodal versus unimodal artificial intelligence capabilities against human expertise.
Objective: To compare the performance of MLLMs, unimodal ChatGPT-5.0, and human clinical experts on a standardized wound care certification examination. Objective: Objective: To compare the performance of MLLMs, unimodal ChatGPT-5.0, and human clinical experts on a standardized wound care certification examination. Methods: Methods: This cross-sectional comparative study evaluated three participant groups on a 25-question wound care certification examination spanning four clinical domains (Diagnosis, Treatment, Complication Management, Wound Subtype Knowledge). Participants included three MLLMs (Med-PaLM 2, LLaVA-Med, BioGPT), one unimodal LLM (ChatGPT-5.0), and four human clinical experts (General Surgeon, Wound Care Nurse, two Internal Medicine Physicians). Statistical analyses included one-way ANOVA with Tukey's post-hoc tests and domain-specific Kruskal-Wallis comparisons Results: Results: Human experts achieved the highest accuracy (86.0%±9.1%), followed by MLLMs (78.7%±12.2%), while ChatGPT-5.0 achieved 64.0%, failing the 70% certification threshold. Significant overall group differences were observed (F(2,5)=8.42, p=0.018, η²=0.74). MLLMs significantly outperformed ChatGPT-5.0 (difference=14.7 percentage points, p=0.032, Cohen's d=1.38), with the multimodal advantage most pronounced in visually-dependent domains: Diagnosis (81% vs 43%, p=0.008) and Complication Management (72% vs 50%, p=0.034). No multimodal advantage was observed for text-based Wound Subtype Knowledge (both 67%). Med-PaLM 2 achieved 92% accuracy, matching the Wound Care Nurse, while the General Surgeon achieved the highest overall performance (96%). Conclusions: Conclusions: MLLMs demonstrate significant performance advantages over unimodal AI in wound care assessment, particularly for visually-dependent clinical tasks. While human experts with specialized wound care experience maintain overall superiority, top-performing MLLMs approach expert-level accuracy, supporting their potential role as clinical decision-support tools
Background: Artificial intelligence (AI) has the potential to transform chest radiography (CXR) interpretation by enhancing diagnostic accuracy, identifying subtle findings, reducing errors, and helpi...
Background: Artificial intelligence (AI) has the potential to transform chest radiography (CXR) interpretation by enhancing diagnostic accuracy, identifying subtle findings, reducing errors, and helping prioritize patient care. Although CXR remains a cost-effective and widely used imaging tool, its effectiveness is limited by overlapping anatomy and variability in clinical expertise. Integrating AI can help overcome some of these challenges, especially in resource-constrained settings. However, robust validation in real-world clinical contexts is essential before widespread implementation. This study protocol evaluates whether AI assistance improves general practitioners' ability to detect radiographic findings on CXR in adults with respiratory complaints or undergoing treatment for respiratory diseases, compared to unaided interpretation. Potential benefits include increased diagnostic safety, higher physician confidence, more efficient workflows, and expanded access to expert support in underserved areas. Objective: This project aims to evaluate whether AI assistance enhances physicians’ ability to detect key radiographic abnormalities— including consolidation or pulmonary opacity, pneumothorax, atelectasis, pleural effusion, and cardiomegaly. The primary outcome is the difference in physicians’ diagnostic accuracy (per examination) when assisted by the AI tool compared with usual practice, using the expert radiologist consensus as the reference value. Methods: This is a protocol for a multicenter, stepped-wedge, cluster-randomized clinical trial following the CONSORT-AI extension and SPIRIT-AI guidelines. The intervention involves the diagnostic support Solution for CXR - Lung Analysis (LuAna), an AI-powered chest X-ray interpretation tool developed in partnership with the Brazilian Ministry of Health. Across nine cities in Brazil, clusters will transition monthly from unaided chest X-ray interpretation by general practitioners to AI-assisted interpretation, with performance benchmarked against thoracic radiologists. The stepped-wedge design ensures all clusters receive the intervention, reflecting real-world coordination, enhancing acceptability, improving power, and strengthening causal inference through repeated measures. Diagnostic performance will be compared to a reference standard established by thoracic radiologists. Results: Thirteen research centers across Brazil will participate, covering all five regions and diverse healthcare settings, from primary care to specialized tuberculosis centers. Next steps involve finalizing regulatory approvals and starting participant enrolment once all sites are fully activated. Conclusions: This intervention is expected to enhance clinical decision-making by supporting earlier treatment initiation and more appropriate diagnostic pathways for patients with respiratory symptoms, while maintaining a favorable safety profile and high physician usability. The findings from this trial will provide real-world evidence on the clinical utility of AI-assisted chest radiography. If effective, LuAna may leverage its scalability and equity advantages to become a replicable model for integrating AI into routine imaging workflows worldwide, especially in regions with limited access to specialist care. Clinical Trial: NCT06686251, Registered on 2024-11-13.
Background: Symptom clusters are closely related to the decline in patients’ quality of life, increased risk of treatment interruption and poor prognosis. Among patients with ovarian cancer, the man...
Background: Symptom clusters are closely related to the decline in patients’ quality of life, increased risk of treatment interruption and poor prognosis. Among patients with ovarian cancer, the manifestation of psychoneurological symptom clusters are particularly prominent, seriously affecting their quality of life and prognosis of the disease. Efficient intervention measures are urgently needed. However, there is still a lack of specific treatment methods for the psychoneurological symptom clusters of ovarian cancer at present. Traditional Chinese medicine shows great potential in improving tumor-related symptom clusters and has unique advantages in overall regulation and comprehensive intervention. Objective: The primary objective of this study is to evaluate the efficacy and safety of the TSZA regimen in alleviating mental and psychological symptoms among ovarian cancer patients. Secondary objectives include assessing its impact on patients’ quality of life and survival outcomes. Furthermore, the study aims to explore the characteristics of the patient population that derives benefit from the TSZA regimen for these symptoms. Methods: A total of 316 ovarian cancer patients aged 18 to 70 with psychoneurological symptom cluster will be included and randomly divided into two parallel groups. Both groups will receive standard treatment for ovarian cancer as the basic treatment. The experimental group will receive the TSZA regimen, that is, Compound Ciwujia Granules (containing Acanthopanax senticosus and Schisandra chinensis) combined with psychological intervention. The control group will receive placebo combined with psychological intervention. The primary outcome measure is the psychoneurological symptom cluster score. Secondary outcome measures included the Pittsburgh Sleep Quality Index (PSQI), the Patient Health Questionnaire -9 (PHQ-9), the Generalized Anxiety Disorder -7 (GAD-7) scale, the revised Piper Fatigue Scale, the EORTC QLQ-C30 Quality of Life Scale, the TCM Syndrome Scale, and the 1-year survival analysis. In addition, this study also set a series of exploratory indicators (including sleep diary, functional magnetic resonance imaging, biomarkers of peripheral blood and tumor tissue, proportion of immune cells, cytokine levels, HPA axis function and immune gene expression analysis) and safety indicators (including vital signs, liver and kidney function and electrocardiogram). The study will be evaluated based on different indicators during the treatment period (baseline and the 1st, 2nd, and 3rd months of enrollment) and the follow-up period (the 6th, 9th, and 12th months of enrollment). Data analysis will be conducted using SPSS 26 software. A p value <0.05 is considered statistically significant. Results: This study is designed to enroll a total of 316 participants. Participant enrollment is set to commence in October 2025, with no recruitment having occurred as of November 2025. The recruitment period will extend until September 2028 or until the target enrollment is met. Data analysis is scheduled for November 2028, with submission of the trial results to a peer-reviewed journal anticipated by May 2029. Conclusions: This study will evaluate the efficacy of the TSZA regimen in managing psychoneurological symptom clusters in ovarian cancer patients, and generate clinical evidence for a new therapeutic option that improves quality of life and alleviates the symptom burden. Clinical Trial: ClinicalTrials.gov NCT07050563; https://clinicaltrials.gov/study/NCT07050563
Background: Sales of heated tobacco products (HTP) expanded rapidly after commercial launch in 2014, yet globally comparable estimates of the number of HTP users remain limited. National surveys rare...
Background: Sales of heated tobacco products (HTP) expanded rapidly after commercial launch in 2014, yet globally comparable estimates of the number of HTP users remain limited. National surveys rarely include standardized HTP measures, and international surveillance platforms do not provide harmonized estimates. A transparent global time series is needed to inform surveillance, modeling, regulation and health policies. Objective: To construct a reproducible, annual global estimate of HTP users for 2014-2024 using publicly available corporate disclosures, to quantify uncertainty, and to compare the resulting totals with user counts derived from nationally representative survey prevalence data where available. Methods: We compiled annual HTP user counts and heated-tobacco stick shipments from public reports of the main transnational tobacco companies with material HTP activity over 2014-2024. In the primary series, we used company-reported user totals where available and converted shipments to implied users where only volumes were disclosed, with +/-50% sensitivity around brand-specific consumption assumptions. As a secondary, shipments-only sensitivity, we ignored all company user counts and converted all companies’ shipments to users using a literature-based consumption intensity estimates. As a complementary check, we used nationally representative current-use prevalence surveys from 35 countries and converted these to user counts using UN population data and summed across countries. Results: Global HTP users increased from negligible levels in 2014 to an estimated 48.9 million in 2024 in the primary disclosure-based series (range 45.6–52.1 million). In the shipments-only sensitivity using literature-based consumption, totals were higher: 67.9 million in 2024 (range 59.7–78.7 million), with a consistent increasing trajectory. Growth was led by PMI throughout the period; BAT gained traction from 2017, and JT contributed smaller but rising shares. The survey-based approach yielded 21.8 million users across 35 countries. Differences across approaches reflect coverage, timing, and counting conventions rather than trend direction. Conclusions: A transparent, updateable global time series indicates rapid HTP uptake from 2014 to 2024. Depending on counting conventions, 2024 totals span 45.6–78.7 million users (primary range 45.6–52.1; shipments-only range 59.7–78.7), with a survey-based lower bound of 21.8 million. As standardized HTP measures enter national surveys, coverage widens, and corporate definitions are clarified, estimates can be recalibrated and uncertainty narrowed.
Background: Digital decision-support tools for labour care remain limited, with few technologies successfully addressing the complex, time-sensitive decisions required during labour triage. Fit4Labour...
Background: Digital decision-support tools for labour care remain limited, with few technologies successfully addressing the complex, time-sensitive decisions required during labour triage. Fit4Labour is a clinician-facing, data-driven research tool, currently under development, that combines computerised cardiotocography interpretation with maternal and fetal risk factors to generate individualised risk scores at labour onset. Its primary aim is to support clinicians in identifying fetuses who may require closer monitoring or expedited delivery, while simultaneously providing reassurance in low-risk cases. By promoting consistent communication and timely escalation of care, Fit4Labour seeks to strengthen clinical decision-making. Understanding and addressing usability and implementation barriers will be critical to its adoption in clinical practice. Objective: To assess whether the Fit4Labour tool, developed through intensive co-development at a single hospital, maintains usability and implementation readiness when tested in hospitals with differing clinical contexts. Methods: We conducted a convergent parallel mixed-methods study in three UK hospitals (December 2022 to May 2025). Phase 1 involved iterative co-development with midwives and doctors at Oxford University Hospitals NHS Foundation Trust; Phase 2 validated the locked version at Birmingham Women’s and Children’s NHS Foundation Trust and Buckinghamshire Healthcare NHS Trust. Midwives and doctors participated in scenario-based usability sessions evaluated with the System Usability Scale (SUS) and Single Ease Question (SEQ) and task completion time to assess efficiency, followed by focus groups and interviews analysed thematically. Results: Twenty-six healthcare professionals participated: 12 in co-development (seven midwives, five doctors) and 14 in validation (eight midwives, six doctors). There was an incremental improvement with validation sites having higher SUS scores (85.8 ± 10.2) for the locked version (v4.0) compared to the initial version (1.0) tested in Oxford (77.5 ± 15.1). Task efficiency improved by 16.9% (from a mean of 11.8 to 8 minutes) with a 28% reduction in performance variability, indicating consistent usability across sites. SEQ scores were consistently high (mean 6.1/7.0).
Thematic analysis identified 12 themes within three domains: Clinical Integration and Workflow, Technology Adoption and Implementation, and Patient Safety and Decision-Making. Participants described the Fit4Labour tool as a supportive tool, “like a co-pilot”, improving confidence in their decisions with the potential to aid assessment and triage. Perceived limitations included an incomplete risk factor profile and the need for minor technical adjustments or integration with existing hospital systems to facilitate adoption. Conclusions: Through systematic co-development, the Fit4Labour tool demonstrated high usability and consistent performance across multiple hospitals, suggesting potential for integration into existing workflows with minimal local adaptation. Clinicians viewed the tool as a supportive aid that enhanced decision-making while preserving clinical autonomy. While further testing in clinical environments is needed, these findings demonstrate that intensive co-design can produce decision-support tools that transfer effectively across hospitals with differing clinical practices. Clinical Trial: NA
Artificial intelligence (AI) is rapidly reshaping the landscape of health, from clinical diagnostics and disease surveillance to the prediction of individual health risks. Yet, its immense promise wil...
Artificial intelligence (AI) is rapidly reshaping the landscape of health, from clinical diagnostics and disease surveillance to the prediction of individual health risks. Yet, its immense promise will only materialize if the tools we deploy work for everyone. When algorithms are trained on incomplete or biased datasets, they risk embedding historical health disparities and can replicate patterns of uneven data representation that limit accuracy and generalizability across population groups (1). Addressing algorithmic bias should be treated as a health quality standard, comparable in importance to safety and efficacy evaluations, ensuring consistent performance across all segments of the population. This editorial aims to inform both policymakers and technical experts, offering a framework that bridges scientific rigor with practical, regionally grounded governance models.
Background: This randomized controlled trial evaluated the efficacy of the “Prostate Cancer (PCa) Education & Resources for Couples” (PERC) eHealth intervention to improving the outcomes for patie...
Background: This randomized controlled trial evaluated the efficacy of the “Prostate Cancer (PCa) Education & Resources for Couples” (PERC) eHealth intervention to improving the outcomes for patients and their partners.
Methods: We enrolled 280 dyads (560 individuals) of patients with localized PCa who recently completed treatment, and their partners. Dyads were randomized to PERC or a control group who accessed an an NCI PCa website. Validated questionnaires assessed quality of life (QOL, FACT-G total; primary outcomes) and FACT-G subdomains, symptom and psychosocial measures (secondary outcomes) at baseline and 4, 8, and 12 months (T1-T4). Multilevel linear mixed models tested intervention effects.
Results: FACT-G total, subdomain, and overall overall psychosocial outcomes did not differ significantly between groups over time. PERC patients reported marginally higher physical QOL (95% CI: –0.1 to 1.9, d=0.33), better illness appraisal (95% CI: 0.0 to 0.4, d=0.38), lower pain (95% CI: –5.3 to –0.2, d=0.38) at T4, and less frequent fatigue across time (95% CI: –3.9 to –0.4). PERC partners reported less urinary symptom bother at T3 (95% CI: -1.0 to14.1, d=0.44).
Discussion: PERC demonstrated exploratory benefits including patients’ improved physical QOL, less fatigue, lower pain, improved illness appraisal in patients, and less urinary bother in partners.
Background: Exercise-induced reductions in body mass index (BMI) have been shown to lower serum uric acid (SUA) levels and mitigate cardiovascular disease (CVD) risk in obese individuals with gout. Ho...
Background: Exercise-induced reductions in body mass index (BMI) have been shown to lower serum uric acid (SUA) levels and mitigate cardiovascular disease (CVD) risk in obese individuals with gout. However, poor exercise adherence remains a major barrier. Therefore, developing engaging and effective interventions to promote long-term adherence is essential. Objective: This study aimed to evaluate the effects of a novel intervention combining exergames with ice therapy on BMI, pain, quality of life, SUA levels, kinesiophobia, and psychological well-being in obese patients with gout. Methods: In this randomized controlled trial, 28 obese gout patients were randomly assigned (1:1) to either an exergames-only group (E group) or an exergames plus ice therapy group (E+I group). Both groups underwent a 4-week intervention, performed 3–5 times per week. Assessments were conducted at baseline (week 0), post-intervention (week 4), and follow-up (week 12). Primary outcomes included BMI, SUA, waist circumference (WC), hip circumference (HC), waist-to-hip ratio (WHR), and blood pressure. Secondary outcomes included fasting blood glucose, lipid profiles (TC, TG, HDL, LDL), Visual Analogue Scale (VAS) scores, the Gout Impact Scale (GIS), the Tampa Scale of Kinesiophobia (TSK), and the Positive and Negative Affect Schedule (PANAS). Results: Both groups demonstrated significant improvements in weight, BMI, WC, blood pressure, and psychological outcomes (p < 0.05). The E+I group exhibited greater improvements in quality of life, positive affect, and reduction of kinesiophobia compared to the E group (p < 0.05). However, no significant changes were observed in biochemical markers such as SUA, fasting glucose, or lipid profiles (p > 0.05). Conclusions: Both exergames alone and in combination with ice therapy improved body composition, blood pressure, and psychological well-being in obese patients with gout. The combined intervention may offer additional benefits, particularly in enhancing emotional health and reducing movement-related fear, thus supporting its potential for broader clinical application. Clinical Trial: Chinese Clinical Trial Registry (ChiCTR2300070029). Registered on 31 March 2023.
Background: Maternal outcomes remain inequitable worldwide. Severe morbidity persists, and current risk assessment tools are largely arbitrary, focusing on biomedical factors while overlooking social...
Background: Maternal outcomes remain inequitable worldwide. Severe morbidity persists, and current risk assessment tools are largely arbitrary, focusing on biomedical factors while overlooking social determinants of health. There is a need for data-driven, artificial intelligence (AI) models to improve early pregnancy risk identification and management. Objective: To develop and internally validate first-trimester AI-based antenatal risk assessment models across three geographically and socio-ethnically diverse populations (Sweden, Chile, and Singapore), and to compare their performance against existing clinical risk assessment strategies. Methods: Retrospective population-based data from over 500,000 pregnancies from Sweden, Chile, and Singapore were used to develop Machine learning (ML) models predicting a composite of adverse maternal and neonatal outcomes. Models were trained and validated separately for each population using first-trimester variables. Model discrimination, measured by the area under the receiver operating characteristic (AUROC) curve, was compared with corresponding real-world first trimester risk assessment approaches. Results: The prevalence of the composite adverse outcome was 10.4% (75,647/727,354) in Sweden, 21.9% (1,302/5,934) in Chile, and 16.3% (6,165/37,813) in Singapore. In Sweden, the guideline-based risk assessment achieved an AUROC of 0.53, compared with 0.65 for the LightGBM model (P<.001). In Chile, the midwifery-led risk assessment achieved an AUROC of 0.52, versus 0.66 from the Traditional ML CatBoost model (P<.001). In Singapore, healthcare professional-based risk assessment reached an AUROC of 0.56, compared with 0.60 for the LightGBM model (P<.05). In the Swedish and Singapore, sociodemographic variables were among the most influential predictive features. Conclusions: AI-based models developed using first-trimester data surpassed that of existing first-trimester clinical risk stratification strategies across all three distinct populations. These findings highlight the potential of integrating social, demographic, and behavioural determinants into AI-driven, clinician-augmented antenatal care frameworks to promote more equitable and personalized pregnancy risk assessment.
Background: Digital mental health tools (DMHTs) offer scalable support, but engagement varies. Understanding what shapes initiation and ongoing use is essential for effective design and implementation...
Background: Digital mental health tools (DMHTs) offer scalable support, but engagement varies. Understanding what shapes initiation and ongoing use is essential for effective design and implementation. Objective: To synthesise determinants of adults’ initiation and engagement with DMHTs, organised through two lenses: (a) psychological factors aligned with the Theory of Planned Behavior (TPB) and (b) design and access features. Methods: A systematic search of nine databases (June 2025) identified qualitative and mixed-methods primary studies reporting end-users’ experiences with DMHTs. Studies were screened and reported in accordance with PRISMA. Quality appraisal used QuADS. Data were synthesised using a framework-guided thematic approach, mapping findings to TPB constructs and complementary design/access domains. Results: 22 studies met inclusion criteria. Findings clustered into two interdependent domains. TPB constructs explained how beliefs, social expectations, and perceived control shaped decisions to start and persist with DMHTs. Design and access features frequently acted through these same pathways, especially by altering perceived behavioral control, with cost, connectivity, device constraints, and time flexibility affecting feasibility, with content design and privacy shaping perceived value and trust. Perceived fit (goals, cultural/linguistic relevance, and routine alignment) consistently influenced both initiation and continuation. Several features operated bidirectionally, depending on context, the same feature could facilitate or hinder engagement. Conclusions: Engagement with DMHTs is jointly determined by users’ beliefs and the design and access conditions within which tools are offered. Implementation should pursue a dual strategy, strengthen willingness to seek support (addressing attitudes, norms, and perceived control) while engineering low-effort, trustworthy, and context-appropriate experiences. Priorities include equity-focused policies (data costs, devices, connectivity), transparent data practices, co-design with diverse communities, and consistent, theory-informed outcome measures.
Background: Obesity imposes a substantial economic burden, accounting for an estimated 10% of total healthcare expenditures in Germany. Digital health applications have demonstrated effectiveness in s...
Background: Obesity imposes a substantial economic burden, accounting for an estimated 10% of total healthcare expenditures in Germany. Digital health applications have demonstrated effectiveness in supporting weight management among individuals with obesity; however, evidence regarding their long-term economic impact remains limited. Objective: The objective of this study was to evaluate the long-term cost-effectiveness of the Oviva Direkt app for obesity treatment in the German healthcare context. Methods: A cohort-based Markov model was developed, informed from the Core Obesity Model, to evaluate the cost-effectiveness of a digital health application for weight management versus care as usual over a 10-year time horizon from a societal perspective. The model simulates disease progression using 6-month and annual cycles after an initial monthly phase and includes health states for key obesity-related comorbidities such as type-2-diabetes (T2D), acute coronary syndrome (ACS), stroke, cancer, and obstructive sleep apnoea. Patients enter the model at age 46 with a BMI of 30–45 kg/m², based on trial data. The analysis considered direct and indirect costs, life-years, and quality-adjusted life-years (QALYs). The primary outcome was the incremental cost-effectiveness ratio (ICER), complemented by net monetary benefit (NMB) analysis. Weight trajectories were extrapolated based on trial results using three scenarios (base case decay model, weight maintenance, full regain). Sensitivity analyses were conducted to assess uncertainty. Results: Under base case assumptions, the digital health application dominated care as usual, yielding cost savings of 3,511.85 € and a QALY gain of 0.0683 (≈3.6 weeks in perfect health). Direct medical costs were reduced by nearly 520 €. T2D prevalence was 1.6 percentage points lower, reducing time lived with diabetes by 8 months. Scenario analyses confirmed consistent cost-effectiveness. Conclusions: Digital health applications for weight management are cost-effective and clinically beneficial for individuals with obesity in Germany. These results add to growing evidence for digital health solutions, aligning with findings from applications for other conditions such as depression and multiple sclerosis.
Black, Caribbean, and African (BCA) immigrant communities in Canada face systemic inequities that undermine their mental health and limit access to culturally relevant mental health promotion (MHP) st...
Black, Caribbean, and African (BCA) immigrant communities in Canada face systemic inequities that undermine their mental health and limit access to culturally relevant mental health promotion (MHP) strategies. While national policy frameworks increasingly recognize these disparities, there remains a lack of consolidated evidence on existing MHP programs and initiatives developed for, with, or by BCA populations. This scoping review aims to comprehensively map the landscape of MHP strategies, programs, and activities targeting BCA immigrant communities in Canada. It will identify barriers and facilitators influencing implementation and uptake and illuminate gaps in research, policy, and practice.
Guided by Arksey and O’Malley’s scoping review framework, the review will draw on six databases Medline (OVID) APA PsychINFO (OVID), EMBASE (OVID), PubMed, CINAHL Plus (EBSCOhost), and Google Scholar, and include grey literature such as community reports and government-funded initiatives. The eligibility criteria focus on English-language sources addressing MHP or mental illness prevention within the Canadian BCA immigrant context. Data will be charted in duplicate and analyzed descriptively then organized using tables and narrative synthesis to highlight thematic trends and opportunities for system transformation.
The implications of this review are far-reaching. It will inform evidence-based policy development, support culturally responsive service design, and contribute to equity-driven public health practices. Moreover, it seeks to validate community-led innovations and knowledge systems that are often excluded from formal research. By illuminating both the strengths and silences in current MHP efforts, this study will guide future research and action toward a more inclusive, just, and culturally grounded mental health landscape in Canada.
Background: Dyslipidemia is a prevalent lifestyle and metabolic disorder that poses a significant risk for cardiovascular diseases. From the Ayurvedic standpoint, dyslipidemia may be understood as a d...
Background: Dyslipidemia is a prevalent lifestyle and metabolic disorder that poses a significant risk for cardiovascular diseases. From the Ayurvedic standpoint, dyslipidemia may be understood as a disorder of fat metabolism. Trikatu, a classical Ayurvedic formulation is scientifically recognized for its role in modulating metabolic processes and enhancing bioavailability. This study was undertaken to assess its role on lipid parameters and markers of metabolism. Objective: To assess the efficacy and safety of Ayurvedic Formulation “Trikatu” for improving lipid parameters in dyslipidemia and to assess the changes in Gut Microbiota Correlates Methods: This study is a prospective, single-centre, randomized, double-blind, placebo-controlled clinical trial involving 120 participants aged 30–60 years with dyslipidemia, including borderline cases with low ASCVD risk and BMI between 18.5 and 29.9 kg/m². Participants will be randomized in a 1:1 ratio to receive either Trikatu (1000 mg) or a matching placebo, administered orally twice daily after food for 12 weeks, along with standardized dietary and lifestyle guidance. A follow-up assessment will be conducted 28 days post-intervention without medication. The primary outcome is the percentage change in fasting plasma triglycerides at 12 weeks. Secondary outcomes include improvements in total cholesterol, HDL, LDL, apolipoproteins, adiponectin, leptin, glycemic and inflammatory markers, gut microbiota profile, blood pressure, insulin resistance (HOMA-IR), and the proportion of participants achieving lipid targets. Drug compliance and any adverse events or drug reactions will be systematically documented. Results: The screening and recruitment process for this trial started on 29.12.2022. The trial is completed, but data analysis work is not yet initiated. Conclusions: The early intervention in dyslipidemia—especially in borderline cases with low ASCVD risk—is a sustainable strategy to curb the epidemic. Clinical Trial: Clinical Trial Registry of India (CTRI/2022/11/047322) Registered on 15/11/2022.
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.
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.
Mental health research increasingly pursues societal impact and addresses urgent challenges, which places researchers at the intersection of two powerful forces: the drive for innovation, and the impe...
Mental health research increasingly pursues societal impact and addresses urgent challenges, which places researchers at the intersection of two powerful forces: the drive for innovation, and the imperative of ethical responsibility. Drawing on the NEON Young Norway Study, a research project co-developed with youth, clinical, and technology partners, this paper explores four ethical tensions in youth mental health research. Four tensions appear broadly relevant across contexts: (1) informational rigor vs. methodological flexibility; (2) formal ethical standards vs. youth-friendly communication; (3) safeguarding against harm vs. enabling youth participation; and (4) pseudonymization vs. authentic storytelling. These tensions create a significant gap between scholarly ethical frameworks and practical guidance for youth mental health research. We argue that responsible research must collaboratively develop and codify ethical norms in youth mental health research that shape and influence governance. Critically, ethics should function not as an innovation barrier but as a dynamic compass for responsible, inclusive, and impactful research. When ethical frameworks inadvertently exclude populations in vulnerable situations, knowledge gaps emerge that may perpetuate harm. Thus, ethical practice must actively enable safe and equitable inclusion, not merely prevent it.
Background: The ubiquitous use of smartphones has given rise to addictive patterns of use, often termed “problematic smartphone use” (PSU), which disproportionately impacts children and young peop...
Background: The ubiquitous use of smartphones has given rise to addictive patterns of use, often termed “problematic smartphone use” (PSU), which disproportionately impacts children and young people and is associated with poor mental health. Emerging evidence suggests that patterns of smartphone use (e.g., PSU and high screen time) may also influence eating patterns and contribute to symptoms associated with eating disorders (ED), although the nature of this relationship remains poorly understood. Objective: The aim of this systematic review was to examine the association between PSU and ED psychopathology or ED-related outcomes (e.g., body dissatisfaction, emotional eating, food addiction) in clinical and non-clinical populations and explore potential moderators and mediators. Methods: This pre-registered systematic review conducted according to PRISMA guidelines searched three databases (PubMed, EMBASE and Web of Science) for studies published after January 2011 reporting data on PSU and ED psychopathology. Results: Thirty-six studies met the pre-specified eligibility criteria, with almost all reporting cross-sectional data in non-clinical populations (mean±SD age 17.0±5.5). Most studies were assessed as being of good quality (n = 28; 78%). In these non-clinical samples, the vast majority of studies reported a positive association between PSU and ED psychopathology, which was largely consistent across age groups and countries. Identified mediators of this relationship included greater emotional regulation difficulties, and anxious and depressive symptoms. Positive associations were also found across studies between PSU and several ED-related outcomes including food addiction, body dissatisfaction, uncontrolled eating and emotional overeating. Daily smartphone use was consistently related to higher ED psychopathology. Conclusions: PSU and greater daily screen time are associated with higher ED symptoms, body image dissatisfaction, and broader disordered eating behaviours. Due to a paucity of studies in clinical populations, these conclusions are generalisable only to non-clinical populations (i.e., those without a formal diagnosis of an ED). Further longitudinal research in clinical populations is needed to fully contextualise the impact of PSU and screen time on ED risk and severity.
Background: Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality globally. It is often diagnosed at advanced stages or in patients unsuitable for traditional curative tre...
Background: Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality globally. It is often diagnosed at advanced stages or in patients unsuitable for traditional curative treatments. Stereotactic body radiation therapy (SBRT), also known as stereotactic ablative radiotherapy (SABR), has emerged as a highly effective and non-invasive local treatment, demonstrating excellent local control rates across various HCC stages. This bibliometric study aims to comprehensively analyze the research trends, hotspots, and collaborative patterns in the field of SBRT for HCC from 2004 to 2025. Objective: This bibliometric study aims to comprehensively analyze the research trends, hotspots, and collaborative patterns in the field of stereotactic body radiation therapy (SBRT) for hepatocellular carcinoma (HCC) from 2004 to 2025. Methods: Literature on SBRT for HCC published between January 1, 2004, and November 12, 2025, was retrieved from the Web of Science Core Collection (WoSCC) database. A total of 2,216 relevant publications were identified and analyzed using bibliometric software including Biblioshiny (R version 4.4.1), VOSviewer (version 1.6.17), ScimagoGraphica (version 1.0.41), CiteSpace (version 6.1. R6), and Microsoft Excel (2019 edition). The analysis covered publication output, country/region, institution, author contributions, journal distribution, co-citation patterns, and keyword trends. Results: A total of 2,216 publications (1,796 articles, 420 reviews) were analyzed, revealing a significant upward trend in annual publications, with a peak of over 220 in 2021. The United States and China emerged as leading countries in research output and international collaboration, with the United States also having the highest citation count. Key institutions included the University of Toronto, the University of Texas System, and Harvard University. Laura A. Dawson and Marta Scorsetti were identified as the most prolific and influential authors. The International Journal of Radiation Oncology Biology Physics was the core journal, leading in H-index and citations. Keyword analysis identified “stereotactic body radiotherapy,” “radiotherapy,” “SBRT,” and “radiofrequency ablation” (RFA) as high-frequency terms. Recent research hotspots (2023-2025) include “Y90 radioembolization,” “open-label studies,” and “motion management,” indicating a shift towards integrated and technologically advanced approaches, along with growing interest in combining SBRT with immunotherapy and targeted therapy. Conclusions: The field of SBRT for HCC has experienced rapid growth over the past two decades, with the United States and China at the forefront of research. SBRT has proven effective across early-stage, recurrent, and locally advanced HCC, particularly in combination with systemic therapies. Future research should prioritize large-scale randomized controlled trials (RCTs), explore SBRT’s synergy with novel immunotherapies, optimize patient selection with biomarkers, and refine advanced delivery techniques to further enhance patient outcomes and broaden its clinical application.
Background: Breast cancer recurrence (BCR) significantly impacts patient survival, quality of life, and overall treatment efficacy, underscoring the critical need to identify causal factors influencin...
Background: Breast cancer recurrence (BCR) significantly impacts patient survival, quality of life, and overall treatment efficacy, underscoring the critical need to identify causal factors influencing recurrence. Although Sociocultural factors of Mental Health(SFOMHs) have been extensively associated with breast cancer outcomes, precise causal relationships remain poorly understood due to limitations in traditional correlational methods. Objective: The objective of this study is to (1) develop and evaluate a comprehensive, multi-step framework to rigorously detect and estimate the causal effects of 22 Sociocultural factors on BCR, and (2) benchmark the proposed framework against established causal models to ensure generalizability and reliability across diverse datasets. Methods: We first developed a Clinical Longformer Multi-Task Multi-Label Classifier (CLMT-MLC) to accurately detect and classify Sociocultural factors from unstructured clinical notes. Next, we designed a novel Siamese Neural Network based subgroup discovery (SNN-SD) method, combined with a Causal Effect Variational AutoEncoder (CEVAE), to estimate subgroup-specific Conditional Average Treatment Effects (CATE). A new dataset, SFOMH-OncoBreast-Clinic, comprising Sociocultural factor–annotated clinical notes and BCR annotations, was created in collaboration with experts and sub-sampled from the MIMIC-IV dataset. Performance was benchmarked against state-of-the-art causal models on the Infant Health and Development Program (IHDP) dataset. Results: The proposed framework significantly outperformed state-of-the-art causal models on the IHDP dataset. Applied to the SFOMH-OncoBreast-Clinic dataset, the model reliably identified actionable causal determinants of BCR, demonstrating its ability to advance understanding of Sociocultural factors as key predictors of recurrence. Conclusions: This study establishes a robust causal inference framework integrating Clinical Longformer, SNN-SD, and CEVAE, supported by a novel annotated dataset. The approach enhances detection of actionable Sociocultural factors, informing personalized care strategies and policy development to reduce breast cancer recurrence and health disparities.
Background: Conversational agents for women’s health often fail to meet user needs, offering either clinically sterile advice or unreliable peer anecdotes. This limitation creates a tension between...
Background: Conversational agents for women’s health often fail to meet user needs, offering either clinically sterile advice or unreliable peer anecdotes. This limitation creates a tension between the need for factual safety and emotional resonance in sensitive health contexts. Objective: We aimed to address this gap by developing and conducting a formative evaluation of HerCare, a conversational agent built on a novel dual-source Retrieval-Augmented Generation (RAG) architecture. The system systematically fuses expert medical knowledge with peer narratives to act as a “trust-calibration mechanism” through transparent source attribution. Methods: We conducted a remote, single-session field study with 243 participants to evaluate the system's usability, trustworthiness, and affective dynamics. Participants interacted with the agent regarding a women's health topic of personal interest. We employed a mixed-methods approach, combining standardized self-report metrics—the Chatbot Usability Questionnaire (CUQ) and Net Promoter Score (NPS)—with computational linguistic analyses (VADER sentiment analysis and NRC Emotion Lexicon) of 1,191 conversational turns. Results: Participants reported high system usability, with a mean CUQ score of 75.67 (SD 15.50). The system achieved a Net Promoter Score (NPS) of 60.0, indicating strong willingness to recommend the tool. In the Post-Interaction Questionnaire (5-point scale), participants rated Helpfulness (mean 4.55) and Trustworthiness (mean 4.35) highly. Computational analysis revealed a consistent conversational polarity shift: while user queries often skewed neutral-to-negative (compound scores down to -0.181), agent responses were consistently and strongly positive (compound scores +0.555 to +0.826). Emotion profiling identified a recurring “Validate, then Redirect” strategy, where the agent acknowledged distress (Sadness) before pivoting to a frame of Trust and Anticipation. Conclusions: The dual-source architecture successfully balanced clinical accuracy with emotional support, resulting in high perceived empathy and trust. These findings demonstrate the feasibility of weaving clinical sources with lived experiences to create safer, more resonant health AI, offering a transferable design pattern for future empathy-attuned systems.
Background: Climate change is a critical global health emergency with direct consequences for public health. As frontline health promoters and agents of change, nurses must develop specific competenci...
Background: Climate change is a critical global health emergency with direct consequences for public health. As frontline health promoters and agents of change, nurses must develop specific competencies to implement sustainable practices and respond to climate-related health challenges. Objective: This overall work aimed to translate the Climate, Health, and Nursing Tool (CHANT) into French, culturally adapt it and conduct a content validation. The CHANT assesses nurses’ awareness, motivations, concerns and self-reported behaviours regarding climate change by evaluating both item-level content validity indices (I-CVI) and scale-level content validity indices (S-CVI). The secondary aim was to explore potential associations between the sociodemographic and professional characteristics of our expert panel members and their CVI ratings of the CHANT’s items. Methods: A descriptive, international study design was used, involving a three-round, modified e-Delphi technique to explore the relevance and comprehensiveness of each item and response option in the translated tool. The study was conducted between January and June 2025 in Switzerland’s French-speaking regions, France and Belgium. A panel of experts in nursing, planetary health and environmental science participated in the content validation process, contributing to an iterative refinement of the tool’s items. To ensure methodological transparency and rigour, this Delphi study was conducted and reported in accordance with the guidance on Conducting and REporting of DElphi Studies developed by Jünger et al. (2017). Results: Over three rounds, 57 experts evaluated the overall comprehensibility, relevance and response options of the CHANT’s 12 items. After Round 1, the comprehensibility I-CVI ranged from 0.786–1.0 across individual items, and the S-CVI for the full 12-item scale was 0.935. Complete consensus (100%) was achieved on every item. After Round 2, I-CVI ratings ranged from 0.71–1.0, and the S-CVI was 0.91, with 92% expert consensus. Item 2, suggested by the expert panel during Round 1, did not reach the predefined consensus threshold. In Round 3, I-CVI ratings ranged from 0.82–1.0, and the S-CVI remained at 0.935, confirming the expert panel’s continued consensus. The process concluded with a cognitive debriefing, during which the complete consensus on every item and response option was reaffirmed, as reflected by consistently high I-CVI and S-CVI ratings, thereby supporting the CHANT’s internal content validity. Conclusions: The rigorous validation of the CHANT’s French translation is a foundational step toward developing and strengthening climate-related competencies in nursing education and clinical practice. This validation supports the development and implementation of targeted strategies to strengthen eco-literacy in healthcare settings.
Background: Exfoliative cheilitis (EC) is a rare, chronic inflammatory disorder of the vermilion border, marked by persistent desquamation, crusting, and hyperkeratosis. It often affects individuals f...
Background: Exfoliative cheilitis (EC) is a rare, chronic inflammatory disorder of the vermilion border, marked by persistent desquamation, crusting, and hyperkeratosis. It often affects individuals from adolescence to late adulthood and is linked to behavioral habits, psychiatric comorbidities, nutritional deficiencies, and hypersensitivity responses. Conventional therapies, including topical corticosteroids, antibiotics, antifungals, and immunomodulators, rarely achieve sustained remission, prompting investigation into novel therapeutic options. Objective: To evaluate the efficacy, safety, and clinical outcomes of laser-based interventions for the management of refractory EC. Methods: A systematic literature review was conducted using PubMed, Scopus, and Web of Science (2015–2025), identifying case reports, case series, and experimental studies involving laser or phototherapy for EC. Extracted data included laser type, parameters, number of sessions, clinical response, recurrence rates, adverse effects, and patient-reported outcomes. Results: Three main laser modalities were identified: excimer laser (308 nm), fractional CO₂ laser (10,600 nm), and the CO₂ laser pinhole method. Excimer laser therapy led to marked symptom improvement within five weeks, with remission sustained up to 23 months. Fractional CO₂ laser therapy achieved up to 90% clinical improvement over three sessions and demonstrated significant reductions in DLQI scores. The CO₂ pinhole method promoted durable lesion resolution with minimal discomfort or scarring. Mechanistically, lasers induce neocollagenesis, normalize keratinocyte turnover, suppress inflammation via T-cell apoptosis, and restore skin barrier function. Compared to conventional treatments, laser therapies demonstrated superior symptom resolution, prolonged remission, and favorable safety profiles with manageable, dose-dependent side effects such as transient burning or blistering. Conclusions: Laser therapies offer a targeted and effective treatment approach for treatment-refractory and chronic exfoliative cheilitis, with benefits extending beyond symptom relief to improved quality of life and reduced corticosteroid dependence. While early evidence is promising, standardized protocols and larger controlled studies are needed to guide clinical practice, establish long-term efficacy, and optimize patient selection criteria.
Background: Therapeutic chatbots are increasingly deployed across digital mental health services, yet most evaluation efforts remain diagnostic rather than actionable. As a result, organizations lack...
Background: Therapeutic chatbots are increasingly deployed across digital mental health services, yet most evaluation efforts remain diagnostic rather than actionable. As a result, organizations lack structured pathways to translate evaluation findings into validated quality improvements suitable for clinical governance. Objective: This study introduces EvaluationPlus, a decision support framework that operationalizes a reproducible evaluation→enhancement loop for therapeutic AI systems. We aimed to demonstrate its feasibility through expert-guided diagnosis, multi-LLM enhancement mapping, and within-subject validation. Methods: Using the bilingual mental health chatbot Dr.CareSam, we conducted three iterative enhancement cycles. Two clinical psychologists performed structured diagnostic reviews using think-aloud protocols to identify competency-specific deficits. Three large language models (GPT-4.0, Claude 4.0 Sonnet, Gemini 2.5) generated prescriptive enhancement strategies aligned with a seven-dimension therapeutic competency rubric. A double-blind, within-subject A/B study with Korean university students (N=15; IRB-approved) compared baseline and enhanced versions across standardized scenarios. Results: Enhancement cycles yielded substantial overall improvement in therapeutic quality (+21%; 6.26→7.59; dz=0.71). Targeted dimensions (Active Listening and Questioning, Personalization, Complex Thinking) improved by +3.04 points, exceeding gains observed in non-targeted domains (+1.62). User preference strongly favored the enhanced system (87%), and expert ratings confirmed maintained safety and therapeutic appropriateness. Conclusions: EvaluationPlus provides a governance-ready framework for continuous quality assurance of therapeutic AI systems. By linking expert diagnostic procedures with prescriptive LLM-driven enhancements and multi-stakeholder validation, the framework supports reproducible improvement cycles suitable for clinical deployment. Clinical Trial: Not applicable. This study was a non-clinical performance-evaluation experiment, not a clinical trial.
Background: Health information technology (HIT) interventions are complex, context-dependent, and often insufficiently theorized, which can hinder their design, implementation, and evaluation. Program...
Background: Health information technology (HIT) interventions are complex, context-dependent, and often insufficiently theorized, which can hinder their design, implementation, and evaluation. Programme theory approaches such as logic models and Theory of Change (ToC) are well established in public health and implementation science for articulating causal assumptions and guiding evaluation. Their use in medical informatics, however, appears inconsistent and insufficiently understood. A systematic overview of how logic models and ToC have been applied to HIT interventions is therefore needed to support theory-informed development and cumulative learning in the field. Objective: This scoping review aimed to map how logic models, ToC, and related programme theory approaches have been conceptualized, constructed, and applied in HIT-related interventions across disciplines. A secondary objective was to identify implications for medical informatics research and practice. Methods: Following PRISMA-ScR, a protocol was preregistered. Searches were conducted in PubMed, Web of Science, Academic Search Elite, APA PsycArticles and CINAHL. Eligible publications explicitly used a logic model, ToC, or related construct within an HIT intervention in any healthcare or social-care setting. Two reviewers independently screened records and extracted data on study characteristics, type and purpose of HIT, model structure, theoretical foundations, and reported benefits and challenges. Findings were synthesized descriptively and thematically. Results: Sixty-nine publications (2012–2025) met the criteria. Use of programme theory increased markedly after 2020 and spanned medical informatics, public health, health services research, and implementation science. Logic models were most frequently applied to patient-facing and self-management technologies, particularly mobile health, telehealth, and home-based remote monitoring. Most models were used to support HIT development or evaluation. Eighty-seven percent of studies provided a visualization, although structures varied considerably. Seventy-two percent cited guidelines for model development, most commonly MRC guidance, realist evaluation, or the Kellogg Logic Model. Forty-one percent used behavioural or implementation frameworks such as COM-B, CFIR, ERIC, FITT, or NASSS to populate model content. Only three studies reused an existing model. Reported benefits concerned improved theorization, structured evaluation, and stakeholder engagement; challenges included limited empirical evidence, high resource demands, and tensions between context specificity and generalizability. Conclusions: Programme theory approaches are increasingly used to conceptualize and evaluate HIT interventions, yet their application in medical informatics remains fragmented and inconsistently reported. More systematic and theory-informed use of logic models could enhance conceptual clarity, methodological rigour, and cumulative learning. Future work should promote model reuse, establish repositories, strengthen reporting standards, and integrate programme theory in HIT education and research to support coherent development, evaluation, and scaling of digital health interventions. Clinical Trial: Ammenwerth E, Bindel M, Hörhammer I. Logic Models on Interventions Including Health Information Technology: A Scoping Review Across Disciplines (Protocol) [Internet]. Open Science Framework (OSF). 2025. Available from: https://osf.io/zru3n/overview
Background: Digital twins (DTs) offer a transformative paradigm for healthcare by creating dynamic, individualized models that simulate disease trajectories and support personalized interventions. How...
Background: Digital twins (DTs) offer a transformative paradigm for healthcare by creating dynamic, individualized models that simulate disease trajectories and support personalized interventions. However, DT development remains limited by the scarcity of standardized, temporally structured, and multidomain data suitable for modeling chronic disease progression. Most existing DT studies rely on narrowly scoped or proprietary datasets, restricting generalizability. Public health datasets such as the Midlife in the United States (MIDUS) study provide rich biopsychosocial information but are underused due to their structural complexity and lack of semantic integration frameworks. Objective: This study aimed to design, implement, and evaluate a scalable, ontology-guided, and agentic-AI framework for constructing personalized, simulation-capable digital twins from large public health datasets. Using diabetes as a case study, the framework integrates multi-agent coordination, medical ontologies, and large language model (LLM) reasoning to enable explainable feature selection, risk prediction, and disease-progression simulation. Methods: A six-stage DT framework was developed and applied to MIDUS Wave 2 (baseline) and Wave 3 (follow-up) data. Ontology- and LLM-assisted feature selection identified predictors across biological, behavioral, psychosocial, and socioeconomic domains. Cleaned and harmonized data were used to train predictive models (Random Forest, XGBoost, Logistic Regression) to estimate diabetes onset at follow-up. A state-transition simulator was then implemented to model progression dynamics, quantify transitions across low-, medium-, and high-risk states, and evaluate counterfactual “what-if” interventions such as weight reduction and lifestyle improvement. Model performance was assessed using accuracy, F1-score, AUC, and calibration metrics. Results: From 9,976 candidate variables, ontology- and LLM-guided selection retained the top 200 most relevant predictors spanning biological, behavioral, psychosocial, and socioeconomic domains. Predictive modeling achieved strong discrimination, with Random Forest (AUC = 0.97, accuracy = 0.91) and XGBoost (AUC = 0.97, accuracy = 0.90) outperforming Logistic Regression (AUC = 0.94). The state-transition simulator reproduced realistic progression patterns: 33.9% of participants changed risk states between waves, and the high-risk group increased from 10.8% to 32.2%. Next-state prediction accuracy reached 92.5%, confirming the simulator’s ability to capture longitudinal dynamics. Counterfactual simulations demonstrated actionable outcomes: a uniform 10% weight reduction improved risk states for 6.7% of participants and reduced predicted diabetes incidence by 98 cases (576 → 478). A placebo test (0% weight change) produced < 0.3% difference in risk distribution, confirming model stability. Conclusions: This study introduces a generalizable, ontology-guided, and multi-agent framework for constructing personalized digital twins from public datasets. By combining semantic reasoning, multidomain predictors, and progression simulation, the framework transforms static population data into dynamic, interpretable representations of individual health trajectories. The proof-of-concept application to diabetes demonstrates that public health data can support robust, explainable, and intervention-aware digital twins for chronic disease prevention and management.
Background: More than 300 million surgical procedures are performed worldwide each year, and opioids remain a primary approach for managing acute postoperative pain. Studies have demonstrated that a s...
Background: More than 300 million surgical procedures are performed worldwide each year, and opioids remain a primary approach for managing acute postoperative pain. Studies have demonstrated that a significant number of patients do not discontinue opioid treatment and continue to use opioids for months or even years after surgery. Tapering and management of prescription opioids is a well-known practice and is a part of the current clinical guidelines on safe prescribing. Every patient should receive thorough monitoring, education, and a tapering plan when prescribed opioids or receiving refills after a prolonged treatment. There are challenges associated with tapering, including close follow-up, patient education, clinician time, and withdrawal safety. The evolution of smartphone app use for follow-up has shown promising results in some fields of medicine, and patients are increasingly interested in this approach. Objective: The objective of this study is to investigate the efficacy and safety of the Prescriby Clinic and digital support in the form of a software with a patient and clinician facing application as a tapering intervention in patients after knee or hip replacement surgery. Methods: Efficacy for the outcomes will be measured in tapers successfully completed, doses successfully lowered during tapering, number of active users, satisfaction with the intervention. Participant safety will be monitored by assessing adverse effects during tapering using the numeric pain rating scale to assess the severity of pain. Participants are recruited via referrals from orthopedic departments in the hospital after surgery during the 7-month study period to the Prescriby clinic, where they will receive a personalized tapering treatment and follow-up with a clinical pharmacist. Results: All in all, 75 patients were enrolled during the 7-month enrollment period of which two were lost to follow-up, so 73 finished the tapering program. Out of these, 57.5% were female, with an average age of 65. Out of 73 patients who received tapering treatment, 72 patients completely tapered off their opioid medication, while 1 patient tapered down to the same dose as before treatment. The average medication starting dose in morphine milligram equivalents (MME) was 21.55 MME (range: 4.5 - 60). The average duration of taper was 23.7 days (range: 7-97 days). The average adherence was 51% of days, and 42.2% of patients can be described as “active users” with over 75% adherence to usage. Agreeableness with positive statements on the service was on average between "agree" and “agree a little”, leaning closer to “agree”. Conclusions: Age distribution and gender ratio comparable to patients who usually undergo elective hip- and knee replacement. Comorbidities are comparable to similar groups in other countries with a few exceptions. Tapering length was reasonable and patient adherence was acceptable with room for improvement. Patient experience is great overall. The support clinic proved to be an essential part in this pain management during tapering, which led to more personalization for each patient not only at the start of tapering, but throughout the tapering progress. Clinical Trial: The National Bioethics Committee of Iceland has given approval for a study. This study has been given the registration number VSNb2024090010/03.01.
Background: Keep-on-Keep-up Nutrition (KOKU-Nut) is a free, tablet-based digital platform that focuses on increasing physical activity and improving the dietary intake of older adults. Objective: The...
Background: Keep-on-Keep-up Nutrition (KOKU-Nut) is a free, tablet-based digital platform that focuses on increasing physical activity and improving the dietary intake of older adults. Objective: The aim of this study is to assess the feasibility of using KOKU-Nut among community-dwelling older adults. Feasibility was assessed by considering recruitment and retention rates, and acceptability of the intervention and study design. Methods: Participants (community-dwelling adults ≥65 years) were randomised 1:1 to either the intervention or control group. The intervention group were asked to engage with KOKU-Nut three times a week for 12 weeks. Participants in the control group received a leaflet promoting a healthy lifestyle. All participants completed questionnaires at baseline and 12 weeks. A sample of participants were asked to complete an optional interview. The study collected data on anthropometry (height, weight), dietary intake (24-hour food diary), physical function (grip strength, 5 times sit-to-stand), usability of the intervention (system usability scale) and safety (adverse events). Results: Of 51 participants assessed for eligibility, 31 were randomised and 28 completed the 12-week follow-up. Ten of the participants in the intervention group (71.4%) reported engagement with KOKU-Nut three or more times a week. There was no difference in physical function or dietary intake at 12-weeks between participants in the intervention and control group after adjusting for age and baseline values. Conclusions: This feasibility RCT demonstrates the study design was appropriate and acceptable to older adults as demonstrated by the recruitment and retention rates. This is promising and demonstrates the potential for conducting a future powered RCT to assess the effectiveness of KOKU-Nut. Clinical Trial: The trial was registered prior to recruitment (NCT05943366).
Background: Preventive campaigns for older adults must decide how to allocate limited resources across media channels. However, these channel allocation and budget decisions rarely use explicit criter...
Background: Preventive campaigns for older adults must decide how to allocate limited resources across media channels. However, these channel allocation and budget decisions rarely use explicit criteria for distributional equity or digital health strategic planning. As a result, health systems may optimize average uptake while leaving large gaps across socioeconomic groups and media-use profiles. Objective: This study aimed to develop and apply a data-driven agent-based model as a strategic planning tool for older-adult preventive campaigns, comparing channel allocation, personalization, and loss framing options under explicit budget and equity constraints. Methods: We built an agent-based simulation calibrated to national survey data on influenza vaccination and routine health screening among older adults in South Korea. Fifteen prespecified campaign scenarios varied channel allocation across television (TV), digital, and print; total exposure budgets; two equity-focused personalization strategies; and graded loss framing. Primary outcomes were final adoption and time to adoption. Equity outcomes included the minimum class-level adoption and the 90–10 gap across latent classes. Each scenario was simulated over 12 monthly steps with 100 Monte Carlo replications. We also compared scenario portfolios using logistic and clipped-linear link functions and varied the balance of media versus social reinforcement weights, the social reinforcement threshold, and network realizations in sensitivity analyses. Results: TV-only and high-budget strategies produced some of the highest mean adoption rates for both vaccination and screening but often failed to meet equity guardrails for minimum class coverage and between-class gaps. In contrast, personalization strategies that modestly reweighted exposure toward the lowest-uptake class or assigned class-tailored channel portfolios maintained or improved mean adoption. These strategies also substantially raised minimum class-level coverage and narrowed disparities. When efficiency and distributional equity were considered jointly, these personalized portfolios emerged as the most attractive options under fixed budget constraints. Loss framing acted as a secondary tuning lever: within the tested range, stronger loss framing yielded small, monotonic gains in adoption and shorter time to adoption without worsening equity metrics. Scenario rankings were stable across sensitivity analyses, suggesting that the main patterns reflected underlying diffusion dynamics rather than any single modeling choice. Conclusions: This agent-based simulation shows how ex ante planning for preventive campaigns can move beyond intuition by comparing channel allocation and personalization options under explicit equity and budget criteria. For campaigns targeting older adults, modest equity-oriented personalization of TV and digital exposure improved or preserved mean uptake. It also consistently improved distributional equity, whereas diversified channel mixes without personalization were less efficient and less equitable. These findings support integrating equity guardrails and channel-allocation guardrails into early-stage campaign design and prioritizing targeted personalization over simple channel diversification. Future work should validate these patterns in other populations and health systems and link simulated diffusion trajectories with observed exposure and engagement in real-world digital and traditional-media campaigns.
Background: Large language model (LLM) proofreaders for radiology reports generate many false positives (FP) due to the low prevalence of errors. Objective: This study aimed to determine whether an op...
Background: Large language model (LLM) proofreaders for radiology reports generate many false positives (FP) due to the low prevalence of errors. Objective: This study aimed to determine whether an optimized LLM framework could improve both precision and cost-efficiency without compromising error detection capability. Methods: In this retrospective study, 1,000 radiology reports (radiography, ultrasonography, CT, and MRI; 250 each) were sampled from the Medical Information Mart for Intensive Care III (MIMIC-III) database. Two public chest radiography corpora (CheXpert and Open-i) served as external test sets. Three LLM frameworks were evaluated: single-prompt detector (Framework 1); report extractor plus single-prompt detector (Framework 2); and extractor, detector, and false positive verifier (Framework 3). Precision for each framework was assessed using positive predictive value (PPV) and detected errors per 1,000 reports (DE/1k). Overall efficiency was estimated using model inference computational costs. Results: PPV increased from 0.063 [95% CI, 0.036–0.101] in Framework 1 to 0.079 (0.049–0.118) in Framework 2 and 0.159 (0.090–0.252) in Framework 3 (P<.001). Despite improved PPV, detected errors remained stable (DE/1k: 12–14). Human review burden decreased from 192 to 88 reports. Framework 3 also reduced costs to $5.58 per 1,000 reports (vs $9.72 and $6.85 for Frameworks 1 and 2; 42.6% and 18.5% reductions). External validation confirmed similar improvements. Conclusions: A three-pass LLM framework more than doubled precision and halved the cost of radiology report error detection without compromising error detection capability, offering sustainable strategies for AI-assisted quality assurance in both radiological practice and research.
Interventions are sorely needed to address the lack of PrEP awareness and mitigate barriers related to PrEP use. One such intervention modality is social media, as PrEP awareness and communicating iss...
Interventions are sorely needed to address the lack of PrEP awareness and mitigate barriers related to PrEP use. One such intervention modality is social media, as PrEP awareness and communicating issues, such as access and cost, are easily addressable via clear social media messages on platforms PrEP-eligible people, and especially young people, use frequently. This study seeks to extend understanding of PrEP awareness and usage by examining PrEP-related communication across 3 popular social media platforms (Facebook, Instagram, and Twitter), and identifying message and source characteristics. In February 2023, we used CrowdTangle (a public-insights tool owned by Facebook, now known as Meta) to gather a total of 39,790 Facebook posts and 5,628 Instagram posts. We also used Twitter’s public API to collect 14,061 Twitter posts during the same time frame. Of these, we drew a random sample of social media posts from each platform [Facebook (N = 1,000), Instagram (N = 1,000), and Twitter (N = 811)] in February 2023 and analyzed them using a quantitative content analysis. Our findings showed some differences in the type of text-based content most likely to appear on each platform. We also uncovered similar patterns across all 3 platforms. Across all platforms, we observed that definitions of and indications for PrEP were the most common type of text-based content in posts likely to be shared, information about PrEP appearing in social media posts did not seem to draw from traditional sources, and men who have sex with men (MSM) represented the most frequently mentioned target population. Although our study did not detect a large presence of theory-based concepts from behavior change theory such as the reasoned action approach (RAA), across all platforms, attitude emerged most frequently, followed by self-efficacy. These findings shed light on the PrEP-related beliefs shaping young people’s perceptions and engagement. Such insights can guide the design of future social media–based messages, targeting the most influential beliefs to strengthen HIV prevention efforts. They also provide a foundation for advanced machine learning models capable of predicting and explaining the diffusion potential of PrEP-related content.
Background: The differentiation of ischemic from non-ischemic T-wave inversion (TWI) on electrocardiograms (ECGs) is a critical diagnostic challenge in cardiology. The non-specific nature of TWI leads...
Background: The differentiation of ischemic from non-ischemic T-wave inversion (TWI) on electrocardiograms (ECGs) is a critical diagnostic challenge in cardiology. The non-specific nature of TWI leads to high false-positive rates, resulting in unnecessary, costly, and risky invasive procedures for patients. Existing deep learning models are often limited by being single-modality "black boxes". Objective: The objective of this study is to develop a novel diagnostic framework designed to address the critical clinical challenge of accurately differentiating ischemic from non-ischemic TWI. By utilizing a multi-modal Vision-Language Model trained with a Reinforcement Learning (RL) paradigm, this study aims to improve diagnostic accuracy and provide interpretable reasoning. Methods: We develop ECG-R1, a multi-modal framework using the Qwen2-VL-2B Vision-Language Model to analyze both ECG waveform images and associated clinical text. Instead of SFT, the model is trained using a RL paradigm with the Group Relative Policy Optimization (GRPO) algorithm. The model is trained to generate a structured output containing an explicit reasoning trace and a final "Yes" or "No" answer. A two-component, rule-based reward function is designed to assess both format adherence and diagnostic accuracy. Performance is compared against strong Supervised Fine-Tuning (SFT) baselines. Results: On a multi-modal dataset of 12,917 cases with TWI, our GRPO model achieves an average accuracy of 74.07%, demonstrating strong generalization with 72.93% accuracy in cross-hospital validation. This result is an improvement of ~24 % over the ~50% diagnostic accuracy of clinicians and 8.2% higher than the best SFT baseline, using ~71% fewer parameters. Conclusions: The RL-based ECG-R1 framework successfully differentiates ischemic from non-ischemic TWI and demonstrates significantly better generalization than standard SFT methods. By enhancing diagnostic accuracy and providing interpretable reasoning, this approach offers a more robust and trustworthy tool to support clinical decision-making in cardiology.
Background: Qualitative research methods offer vital insights into how patients make treatment decisions, but these approaches are labor-intensive, limited by small samples, and difficult to scale. Na...
Background: Qualitative research methods offer vital insights into how patients make treatment decisions, but these approaches are labor-intensive, limited by small samples, and difficult to scale. Natural Language Processing (NLP) provides a promising solution by automating the analysis of large volumes of unstructured clinical text, improving efficiency and enabling deeper understanding of complex interactions in cancer care. Objective: The objective of this study was to develop, test, and validate a Natural Language Processing (NLP) application capable of transforming large-scale qualitative clinical communication data into structured formats, thereby reducing the need for manual coding. Methods: Using 434 transcripts of physician–patient encounters collected from a prior study, we evaluated the feasibility of advanced NLP methods to analyze cancer care communication. Results: Transformer-based models demonstrated strong performance in extracting clinically relevant information, with RoBERTa achieving the highest F1 score (76%), outperforming both BERT (71%) and the rule-based SpaCy baseline (36%). Conclusions: These findings underscore the advantages of context-aware transformer architectures, which are better suited to capturing the complexity of medical dialogues than traditional rule-based approaches. Notably, while transformers provided the greatest accuracy, results also suggest the value of hybrid systems that integrate rule-based precision with the contextual depth of transformer models. Such approaches may be especially useful for capturing longer conversational sequences, such as emotional expressions, question–answer exchanges, and multi-topic utterances. Overall, this study demonstrates the potential of NLP to improve the efficiency and scalability of clinical communication analysis, expand institutional capacity to deliver standardized feedback, and enable large-scale, multi-site research on communication processes in cancer care. Clinical Trial: N/A
AI triage in GP practices is developing rapidly within the primary care digital transformation, promising efficiency gains, and safety standardisation in overwhelmed primary care systems. However, cur...
AI triage in GP practices is developing rapidly within the primary care digital transformation, promising efficiency gains, and safety standardisation in overwhelmed primary care systems. However, current evidence is drawn from retrospective validations, emergency settings, or vignettes, with scant evaluation of real-world outcomes and almost no equity-stratified safety data, despite known disparities across age, ethnicity, language, and deprivation. From a sociotechnical standpoint i.e., focusing on the fit between people, tasks, technology, and organisational context, key risks stem not only from algorithmic bias and under-triage but also from human factors, workflow misalignment, governance gaps, and lack of post-deployment monitoring. We argue that ensuring AI triage is safe and equitable requires real-world evaluations in primary care settings, equity-focused performance reporting using theoretically informed frameworks, and rigorous post-market surveillance. Without these, deployment may widen existing health inequalities rather than moderate them.
Background: Artificial intelligence (AI) is revolutionizing healthcare, significantly enhancing diagnostic accuracy, clinical decision-making, and operational efficiency. However, the pace of AI integ...
Background: Artificial intelligence (AI) is revolutionizing healthcare, significantly enhancing diagnostic accuracy, clinical decision-making, and operational efficiency. However, the pace of AI integration into medical education has lagged behind, leaving students inadequately prepared for the emerging challenges AI brings to healthcare. Key issues such as AI’s ethical implications, transparency, and inherent biases remain critical concerns that need to be addressed. While there is growing support for AI’s role in medical practice, many medical curricula still lack structured AI training programs, limiting students’ ability to fully leverage AI’s potential. Objective: This paper reviews the current state of AI education programs in medical and psychology training and proposes a model AI curriculum that can be used as a case example to illustrate how AI can be integrated effectively into medical education. Methods: A scoping review was conducted following the PRISMA-ScR guidelines to analyze existing AI education programs. Searches were performed in PubMed, PsycINFO, and Web of Science (2008–2023) for relevant studies. The inclusion criteria focused on programs designed for medical and psychological professionals. Data were extracted, synthesized narratively, and visualized. Screening was performed using Rayyan, and disagreements were resolved by reviewers. Results: From 5,364 records, 20 relevant programs were identified. The majority of programs (50%) were from the United States, with others coming from Canada, Germany, France, China, and the Netherlands. Topics covered included foundational AI concepts, programming, ethical concerns, governance, and AI’s role in clinical decision-making. Most programs were extracurricular (60%), and evaluation results highlighted that while technical skills were often taught, many programs lacked in-depth practical applications or hands-on experience with AI tools. Ethical and governance topics were also a common focus. In light of these findings, we propose five principles for a successful curriculum with a strong psychiatric perspective in order to both improve skills on AI and increase the attractiveness of psychiatry among medical students. Conclusions: The integration of AI into medical and psychology curricula is essential for producing well-rounded healthcare professionals. To prepare students for AI’s role in healthcare, educational programs should be mandatory and focus on foundational AI knowledge, ethical considerations, data privacy, and clinical decision-making. These programs should align with the WHO’s guiding principles, ensuring that topics such as Explainable AI, Natural Language Processing (NLP), and algorithmic biases are comprehensively covered. Furthermore, it is crucial to foster collaboration with universities in low- and middle-income countries (LMICs) to ensure equitable access to AI education, bridging global disparities in healthcare technology. Such efforts will contribute to the sustainable, inclusive growth of AI in healthcare, enabling all healthcare systems to benefit from advancements in AI technologies.
Background: Artificial intelligence (AI) is increasingly prevalent in healthcare. However, little is known about how frequently Australian general practitioner (GP) trainees (registrars) use AI or how...
Background: Artificial intelligence (AI) is increasingly prevalent in healthcare. However, little is known about how frequently Australian general practitioner (GP) trainees (registrars) use AI or how they perceive its potential impact on training. Objective: This study aimed to explore how frequently Australian GP registrars use AI tools during training, what types of AI technologies they use, and their perceptions of AI’s benefits, risks, and training needs. Methods: A cross-sectional national online survey was conducted from May 7 to June 4, 2024.
GP registrars (3743) enrolled in the Australian General Practice Training and the Fellowship Support programs run by the Royal Australian College of General Practitioners were invited to participate; 727 (19.5%) responded. The survey included 13 questions exploring self-reported frequency of AI use in clinical, educational, and personal contexts, practice AI governance, context and types of AI tool use, perceived impacts of AI on training, extent of AI-related education received, and ethico-legal concerns. Results are reported as percentages. Results: Most registrars reported infrequent or no use of AI. For clinical tasks, 7.3% (53/727) used AI at least weekly. Similarly, only 4.9% (36/727) used AI weekly or more for educational tasks. Practice-level adoption was limited, with 10.8% (78/725) of registrars reporting their practice subscribed to any AI tools. General large language models, remote patient monitoring devices and note-taking voice-to-text tools were the most common AI tools used in the clinic. For education, general large language models dominated, used by 63.5% of those using AI (141/222), far more than any other tool. Most registrars anticipated benefits from AI, particularly for learning efficiency and reducing administrative workload. However, 25% (181/724) believed AI could increase medicolegal risk. Formal training was rare; most had received minimal or none and thus relied on self-directed learning. Most registrars rated formal training in AI competencies as at least moderately important. Conclusions: At the time of the survey, AI use among registrars was limited, though most recognized its potential. Enthusiasm was tempered by concerns about safety, ethics, and legal risks. Structured AI training and clear guidelines are needed to support safe, effective adoption in general practice and education.
Background: Large Language Models (LLMs) are Artificial Intelligences that predict desired user outputs. There are gaps in Dermatology education that could benefit from the incorporation of LLMs. Howe...
Background: Large Language Models (LLMs) are Artificial Intelligences that predict desired user outputs. There are gaps in Dermatology education that could benefit from the incorporation of LLMs. However, efforts to do so have been hindered by concerns over the accuracy, transparency, and reproducibility of responses. Furthermore, LLMs have historically performed inconsistently on standardized medical questions, possibly due to a lack of representative data within an LLM’s armamentarium. NotebookLM (NLM) by Google, an LLM that advertises to develop answers from user-uploaded sources and provide reliable citations, is a source-based LLM that may offer a possible solution to these shortcomings. It also has the potential to integrate student-developed notes into teaching and thereby utilize principles from Vygotsky’s Zone of Proximal Development, along with Cognitive Load Theory, to enhance learning quality. Objective: To evaluate how the provision of extensive student-created study guides affects NotebookLM performance with implications for its usability within the classroom and to compare its performance to other industry LLMs in answering Step1 Dermatology questions. Methods: Four LLMs were used in experimentation - NLM with uploaded pre-clerkship study guides, NLM with an inputted blank sheet of paper, ChatGPT-4o mini, and Google Gemini 1.5 Flash. Each model completed three trials of 121 text-based USMLE Step 1 Dermatology questions from the AMBOSS question bank. They were evaluated for overall accuracy, accuracy by question difficulty, reproducibility of responses across trials, and agreement in answer selection between different models. Data on each of these categories was gathered, charted, and analyzed using Chi-Squared tests of Independence and Fleiss’s Kappa statistics. Results: NLM w/ Notes exhibited significantly more omissions (unanswered questions) than other LLMs (10.5% vs ≤1.65%). When omissions were excluded from statistical analysis, ChatGPT-4o Mini had the greatest accuracy (86%). NLM had unchanged accuracy when compared with inputted study guides and without (76% vs 76%); among all other LLMs, NLM with inputted material had the highest rates of reproducibility (Fleiss Kappa of 0.939). Conclusions: All the LLMs tested here performed higher than previously reported in literature, demonstrating a rapid progression in LLM capabilities. NLM has improved response completeness and reproducibility with user-inputted data, but not factual accuracy. One interpretation is that user-inputted data is being utilized more as an end-state 'filter' rather than being integrated within core reasoning processes, but the unclear nature of LLM cognition precludes definitive answers. More research is needed to harness source-based LLM potential within the classroom under structured, theory-informed educational roles.
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.
Background: Although recent advances in machine learning have accelerated the development of algorithms capable of predicting mental health symptoms and maladaptive behaviors, research aiming to forec...
Background: Although recent advances in machine learning have accelerated the development of algorithms capable of predicting mental health symptoms and maladaptive behaviors, research aiming to forecast the emergence of addiction-related problems in real-life contexts remains limited. Notably, despite longstanding evidence that emotional and temperamental vulnerabilities are core predictors of alcohol-related problems, few studies have incorporated these factors into evidence-based machine learning or AI models. Objective: This study evaluated the extent to which machine learning (ML) models can accurately predict weekly drinking episodes among high-risk drinkers by integrating real-time health data collected from wearable devices with self-reported emotional and temperamental vulnerability indicators across a 4-week follow-up period. Methods: Using a prospective observational design, we collected weekly self-report surveys (five time points) and wearable-derived data (Fitbit; heart rate, physical activity, sleep) from adults in their twenties over four weeks. All variables were aggregated at the weekly level. Positive labels were defined according to the AUDIT-K hazardous-use cutoffs (≥20 for men, ≥10 for women). XGBoost and Random Forest models were trained, and performance was evaluated using K-fold cross-validation, yielding Accuracy, Precision, Recall, F1-score, and ROC AUC for comparison among self-report-only, wearable-only, and integrated models. Results: For week-ahead prediction of drinking episodes, the integrated model showed superior performance. XGBoost achieved an Accuracy of 0.887, Recall of 0.824, and ROC AUC of 0.906, while Random Forest achieved an Accuracy of 0.903, Recall of 0.824, and ROC AUC of 0.937—outperforming both self-report-only and wearable-only models. Conclusions: The combination of self-report and wearable biosignal data demonstrated strong utility for predicting weekly high-risk drinking episodes. These findings suggest that digital mental-health indicators can serve as personalized early-warning signals and intervention triggers in clinical and public-health settings. To support real-world deployment, future work should include threshold optimization, external validation, and time-series split validation to assess model generalizability.
Background: Chronic pain is a widespread condition that impairs quality of life and is often managed primarily with medications. National guidelines now recommend nonpharmacologic, mind–body, and be...
Background: Chronic pain is a widespread condition that impairs quality of life and is often managed primarily with medications. National guidelines now recommend nonpharmacologic, mind–body, and behavioral approaches as first-line or complementary treatments. However, access to these evidence-based options remains limited. Digital health technologies offer a scalable way to deliver integrative, self-care interventions that empower patients to live well with pain. Objective: This study examined engagement and perceived usefulness of a patient- and provider-informed mobile app designed to deliver behavioral and educational content to support pain self-management. Methods: Adult primary care patients with chronic pain were enrolled in a 12-week feasibility trial. The app included lessons addressing the physical, emotional, and social aspects of pain; tracking and personalized insights; self-screenings; and optional in-app coaching. Participants completed baseline and 3-month surveys assessing usability and satisfaction. Engagement was evaluated through app analytics and milestone completion. Results: Of 49 patients assigned to the app, 82% activated it. Participants used the app for an average of 27 unique days and completed 26 core lessons. Engagement highlights included 43% completion of the valued living module, 25% completion of all lessons, and 50% use of daily check-ins. Usability ratings were high, with 87% reporting the app helped them better understand or manage their pain and 93% recommending it to others. Conclusions: Adults with chronic pain engaged consistently and reported high satisfaction with this evidence-informed digital mind–body program. Findings support the potential of digital tools to expand access to nonpharmacologic, integrative pain self-care and complement traditional clinical approaches.
A cross-sectional survey was answered by medical students to evaluate awareness of diathermy-related safety risks compared to that of surgeons, results highlighted educational gaps that could guide cu...
A cross-sectional survey was answered by medical students to evaluate awareness of diathermy-related safety risks compared to that of surgeons, results highlighted educational gaps that could guide curriculum improvement in surgical safety training.
Background: Depression has become a major global public health challenge, and early intervention is critical for improving patient outcomes. Current depression detection techniques based on social med...
Background: Depression has become a major global public health challenge, and early intervention is critical for improving patient outcomes. Current depression detection techniques based on social media data (Traditional Risk Detection, TRD) rely heavily on users’ complete historical information, which cannot meet the timeliness requirements of early intervention. This underscores the need for Early Risk Detection (ERD) methods emphasizing early-stage and real-time warning. However, existing ERD studies have notable limitations: (1) they overlook sleep–wake rhythms hidden in posting timestamps, missing vital warning signals; and (2) they depend on static templates or resource-intensive sequence models, resulting in limited interpretability and inefficient use of early data, ultimately constraining their clinical applicability for early intervention. Objective: To address these issues, this study aims to develop an efficient, reliable, and interpretable ERD model. The core objectives are: to extract sleep–wake rhythm features from posting timestamps, thereby to enrich the feature dimensions for risk warning; to leverage large language models (LLM) for improved text filtering precision and depression-related factor analysis; and ultimately to achieve accurate early detection of depression, supporting early clinical intervention. Methods: We propose the Monitoring of Individual Nighttime Dynamics and LLM Analysis (MIND) model, which integrates two key innovations:
(1)Sleep Dynamics: posting timestamps are transformed into sleep–wake rhythms, analyzing fluctuations in posting frequency and time to derive sleep-related features, thereby compensating for the limitations of text-only approaches.
(2)LLM Depression Profiler: LLM is used for dynamic text filtering, automatically removing irrelevant noise and focusing on potential depression-related cues. Based on LLM semantic understanding, latent depression risk factors are identified, enhancing interpretability for clinical treatment and robustness to noise. Results: Experiments on the eRisk2017 benchmark dataset demonstrated that MIND significantly outperformed existing baseline models in early detection sensitivity, specificity, and accuracy. By combining sleep features with text analysis, the model achieved interpretable, traceable predictions that can support clinical treatment. Relevant experimental code is publicly available Conclusions: The MIND model innovatively combines sleep–wake rhythm features with LLM-based text analysis, addressing the challenges of poor interpretability and inefficient use of early-stage data in existing ERD methods. It significantly enhances early detection performance, offering a new paradigm for applying social media data in ERD task, thereby enabling earlier intervention and reducing the public health burden of depression.
Background: Cognitive frailty—typically described as the co-occurrence of physical frailty and mild cognitive impairment in the absence of dementia—has increasingly been viewed as a potentially re...
Background: Cognitive frailty—typically described as the co-occurrence of physical frailty and mild cognitive impairment in the absence of dementia—has increasingly been viewed as a potentially reversible geriatric condition. It is closely tied to functional decline, disability, and later development of dementia. Although conventional motor–cognitive or physical training programs can offer benefits, they often struggle with poor engagement and limited relevance to daily life. In recent years, immersive virtual reality (VR) systems have emerged as a promising approach because they provide interactive environments that may stimulate both cognitive and motor processes in ways traditional programs cannot. Several trials have begun to test VR in older adults with cognitive frailty, but the overall effect remains uncertain, and existing reviews have generally been broad, including mixed populations or non-immersive VR. A focused evaluation of immersive motor–cognitive VR specifically in individuals diagnosed with cognitive frailty is still lacking. Objective: To determine whether immersive motor–cognitive VR training improves cognitive performance and physical frailty among adults with cognitive frailty Methods: We searched five major databases through November 14, 2025, for randomized controlled trials involving immersive or semi-immersive VR motor–cognitive interventions in adults diagnosed with cognitive frailty. The primary outcome was global cognitive function; physical frailty was examined as a secondary outcome. Effect sizes were synthesized using standardized mean differences (SMDs) or mean differences (MDs) with 95% confidence intervals. Risk of bias was evaluated using Cochrane criteria, and certainty of evidence was graded using GRADE. Results: Three studies involving 344 participants were included in this meta-analysis comparing VR-based intervention versus non-VR (standard care) in older people. VR intervention was associated with a significant improvement in the global cognitive function compared with non-VR (SMD = 0.42; 95% CI 0.21 to 0.64; p = 0.0001; I² = 39%; moderate heterogeneity). Only two trials reported physical frailty outcomes and involved whole-body motor–cognitive VR; therefore, the third trial was excluded from this analysis. Two studies involving 278 participants were included in comparing VRMCT (Virtual Reality Motor-Cognitive Training) versus MCT (Motor-Cognitive Training) in older people. The VRMCT showed a significant improvement in the physical frailty (Mean Difference = –0.26; 95% CI –0.47 to –0.04; p = 0.02; I² = 0%; very low heterogeneity). Conclusions: In adults with cognitive frailty, immersive VR-based motor–cognitive rehabilitation appears to provide benefits for both cognitive performance and frailty severity compared with conventional training approaches. Further high-quality trials are still needed, but current evidence supports the growing role of immersive VR in geriatric rehabilitation. Clinical Trial: CRD420251234169
Background: Effective communication between healthcare providers (particularly physicians and nurses) and patients is crucial. However, numerous challenges exist across various care settings. This stu...
Background: Effective communication between healthcare providers (particularly physicians and nurses) and patients is crucial. However, numerous challenges exist across various care settings. This study aims to employ a scoping review methodology to identify the challenges in healthcare provider-patient communication, as well as the facilitating and inhibiting factors involved. Objective: This protocol aims to conduct a comprehensive scoping review to identify and analyze the challenges, barriers, and facilitators in healthcare provider-patient communication. The findings will inform the development of practical strategies and educational interventions to enhance the quality of care and patient satisfaction. Methods: This scoping review will be conducted following the Joanna Briggs Institute (JBI) methodology for scoping reviews. It will focus on research concerning communication between healthcare providers (including physicians, nurses, and clinical specialists) and patients. A comprehensive search strategy will be implemented using keywords such as "physician," "healthcare provider," "healthcare worker," "healthcare personnel," "healthcare professional," "patient," "client," "communication," and "communication skills." The search will be performed in two major databases, PubMed and Embase, for studies published between January 2000 and June 2023. There will be no restrictions on publication type or study design, and both quantitative and qualitative studies will be included. Participants in the selected studies will include healthcare providers and professionals with direct patient contact, as well as the patients themselves. For quantitative studies, appropriate checklists will be used depending on their methodology. The SRQR (Standards for Reporting Qualitative Research) checklist will be utilized for the quality assessment of qualitative studies. Results: This scoping review will synthesize and map the available evidence against the predefined objectives. The findings will be presented graphically and in a summary table, encompassing study characteristics, key themes related to challenges and facilitators, as well as communication outcomes. Conclusions: This article presents the protocol for a study that will identify communication challenges between healthcare providers, including nurses and physicians, and their patients. The findings of this research can assist health policymakers in planning strategies to enhance communication and improve the quality of healthcare services.
Background: Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by social communication deficits, repetitive behaviors, and cognitive impairment. Side effects and inconsiste...
Background: Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by social communication deficits, repetitive behaviors, and cognitive impairment. Side effects and inconsistent efficacy often limit current interventions such as pharmacological treatments and behavioral therapies. Massage therapy—including Tuina, acupressure, and other manual techniques—has been used as a non-pharmacological, low-risk complementary approach to improve cognitive symptoms in ASD, yet its overall efficacy and safety have not been systematically evaluated. Objective: This systematic review and meta-analysis aims to evaluate the efficacy and safety of massage therapy for improving cognitive impairment in individuals with ASD. Methods: Randomized controlled trials (RCTs) will be identified through searches of PubMed, Web of Science, Scopus, Cochrane Library, Embase, ClinicalTrials.gov, the Chinese Clinical Trial Registry, CNKI, Wanfang, VIP, and CBM until September 2025. Two reviewers will independently perform study selection, data extraction, and risk of bias assessment. For data synthesis, mean differences (MD) or standardized mean differences (SMD) will be used for continuous outcomes (e.g., ABC, CARS, ATEC scores), and odds ratios (OR) will be used for dichotomous outcomes (e.g., clinical efficacy rates). A random-effects model will be applied if the I²≥ 50%; otherwise, a fixed-effects model will be used. Subgroup, sensitivity, and meta-regression analyses will be performed to explore heterogeneity. The GRADE approach will be used to assess the quality of evidence. Results: This review will summarize evidence on the effect of manual therapy on cognitive outcomes measured by scales such as ABC, CARS, and ATEC, as well as safety profiles. Conclusions: The findings will provide evidence regarding the role of massage therapy in managing cognitive impairment in ASD and support clinical decision-making. Clinical Trial: PROSPERO 2025 CRD420251038194
Background: Global drug shortages have posed persistent public health challenges, while internet-based drug sales have recently emerged as a promising solution, especially during the COVID-19 pandemic...
Background: Global drug shortages have posed persistent public health challenges, while internet-based drug sales have recently emerged as a promising solution, especially during the COVID-19 pandemic. However, systematic analyses of national-level regulatory policies for online pharmacies regulation remain limited. Objective: To explore the structure, thematic focuses, and internal consistency of China's national policies regulating online pharmacies. Methods: Two researchers independently retrieved and screened 22 national policies issued by Chinese government agencies by April 1, 2025, based on predefined criteria. Latent Dirichlet Allocation (LDA) was used for topic extraction, and a Policy Modeling Consistency (PMC) Index Model was constructed, incorporating literature review and focus group insights to quantitatively assess policy comprehensiveness and quality. Results: First, the issuance of online drug sales regulation policies in China has accelerated significantly between 2016 and 2022. Second, Latent Dirichlet Allocation (LDA) analysis categorized the 22 policies into five major thematic areas: (1) Internet Drug Trading and Health Service Management; (2) Regulation of Illegal Sales and Pharmacy Online Transactions; (3) Integration of Traditional Medicine and Treatment Data; (4) Diagnosis Qualification and Medical Institution Accreditation; and (5) Healthcare Personnel Management and Institutional Development. Third, a PMC evaluation framework was constructed, encompassing 10 primary variables and 43 secondary indicators, covering dimensions such as Policy Nature, Timeliness, Issuing Agency, Incentives and Constraints, Policy Content, and Stakeholder Protection. Fourth, the average PMC Index score was 5.04, classifying the policies as “acceptable,” with individual scores ranging from 2.45 to 6.93. Strengths were identified in regulating online drug transactions, curbing counterfeit drug incidents, enhancing traceability and recall mechanisms, protecting consumer rights, and promoting compliant online pharmacies. However, deficiencies were notable in Policy Nature, Timeliness, and Issuing Agency dimensions. Conclusions: While China’s regulatory efforts in internet-based drug sales have advanced, critical gaps persist. Strengthening oversight of drug circulation and storage in online transactions and mitigating risks from prescription-free drug purchases remain urgent priorities. Future policy refinements could benefit from international best practices, such as Germany’s rigorous online prescription verification systems. Clinical Trial: None
This article introduces the strategic readiness level for digital health (SRL-DH), conceptualized as a multidimensional strategic scale for assessing the readiness of digital health systems. The frame...
This article introduces the strategic readiness level for digital health (SRL-DH), conceptualized as a multidimensional strategic scale for assessing the readiness of digital health systems. The framework integrates seven critical dimensions: the cognitive maturity of regulators, interorganizational coordination, data readiness, stakeholder synergy, strategic risk, technological fragmentation, and epidemiological volatility.
Background: Patient navigation is a critical component of health care delivery, facilitating connections with appropriate services. A new era of virtual navigators are being developed that can be acce...
Background: Patient navigation is a critical component of health care delivery, facilitating connections with appropriate services. A new era of virtual navigators are being developed that can be accessed through the web or smartphone application. However, it is unknown what features make virtual navigators accessible and reliable. Objective: The objective of this scoping review is to understand the current landscape of existing virtual navigation systems. In this review we will determine the features of these systems and barriers to accessibility they’ve identified. Methods: This review will follow the guidelines for scoping reviews outlined by the Joanna Briggs Institute (JBI) methodology. A search strategy will be used to locate both published and unpublished literature. The databases to be searched include PubMed, PsycINFO (ProQuest), Cochrane Library, Web of Science Core Collection (Clarivate), Cumulative Index of Nursing and Allied Health Literature (EBSCO), ScienceDirect, IEEE Xplore, and ACM Digital Library. Articles will be screened, selected, and extracted by 2 independent members of the research team. Results will be presented in table format and accompanied by a narrative summary. Results: As of September 2025, the preliminary stages of the scoping review have been completed. We anticipate the full scoping review manuscript will be prepared for submission by February 2026. Conclusions: This review will synthesize the current literature of virtual navigation tools and recommendations systems that aim to connect users to the appropriate health care services. By identifying trends and gaps, this review will provide critical information for the development of new and innovative systems that can support healthcare and public health systems.
Background: Digital mental health interventions (DMHIs) can be effective in supporting patients experiencing mental health distress and can offer alternative and novel therapeutic tools for healthcare...
Background: Digital mental health interventions (DMHIs) can be effective in supporting patients experiencing mental health distress and can offer alternative and novel therapeutic tools for healthcare professionals. However, the evidence base and the quality of available DMHIs in the commercial marketplace remain questionable. In particular, DMHIs targeting individuals who self-harm are scant. To address this need, the SAFE app was designed and developed for individuals at risk of self-harm and for their healthcare providers. Objective: The study aimed to evaluate the quality of SAFE from the perspective of healthcare professionals. Methods: The study employed an open survey design to evaluate healthcare professionals’ acceptance of the SAFE app. Healthcare professionals were recruited from a range of psychiatric and educational settings. The Mobile Application Rating Scale: User Version (uMARS) was used to assess the quality of the app. The scale assesses four objective domains of quality: (A) engagement, (B) functionality, (C) aesthetics, and (D) information quality. Each domain is rated on a 5-point Likert scale with ratings below 3 poor, 3-3.5 adequate, 3.5-4 good, and 4+ very good. Data was collected online via the SurveyXact platform. A descriptive analysis was conducted to summarise the participants’ evaluations of SAFE. Results: A total of 121 healthcare professionals participated in the study: 66.9% (81/121) completed the uMARS; 24% (29/121) partially completed it; and 9% (11/121) did not respond. Overall, the quality of SAFE was rated as very good, with a total uMARS mean score of 4.08 (SD 0.68). Functionality, aesthetics, and information quality were evaluated as very good (mean >4). Engagement scored lowest with a mean score of 3.82 (SD 0.85) but was still perceived as good. Conclusions: This study systematically evaluated different aspects of the quality of the SAFE app from the perspective of healthcare professionals. The SAFE app was rated highly across a range of domains using a validated measure (uMARS), indicating good quality and usability. High usability can facilitate utilisation of the app in clinical practice by mental health staff and people at risk of or engaging in self-harm, although further systematic assessment of the app is required to determine its efficacy regarding reducing distress and engaging in self-harm.
Background: Background: Ultrasound education across health sciences has traditionally relied on onsite, instructor-led training. The growing need for scalable, cost-effective, and competency-based edu...
Background: Background: Ultrasound education across health sciences has traditionally relied on onsite, instructor-led training. The growing need for scalable, cost-effective, and competency-based education has accelerated the adoption of simulation-based e-learning. WAZO is an online ultrasound simulator designed to reproduce practical scanning experiences through interactive digital training. Objective: Objective: This study aimed to evaluate the effectiveness of WAZO as an online simulation-based platform for teaching musculoskeletal ultrasound compared with traditional onsite training. Additionally, we used Item Response Theory (IRT) to assess the psychometric properties and discriminative performance of the practical evaluation items. Methods: Methods: A prospective randomized blinded study was conducted with 68 second-year physiotherapy students at the University of Alcalá (Spain). Participants were randomized into two groups: online training using the WAZO platform (experimental) and traditional onsite instruction (control). Both groups completed identical theoretical and practical exams. Rasch modeling under the IRT framework was used to examine item difficulty (δ), student ability (θ), and model fit. Reliability and agreement were analyzed using Intraclass Correlation Coefficients (ICCs), Standard Error of Measurement (SEM), and Minimal Detectable Change (MDC). Results: Results: No significant differences were found between online and onsite groups in theoretical or practical performance (P>.05). IRT analysis revealed an adequate unidimensional model fit, identifying “image optimization,” “structure diameter,” and “surface distance” as the most difficult items, and “probe handling” and “structure identification” as the most informative for competency assessment. The model achieved excellent discrimination (area under the curve 0.93) with high sensitivity (87%) and specificity (80%). Conclusions: Conclusions: Simulation-based e-learning using WAZO achieved comparable learning outcomes to traditional ultrasound instruction while offering enhanced scalability, accessibility, and potential cost-effectiveness. The use of Item Response Theory provided deeper psychometric insight into assessment design, supporting its inclusion as a methodological asset in future digital ultrasound education research.
Background: The French West Indies constitute a vulnerable territory when faced with major health emergencies, particularly epidemiological and biological hazards, as illustrated by the COVID-19 pande...
Background: The French West Indies constitute a vulnerable territory when faced with major health emergencies, particularly epidemiological and biological hazards, as illustrated by the COVID-19 pandemic. In response to the first cases of COVID-19 and the uncertainty regarding its progression at both the individual and population levels, the Call Regulation Center of the Martinique Emergency Medical Service (EMS) implemented a teleconsultation platform to monitor ambulatory patients with suspected SARS-CoV-2 infection. Objective: The objective of this study was to describe the evolving clinical and demographic characteristics of patients monitored by the SAMU teleconsultation unit during the first wave of COVID-19, and to assess their satisfaction. Methods: This was a single-center, retrospective, descriptive study conducted at the Emergency Medical Service (SAMU) of the University Hospital of Martinique during the first wave of the COVID-19 pandemic, between March 10 and May 31, 2020. The objective was to describe the changing characteristics of adult patients monitored by the SAMU follow-up unit and to assess patient satisfaction. Results: A total of 908 patients were included in the study. The study population was predominantly female (57.6%), with a mean age of 45 ± 17 years, and 723 patients (79.7%) reported no prior medical history. During follow-up, 68 patients (7.5%) required hospitalization, and two (0.2%) died during their hospital stay. The presence of at least one pre-existing medical condition and older age were significantly associated with hospitalization for suspected COVID-19 (p < 0.05). Before follow-up, the mean anxiety level was 3.9 ± 0.8 on a 5-point scale. After follow-up, it decreased to 1.4 ± 1.1, reflecting a significant reduction of 64.1% (p < 0.0001). Lastly, the COVID-19 telephone monitoring unit of the SAMU in Martinique received a mean patient satisfaction score of 8.7 ± 1.2 on a 10-point scale. Conclusions: In the context of a novel and uncertain pandemic, follow-up teleconsultation appears to be an effective tool to address public health challenges while limiting the risk of transmission. It helps ensure continuity of care, optimize patient flow, and provide close individual monitoring, thereby reducing the strain on healthcare systems.
Background: Chemotherapy-related side effects are common, yet most are mild and manageable at home with appropriate supportive care. However, many patients find standard education overwhelming or insu...
Background: Chemotherapy-related side effects are common, yet most are mild and manageable at home with appropriate supportive care. However, many patients find standard education overwhelming or insufficiently tailored to their symptoms. Smartphone-based chatbots offer accessible, personalized medical guidance that may reduce unnecessary hospital visits and improve quality of life (QoL). The ChulaCancer LINE Chatbot was developed as a Thai-language digital assistant to support patients receiving chemotherapy. Objective: To evaluate the feasibility and preliminary efficacy of the ChulaCancer LINE Chatbot for managing chemotherapy-related adverse events compared with usual care. Methods: This single-center, open-label, randomized controlled pilot trial was conducted at King Chulalongkorn Memorial Hospital from December 2024 to May 2025. Adults aged ≥18 years with early-stage breast or colorectal cancer scheduled for adjuvant or neoadjuvant chemotherapy (AC, TC, or CAPOX) and an ECOG performance status of 0–2 were randomized 1:1 to the Chatbot or Usual Care groups, stratified by cancer type.
The chatbot was developed using the BOTNOI platform and integrated into the LINE messaging application. Educational content was adapted from existing hospital materials, and a closed-loop system ensured medical accuracy. Usual care included standard counseling, printed materials, and educational videos. The Chatbot group received proactive video content and interactive symptom-specific guidance.
The primary outcome was the rate of additional hospital visits due to chemotherapy-related side effects within 12 weeks. Secondary outcomes included QoL changes measured by EORTC QLQ-C30 at baseline, week 6, and week 12, and incidence of grade ≥3 adverse events (CTCAE v5.0). Fisher’s exact test and linear mixed-effects models were used for analyses. Results: Forty patients were enrolled (20 per group). The median age was 54 years, 80% were female, and baseline characteristics were balanced. Across 12 weeks, 508 chatbot interactions were recorded; the most frequently accessed topics were cancer knowledge, nausea and vomiting, nutrition, chemotherapy information, and fever.
Additional hospital visits occurred in 3 patients (15%) in the Chatbot group versus 7 (35%) with Usual Care (P=.24). Grade ≥3 adverse events occurred in 1 patient (5%) and 2 patients (10%), respectively (P=1.00). The Chatbot group showed a significant improvement in fatigue scores at week 12 (P=.016), with nonsignificant trends favoring the chatbot across other QoL domains. Conclusions: Use of a LINE-based chatbot is feasible and shows promising signals of benefit in supporting patients undergoing chemotherapy. Improvements in fatigue suggest that timely, personalized communication may enhance symptom management. Although limited by small sample size and short follow-up, this pilot study supports further evaluation of conversational agents in oncology, particularly in resource-limited settings.
Background: Medical errors occur more frequently in healthcare than in other industries because of challenges in patient safety education for nurses and students. To address this, it is important to i...
Background: Medical errors occur more frequently in healthcare than in other industries because of challenges in patient safety education for nurses and students. To address this, it is important to identify the factors, structure, and nature of clinical errors and apply these insights to backward-designed educational programs for clinical nurses and nursing students. Medication-related errors are highly preventable with appropriate interventions, underscoring the need for data-driven safety education and systematic frameworks. Previous research suggests that unit adaptation, rather than clinical experience alone, plays a critical role in error occurrence. Focusing on “adaptive performance,” an underexplored concept in nursing, can help identify new educational strategies and interventions. Objective: This study aimed to clarify the structural differences in medication-related errors among nurses through Bayesian network modeling (BNM) based on unit experience. The secondary aim was to examine the influence of total nursing experience on these models. Methods: This mixed-methods study performed a qualitative Root Cause Analysis (RCA) of medication error reports to extract causal factors. BNM, an Artificial Intelligence-based approach, was used to visualize error-generation flows and compare models with years of experience within the current unit. Data were obtained from 2023 medication-related error reports submitted by nurses to the Japan Council for Quality Health Care. Results: RCA of 119 reports identified 10 types of medication-related events and 23 contributing factors. The cases were categorized into low-, moderate-, and high-adaptation groups, and separate Bayesian network models were constructed. The moderate- and high-adaptation models exhibited fewer complex error networks, with weaker chains of unsafe conditions or actions than the low-adaptation model. Extensive clinical experience did not always prevent errors; rather, it was often linked to lapses in verification behavior. Conclusions: Nurses’ adaptation to clinical units plays a pivotal role in patient safety. Higher adaptation enhances flexibility, resilience, and communication, thereby reducing medication errors, whereas excessive experience may lead to complacency. Patient safety education should promote adaptation among new and transferred nurses and include ongoing reminders for all staff. Future data-driven research should inform patient safety education aligned with institutional contexts and clinical needs.
Background: Digital Entrustable Professional Activities (EPAs) in simulated environments may accelerate competency acquisition, but adoption depends on learner acceptance. The Technology Acceptance Mo...
Background: Digital Entrustable Professional Activities (EPAs) in simulated environments may accelerate competency acquisition, but adoption depends on learner acceptance. The Technology Acceptance Model (TAM) posits that perceived usefulness (PU) and perceived ease of use (PEU) shape attitudes (AT) and, in turn, behavioral intention (BI). Objective: We examined medical students’ acceptance of digital EPAs and tested the hypothesized TAM relationships among PU, PEU, AT, and BI. Methods: Clinical-phase medical students at Ludwig-Maximilians-Universität Munich completed a TAM-based survey (7-point Likert scales) after reading a canonical analog EPA and its digital counterpart. Confirmatory analyses comprised bivariate correlations, and hierarchical regressions testing TAM paths. Exploratory analyses comprised paired-sample t-tests comparing analog vs digital ratings and path modeling to evaluate global TAM fit. Results: N = 72 medical students provided complete responses per construct. Mean ratings were favorable (≈5/7). Internal consistency was acceptable (ω=.67–.80). Within the digital EPAs, PU strongly predicted AT (β=.59, p<.001), and AT predicted BI (β=.58, p<.001. For the analog EPAs, PU (β=.54, p<.001) and PEU (β=.28, p=.005) predicted AT; both AT (β=.42, p<.001) and PU (β=.36, p=.002) predicted BI. Attitudes were modestly higher for analog vs digital (M=5.18 vs 4.87; t(71)=−2.50, p=.015, d=−0.30) but PU, PEU, BI did not differ significantly. The path models indicated excellent fit for both formats (CFI=1.00; RMSEA=0.00; SRMR≤.01). Conclusions: Students reported high acceptance of digital EPAs. Acceptance was driven primarily by PU (via AT), whereas PEU contributed to AT only for analog EPAs. Implementation should emphasize demonstrable educational value and cultivate positive attitudes; subsequent work should link acceptance to actual use and learning outcomes. Clinical Trial: PRR1-10.2196/59326
Background: Large language models (LLMs) are poised to transform mental healthcare, offering advanced capabilities in diagnosis, prognosis, and decision support. Since their inception, numerous mental...
Background: Large language models (LLMs) are poised to transform mental healthcare, offering advanced capabilities in diagnosis, prognosis, and decision support. Since their inception, numerous mental health-focused LLMs have emerged in the scientific literature, reflecting the growing interest in leveraging these models across various clinical applications. With a broad range of models available, diverse tuning strategies, and multiple use cases, reviewing the current landscape is critical to understanding how LLMs are being applied. Objective: This study investigates the use of LLMs in the mental health domain, particularly in a clinical setting. We evaluated how LLMs were used in diagnostic, prognostic, and decision support tasks. Methods: We screened 3,121 papers from PubMed, Scopus, and Web of Science focusing on model type and clinical use case from January 2023 to October 2025. After removing duplicates and manual filtering, 42 studies were included in our final analysis. These were categorized based on the use-cases and the type of dataset that they use. Results: Most studies utilized OpenAI’s GPT series—GPT-4 (25 studies, 59.5%) and GPT-3.5 (16 studies, 38.1%) were the most common. Other frequently used models included BERT derived models (7 studies, 16.7%), LLaMA (8 studies, 18.6%), and RoBERTa derived models (6 studies, 14.0%). While all studies initially applied untuned LLMs, several adapted them through few-shot learning or fine-tuning to better align with specific research goals. Most models were used for diagnostic tasks (30 studies, 69.8%). The most common target conditions were depression (11 studies, 26.2%), followed by disorders such as ADHD, OCD, and suicidality. A subset of studies also examined general medical cases, which were included when mental health-related content was present. Conclusions: Despite rapid growth and diversity of LLM applications in mental health, the field remains nascent and exploratory. Future developments must emphasize responsible development, enhanced explainability, and deeper investigations into implementation and deployment practices centered on patient wellbeing.
Background: The COVID-19 pandemic highlighted the significant impact of telemedicine-delivered Medication for Opioid Use Disorder Treatment (tMOUD). While the adoption of tMOUD provides opportunities...
Background: The COVID-19 pandemic highlighted the significant impact of telemedicine-delivered Medication for Opioid Use Disorder Treatment (tMOUD). While the adoption of tMOUD provides opportunities to improve OUD treatment access and quality, more information is needed about patient perspectives on various components that can be integrated into tMOUD delivery for enhancing medication adherence. Objective: This qualitative study examined experiences of patients enrolled in a clinical trial that utilized the MySafeRx platform, a mobile intervention designed to integrate remote buprenorphine self-administration and motivational interviewing (MI) based recovery coaching, to increase medication adherence. Methods: Participants were interviewed about their experiences using the MySafeRx platform in combination with their standard MOUD treatment. Interview questions explored the utility of intervention components: text message reminders, electronic pill dispensers, MI-based mobile recovery coaching, and self-administration of medication via videoconferencing. Interviews were digitally recorded through Zoom, transcribed through Rev.com, and analyzed with ATLAS.ti. Results: Nine participants were interviewed. The MI-based mobile recovery coaching was reported to be the most favorable component of the platform. Participants felt it was helpful to have text reminders about upcoming coaching sessions. Video session experiences were overall positive. The electronic pill dispenser received mixed assessments: most participants felt that the supervised self-administration of medication was acceptable, while one participant reported feeling mistrusted. Conclusions: With the expansion of telemedicine, it is critical to understand patient perspectives of tMOUD and platform components that may increase treatment adherence. MI-based mobile recovery coaching may be particularly appealing to those receiving MOUD. These findings may be useful for the development and delivery of future tMOUD interventions to ensure acceptability and patient satisfaction alongside considerations for efficiency and resource allocation. Clinical Trial: NCT02778282, NCT04449744
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.
Background: Acute respiratory infections (ARIs) remain a major global health concern, yet evidence on the impact of long-term air pollution exposure on both incident and recurrent ARIs in the general...
Background: Acute respiratory infections (ARIs) remain a major global health concern, yet evidence on the impact of long-term air pollution exposure on both incident and recurrent ARIs in the general population is still limited. Objective: This study aimed to assess the risk of ARIs among residents of a community-based cohort in Shanghai and to investigate the associations between long-term exposure to air pollutants and the risks of both incident and recurrent ARIs. Methods: We established a prospective cohort of 3,631 residents in Shanghai, China, who were followed weekly for one year. Individual-level PM2.5, PM10, NO2, and O3 concentrations were estimated using 1 km × 1 km satellite-based models at residential addresses, and exceedance days were calculated. Incident ARIs were analyzed using Cox proportional hazards models, while recurrent ARIs were examined using marginal Cox models with robust standard errors. We further fitted time-varying Cox models to estimate hazard ratio trajectories over the follow-up period. Stratified analyses were conducted to assess potential effect modification by key covariates, and sensitivity analyses were performed using alternative exposure windows, two-pollutant models, and additional models for recurrent ARIs to assess robustness. Results: During 3,498 person-years of follow-up, a total of 874 ARIs were recorded (0.25 per person-year). For incident ARIs, each 1 μg/m3 increase in PM2.5, PM10, and O3 was associated with HRs of 1.25 (95% CI: 1.12–1.40), 1.16 (95% CI: 1.03–1.30), and 1.24 (95% CI: 1.18–1.30), respectively. Stronger associations were observed for recurrent ARIs with PM2.5 and PM10, while the effect of O3 remained stable. Time-varying Cox models revealed that the effects of PM2.5 and PM10 gradually attenuated, whereas the effect of O3 appeared later and strengthened over time. Conclusions: Long-term exposure to PM2.5, PM10 and O3 significantly increases the risk of both incident and recurrent ARIs, with differential time-varying patterns across pollutants.
Background: The laparoscopic-guided subcostal transversus abdominis plane (TAP) block has been introduced as a surgeon-performed alternative for postoperative analgesia following laparoscopic cholecys...
Background: The laparoscopic-guided subcostal transversus abdominis plane (TAP) block has been introduced as a surgeon-performed alternative for postoperative analgesia following laparoscopic cholecystectomy (LC). This approach allows direct visual confirmation of local anesthetic distribution without reliance on ultrasound guidance. However, evidence regarding its efficacy in patients with complicated gallstone disease remains limited. Objective: To evaluate the clinical outcomes and perioperative factors associated with postoperative opioid requirement following laparoscopic-guided subcostal TAP block. Methods: A prospective observational study was conducted between November 2023 and October 2024 at Srinakharinwirot University Hospital, Thailand. Adult patients (18–80 years) undergoing LC for uncomplicated or complicated gallstone disease received a laparoscopic-guided subcostal TAP block with 0.25% bupivacaine. Postoperative pain intensity was assessed using the Visual Analogue Scale (VAS) at 2, 4, 6, 8, 12, and 24 hours. Morphine administration within 24 hours was recorded. Perioperative variables were analyzed using univariate and exploratory multivariable logistic regression. The study was approved by the Institutional Ethics Committee (SWUEC-004/2566F), and the trial was registered with the Thai Clinical Trials Registry (TCTR20250314002). Results: Forty-two patients were included in the analysis. Half of the patients did not require postoperative opioids, whereas the remaining patients received a mean cumulative morphine dose of 3.86 ± 1.39 mg. Pain scores were significantly lower at 2, 4, and 12 hours postoperatively in the morphine-free group (p < 0.05). Higher ASA classification independently predicted postoperative morphine requirement (OR = 6.51; 95% CI, 1.37–30.96; p = 0.018). No major complications or local anesthetic toxicity were observed. Conclusions: The laparoscopic-guided subcostal TAP block provides effective early postoperative analgesia and a clinically meaningful opioid-sparing effect after LC, including in patients with gallstone-related complications. A higher ASA class was associated with increased opioid requirement, emphasizing the need for individualized, risk-adapted analgesic strategies within comprehensive perioperative care. These findings support the feasibility and potential integration of surgeon-performed TAP block into Enhanced Recovery After Surgery (ERAS) protocols to optimize multimodal analgesia and enhance postoperative recovery. Clinical Trial: Trial registration: Thai Clinical Trials Registry (TCTR20250314002).
Background: Web-based advertisements, specifically social media advertisements, are a popular recruitment avenue among research projects involving human participants. Social media recruitment has adva...
Background: Web-based advertisements, specifically social media advertisements, are a popular recruitment avenue among research projects involving human participants. Social media recruitment has advantages over other methods (e.g., in-person recruitment), such as aiding teams in reaching the population of interest and increasing enrollment pace at a relatively low cost. Nonetheless, social media recruitment comes with the challenge of fraudulent responses, and therefore effective identity verification procedures must be put in place in order to maintain the integrity of the final sample and data. Objective: In this paper, we outline the identity verification methods (herein referred to as “checks”) used in the recruitment process for a pilot study featuring a mobile health (mHealth) intervention app for emerging adults (EAs; aged 18-25) who regularly use cannabis. Each identity verification check is examined for its rate of passing. Methods: Participants were recruited via social media advertisements that linked directly to a study eligibility screening survey. Advertisements were posted on Meta (Facebook and Instagram), Snapchat, and TikTok. Participants were enrolled if they met study inclusion criteria (e.g., aged 18-25, reported regular cannabis use), completed the baseline consent and survey, downloaded the app, and passed all identity verification checks. Identity verification checks happened at two checkpoints: directly following screening survey completion (e.g., geolocation check, duplicative IP address check, social media check) and directly following app download and login (duplicative device ID and/or push token check). Failing an identity verification check resulted in exclusion from the study. Results: Identity checks were non-exclusive such that a single eligible screening response could undergo multiple checks. Of the 573 eligible screening responses that went through the identity verification process, a total of 3,031 identity verification checks were completed. Of these 3,031 aggregate checks, 396 failed the verification criteria (13.1%), and therefore 396 of the 573 eligible respondents were excluded from continuation in the enrollment process (69.1%). Social media checks, wherein study staff ensured the individual’s public-facing account had personally relevant information, had the highest failure rate (61.5%). The second most common failed check was due to a duplicate device ID upon logging into the app (10.0%), followed by the geolocation check (4.9%), the duplicate IP address check (4.2%), the combination check (time zone; 4.1%), and duplicate push token check (3.2%). Conclusions: This paper describes a participant identity verification process for app-based mHealth studies using social media as a recruitment source. A combination of identify verification safeguards is suggested to maintain integrity of the study sample and data. Clinical Trial: ClinicalTrials.gov NCT05824754; University of Michigan IRB: HUM00222194
Background: The integration of Virtual Reality (VR) tools in mental healthcare, such as VR relaxation, shows promise for supporting stress reduction and mental well-being. However, implementation acro...
Background: The integration of Virtual Reality (VR) tools in mental healthcare, such as VR relaxation, shows promise for supporting stress reduction and mental well-being. However, implementation across healthcare settings remains complex and context-dependent, influenced by organizational capacity, stakeholder readiness, and external factors such as policy and funding. This study explores how barriers, facilitators, and evolving implementation strategies shape the use of VRelax, a VR relaxation tool, in primary, secondary, and tertiary mental healthcare settings in the Netherlands. Objective: To identify shared and context-specific barriers and facilitators, and to develop and refine tailored implementation strategies for the integration of VR relaxation in primary, secondary, and tertiary mental healthcare, with a focus on learning from the implementation approach used within the research process itself. Methods: A qualitative, comparative study was conducted using a participatory approach with 33 healthcare professionals and eight patients across primary, secondary, and tertiary mental healthcare settings in the Netherlands, involving 18 interviews and eight focus groups. Thematic analysis, guided by the Consolidated Framework for Implementation Research, was used to assess implementation barriers and facilitators. The Expert Recommendations for Implementing Change tool was applied to match found barriers with evidence-based implementation strategies. Results: Across all settings, key lessons emerged about what supports and hinders the implementation of VR relaxation in mental healthcare. While challenges such as equipment costs, limited staff capacity, technical issues, and lack of structural funding persisted, they also revealed opportunities for improvement. In primary care, collaboration with community organizations enabled low-threshold, accessible use. In secondary care, staff feedback refined strategies and strengthened team learning. In tertiary care, co-development with professionals and patients advanced person-centered care, though time constraints and fragmented organizational structures limited full adoption. Across settings, the gap between professional assumptions about patient suitability and patients’ actual enthusiasm underscores the need for shared decision-making, patient involvement, and flexible, hybrid approaches to care. Conclusions: Successful integration of VR relaxation in mental healthcare requires balancing flexibility with structured, setting-specific strategies while addressing system-wide barriers. Collaboration with community facilities, iterative refinement through staff feedback, and co-development with patients show how VR can strengthen person-centered, hybrid, and sustainable mental healthcare. These findings align with efforts to ensure accessible, appropriate, and future-ready care across all mental healthcare settings. They also underscore that effective implementation requires both localized adaptation and system-level solutions, including shared infrastructure, post-discharge continuity, and long-term funding models.
Background: With the rapid development of medical technology and the emphasis on early lung cancer screening, the incidence of multiple primary lung cancer (MPLC) has increased in recent years. Howeve...
Background: With the rapid development of medical technology and the emphasis on early lung cancer screening, the incidence of multiple primary lung cancer (MPLC) has increased in recent years. However, the prognostic determinants and clinical characteristics of MPLC patients remain poorly characterized. Objective: This study aimed to develop and validate a nomogram for predicting overall survival (OS) in MPLC patients using data from the Surveillance, Epidemiology, and End Results (SEER) database. Methods: This study was reported in accordance with the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guideline. A cohort of 4,177 MPLC patients (2007–2015) was collected from the Surveillance, Epidemiology and End Results (SEER) database. The patients were randomly divided into training (n=2,923) and validation (n=1,254) cohorts at a 7:3 ratio. Backward stepwise Cox regression identified 11 independent risk factors, which were integrated into a nomogram predicting 3-, 5-, and 8-year OS rates. Results: The nomogram demonstrated superior discriminative ability compared to the AJCC staging system, with higher AUC values for 3-/5-/8-year overall survival (OS) predictions in both cohorts (training cohort: 0.743, 0.751, 0.759; validation cohort: 0.737, 0.734, 0.695). Calibration curves and decision curve analysis confirmed its clinical utility. Conclusions: This study establishes a validated nomogram incorporating clinical and socioeconomic variables to optimize prognostic assessment and personalized treatment planning for MPLC patients.
Background: Systemic arterial hypertension is an important risk factor for cardiovascular diseases and a major public health problem. Despite available treatments, low adherence to treatment and insuf...
Background: Systemic arterial hypertension is an important risk factor for cardiovascular diseases and a major public health problem. Despite available treatments, low adherence to treatment and insufficient levels of disease control are still observed. Therefore, innovative methodologies to improve this scenario are needed. Mobile health interventions (apps) are emerging as an alternative, showing improvements in various parameters related to the treatment of hypertension. However, most of these interventions are conducted in high-income countries. Objective: This study aimed to evaluate the effects and the usability and acceptability of a mobile application-based intervention (Pressão na Boa) on blood pressure (BP), medication adherence, and lifestyle in hypertensive adults. Methods: This trial was conducted with hypertensive adults, randomly allocated into two groups: control group (CG) and App group (AG). The AG was submitted to an intervention based on a mobile app (Pressão na Boa) during eight weeks. Laboratory BP, medication adherence, lifestyle, accelerometry, usability and acceptability of the App were assessed at pre- and post-intervention. Results: Fifty-two individuals participated in the study (AG: n = 26 and CG: n = 26). A significant main effect of time was observed on BP with reductions from pre- to post-intervention in both groups (p<0.001). A significant group-by-time interaction was found for sedentary behavior (p = 0.004) and light PA (p = 0.012), with the CG showing great effect sizes. The app received positive evaluations regarding the system usability. Most participants emphasized the functionality in supporting BP control but some barriers to its use, such as small icons and fonts, the lack of memory to record diary physical activities, and the lack of familiarity and comfort with the technology. Conclusions: The intervention using the mobile app Pressão na Boa led to a decrease in BP for hypertensive patients, showing a slightly larger effect compared to a counseling lecture on hypertension treatment. While the app was evaluated positively overall, it needs enhancements to improve its support for managing hypertension. Clinical Trial: Registry name: Effectiveness and Usability of a Mobile Application to Assist in the Treatment of Arterial Hypertension.
URL: https://clinicaltrials.gov/study/NCT05575232
Registration number: NCT05575232.
Background: The use of social media and access to the web has grown significantly in recent years, and this can affect mental health, especially in adolescents. Objective: This study was conducted to...
Background: The use of social media and access to the web has grown significantly in recent years, and this can affect mental health, especially in adolescents. Objective: This study was conducted to examine the impact of internet usage and cyberchondria on the mental health and body image concerns of adolescents. Additionally, it explored the mediating role of family support in the relationship between internet usage, cyberchondria, mental health, and body image concerns based on path analysis. Methods: The present study was a cross-sectional study involving girls and boys aged 13 to 15. Sampling was done using a stratified random sampling method and was proportional to gender. The sample size in this study was 287 adolescents. Data collection tools included the General Health Questionnaire (GHQ-28) and scales related to social media use and cyberchondria. Descriptive, correlational, and regression statistical methods were used to analyze the data. Results: The findings indicate a significant positive association between the severity of Internet addiction and the severity of Cyberchondria among adolescents (r=0.276; P<0.01). The severity of Internet addiction is also significantly correlated with worse mental health and body image concerns. The relationship between internet addiction and family support is an inverse relationship. The severity of Cyberchondria is positively correlated with worsening mental health and body image concerns. The indirect pathway from IAT to BIC and GHQ through Family Support was significant. Upon the inclusion of the mediator variable within the analytical model, the path coefficient remained statistically significant. This phenomenon is classified as partial mediation. Conclusions: The findings of the current study on adolescents revealed that Internet addiction and the severity of Cyberchondria are linked to mental health challenges and concerns about body image. However, when family support was incorporated into the analysis, the outcomes shifted. Family support emerged as a mitigating factor, playing a significant role in reducing mental health issues and improving body image perceptions. Clinical Trial: None
Background: Efficiently finding and exploring relevant health studies is critical for informed evidence-based healthcare. However, study information remains distributed across multiple resources, hind...
Background: Efficiently finding and exploring relevant health studies is critical for informed evidence-based healthcare. However, study information remains distributed across multiple resources, hindering interoperability, search, and reuse. Enhancing the findability of study data is a key challenge in promoting the FAIR principles in health research. Objective: This work aimed to improve the findability and comparability of health studies by developing a semantically enriched graph-based framework that supports intuitive search and exploration for diverse stakeholders, including clinicians, researchers, and patients. Methods: We developed the BRAinS-Graph (“Biomedical Knowledge Graph for Recommending and Analysing Health Studies”), a semantically enriched knowledge base that integrates data from ClinicalTrials.gov, the Portal for Medical Data Models, the Unified Medical Language System (UMLS), and Medical Subject Headings (MeSH) into a single graph database. The framework applies an extract–transform–load (ETL) process to integrate heterogeneous data structures and link related information across study resources and biomedical ontologies. Results: The BRAinS-Graph supports fine-grained, semantic searches across study metadata, eligibility criteria, and structural properties. Use cases illustrate its potential for clinicians, patients, and researchers, including analyses of study type distributions for meta-analyses and the identification of studies relevant for individual patients. Conclusions: By integrating heterogeneous study data into one interconnected knowledge base, the BRAinS-Graph improves the findability, accessibility, and reusability of study information, thereby advancing the FAIRness of health research. The present work establishes a foundation for graph-based study recommendation systems and cross-institutional research infrastructures.
Wound care is an increasing global challenge, with older adults among those most affected. As populations age, the demand for effective and efficient wound care increases. Over the years, various wou...
Wound care is an increasing global challenge, with older adults among those most affected. As populations age, the demand for effective and efficient wound care increases. Over the years, various wound assessment and care techniques have been developed, including digital wound care technology (DWCT) which use innovative artificial intelligence. Many older adults, especially those living in rural and remote areas, face significant barriers in obtaining timely and effective wound care, leading to poorer health outcomes and increased healthcare costs related to wound care. These challenges underscore the urgent need to implement wound care models which equitably improve access to care and enhance clinical outcomes, particularly for older adults, to promote healthy aging and age-in-place.
Based on evidence from the literature and the initial implementation of a DWCT in two community health systems in Ontario, this viewpoint paper encourages clinicians and healthcare leaders to embrace and expand the implementation of an AI-driven DWCT to address inequities in access to high-quality, timely care. The experiences from these implementations indicate that the use of AI can support clinical decision-making and extend access to care for individuals in rural and remote communities in Canada. By leveraging DWCT powered by AI, healthcare providers can enhance the accuracy and consistency of wound assessments, improve communication, streamline care processes, and more effectively allocate resources, ultimately aiming to reduce disparities in wound care outcomes.
Background: eHealth interventions have demonstrated potential to address challenges related to health and the health care system in low- and middle-income countries. To effectively leverage eHealth in...
Background: eHealth interventions have demonstrated potential to address challenges related to health and the health care system in low- and middle-income countries. To effectively leverage eHealth in supporting health care in Ethiopia, the assessment and development of eHealth literacy of patients is essential. Objective: This study aimed to translate and culturally adapt the eHealth Literacy Questionnaire (eHLQ) to Amharic and assess its psychometric properties. Methods: We applied a systematic process of translation and cultural adaptation, including forward and backward translation, expert review, and cognitive interviews. Then we conducted a cross-sectional questionnaire-based study using a convenience sample (N=300) of patients with internet access in the primary health-care level between January and March 2025 in the capital and a larger city of Ethiopia. Internal consistency was assessed using Cronbach α and McDonald ω. Factor structure was assessed using Confirmatory Factor Analysis. Convergent and discriminant validity were examined by calculating Spearman correlations between each eHLQ scale and the total score of the eHealth Literacy Scale (eHEALS). Results: A total of 300 participants were included in the analysis. The mean age was 30.4 years (SD 6.8; range 18–55), and 69.7% (209/300) were women. Internal consistency was acceptable for all scales (Cronbach α=0.72–0.91; McDonald ω=0.79–0.96), except for Scale 4 (α=0.62; ω=0.70). The 7-factor model showed satisfactory fit, with a Comparative Fit Index of 0.97, Tucker-Lewis Index of 0.97, and Standardized Root Mean Square Residual of 0.07. Factor loadings exceeded 0.40 for all items except one. Strong correlations between Scales 1–3 and eHEALS (range r=0.69–0.74) supported convergent validity, while moderate correlations between Scales 5–7 and eHEALS (range r=0.66–0.67) indicated limited discriminant validity. Conclusions: The Amharic eHLQ demonstrated generally satisfying psychometric properties and can be considered as a valid tool for assessing eHealth literacy among patients with internet access in Ethiopia, marking the first validation of the eHLQ in Sub-Saharan Africa. Future studies could provide additional evidence to substantiate the psychometric robustness of Scale 4 (“Feeling Safe and in Control”). Overall, the Amharic eHLQ can support the development of tailored eHealth interventions in Ethiopia.
Background: Understanding how attitudes toward COVID-19 mitigation measures spread on social networks is crucial to inform infectious disease modelers and policymakers. Even though previous studies ha...
Background: Understanding how attitudes toward COVID-19 mitigation measures spread on social networks is crucial to inform infectious disease modelers and policymakers. Even though previous studies have described social media interactions during the pandemic, there remains potential to model the underlying dynamics of sentiment contagion and polarization. Objective: This study investigated the emergence and evolution of discussions on COVID-19 mitigation measures within the Belgian Reddit community (r/Belgium), focusing on how sentiments diffused among users over time. Concretely, it examined whether topic discussions exhibited patterns of social contagion and how expressed sentiments were shaped by prior interactions, contributing to homophily and polarization. Methods: We analyzed posts created on r/Belgium between 1 January 2020 and 30 June 2022. Posts were classified into three mitigation topics, lockdowns, masks, and vaccination, using a BERT-based topic model. Sentiment was assigned to English posts using a RoBERTa-based sentiment classifier. We examined temporal patterns of post volume and tested for social contagion in topic initiation using null models. Sentiment homophily was quantified by comparing observed comment-parent sentiment pairs to null distributions. We developed the Smooth Internal Expressed Bounded Confidence (SIEBC) model and tested it against two alternatives, to add mechanistic intuition to the observed homophily. Results: The analysis of 655,642 posts made by 28559 users revealed that post volume was strongly associated with external events such as policy announcements and media reports, but not with within-Reddit interactions. There was no evidence of social contagion in topic initiation. However, sentiment exhibited significant homophily, with comment sentiment correlating with parent comment sentiment. The SIEBC model reproduced observed sentiment patterns, with Kolmogorov Smirnov statistic between predicted and observed sentiment distributions ranging from 0.043 to 0.067. It slightly underestimated homophily, but still outperformed alternative models. The model revealed that expressed sentiment adapts more strongly to parent comments than internal sentiment adapts to other interactions (proportion of users showing this pattern: 0.75, 0.70, and 0.53 for lockdowns, masks, and
vaccination). Conclusions: Topic discussions on r/Belgium are driven primarily by external events rather than social contagion within the platform. In contrast, for sentiment there is observed homophily. This can be explained by users adapting their expressed sentiment to match the conversational context of threads. The SIEBC model demonstrates that expressed sentiment may not reflect users’ internal attitudes, highlighting the importance of handling the former with care. These findings suggest that epidemic-social models would benefit from incorporating external information sources for topic dynamics and using complex mechanisms, such as a bounded confidence kernel, for sentiment spread.
Background: Digital health technologies (DHTs) are increasingly integrated into clinical practice, yet economic evaluations remain scarce, particularly in early development stages. Within the NICE Evi...
Background: Digital health technologies (DHTs) are increasingly integrated into clinical practice, yet economic evaluations remain scarce, particularly in early development stages. Within the NICE Evidence Standards Framework, Tier C DHTs comprise technologies with direct clinical implications and measurable health outcomes, for which robust economic evidence is essential. Early-stage assessments are particularly important to inform subsequent development, refinement, and adoption decisions across the digital health lifecycle. Objective: This study aims to explore the feasibility of integrating a full trial-based economic evaluation within an early-stage pilot comparing a chatbot-supported remote patient monitoring (RPM) solution for anticoagulation management with standard of care (SOC). Methods: A cost-effectiveness analysis was performed alongside a pilot crossover trial among adult cardiac surgery patients receiving vitamin K antagonists. Participants were allocated to two 6-month sequences (SOC→RPM or RPM→SOC). The intervention consisted of a rule-based chatbot integrated with home-based international normalized ratio self-testing using portable coagulometers to support communication and therapy management. Effectiveness was measured as time in therapeutic range (TTR), and costs were estimated from the Portuguese National Health Service and a limited societal perspective over a 1-year horizon. The analysis (i) applied a within-patient cost-effectiveness approach to estimate incremental costs, incremental TTR, and incremental cost-effectiveness ratios (ICERs). Uncertainty was explored through non-parametric bootstrapping (5,000 replications) and deterministic sensitivity analyses. Complementary comparisons examined differences between sequences (analysis ii), between periods (analysis iii), and within each sequence (analysis iv). Results: A total of 19 patients were included in the analyses. In the analysis (i), RPM improved anticoagulation control, with a mean within-patient increase of 10.43 percentage points in time in TTR. The mean incremental costs were €198.61 from the SNS perspective and €270.05 from the limited societal perspective. The corresponding ICERs were €19.03 and €25.88 per additional percentage point of TTR gained. Sensitivity analyses produced consistent estimates across parameter variations. Complementary analyses (ii–iv) suggested that RPM tended to be more cost-effective when implemented after the initial 6-month postoperative period. Conclusions: This proof-of-concept study demonstrates that full trial-based economic evaluation can be feasibly embedded within an early-stage Tier C DHT. The intervention showed improved anticoagulation control alongside higher costs, providing initial insights on its cost-effectiveness profile. Positioned within the digital health evidence continuum, such assessments can function as a learning stage within the lifecycle. To address the persistent adoption–evidence gap, tier- and stage-aligned frameworks are needed to guide the economic evaluation of DHTs. This study contributes to that goal by providing a set of recommendations specifically for Tier C DHTs. Clinical Trial: ClinicalTrials.gov NCT06423521
Background: Stigmatized women's health issues, such as polycystic ovary syndrome (PCOS) and endometriosis, are often marginalized or dismissed in traditional clinical settings. This drives individuals...
Background: Stigmatized women's health issues, such as polycystic ovary syndrome (PCOS) and endometriosis, are often marginalized or dismissed in traditional clinical settings. This drives individuals to seek peer support in anonymous online communities like Reddit. While these digital platforms host critical discussions, they are often designed as static information repositories, failing to account for the complex emotional, temporal, and cultural dynamics that shape users' support needs. There is a disconnect between the lived experiences of users—particularly feelings of clinical dismissal and the need for culturally-specific advice—and the design of the sociotechnical systems they rely on. Objective: This study aimed to deconstruct support practices in online women's health forums to provide a formative basis for designing more responsive digital health systems. We analyzed the intersections of discussion topics, emotional expression, temporal shifts (specifically the impact of the COVID-19 pandemic), and culturally-situated discourse to identify unmet user needs and effective peer-support patterns. Methods: We conducted a large-scale, mixed-methods analysis of 4,995 posts and 460,317 comments from five major women's health subreddits (r/WomensHealth, r/TwoXChromosomes, r/BirthControl, r/Endometriosis, and r/PCOS). Computational methods included Latent Dirichlet Allocation (LDA) for topic modeling, VADER for sentiment analysis, and the NRC Emotion Lexicon for granular emotion classification. We segmented the data into pre-, during-, and post-COVID-19 periods to analyze temporal shifts. This quantitative analysis was complemented by a two-phase qualitative thematic analysis to identify and characterize engagement patterns within 147 validated culturally-situated threads. Results: Our analysis revealed that the most prevalent and emotionally negative topic was "Pain & Doctor Visits," which was uniquely characterized by high levels of fear and sadness linked to systemic clinical dismissal. The COVID-19 pandemic triggered a significant topical "turn inward," with discussions shifting away from social/political issues and toward somatic concerns (e.g., "PCOS," "Pain & Doctor Visits"). Paradoxically, this period saw a simultaneous rise in both negative emotions (eg, fear, sadness) and expressions of community trust. Critically, our qualitative analysis of culturally-situated discourse uncovered a consistent three-stage "playbook" for effective support: (1) Affirmation, to establish psychological safety and validate cultural experiences; (2) Information Scaffolding, to provide actionable, culturally-tailored advice; and (3) Inter-Cultural Bridging, to facilitate community-wide learning and empathy. Conclusions: Online health forums operate as essential, resilient sociotechnical infrastructures that actively compensate for failures and gaps in formal healthcare. The "Affirmation-Scaffolding-Bridging" model identified in our research provides a clear, formative framework for designing future digital health interventions. These findings can guide the development of new platforms that are emotionally aware, culturally responsive, and adaptive to user needs and external crises.
Introduction: This study evaluated the effectiveness of a mobile short messaging service (SMS) on the perioperative outcomes.
Methods: The study is a randomized controlled trial. Data were collected...
Introduction: This study evaluated the effectiveness of a mobile short messaging service (SMS) on the perioperative outcomes.
Methods: The study is a randomized controlled trial. Data were collected using electrical questionnaires from 110 child-caregivers. Short messaging service was utilized to provide educational support to the intervention group.
Results: Baseline Family Caregiver Task Inventory scores did not differ between groups but were significantly higher in the intervention group on postoperative day 7. The quality of recovery score was significantly higher in the intervention group on postoperative days 1 and 2, but not on day 3. The Client Satisfaction Questionnaire-8 score was also significantly higher in the intervention group. No significant differences were observed in surgery cancellation, unanticipated hospital visits, or postoperative complications.
Discussion: The use of an SMS-based educational support significantly enhanced caregiver competence, postoperative recovery, and patient satisfaction among children undergoing day surgery for phimosis.
Background: Validated measurement tools that focus solely on assessing adolescents´ involvement in their treatment are scarce. Objective: We aimed to validate five Patient-Reported Outcome (PRO) ques...
Background: Validated measurement tools that focus solely on assessing adolescents´ involvement in their treatment are scarce. Objective: We aimed to validate five Patient-Reported Outcome (PRO) questions regarding patient involvement among adolescents with type 1 diabetes. Methods: 447 adolescents (11-18 years of age) completed a short survey including five PRO questions regarding patient involvement. Twenty participants were interviewed cognitively. The PRO questions' content validity, feasibility, internal consistency, and construct validity were analyzed. Results: Assessment of content validity revealed that most participants encountered no or minor difficulties comprehending the PRO questions. Specifically, all participants demonstrated a satisfactory cognitive understanding of three of the five PRO questions. However, the terms "appropriate" and "experiences" posed a challenge for 3 out of 20 subjects. The feasibility, internal consistency, and construct validity analyses uncovered only two limitations in validity. Firstly, the clinical utilization of the questions limits anonymity, potentially introducing bias. Secondly, adolescents below 15 years had more difficulties with the item "I talked to the healthcare staff about the questions or concerns I had." Conclusions: The PRO questions are valid for measuring patient involvement among adolescents with type 1 diabetes. Respondents stated that the PRO questions were easy to understand, relevant, and comprehensive. However, employing these PRO questions in both clinical settings and research demands thoughtful considerations regarding their application and setup, particularly when considering the age of respondents and circumstances. It is worth noting that these PRO questions might hold validity challenges for application among adolescents below 15 years with type 1 diabetes.
Background: Blended therapy (BT) combines digital applications with face-to-face treatment and has become an increasingly important component of psychiatric care. Evidence indicates that BT can achiev...
Background: Blended therapy (BT) combines digital applications with face-to-face treatment and has become an increasingly important component of psychiatric care. Evidence indicates that BT can achieve outcomes comparable to, or even superior to those of traditional face-to-face therapy. Despite certain advantages, routine implementation of BT remains challenging, and clinical practice suggests that while some inpatients engage with BT, many either discontinue early or do not initiate its use at all. To better understand these patterns, this multicentric retrospective observational study investigates factors associated with non-initiation and dropout among inpatients offered BT. Objective: In this study, data from 278 inpatients were analysed to examine the influence of sociodemographic variables, comorbidities, and symptom severity on the uptake and continued use of BT. The objective was to identify predictors of non-initiation and dropout. Methods: Multivariable logistic regression models were conducted to identify significant predictors of non-initiation and dropout. Results: The findings indicate distinct patterns of association for non-initiation and dropout. Specifically, increasing age was linked to a lower risk of non-initiation (OR (per year age difference) = 0.98, 95% CI [0.96, 1.00], p = 0.013), while the presence of a comorbid anxiety disorder was associated with a reduced risk of dropout (OR = 0.23, 95% CI [0.08, 0.66], p = 0.007). Several variables showed no association with either non-initiation or dropout across all analyses, including sex, overall symptom severity, and certain comorbidities such as personality disorders and depression. Conclusions: The age-related finding aligns with existing literature suggesting that older adults show higher willingness to continue with internet-based treatment. Explanations for this could be having more realistic expectations regarding treatment, greater persistence, or the likelihood that only intrinsically motivated older adults choose to even engage in digital therapies. Regarding comorbid anxiety disorders, previous literature provides no consistent conclusions about its role in dropout. However, the lower dropout rates observed in this subgroup may reflect specific personality traits or indicate that these patients benefit more from the highly structured nature of BT. It is possible that the modules offered on the platform are particularly well-suited to addressing core mechanisms of anxiety disorders, thereby enhancing perceived relevance and user engagement.
In conclusion, the findings highlight the importance of identifying patient characteristics that predict successful engagement with BT. Tailoring the use of BT to those more likely to adhere may support more effective and resource-conscious implementation in clinical inpatient settings.
Background: Ecological momentary assessment (EMA) is a tool that captures emotional states, experiences, and behaviors in real or near-real time. Using continuous glucose monitoring (CGM) data and EMA...
Background: Ecological momentary assessment (EMA) is a tool that captures emotional states, experiences, and behaviors in real or near-real time. Using continuous glucose monitoring (CGM) data and EMA in unison may be beneficial to understand associations between psychosocial factors and momentary glucose levels. An in-depth understanding of these relationships is crucial for future interventions targeting psychosocial factors in chronic diseases such as diabetes mellitus. Objective: The goal of this scoping review was to summarize the objectives, methodologies, and outcomes of studies analyzing concurrent psychosocial EMA and CGM data. Methods: This study was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews. One-hundred and six studies were identified from PubMed, Embase, and EBSCOhost from May 2009-Jan 2025. Thirteen original research articles that collected and analyzed simultaneous EMA and CGM data were included. Methodological data abstracted included study characteristics, EMA protocols and outcomes, CGM outcomes, and integrated EMA and CGM study objectives. Results: Studies primarily recruited adult (92%) populations with type 1 diabetes (T1DM) (69%). EMA delivery protocols and outcomes varied significantly and included emotion, self-care behaviors, disordered eating behaviors, interpersonal interactions, cognition, sleep, workload, and impacts of hypoglycemia. Most (69%) studies analyzed blinded CGM data and CGM outcomes included both standardized and non-standardized glucose outcomes. Integrated EMA and CGM data answered study objectives including evaluating impacts of psychosocial and lifestyle factors on momentary glucose metrics; the influence of momentary glucose on emotional states, mood, personal behaviors, sleep, and cognition; and study protocol or mobile application optimization among others. Conclusions: The combination of EMA and CGM data provides an opportunity to elucidate the relationship between psychological and behavioral factors with momentary glucose. In this review, we describe a broad range of study characteristics, protocols, outcome measures, and objectives using these novel combined methodologies. Clinical Trial: Not applicable
Background: In South Korea, where plain packaging has not been adopted, tobacco packaging remains primarily a marketing tool for the tobacco industry, using texts, colors, and imagery to attract consu...
Background: In South Korea, where plain packaging has not been adopted, tobacco packaging remains primarily a marketing tool for the tobacco industry, using texts, colors, and imagery to attract consumers. Among these, flavor cues are especially important as they enhance product appeal. Cigarette sticks also serve as marketing through features such as colors and capsule indicators. Objective: We aimed to examine the packaging of tobacco products, with a focus on flavor cues and additives in products. Methods: This surveillance study was conducted in November 2024 using an adapted Tobacco Pack Surveillance System protocol (TPackSS). Tobacco products were purchased from convenience stores located in Seoul, supplemented by cross-referencing with national market monitoring data. Of 353 identified products, 214 (150 cigarettes and 64 heated tobacco products [HTPs]) were collected. Flavor cues were categorized by pack design features, while additives were identified through sensory analysis of product components. Results: Among the collected products, 63.6% had both flavor cues (68.0% of CCs and 95.3% of HTPs) and flavor additives (59.3% of CCs and 84.4% of HTPs), while 20.6% had neither. Pack color was the most common cue and additives were most often delivered through crushable capsules. HTPs used a wider range of flavoring methods, including flavoring in tobacco leaves and inner wrappers. Conclusions: Tobacco packaging and stick design in South Korea remain important marketing channels. Flavor cues and additives are widely used in tobacco products, particularly in HTPs. These findings highlight the need for plain packaging and flavor bans on tobacco products.
Background: The transition from simulation-based learning to the clinical environment is a pivotal stage in undergraduate nursing education. This period can influence students’ psychological well-be...
Background: The transition from simulation-based learning to the clinical environment is a pivotal stage in undergraduate nursing education. This period can influence students’ psychological well-being, adaptability, and sense of belonging within the clinical setting which is a dimensions essential to professional learning and patient safety. Although simulation aims to prepare students for clinical realities, the extent to which it supports their emotional and social readiness for real practice remains unclear. Objective: The primary objective of this scoping review is to map existing literature addressing the well-being, adaptability, and sense of belonging of undergraduate nursing students as they transition from the simulation lab to the clinical environment. Secondary objectives include identifying educational interventions or strategies that foster these dimensions and highlighting gaps and directions for future research. Methods: The review will follow the Joanna Briggs Institute (JBI) methodology for scoping reviews and the PRISMA-ScR reporting guidelines. A systematic search will be conducted in PubMed, CINAHL, APA PsycINFO, and ScienceDirect for studies published between 2015 and 2025 in English or French. Eligible studies will include empirical (quantitative, qualitative, or mixed-method) and review papers exploring nursing students’ experiences of transitioning from simulation-based to real clinical settings. Two reviewers will independently screen titles, abstracts, and full texts, extract relevant data, and synthesize findings using thematic analysis. Results: Data collection and analysis are expected to be completed by June 2026. Results will summarize current definitions, measures, and interventions related to students’ well-being, adaptability, and belonging, as well as identify evidence gaps and conceptual trends in literature. Conclusions: This scoping review will offer a comprehensive synthesis of how undergraduate nursing students experience the transition from simulation-based learning to clinical practice, with a particular focus on psychosocial dimensions such as well-being, adaptability, and sense of belonging. By identifying key factors and interventions that support these dimensions, the findings will inform the development of targeted educational strategies and contribute to enhancing transition frameworks within nursing education.
Background: Wearable accelerometers have become integral to mobile health (mHealth) research, particularly for delivering real-time physical activity (PA) monitoring and applications in interventions...
Background: Wearable accelerometers have become integral to mobile health (mHealth) research, particularly for delivering real-time physical activity (PA) monitoring and applications in interventions such as Just-in-Time Adaptive Interventions (JITAIs). One critical yet underexplored factor in real-time PA monitoring is epoch length, which is the time interval over which raw accelerometry data are aggregated to classify activity intensities and levels. Shorter epochs (e.g., 1 second) enhance precision but increase computational and battery demands, while longer epochs (e.g., 60 seconds) reduce data burden but may miss brief activity bouts. Although previous studies have examined epoch effects using post-processed data, limited evidence exists regarding their influence on real-time, wrist-based PA estimates, especially for moderate-to-vigorous PA (MVPA). Identifying an optimal epoch length for real-time PA measurement remains a critical gap in supporting scalable and efficient mHealth interventions. Objective: This study determined the impact of varying epoch lengths on real-time MVPA estimates derived from a wrist-worn accelerometer to identify an optimal epoch that balances measurement accuracy with practical feasibility for mHealth applications. Methods: Twenty adults (Age: 32.5 ± 15.1 years) completed a series of carefully selected simulated free-living activities in a controlled laboratory setting. Participants wore the MotionSense HRV wristband, which computed real-time Euclidean Norm Minus One (ENMO) values, and a COSMED K5 indirect calorimetry for metabolic reference. ENMO values were aggregated into 5-, 10-, 15-, 30-, and 60-second epochs. MVPA was classified using validated ENMO cut-points. Epoch-level MVPA estimates were compared against the 1-second reference using mean absolute percent error (MAPE), Pearson’s correlations, Bland-Altman (BA) plots, and equivalence testing with a ±10% equivalence zone. Results: MVPA estimates from all epoch lengths were statistically equivalent to the 1-second standard. The 15-second epoch demonstrated the best trade-off between accuracy and efficiency, with minimal bias (0.05 min), low MAPE (6.3%), and strong correlation (r = 0.97). However, indicators of individual-level error increased with longer epochs; MAPE increased to 9.5% at 60 seconds, and the limits of agreement widened (from ± 2.9 min at 15s to ± 4.9 min at 60s), suggesting greater potential misclassifications in estimating MVPA with longer epochs. Conclusions: Although MVPA estimates using the MotionSense HRV wristband were robust across all epoch lengths, findings from this study suggest that a 15-second epoch provides an optimal balance between measurement precision and processing efficiency, making it well-suited for mHealth interventions, such as JITAIs that rely on timely activity detection.
Background: Background: Sepsis is defined as a life-threatening condition caused by a dysregulated host immune response to infection, often resulting in organ dysfunction. The heterogeneous nature and...
Background: Background: Sepsis is defined as a life-threatening condition caused by a dysregulated host immune response to infection, often resulting in organ dysfunction. The heterogeneous nature and clinical presentation, overlapping with other acute conditions, often leads to diagnostic delays of sepsis, and consequently, to unacceptably high morbidity and mortality levels. Objective: This scoping review aims to identify and synthesise studies that use electronic medical record (EMR) data to develop or validate artificial intelligence (AI)-based models for the early detection or prediction of sepsis. Methods: Peer-reviewed studies published between March 2016 and February 2024 that use EMR data to develop, test, or evaluate AI or machine learning models for predicting sepsis or septic shock will be included. Adult participants (>18) will be included with no restriction on geographical location in this study. This scoping review will be guided by the updated JBI (formerly Joanna Briggs Institute) methodology. The search strategy will include relevant keywords and MeSH terms related to sepsis, electronic health records, and machine learning. Major electronic databases, including MEDLINE, PUBMED, EMBASE, CINAHL, and Cochrane Database of Systematic Reviews/Central Register of Controlled Trials, will be searched. Titles, abstracts, and full texts will be screened by two reviewers independently, with discrepancies resolved by consensus. Results: We will use publicly available data. No primary data will be collected. Ethical approval will not be required. Conclusions: Results will be extracted into a full report to be submitted to a peer-reviewed scientific journal and disseminated to stakeholders and partners in appropriate formats.
Background: Background: As individuals with HIV live longer, many now face the health consequences of aging and multimorbidity, known as disability. Exercise can mitigate disability, however, engageme...
Background: Background: As individuals with HIV live longer, many now face the health consequences of aging and multimorbidity, known as disability. Exercise can mitigate disability, however, engagement in exercise among adults living with HIV varies. Technology-based interventions, such as telerehabilitation, may help mitigate geographical, financial and time barriers to community-based exercise (CBE). However, little is known about the experiences with technology uptake and usage among adults living with HIV. Understanding these experiences is essential to inform design of inclusive, accessible, and sustainable online interventions. Objective: Objectives: Our aim was to describe experiences with technology uptake and usage among adults aging with HIV participating in a six-month online community-based exercise (CBE) intervention, and explore how these experiences changed over time, from baseline to post-intervention. Methods: Methods: We conducted a longitudinal qualitative descriptive study using interview data from adults living with HIV engaged in a CBE intervention study in Toronto, Canada. Participants engaged in a six-month online CBE intervention consisting of thrice weekly exercise, supervised biweekly with online personal coaching sessions, weekly group exercise classes, and monthly self-management education sessions (via Zoom). Technology included Zoom software and webcam; Sweat for Good YMCA App and YMCA Virtuagym Website; and participants wore a wireless physical activity monitor (Fitbit Inspire 2) throughout. Participants completed interviews at baseline and post-intervention. We conducted a group-based content analysis of interview transcripts, focusing on digital access, setup, usage, and perceptions of technology. Questionnaire data describing digital literacy and access to technology provided additional context to the interview data. Results: Results: Eleven participants completed at least one interview. We analyzed 19 interview transcripts from 11 participants (six women, five men; median age 52 years). Experiences with technology uptake and usage among adults aging with HIV were characterized by four components: i) preparations for technology (technology set up); ii) interactions with technology (preferences for different types of technology, preferences for mode of delivery, ease of usage); iii) facilitators and satisfaction with technology (facilitators to technology uptake and usage and satisfaction with technology); and iv) challenges and frustrations with technology (barriers technology uptake and usage, frustrations with technology). Experiences with technology across participants were influenced by intrinsic contextual factors (prior exposure with technology) and extrinsic contextual factors (COVID-19 pandemic, technological and social support). Conclusions: Conclusion: Experiences with technology among adults aging with HIV engaging in an online CBE intervention varied from increasing ease of use, to increasingly burdensome over time. Results highlight the need to incorporate personal preferences, and ongoing technological support when implementing online CBE with adults aging with HIV. Clinical Trial: NCT05006391
Background: Healthy aging encompasses maintaining physical functions, promoting mental health, and preventing mental illness. In recent years, the prevalence of mental disorders among senior citizens...
Background: Healthy aging encompasses maintaining physical functions, promoting mental health, and preventing mental illness. In recent years, the prevalence of mental disorders among senior citizens has gradually increased. How to evaluate and regulate emotions in an effective way has become an important issue to improve the level of mental health. The rise of the silver-haired economy has promoted the transformation of the consumption concept of senior citizens, and they gradually show interest and acceptance of electronic technology products, such as commercial electronic fitness games (Exergame) that combine entertainment and fitness. Objective: Existing research focuses on the improvement of commercial exergames on the physical and cognitive functions of senior citizens, while research on their mood state and mental health promotion is relatively scarce. This research aims to explore the impact of exergames on the emotional experience of senior citizens and provide novel ideas and application directions for healthy aging. Methods: This study included a total of 30 senior citizens (age range: 60-89 years old) who had no cognitive impairment and could move independently. They were randomly divided into two groups. The intervention group (n=15) conducted exergame training, using the Nintendo Switch game Ring Fit Adventure. The comparison group (n=15) carried out traditional indoor exercise. Both groups of sample sets focus on the four dimensions of aerobic exercise, muscle exercise, balance exercise, and flexibility exercise. The exercise cycle is four weeks, twice a week, with eight sessions. The Brunel Mood Scale is employed to assess participants’ mood states(anger, confusion, depression, fatigue, tension, vigor). Using a repeated-measure study design to compare the mood changes of the participants from the front to the back test under different experimental conditions. Results: Significant group x time interaction effects were found for confusion[F(7,28)=7.37, p<.001, η²=0.208]; depression[F(7,28)=5.90, p<.001, η²=0.174]; fatigue[F(7,28)=6.59, p<.001, η²=0.191]; tension[F(7,28)=3.03, p=.005, η²=0.098]; vigor[F(7,28)=14.23, p<.001, η²=0.337]. There was no statistically significant interaction between groups and time points in terms of anger (p>0.05). Confusion, depression, and fatigue in the intervention group were generally lower than those in the comparison group, while tension and vigor were higher than those in the comparison group. Conclusions: Research results show that both commercial exergames and traditional indoor sports can help improve the acute mood state of senior citizens. The training method based on exergames is more effective in reducing confusion, depression, and fatigue, and increasing vigor. This study explores the potential of commercial exergames for improving the mood state of senior citizens as a non-drug intervention method. Clinical Trial: The protocol was registered on Chinese Clinical Trial Register (Registration Number: ChiCTR2500111853). This study was approved by the Institutional Review Board of Zhejiang University in China (Approval Number: cmic20250934).
Background: Virtual reality (VR) provides an accessible, standardized environment to objectively evaluate functional cervical movement control by leveraging built-in head-tracking and game-based tasks...
Background: Virtual reality (VR) provides an accessible, standardized environment to objectively evaluate functional cervical movement control by leveraging built-in head-tracking and game-based tasks. Making such tools available through VR app stores can bridge the lab-to-clinic gap and enable remote administration, allowing clinicians to establish baselines and track therapy response over time with repeated, comparable measurements. Objective: This study aimed to (a) develop a novel VR serious game designed for the assessment of neck movement control and kinematics and (b) to evaluate its reliability, side effects, sense of presence, perceived mental workload and usability. Methods: A multidisciplinary team developed an immersive VR serious game using Unity engine and Meta Quest 3 headsets. The game places users in a space station scenario where they destroy meteorites/space debris by precise head movements over 4 minutes. A descriptive, observational repeated-measures reliability study was conducted with 49 asymptomatic participants (aged 18-65 years). Participants completed three sessions separated by one week to assess test-retest reliability. Variables analyzed included final score, meteorites destroyed/failed, and maximum level reached. Reliability was measured using intraclass correlation coefficients. Side effects (Simulator Sickness Questionnaire), presence (Igroup Presence Questionnaire), mental workload (NASA Task Load Index), and usability (System Usability Scale) were evaluated. Results: The present study allowed for the development of a novel serious game designed to assess neck movement control. It is freely available for use by clinicians or researchers through Meta Quest 3 VR headsets. Forty-five participants completed the protocol (25 men, 20 women; mean age 27.49±10.74 years). The final score showed good reliability (ICC=0.788). The game demonstrated high usability (86.09±13.67), negligible side effects (SSQ: 2.04±2.84), moderate presence levels (IPQ total: 3.43±1.03), and high but acceptable mental workload (NASA-TLX: 61.94±12.85). No major adverse events occurred during 135 game sessions. Conclusions: The novel VR serious game demonstrated good reliability, safety, and usability for neck movement control assessment in asymptomatic individuals. Future research should investigate its application in clinical populations with pain to establish discriminative validity and responsiveness before determining clinical utility.
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.
AI-powered therapy chatbots and virtual companions are rapidly entering mental health care. For attention-deficit/hyperactivity disorder (ADHD), they promise timely psychoeducation, behavioral coachin...
AI-powered therapy chatbots and virtual companions are rapidly entering mental health care. For attention-deficit/hyperactivity disorder (ADHD), they promise timely psychoeducation, behavioral coaching between visits, and structured inputs for measurement-based care across clinics, schools, and homes. Yet practical guardrails—especially for minors—have not kept pace with deployment. This Viewpoint advances a nurse-led, risk-tiered framework to guide safe, effective, and equitable integration of therapy chatbots into ADHD services. Drawing on ADHD care guidelines, emerging AI governance instruments, and lessons from digital health implementation, we identify six persistent gaps: risk classification and predetermined change control plans for adaptive models; child-specific safeguards, including content safety, age gating, and reliable crisis escalation; equity-by-design procurement and reimbursement; interoperable outcomes and safety reporting; workforce readiness with human-in-the-loop oversight led by nursing; and privacy-preserving real-world evidence (RWE). We present a three-layer framework aligning policy levers, implementation enablers, and outcomes, and we compare five complementary policy options: status quo self-regulation; risk-tiered regulation with PCCPs; voluntary certification with public procurement criteria; reimbursement for nurse-delivered digital ADHD care; and a federated RWE and safety surveillance network. A phased roadmap details preparation, sandbox pilots, scale-up with guardrails, and continuous learning, alongside a concise evaluation set spanning access and equity, safety signals, clinical and functional outcomes, experience, and economic value. Taken together, risk-tiered governance, procurement aligned with transparency and age-appropriate design, financing for nurse-led integration, and federated RWE can accelerate safe, equitable adoption of therapy chatbots for youth with ADHD while the evidence base matures.
Background: Hepatitis B virus (HBV) and hepatitis C virus (HCV) are significant global health concerns, particularly prevalent in low- and middle-income countries (LMICs). In China, a significant numb...
Background: Hepatitis B virus (HBV) and hepatitis C virus (HCV) are significant global health concerns, particularly prevalent in low- and middle-income countries (LMICs). In China, a significant number of international migrants from LMICs reside, many of whom are at high risk of HBV/HCV infection with few test utilization. Previous studies presented Pay-it-forward (PIF) strategy has proven effective in promoting sexually transmitted infections (STIs) test in various populations. This study aims to evaluate the effectiveness of a PIF intervention in promoting HBV/HCV testing among international migrants from LMICs in China. Objective: Aims to evaluate the impact of a pay-it-forward (PIF) intervention in promoting hepatitis B virus (HBV) and hepatitis C virus (HCV) testing among international migrants from low- and middle-income countries (LMICs) residing in China. Methods: A two-arm cluster randomized controlled trial will be conducted in Guangzhou, China. Participants will be recruited from a public hospital serving a large migrant community. Eligible participants will be randomly assigned to either the PIF intervention arm or the control arm in a 1:1 ratio. Participants in the intervention arm will receive free HBV/HCV tests donated by previous participants and will have the opportunity to donate to support future tests. The control arm will receive standard medical services with self-paid testing. The primary outcome is the proportion of participants tested for both HBV and HCV. Data will be collected through a self-administered questionnaire and hospital records, and analyzed using generalized estimating equations to account for clustering effects. Results: The data collection has been completed, and the information of 100 participants was included to the data analysis. Conclusions: This study is innovative in targeting international migrants from LMICs in China and employing the PIF strategy to promote HBV/HCV testing. The PIF intervention is expected to increase testing rates by addressing financial barriers and fostering a sense of community support. The findings will contribute to the understanding of HBV/HCV testing promotion among this understudied population, with potential implications for public health policy and practice. Clinical Trial: ChiCTR2400082560. Registered on 1st April 2024.
Background: Aging is inherently linked to increased vulnerability to adverse events, with falls being a primary concern for public health. Falls are the leading cause of severe injury and death among...
Background: Aging is inherently linked to increased vulnerability to adverse events, with falls being a primary concern for public health. Falls are the leading cause of severe injury and death among individuals over 65 years, significantly impacting their health, quality of life, and mobility 1,2. This growing issue significantly burdens healthcare systems, requiring extensive resources for hospitalization, rehabilitation, and long-term care 3. These challenges require innovative solutions that can effectively mitigate falls risk and improve the safety, independence, and quality of life for older adults while also easing the strain on healthcare systems 4.
Several factors increase the risk of falls in older adults, including aging, low muscle mass due to physical inactivity, smoking, alcohol consumption, and malnutrition 5,6. Comorbidities such as diabetes, hearing loss, cognitive decline, poor sleep, chronic pain, and depression further exacerbate falls risk, leading to frailty and mobility limitations 7. Additionally, many older individuals live alone, which increases the likelihood of delayed help after a fall 8.
Declines in physical, cognitive, and sensory functions significantly raise fall risk, making it increasingly important to identify innovative solutions to prevent falls and promote healthy aging 9. As Professor Bernard Isaacs (1924-1995) remarked, "A child takes one year to acquire independent movement and ten years to acquire independent mobility. An older adult can lose both in a single day." This highlights the potentially fatal consequences of falls, which can severely compromise mobility and independence 2. Recent technological advancements have introduced valuable tools for geriatric healthcare, offering solutions that improve quality of life, promote autonomy, and optimize healthcare delivery for older individuals 10.
Technologies such as wearable devices (smartwatches and health sensors), telemedicine, robotic assistance, and health management systems play a central role in monitoring vital signs, facilitating early detection of health issues, and enabling remote care 11,12 In addition, technologies for rehabilitation, such as exoskeletons and exercise applications, provide support for physical activity, aiding in the prevention of falls and enhancing recovery 13,14. Building on the advent of the Internet of Things and Artificial Intelligence, the spectrum of possible solutions and applications continues to grow, with increasing the potential to monitor and predict fall risk and to enhance rehabilitation procedures 15.
Despite these advances, challenges remain, including the accessibility of technologies, their integration into existing care systems, and the need for personalized solutions 16. The lack of coordination among various technological platforms can reduce efficiency and complicate the interpretation of data, while cost and usability can limit access for many individuals 17. Moreover, ensuring that non-pharmacological interventions (e.g. exercise-based technology) are tailored to the unique needs of each individual is essential for long-term success 18.
The search for innovative solutions in public health is driven by the growing challenges of the aging population, particularly in fall prevention, chronic disease management, and overall well-being 19. As life expectancy increases, the prevalence of debilitating conditions also rises, requiring sophisticated and personalized approaches 20. Innovations such as wearable devices and remote monitoring systems offer the potential to enhance care quality, by enabling more precise and effective interventions. However, their adoption depends on accessibility, economic feasibility, and ease of use—especially for older adults with varying levels of digital literacy 21.
The effectiveness of these solutions must be rigorously validated through dedicated studies to ensure improved health outcomes and reduced long-term healthcare costs. Effective technologies can prevent unnecessary complications and hospitalizations, while personalized interventions, based on precise data and continuous monitoring, address the specific needs of each individual 20,22. Digital technologies can offer accessible, customized solutions that overcome the limitations of traditional approaches, promoting safer and healthier aging 23.
Collaboration between healthcare professionals, developers, and final users' contributions may also be important to ensure that solutions are relevant, effective, and sustainable in the long term 24. Identifying and addressing limitations during the research and development process can reduce barriers to adoption and improve implementation, increasing the likelihood that fall prevention technologies will be successfully integrated into care strategies for the aging population 9.
Given these challenges, there is a growing need for innovative and sustainable solutions that address the multifaceted nature of fall prevention. This study aims to lay the foundation for the development of a web-based medical device and app platform that integrates digital technologies, physical-functional assessments, and user-centered approaches to predict and prevent falls in older adults. Objective: To explore, evaluate, and establish the foundation for Stage 1 of the Sustainable and Accessible Fall-Prevention Medical Device Technologies for Older Adults’ Health (S@FHe-RP).
Research Questions
I. What digital medical device technologies are currently available for predicting fall risk in older adults, and how do they compare in terms of functionality, usability, and effectiveness?
II. Which physical-functional assessments are commonly used to evaluate fall risk in older adults, and how can these be adapted or enhanced through medical device–based digital technologies?
III. How do end users, including healthcare professionals and nursing home staff, perceive the acceptability, feasibility, and functionalities of fall-prevention medical devices?
IV. Which bio-socio-demographic factors are essential for developing a reliable and accurate medical device–driven fall prediction model in long-term care facilities?
V. How can the integration of multidisciplinary and technological approaches, including medical device innovation, overcome the limitations of current fall-prevention strategies and support the development of sustainable solutions in long-term care facilities? Methods: This research protocol is structured into four interconnected studies (Stage 1–Stage 4), each designed to systematically align the research process with the overarching objectives of the project. Stage 1 involves a scoping review (Study 1) to identify and map existing digital technologies used to predict fall risk in older adults. Stage 2 (Study 2) focuses on a comprehensive systematic review comparing traditional and technology-adapted physical fitness assessment tests. Stage 3 (Study 3) explores the development of a statistical prediction model based on bio-sociodemographic indicators to estimate fall risk. Finally, Stage 4 (Study 4) integrates the end-users’ perspectives—health professionals and caregivers in long-term care settings—to inform the design and implementation of a sustainable, user-centered fall-prevention technology.
Together, these four stages create a cohesive framework that bridges evidence synthesis, model development, and practical application, ensuring scientific rigor and real-world relevance. The methodology follows recent approaches 25,26, integrating technological, clinical, and user-centered dimensions.
Project Design
A mixed-methods approach will be employed, comprising four complementary and consecutive studies to support the development of a low-cost, web-based app platform for fall prevention in older adults. This platform will comply with European medical device regulations, meeting validation criteria for medical devices 27. The research characterizes contemporary translational research, which involves effectively translating scientific knowledge into new health technologies 28. Furthermore, the development of this medical device will involve researchers from distinct areas of clinical, technological, and industrial sectors, following a User-Centered Design (UCD) approach 29.
Figure 1.
Module A: Scoping review of new digital technologies to predict falls
This phase involves a systematic scoping review of the literature across relevant databases, specialized journals, and grey literature, aiming to analyze studies examining new digital thecnologies used to predict fall risk in older adults. This study will adhere to established methodological guidelines, including the exploratory review framework by Arksey and O'Malley (as refined by 30), and the Joanna Briggs Institute (JBI) guidelines 31. Results will be reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines 32.
A reproducible four-step search strategy, as recommended by JBI 33, will be implemented across databases (see Table 1): Medline, Web of Science, SCOPUS, Cochrane Library, B-On, CINAHL, PsycINFO, Google Scholar, and Open Access Thesis and Dissertations. The search terms included: ((Falls prevention) OR (Fall risk)) AND (Elderly) AND (Digital technologies) AND (Long-term care)). The inclusion criteria, guided by the Joana Brigs framework 34, will include studies involving older adults (≥60 years) (Population), examining technological devices assessing fall risks related to gait performance (Concept), across various settings, countries, and sectors of the economy (Context).
Two reviewers will design the strategy, which will be reviewed by a third expert using the PRESS checklist 35. Selection criteria will include original research, systematic reviews, theses, grey literature, patents and prototypes from the last ten years. Quantitative studies (randomized controlled trials, non-randomized trials, quasi-experimental studies) and observational studies (descriptive, exploratory, and analytical designs) will be considered. The initial search will be conducted in English, and may be expanded to include Spanish and Portuguese depending on preliminary findings. Findings will summarize studies by PCC, answer research questions, and describe study characteristics.
Module B: Review of Traditional and Tech-Adapted Physical Fitness Tests
This comprehensive review will identify traditional and technology-adapted physical-functional tests (PFT) for fall risk assessment in older adults. The review will focus on the effectiveness, ease of use, accuracy, and implementation challenges. A systematic search across multiple databases (Medline via PubMed, Scopus, Web of Science, CINAHL) will target studies from the last decade using terms such as “fall risk assessment,” “functional test,” “older adults,” “physical test,” “digital adaptations,” and “technology for fall prevention.” Inclusion criteria include original research, systematic reviews, and relevant grey literature.
Results will follow PRISMA guidelines 36, Cochrane guidelines for systematic reviews, and the GRADE methodology for evaluating the quality of evidence 37. The PICO framework 38 will structure reporting: Population (≥ 60 years, including clinical subpopulations), Intervention (traditional and adapted PFTs), Comparison (traditional vs. digital/technological adaptations), and Outcome (effectiveness on fall risk assessment).
Module C: Fall risk prediction statistical model
This cross-sectional study is aimed to develop a predictive algorithm for fall risk based on bio-socio-demographic indicators. This study will involve approximately 300 institutionalized older adults from Coimbra, Portugal, levering data from the PRO-HMESCI project 39 project. Participants will provided informed consent, with ethical approval obtained from the Faculty of Sports Sciences and Physical Education Ethical Committee, University of Coimbra (code number: CE/FECDEFUC/0002013, CE/FCDEF-UC/00112024).
Collected data includes sociodemographic data (age, marital status, education), anthropometric measures (weight, height, BMI), cognitive status 40, state of depression 41, comorbidity index 42, functional fitness (e.g. upper and lower body muscle strength and balance), and falls history.
Advanced statistical techniques will be applied to identify significant patterns and relationships between bio-socio-demographic indicators and risk of falls in older adults. Bivariate analysis methods, such as the chi-square test and Pearson's correlation coefficient, will be used to explore associations between categorical and continuous variables. Multivariate logistic regression will be applied to identify the most significant factors contributing to fall risk 43 and construct the predictive model. Model validation will be performed using cross-validation techniques and Receiver Operating Characteristic (ROC) curve analysis to assess the accuracy and predictive power of the algorithm 44.
Module D: End-Users' Perspective study
The objective of this study is to validate the necessity, feasibility, and acceptance of a technology-based fall prevention product by healthcare professionals working in long-term care facilities. This study will adopt a mixed-methods approach, combining qualitative and quantitative techniques to evaluate the acceptance and perceived utility of a new technology-based solution for fall prevention in older persons in care facilities. It seeks to understand user perspectives, barriers, and facilitators, as well as institutional specificities related to long-term care, based on the UCD methodology 45.
The adapted Technology Acceptance Model (TAM) serves as the theoretical framework connecting the study's qualitative and quantitative assessement 46. Insights gathered through qualitative interviews will inform the contextual understanding of perceived usefulness, ease of use, and behavioral intention, which are central to the TAM framework 46. These constructs guide the design and evaluation of fall prevention technologies, as visualized in Figure 2, ensuring alignment with user needs and practical applications.
Participants include higher-education healthcare professionals (nurses, physicians, physiotherapists, gerontologists) with at least one year experience in long-term care. For the qualitative phase, theoretical saturation is expected to occur after 15–20 interviews, following similar previous studies 47. For the quantitative phase, the sample size will be calculated using G*Power, based on the sample size as shown in previous studies 48. Qualitative analysis will employ thematic content analysis using NVivo® 49; quantitative analysis will employ TAM-based questionnaire to evaluate variables such as perceived usefulness (10 items), perceived ease of use (11 items), behavioral intention (4 items), and demographic data (6 items) 50. Data collection will occur via online and in-person distribution, with follow-up to ensure response completeness. Descriptive and inferential statistical analyses will be conducted using SPSS.
Ethical Procedures
The research protocol was approved by the same committee under the project number CE/FCDEF-UC/00112024, which ensures compliance with ethical standards for research involving human participants. Procedures will comply with the European Union directives 51, and relevant guidelines from the International Organization for Standardization 52. Clinical studies will also adhere to the ethical principles outlined in the Declaration of Helsinki and the guidelines for Good Clinical Practice 53. As we are currently in Stage 1 of the project, the Portuguese regulatory body responsible for overseeing the evaluation authorization, and monitoring of medicines, medical devices, and health products in Portugal 54 approval is not required at this point, but it will be necessary in Stage 2 when developing and validating the platform prototype. Results: The study aims to significantly advance fall risk prevention by integrating traditional and technological approaches. A key outcome will be a comprehensive analysis of PFTs, both conventional and technology-adapted, to identify the most effective methods for assessing fall risk. By systematically comparing these approaches, the study will bridge the gap between traditional clinical assessments and emerging technologies 55, providing a roadmap for integrating innovative tools into routine practices and highlighting their complementary potential.
Additionally, the study will support the development and preliminary validation of low-cost, data-driven algorithm to predict fall risk in older adults. By combining bio-sociodemographic, clinical, and functional data, this tool will enhance clinical decision-making and enable timely , targeted interventions 56, with a strong emphasis on early screening and fall prevention 57.
The evaluation of existing fall risk assessment technologies will also play a central role, aiming to verify their reliability and accuracy. This assessment will help to identify strengths and limitations, guide future development, and establish benchmarks for next-generation tools. A particular focus on gait performance and fall risk will contribute to ongoing advancements in smart wearable devices and motion analysis technologies 14.
Moreover, the study will capture end-user perspectives, particularly from healthcare professionals working in long-term care facilities. It will explore factors influencing the adoption of fall prevention technologies, including barriers, facilitators, and overall acceptability. Applying the UCD approach will ensure that future solutions align with the real-world needs and preferences of those who use them 58.
Finally, this work will establish a solid foundation for developing a web-based medical device for fall-risk management. Following evidence generation and ethical ethical approval, the next phase will involve rigorous prototype testing to ensure safety, efficacy, and compliance with international standards, including EU and ISO guidelines 51. Conclusions: The research protocol aims to provide valuable insights into the development and adoption of fall prevention technologies for older adults using a user-centered design approach. By focusing on low-cost, sustainable, and accessible solutions, the study addresses the pressing need for equitable healthcare innovations—particularly in underserved and resource-limited contexts. The findings will contribute to enhancing fall prevention strategies and ensuring that future technologies are not only clinically effective but also aligned with real-world needs and preferences of their users.
Background: Severe tricuspid regurgitation (TR) is most commonly functional, related to left-sided heart disease or pulmonary hypertension. Carcinoid heart disease is a rare cause, and ovarian carcino...
Background: Severe tricuspid regurgitation (TR) is most commonly functional, related to left-sided heart disease or pulmonary hypertension. Carcinoid heart disease is a rare cause, and ovarian carcinoid tumors represent an uncommon source capable of producing isolated right-sided valvular lesions. Objective: To present an educational case of isolated severe TR revealing a probable ovarian carcinoid tumor and to discuss diagnostic reasoning, management, and prognosis in advanced high-risk TR. Methods: We analyzed the clinical, echocardiographic, and imaging findings of an 84-year-old woman admitted for right heart failure. Computed tomography, tumor marker assays, and risk scoring (TRI-SCORE) guided the etiologic and therapeutic approach. Results: Echocardiography revealed a dilated right ventricle with preserved systolic function, severe TR due to leaflet malcoaptation, and massive annular dilation (66 mm). Imaging showed a right latero-uterine cystic mass (53 × 56 mm) suspicious for ovarian neoplasm, with ascites and pericardial effusion. Tumor markers (CEA and CA19-9) were elevated. Given her high TRI-SCORE (≥6), surgical or percutaneous intervention was contraindicated. She was managed medically with high-dose diuretics and referred for oncologic evaluation. Conclusions: Ovarian carcinoid tumors can cause isolated right-sided valvular fibrosis via systemic serotonin release. In advanced high-risk TR, surgery is rarely beneficial, and optimized medical therapy remains the mainstay. This case highlights the importance of multidisciplinary evaluation and early recognition of rare cardio-oncologic presentations.
Background: The Objective Structured Clinical Examination (OSCE) is a prevalent method for evaluating clinical competence in medical education. As OSCEs become increasingly standardized and resource-i...
Background: The Objective Structured Clinical Examination (OSCE) is a prevalent method for evaluating clinical competence in medical education. As OSCEs become increasingly standardized and resource-intensive, alternative evaluation methods are being explored, particularly owing to the limited availability of certified examiners. However, few studies have investigated whether wearable technologies can effectively support OSCE assessment. Wearable devices may present a viable solution for recording and assessing clinical skills from the examiner’s perspective. Objective: This pilot study, conducted in 2024, aimed to investigate the feasibility and utility of a neck-mounted wearable camera for recording OSCE scenarios and to evaluate the assessability of clinical performance based on the obtained footage. Methods: Nine experienced medical educators participated in a simulated OSCE scenario involving ECG lead placement. All participants completed the initial live assessment and post-use questionnaire, while 8 completed the subsequent video-based reassessment. The authors utilized video recordings from both a fixed camera and a neck-mounted camera (THINKLET®) to assess the evaluability of each OSCE item. Following a washout period, evaluators re-assessed the recorded sessions. The authors analyzed interrater agreement using percent agreement and Cohen’s κ coefficient. A post-evaluation questionnaire captured evaluators’ experiences with the wearable device. Results: Cohen’s κ ranged from 0.258 to 0.913 (mean: 0.67), indicating substantial overall agreement. Visibility of fine motor skills, particularly in ECG electrode placement, appeared to be enhanced when using the neck-mounted camera, as inferred from higher assessment completion rates compared to fixed-camera recordings (odds ratio = 11.69; P < .001). Evaluators reported generally positive experiences with the device, although some noted issues with comfort, posture restriction, and limited visibility at low angles. Conclusions: This pilot study suggested that neck-mounted wearable cameras may enhance flexibility and visual accuracy in OSCE assessment, supporting their potential role in supplementing current evaluation practices. Further research is needed to standardize usage and address technical challenges.
Background: Parental burnout is an under-recognised syndrome characterised by emotional exhaustion, detachment from children, and reduced parental efficacy. It is associated with sleep disturbance, ad...
Background: Parental burnout is an under-recognised syndrome characterised by emotional exhaustion, detachment from children, and reduced parental efficacy. It is associated with sleep disturbance, addictive behaviours, suicidal ideation, and increased risk of child neglect and family conflict. Despite its public-health relevance, evidence-based interventions remain limited, particularly in low- and middle-income contexts. Objective: To evaluate the efficacy and safety of a mindfulness- and compassion-based group program—Inter-Care for Parental Burnout (IBAP-PB) —designed to reduce burnout symptoms in teleworking mothers. Methods: A three-arm randomised controlled trial (IBAP-BP, active control, waitlist) was conducted across Chile (December 2022–March 2023) with nine-month follow-up. Participants (n = 593) were women ≥ 18 years teleworking ≥ 1 day/week and living with ≥ 1 child. Exclusion criteria were self-reported severe psychiatric disorders. Randomisation was computer-generated and centrally concealed; data analysts were blinded. The IBAP-BP group attended eight weekly two-hour online sessions plus daily home practice integrating mindfulness and compassion. The active control performed relaxation and reflective journaling matched for duration and structure. The primary outcome was parental burnout (Parental Burnout Assessment, PBA) at nine months; secondary outcomes were mindfulness, balance of risks/resources, and adverse effects. Modified intention-to-treat analyses and multilevel structural models assessed effects over time. Results: Of 665 enrolled participants, 343 completed follow-up. At nine months, IBAP-BP produced greater reductions in parental burnout than the waitlist (mean difference = −0.81, p < 0.05; d ≈ 0.6). No significant difference was found between IBAP-BP and the active control, which showed transient improvements up to three months. Effects remained robust in sensitivity analyses. Adverse events were rare and mild across all groups. Mediation analyses showed inconsistent associations between mindfulness facets and outcomes. Conclusions: The culturally adapted, online IBAP-BP programme is a feasible, safe, and effective approach for reducing parental burnout in working mothers, with effects sustained over nine months. Clinical Trial: ClinicalTrials.gov Identifier: NCT05833269
Background: Large language models (LLMs) such as ChatGPT are increasingly used to support health-related queries and
decision-making. However, these models can be “jailbroken” through adversarial...
Background: Large language models (LLMs) such as ChatGPT are increasingly used to support health-related queries and
decision-making. However, these models can be “jailbroken” through adversarial prompts that bypass safety filters
and elicit harmful or medically inappropriate responses. In healthcare contexts, such vulnerabilities pose serious
risks. Understanding how jailbreak susceptibility varies across languages is essential for developing robust
safeguards and promoting equitable access to safe health information. Objective: This study aims to systematically compare and contrast the vulnerability of a health LLM for jailbreaking across
three languages: English, Spanish, and Hindi (transliterated using the Latin alphabet) based on emoji and
permutation cipher attacks. Methods: We analyzed 1,000 input prompts per language, drawn from the BeaverTails dataset, across three harm categories:
self-harm, violence, and drug abuse. Each prompt was modified using emoji and permutation cipher techniques,
resulting in 6,000 input-output pairs. Model responses were evaluated by human coders to determine the success rate
of jailbreak attempts across languages and cipher types. Results: Hindi prompts showed the highest vulnerability, with 787 successful jailbreaks using emoji ciphers and 873 using
permutation ciphers. Spanish and English followed, with lower success rates across both cipher types. Differences in
jailbreak success across languages and cipher strategies were statistically significant. Additionally, attacks targeting
violence-related prompts were more successful overall than those targeting drug-related or self-harm content,
indicating variation in vulnerability by harm type. Conclusions: The findings of this formative study reveal that LLM safety performance varies substantially across languages and
harm categories, raising concerns about equitable protection in multilingual health communication. Disparities in
access to harmful content may contribute to downstream health risks. Strengthening multilingual content moderation
and developing language-aware safety mechanisms are critical steps toward safer and more inclusive health AI
systems.
Background: Background: Graduate health informatics programs in the United States differ widely in cost, curriculum, and program structure. However, it is unclear how these differences influence affor...
Background: Background: Graduate health informatics programs in the United States differ widely in cost, curriculum, and program structure. However, it is unclear how these differences influence affordability, accreditation value, and preparation for a data-driven workforce. Objective: Objective: This study evaluated the value (tuition and affordability), structure (delivery format, credit load, culminating experience, and accreditation), and curriculum (technology content emphasis) of U.S. graduate health informatics programs, and examined how accreditation and modality relate to program design and tuition efficiency. Methods: Methods: A cross-sectional analysis of 107 U.S. graduate health informatics programs was conducted using publicly available data collected between January and May 2025. Tuition was standardized to cost per credit. Curricular content was coded for technology density and mapped to CAHIIM domains. Comparative statistics, regression models, and cluster analysis were used to assess relationships between tuition, credit requirements, accreditation, delivery format, and curriculum characteristics. Results: Results: Programs varied in delivery format (online 32%, in-person 19%, hybrid 17%), credit requirements (most commonly 31–39 credits), and culminating experiences (capstone 51%, internship 20%, thesis 2%). Delivery format predicted required credit hours (P=.05), while accreditation did not (P=.94). Accreditation did not improve tuition efficiency (P=.35). Programs requiring internships had significantly higher average credit loads (39.0 vs 31.3 credits; P=.005). Cluster analysis identified four program typologies differing in cost, credit requirements, modality, and culminating structure. Conclusions: Conclusions: Accreditation did not consistently improve affordability or tuition efficiency. Program design elements, particularly delivery format and internship requirements, were stronger predictors of credit load and cost. Aligning tuition transparency, structural expectations, and curricular emphasis on technical competencies may enhance equity and value in graduate health informatics education.
Background: Background: Maternal health literacy plays a critical role in promoting maternal well-being and preventing adverse pregnancy outcomes, yet limited studies have focused on primigravida wome...
Background: Background: Maternal health literacy plays a critical role in promoting maternal well-being and preventing adverse pregnancy outcomes, yet limited studies have focused on primigravida women, particularly in low-resource settings like Egypt. The interplay between health literacy, partner support, and self-efficacy in preventing preterm birth is underexplored. Understanding these relationships is crucial for informing interventions that enhance maternal health outcomes. Objective: Aim of the Study
This study aims to examine the moderating role of partner support in the relationship between maternal health literacy and self-management self-efficacy in preventing preterm birth among primigravida women. Methods: Methods: A cross-sectional descriptive study was conducted using a two-stage random sampling approach, following the STROBE guidelines, with 288 primigravida women. Data collection tools included the Maternal Health Literacy Inventory in Pregnancy (MHLIP), Partner Support Scale, Self-Management Self-Efficacy Scale for Premature Birth Prevention (SMSE-PBP), and a socio-demographic questionnaire. Descriptive statistics, Pearson’s correlation, and multiple regression analyses were utilized. Results: Results: Participants exhibited moderate health literacy, excelling in health knowledge. Partner support strongly correlated with self-efficacy (r = 0.45, p < 0.01). Regression analysis revealed that health knowledge and partner support were significant predictors of self-efficacy (R² = 0.47, p < 0.001). Age, education level, and the number of antenatal visits were significant predictors of health literacy (R² = 0.39, p < 0.01). Conclusions: Conclusion: Maternal health literacy, partner support, and self-efficacy are crucial for positive pregnancy outcomes. Antenatal care should integrate these factors through targeted, culturally appropriate interventions, particularly in primary healthcare settings.
Background: While smartwatches have revolutionized personal health monitoring, their utility for comprehensive cardiac assessment remains limited by single-lead electrocardiogram (ECG) acquisition. Th...
Background: While smartwatches have revolutionized personal health monitoring, their utility for comprehensive cardiac assessment remains limited by single-lead electrocardiogram (ECG) acquisition. This study introduces a novel software-based methodology to reconstruct a full 12-lead ECG from sequential smartwatch recordings, aiming for diagnostic comparability with conventional systems. Objective: We present a proof-of-concept clinical validation of this approach, addressing a critical unmet need in accessible cardiac diagnostics. Methods: A signal processing pipeline was developed to preserve ECG morphology across sequential smartwatch lead recordings, achieving synchronization via RR interval–based temporal resampling to generate a coherent 12-lead ECG. A clinical validation study was conducted in 20 patients with diverse cardiac pathologies and 2 healthy controls. Smartwatch-derived ECGs were compared with standard recordings. Diagnostic accuracy and visual interpretability were assessed by 85 healthcare professionals through a structured questionnaire. An independent arrhythmologist provided qualitative clinical validation for all cases. Results: The primary objective of reconstructing a 12-lead ECG from smartwatch signals was successfully achieved. Visual correlation scores between smartwatch-derived and conventional ECGs ranged from 77% to 91.4%, with a mean of 81%, indicating strong concordance. An average of 95.5% alignment in the electrical axis was observed. The highest diagnostic agreement was found in ischemic heart disease and bundle branch blocks, supporting clinical relevance. Conclusions: This study demonstrates proof-of-concept for reconstructing a 12-lead ECG from smartwatch-acquired signals with diagnostic comparability to conventional systems. This method expands smartwatch ECG capabilities, enabling accessible, advanced cardiovascular diagnostics and proactive patient monitoring. A European patent has been filed for this technology (reference:EP25382712.5).
Background:
Shifting surgeries from the stationary to the ambulatory setting is seen as a suitable way of increasing efficiency in the health care system. A significant increase in ambulatory proced...
Background:
Shifting surgeries from the stationary to the ambulatory setting is seen as a suitable way of increasing efficiency in the health care system. A significant increase in ambulatory procedures can therefore be observed internationally – particularly in the field of orthopedics. However, the interests and needs of patients are often insufficiently taken into account in this process. The "Power-AOP" research project was initiated to identify the associated challenges in the area of patient empowerment and to develop solutions. Using the field of orthopedics as an example, it investigates how the self-determination and participation of patients can be strengthened in the context of ambulatory surgery.
Methods:
Using a mixed methods approach, (health policy) recommendations will be developed that aim to strengthen patient empowerment in the context of ambulatory surgery. The project is scheduled to run for three years and comprises six work packages with a total of ten modules. In a first step, a scoping review will be carried out to map the existing literature. This will be followed by focus groups with patients and healthcare providers to gain deeper insights into their experiences and perspectives. The results will be quantified through a questionnaire-based survey. In order to identify a suitable patient population for this survey, an analysis of claims data will be conducted beforehand. The results will then be discussed and refined in two stakeholder workshops with key players in the health care system. In the final phase, a concept will be developed that contains actionable recommendations for strengthening patient responsibility in the context of ambulatory care.
Discussion:
This project will help to improve the patient empowerment in the context of ambulatory surgery. This allows patients to take a more active role in the process. On the one hand, this can lead to greater satisfaction with the process, particularly among patients. On the other hand, more active participation by patients can improve outcomes and avoid unnecessary readmissions or additional treatments.
Background: High-quality wound care requires early and effective interprofessional collaboration between medical, nursing, and pharmacy professionals. However, interprofessional education (IPE) in thi...
Background: High-quality wound care requires early and effective interprofessional collaboration between medical, nursing, and pharmacy professionals. However, interprofessional education (IPE) in this context remains limited in higher education. Immersive virtual reality (iVR) seems to be a promising IPE tool, enabling a standardized, realistic, and safe learning environment that allows multiple learners from different professions to train together. However, its educational effectiveness depends on an instructional design that supports learning while managing cognitive demands during immersive experiences. Objective: This study examined whether a newly developed interprofessional iVR wound-care training improves (1) procedural knowledge and (2) willingness to collaborate among medical, nursing, and pharmacy students, and how cognitive load relates to these outcomes. Methods: A within-subjects design with a pre- and posttest was implemented with 116 students from medicine, nursing, and pharmacy. Students completed two iVR sessions (~25 and 15 minutes) in interprofessional triads, addressing a pressure-ulcer case. The training integrated step-by-step scaffolding for the wound care task, and collaboration scripts to guide teamwork. Procedural knowledge and willingness to collaborate were assessed before and after the sessions, and cognitive load was measured after the sessions. Data were analyzed using repeated-measures ANCOVAs and a mediation model to test the preregistered effects. Results: Procedural knowledge increased significantly from pre- to posttest, F(1,107) = 26.19, p < .001, η² = .08. Cognitive load showed no significant effect on this gain. Willingness to collaborate did not change after the first session, F(1,80) = 3.55, p = .063, η² = .01, and was unaffected by cognitive load. Exploratory analyses showed that willingness to collaborate was significantly higher after the second session, t(64) = 3.16, p = .007, mean difference = 0.202). The effect on procedural knowledge and willingness to collaborate did not depend on the learner’s profession. Conclusions: These findings suggest that the iVR training effectively supported learning by providing clear structure and managing cognitive demands, enabling students from different professions to acquire procedural knowledge without being hindered by excessive cognitive load. The absence of cognitive load effects indicates that the instructional design, combining scaffolding and collaboration scripts, successfully balanced task complexity and guidance within the immersive environment. The delayed increase in collaboration willingness further suggests that attitudinal change requires sustained, repeated engagement in interprofessional contexts rather than a single exposure. Notably, no profession-related differences emerged in either procedural knowledge or willingness to collaborate, indicating that the iVR training supported learners equally across professional backgrounds. Overall, the results highlight the potential of iVR as a scalable, theory-based approach to IPE that can bridge disciplinary boundaries and prepare learners for effective teamwork in clinical practice.
Pretest–posttest pilot (n=38): WISEBOx reduced loot box purchasing at one month (32/38) and residual spending among those who continued purchasing. Further evidence is needed...
Pretest–posttest pilot (n=38): WISEBOx reduced loot box purchasing at one month (32/38) and residual spending among those who continued purchasing. Further evidence is needed
Background: South Australia’s public hospitals transitioned to the Sunrise (Altera Digital Health) electronic medical record (EMR) from 2010 but the new system lacked tools for near-real-time second...
Background: South Australia’s public hospitals transitioned to the Sunrise (Altera Digital Health) electronic medical record (EMR) from 2010 but the new system lacked tools for near-real-time secondary use of data to support clinical care, operations, and research. Objective: To design, implement, and evaluate a scalable Health Informatics System (HIS) within the Central Adelaide Local Health Network (CALHN) that securely integrates live EMR data and enables user-centred digital tools across clinical, coding, and operational domains. Methods: A cloud-native Health Informatics System (HIS) was developed on Microsoft Azure and Red Hat OpenShift using Terraform and ArgoCD for secure, reproducible deployment. Services employed CQRS, event-sourcing, and Apache Kafka for real-time data processing, with applications built in Scala.js and D3.js for responsive clinical interfaces. Governance frameworks aligned with SA Health’s Security Impact Assessment and Information Asset Classification processes and ensured compliance and safety. Clinical, corporate, and ethical committees oversaw EMR integration, data access, and research use. AI models were trained in secure, GPU-enabled environments with full audit trails. Sustainability was achieved through commercial agreements between CALHN, AusHealth, and HeartAI Pty Ltd, and was further supported by blended grant, professional services and equity funding. Results: Three sequential projects demonstrated coverage across key user groups:
1. Critical Care Informatics System extracted live EMR data for ward/bed-level displays, automated registry submissions and underwent iterative usability testing
2. CODEXA Clinical Coding Software system applied a custom Transformer based AI model to associate likely diagnostic codes using patient notes and estimate reimbursement amounts
3. The Patient Flow project is in the planning phases to establish a data-driven command centre for use in the Network Operations Centre Conclusions: A secure, scalable HIS can be deployed in a complex public health network through layered architecture, rigorous governance, and co-design with end users. The platform enabled real-time clinical displays, AI-assisted coding, and enterprise flow planning while maintaining safety and compliance. Clinical Trial: This trial is not registered.
Background: Loss of self-feeding ability due to severe upper extremity motor impairment (UEMI) limits independence, compromises nutrition, and reduces social participation. Robotic feeding devices are...
Background: Loss of self-feeding ability due to severe upper extremity motor impairment (UEMI) limits independence, compromises nutrition, and reduces social participation. Robotic feeding devices are an emerging category of assistive technology. Obi3 is a 3rd generation, Class I durable medical equipment (DME) robot intended to compensate for the movement of a human arm to restore self-feeding performance. The device is designed to enable patients to independently select and consume food and liquid using accessible switches or interfaces while maintaining culturally normative dining practices. Although earlier Obi generations showed promise, comprehensive usability data across multiple stakeholder groups remains limited. Objective: This study evaluated the usability, safety, and clinical relevance of Obi3 from the perspectives of patients, caregivers, and rehabilitation providers in real-world use. The findings were intended to generate evidence that informs clinical decision-making and determinations of medical necessity for DME consistent with the device’s FDA-cleared Indications for Use and clinician-supervised implementation. Methods: A mixed-methods usability study enrolled 50 participants, of whom 42 completed follow-up surveys after a one-week home trial of Obi3. Participants included 15 patients (pediatric and adult), 14 caregivers, and 13 providers. Quantitative measures included the System Usability Scale (SUS), survey items adapted from the Matching Person and Technology (MPT) framework, and self-feeding impairment ratings based on the International Classification of Functioning, Disability and Health (ICF). Paired-sample t tests, Wilcoxon signed-rank tests, and effect sizes (Cohen d) assessed changes in self-feeding scores. Qualitative data from open-ended survey responses and follow-up semi-structured interviews underwent thematic content analysis to complement quantitative findings. Results: Patients’ mean self-feeding impairment scores decreased significantly from 3.80 (SD 0.41) at baseline to 0.60 (SD 0.74) posttrial (n = 15; t₍₁₄₎ = 10.75, P < .001; Cohen d = 3.40). All patient participants demonstrated improvement in functional self-feeding ability. Mean SUS scores exceeded the benchmark for acceptable usability (≥68) across stakeholder groups: patients, 82.8 (SD 17.2); caregivers, 85.2 (SD 12.6); and providers, 85.0 (SD 10.7). Responses to Matching Person and Technology (MPT) items indicated strong alignment between user goals and device capabilities, perceived safety, and low complexity. Caregivers reported reduced feeding-related workload and stress, and providers endorsed ease of clinical integration. No adverse events or device malfunctions occurred. Conclusions: Obi3 demonstrated high usability, safety, and clinical utility in restoring self-feeding independence among individuals with severe UEMI. Findings across patients, caregivers, and providers support Obi3 as an effective piece of DME that enhances user autonomy and reduces caregiver burden. Future multisite and longitudinal research is warranted to confirm long-term outcomes, maintenance, and integration into rehabilitation practice. Clinical Trial: The study was approved by WCG IRB (protocol #20251472), and all participants provided informed consent.
Background: Acute ischemic stroke (AIS) management in rural regions is challenged by interhospital transfer delays, impacting outcomes. Objective: This study evaluates the efficacy of video-conferenci...
Background: Acute ischemic stroke (AIS) management in rural regions is challenged by interhospital transfer delays, impacting outcomes. Objective: This study evaluates the efficacy of video-conferencing teleconsultation on improving transfer efficiency and clinical outcomes in a hub-and-spoke model. Methods: This retrospective cohort study (January 2022–December 2024) included AIS patients identified as potential EVT candidates, transferred from a primary stroke center (PSC) to a comprehensive stroke center (CSC). Patients were categorized into teleconsultation and standard referral process (SRP) groups. The primary outcome was door-in-door-out (DIDO) time, with additional analyses on its components. Secondary outcomes included stroke-related decision-making and 90-day functional outcome using modified Rankin Scale (mRS). Safety outcomes included all-cause mortality within 90 days and symptomatic intracranial hemorrhage after intravenous thrombolysis and/or EVT. Results: A total of 83 patients were included, with 41 in the teleconsultation group and 42 in the SRP group (mean age: 73.3 years), and baseline characteristics were comparable. Teleconsultation significantly reduced DIDO time (95.2±22.9 vs. 132.3±41.5 minutes, p<0.001) by shortening CTA-to-ambulance notification time (44.6±17.4 vs. 79.5±37.6 minutes, p<0.001). Teleconsultation group had higher intravenous thrombolysis rates at the PSC (63.4% vs. 40.5%, p=0.04), higher EVT rates (34.2% vs. 14.3%, p=0.035) and shorter door-to-puncture time (83.0±35.5 vs. 118.5±25.9 minutes, p=0.04) at the CSC, with a significant shift toward better mRS at 90th day (OR: 4.55, p<0.001 vs. OR: 1.35, p=0.07). Safety outcomes were comparable between groups. Conclusions: Video-conferencing teleconsultation improves interhospital transfer efficiency, stroke-related decision-making, and functional outcomes. This study highlights its potential in optimizing rural stroke care.
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.
Background: Youth who identify as lesbian, gay, bisexual, transgender, queer, or as other sexual and gender minorities (LGBTQ+) encounter everyday expressions of prejudice and discrimination via infor...
Background: Youth who identify as lesbian, gay, bisexual, transgender, queer, or as other sexual and gender minorities (LGBTQ+) encounter everyday expressions of prejudice and discrimination via information and communication technologies. This includes sentiments and interactions that may be conceptualized as digital microaggressions. Objective: A preliminary measure of anti-LGBTQ+ digital microaggressions was compiled from emerging research and existing microaggressions scales, then tested on an internet-based sample of LGBTQ+ youth (aged 14–24) across three countries (n = 1,804). Methods: An exploratory factor analysis was conducted to understand emergent factor structures and vetted using a confirmatory factor analysis. Results: Results: Findings support further appraisal of the 45-item Anti-LGBTQ+ Digital Microaggressions Scale (ADMS), consisting of two three-factor subscales that measure (1) the experience of digital microaggressions directed at oneself and (2) witnessing digital microaggressions directed at others. The direct subscale (23 items) examines discrimination towards LGBTQ+ youth as individuals, including minimization and dismissal of their identity-based experiences. The indirect subscale (22 items) assesses discriminatory content directed at others or ambiently present in internet-based contexts, including negative portrayals of LGBTQ+ individuals and communities. Conclusions: Findings raise serious concerns about the prevalence of digital microaggressions in the lives of LGBTQ+ youth and offer a mechanism to support future investigations into this emerging area of research.
Background: Dehydration—specifically hyperosmolar dehydration (HD)—in adults is a common clinical condition associated with increased morbidity, mortality, and hospital admissions. Despite these a...
Background: Dehydration—specifically hyperosmolar dehydration (HD)—in adults is a common clinical condition associated with increased morbidity, mortality, and hospital admissions. Despite these alarming statistics, current treatment guidelines often fail to distinguish HD from other types of low body fluid status (e.g. hypovolaemia). This review aims to explore the existing evidence on treatment and to identify gaps in the evidence to guide future research Objective: The objective is to identify and summarize existing studies on treatment of dehydration in adults and to map the current evidence, highlight gaps in the literature and guide future research. Methods: The planned review will be conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. A systematic search will be performed across major peer-reviewed databases, with additional examination of reference lists and citations. Original studies of any design concerning treatment for HD in adults, specifically involving treatment with oral, subcutaneous, or intravenous fluids for dehydration, will be included. Data charting will contain study characteristics, participant demographics, details of HD diagnosis, treatment approaches, and reported outcomes. Results: The findings from the included studies will be reported through a narrative summary, supported by descriptive analysis of quantitative data if suitable. Conclusions: This scoping review will provide an overview of the current evidence on the treatment of HD in patients referred to hospital, identifying key insights and evidence gaps to inspire future research areas. Clinical Trial: The protocol will be registered at Open Science Framework? https://osf.io/ym65x
Background: Adolescents and young adults with chronic health conditions often struggle to adhere to their daily oral medications. Transdiagnostic mobile health interventions have the potential to supp...
Background: Adolescents and young adults with chronic health conditions often struggle to adhere to their daily oral medications. Transdiagnostic mobile health interventions have the potential to support medication adherence by reaching youth in their daily lives and at a large scale. Objective: The aim of this study was to partner with a Community Advisory Board to design an adaptive medication adherence mobile health intervention and assess the feasibility, usability, and acceptability of the intervention during iterative design cycles. Methods: Using human-centered design methods, researchers collaborated with a Community Advisory Board of young adult patients to design an intervention prototype, collect mixed methods feedback from (N = 22) 15-20 year old patients, and iteratively refine the intervention to optimize feasibility, usability, and acceptability. Results: The design process produced a moderately feasible, usable, and acceptable intervention. However, prospective acceptability (before trying the intervention) is still insufficient, and more refinement is needed to support users in learning to use the intervention, increase variety and interest during use, and dynamically adapt the intervention to the specific user over time. Conclusions: Partnering with community members early in the development of an intervention may improve the ultimate feasibility, usability, and acceptability of digital heath tools. Human-centered design offers a rapid, practical, creative framework for identifying what works and what needs to be improved early in the lifecycle of a new intervention. Clinical Trial: NCT05719064
Background: Rheumatic heart disease (RHD) is a sequela of recurrent, untreated Group A Streptococcus (GAS) infections. RHD disproportionately affects children and young adults in the Global south. Int...
Background: Rheumatic heart disease (RHD) is a sequela of recurrent, untreated Group A Streptococcus (GAS) infections. RHD disproportionately affects children and young adults in the Global south. Intermittent mass screening of early RHD using affordable tools in these disease endemic regions is essential for effective prevention. Objective: This study examined multimodal physiologic data for assessing the prevalence of early RHD in a cohort of asymptomatic, at-risk schoolchildren in a rural Ethiopia. Methods: A total of 584 asymptomatic children, aged 10 to 20 years, were randomly selected for screening, and stratified into two groups (≤14 and >14 years). Electrocardiogram (ECG), Phonocardiogram (PCG), and echocardiography were performed, with diagnoses based on the 2012 World Heart Federation criteria. Results: After excluding 8 (1.4%) children who had non-rheumatic findings, 576 children comprised of 334 (58%) females and 242 (42%) males were analyzed. The mean age of the cohort was 16.1±2.5 years. Echocardiographic evaluation identified 19 RHD cases (10 borderline, 9 definite), with a higher prevalence among females (68%). The prevalence estimate derived from this analysis was 32.5 per 1000 population (95% CI: [18.1, 46.9]). Mitral valve was most affected (47%), followed by combined mitral and aortic involvement (42%). Mitral regurgitation (MR) was the most common (84%), then mitral stenosis (10%) and aortic regurgitation (6%). PCG showed MR (52.6%), MS (10.5%), and silent/unknown in the rest. Prolonged PR intervals was observed in 11% of RHD cases. Conclusions: The study confirms persistent high prevalence of asymptomatic RHD among schoolchildren in rural regions with female predominance. Clinical Trial: B3222022001075
Background: Decentralized clinical trials (DCTs) are transforming traditional research by allowing participants to remotely take part through digital technologies such as telemedicine, mobile applicat...
Background: Decentralized clinical trials (DCTs) are transforming traditional research by allowing participants to remotely take part through digital technologies such as telemedicine, mobile applications, and digital platforms, overall enhancing participation, safety, accessibility, and overall trial outcomes. The COVID-19 pandemic further accelerated adoption, as there was a pressing need to minimize infection risks, delays, and disruptions. Despite growing innovation and initiatives like Trials@Home, there is limited understanding of how commercial technologies align with and support trial operations in the DCT lifecycle. Objective: This study aimed to map the landscape of commercial technologies used in DCTs and assess their availability, suitability, and alignment with clinical trial operations across the DCT lifecycle. Methods: A market scan of commercial technologies was conducted in 2024-2025 to update and add on previous work in 2020 using a structured approach. Seven peer groups were formed, each assigned to one of seven Basic Building Blocks (BBBs) representing key phases of the clinical trial lifecycle. These groups independently reviewed and categorized relevant solutions. The process included reassessment of previously identified solutions and identification of new technologies through keyword-based web searches, and categorization by the number of BBBs covered. Solutions also were categorized as Single- or Multiple-BBB based on their coverage, and a co-occurrence analysis identified strong and underrepresented pairings in BBB coverage. Results: The scan identified 312 technological solutions supporting DCTs. A similar distribution of solutions across BBBs was observed, with Set-up and Design and Patient Engagement being the most represented, while Operation and Coordination was the least covered. Most tools were specialized, with 226 single-BBB solutions, 48 covering two BBBs, and fewer than 10 addressing five or more. Only 2 solutions covered all seven BBBs. Co-occurrence analysis revealed strong overlaps between Patient Engagement, Intervention and Follow-up, and Operation and Coordination, while Set-up and Design showed minimal overlap with other BBBs. Conclusions: The rapid evolution of DCT technologies highlights the importance of structured assessments to guide technology selection. The balanced distribution of solutions per BBB suggests a broad coverage of trial operations. The stronger coverage in Set-up and Design and Patient Engagement compared with Operation and Coordination indicates areas for further development. The majority of tools were highly specialized with only a few covering multiple trial operations in an integrated manner. This reflects the maturity of specialized solutions and the potential for comprehensive systems spanning the full trial lifecycle. The strong pairs between Patient Engagement with Operation and Coordination and Intervention and Follow-up, together with the minimal overlap of Set-up and Design reveal a gap between planning and execution phases. This study provides a comprehensive catalogue of technologies, and offers practical insights to inform strategic technology selection for more efficient, inclusive, and connected clinical trial operations.
Background: General Practitioners (GPs) play a pivotal role in a patient’s health care journey. However, demands on general practice, including an aging population and complex patient management, wo...
Background: General Practitioners (GPs) play a pivotal role in a patient’s health care journey. However, demands on general practice, including an aging population and complex patient management, workforce shortages and health system fragmentation, have been shown to adversely impact delivery of high-quality care and health outcomes. Integrated care models, particularly those that offer virtual care options, are one way to support improved access to quality care and efficiency of health care delivery across metropolitan and rural areas. The SUSTAIN model of care was created to provide an accessible option for integrated care. It consists of centralised paediatricians supporting general practitioners in their practice through virtual co-consultations, virtual case discussions and phone/email support. There is limited evaluation literature on integrated models of care being implemented in a primary care setting where the GP and family are face-to-face and the non-GP specialist is virtual. To address this gap, a comprehensive implementation evaluation of the SUSTAIN model of care was conducted. Objective: To examine what, why and how different factors impact the uptake of the SUSTAIN model of care from the perspectives of the SUSTAIN paediatricians and metropolitan and rural GPs in New South Wales (NSW), Australia. Methods: Qualitative study as part of the mixed-methods implementation evaluation of the SUSTAIN model of care. Data were collected via recorded online focus groups and interviews with general practitioners, general practice managers and paediatricians at 6- and 12-months post implementation of SUSTAIN. Data were analysed thematically using iterative thematic analysis informed by the Consolidated Framework of Implementation Research. Results: Eighteen focus groups and 13 interviews were conducted. GPs, practice managers and paediatricians found the SUSTAIN model acceptable, with the flexibility and practicality of the model highlighted. GPs valued the learning opportunities, collaboration and support they gained working alongside the paediatricians. Virtual delivery through telehealth was viewed as a positive means of receiving specialist support that would otherwise be inaccessible to many practices. Increased efficiency in workflow and working at the top of scope in paediatric care as well as opportunities for meaningful professional relationships and increased family trust in GP-delivered care were recognised as key benefits that enhanced uptake. The current landscape of Australian general practice, with fee-for-service billing, limited time and workflow pressures, were all recognised as barriers to engagement with the SUSTAIN model of care. GPs and paediatricians recognised the need for more appropriate remuneration to support co-consultation as vital to the sustainability and scalability of the SUSTAIN model. Conclusions: The SUSTAIN model of care expands on our understanding of the benefits of integrated GP-paediatrician models of care in general practice by demonstrating the utility of a paediatrician supporting a GP in their practice via telehealth across metropolitan and rural environments in NSW, Australia. Clinical Trial: Australian New Zealand Clinical Trials Registry ACTRN12623000543684
Background: Psychotic disorders are severe psychiatric conditions that affect approximately 24 million people worldwide. While research has traditionally focused on schizophrenia, other psychotic diso...
Background: Psychotic disorders are severe psychiatric conditions that affect approximately 24 million people worldwide. While research has traditionally focused on schizophrenia, other psychotic disorders are sometimes overshadowed by schizophrenia in the research field. Digital psychiatry offers innovative solutions to improve access to care, reduce stigma, and personalize interventions for patients who face barriers to traditional services. Objective: This scoping review aimed to explore the most recent studies targeting patients with non-schizophrenic psychosis. Methods: This review of literature examined articles published between the 1st January 2015 and 14th May 2025 on digital interventions targeting non-schizophrenic psychoses, identified through MEDLINE/PubMed. Results: Of 368 initial results, 19 studies met eligibility criteria. Interventions were grouped into four categories: internet-based therapies, smartphone applications, virtual reality (VR)-based treatments, and avatar therapy. The studies identified show that digital interventions for psychotic disorders, although mainly focused on schizophrenia and psychotic onset, also offer good prospects for other psychoses. In terms of acceptability, most studies show a good level of patient satisfaction, especially among young people, thanks to the flexibility and possibility of integrating treatment into daily life. However, significant barriers persist: psychotic symptoms themselves (suspiciousness, social withdrawal), technical difficulties, privacy concerns and poor training of clinicians. Finally, there is considerable methodological heterogeneity and limited representation of non-schizophrenic disorders, leading to difficulties in generalizing the results. Conclusions: Overall, digital technologies represent a promising opportunity to personalize interventions, improve access and support treatment continuity, but larger, more inclusive studies conducted in real-world clinical settings are needed.
Background: Large language models are increasingly used by employees at university hospitals for information retrieval or decision support. Self-hosted on-premise systems provide a secure environment,...
Background: Large language models are increasingly used by employees at university hospitals for information retrieval or decision support. Self-hosted on-premise systems provide a secure environment, conform with data privacy and security regulations for handling sensitive personal data. By automation of standard procedures through LLM application, time-consuming administrative tasks can be drastically reduced and analysis of large data sets facilitated. Objective: The objective of our study was to gather feedback from registered AI users on the usability and common use cases of the on-premise LLM infrastructure we established at the University Medicine Magdeburg, in order to optimize the models to the needs at our facility. Methods: We developed an online questionnaire to which registered AI users were given access and were informed via email. Results: Of 322 registered AI users, 98 participated in the user survey. Filtering incomplete responses, results from 91 participants remained for further analysis. Speed and quality received overall high approval rates. A majority of users utilized the platform at least once per week. Forty-four percent reported to save at least 30 minutes of work per week by using our AI platform. A diverse set of use cases could be observed, dependent on the users’ professions, e.g. healthcare and research professionals using the AI platform much more often for tasks of creation or analysis compared to administrative staff. Conclusions: Our data indicates that implementation of a self-hosted on-premise LLM has positive influence on the diverse group of professionals working at a university hospital, saving time and meeting their individual needs.
Background: Vision-enabled large language models (VE-LLMs) have the potential to provide flexible and explainable medical image interpretation. However, their real-world performance on clinical data s...
Background: Vision-enabled large language models (VE-LLMs) have the potential to provide flexible and explainable medical image interpretation. However, their real-world performance on clinical data such as 12-lead electrocardiograms (ECGs) has not been systematically assessed. Objective: This study aimed to evaluate the diagnostic accuracy and reliability of state-of-the-art VE-LLMs in interpreting real-world ECG images. Methods: We tested eight VE-LLMs (ChatGPT-5, ChatGPT-4, Gemini 2.5 Pro, Copilot, Grok-4, Perplexity, Claude Sonnet-4, and Claude Opus-4.1) using 70 de-identified ECG images. A standardized prompt requested nine determinations: rhythm, first-degree atrioventricular (AV) block, intraventricular conduction block and pattern, corrected QT (QTc) prolongation, premature atrial and ventricular contractions, ischemic ST-segment deviation, and axis deviation. An expert consensus served as the reference standard. Model outputs were evaluated using overall and per-class diagnostic metrics. Results: Overall accuracy across models varied significantly from 68.1% to 78.3% (429/630 to 493/630, Cochran’s Q, P<.001). ChatGPT-5 achieved the highest accuracy (78.3%) but had the slowest response time (median 276 seconds), whereas Perplexity and Copilot responded within a median of 2 and 3 seconds, respectively. Rhythm classification reached 72.9%–82.9% accuracy (51/70 to 58/70), but sensitivity for atrial fibrillation was ≤22% (≤2/9). Detection of first-degree AV block was poor (sensitivity 0%–33%; 0/9 to 3/9), and QTc prolongation was also poor (sensitivity 0%–45.5%; 0/22 to 10/22). Intraventricular block was identified with up to 70% accuracy (49/70), but correct subtype assignment was ≤44% (≤11/25). ST-segment deviation sensitivity was <25% for all models (highest 3/14). Agreement with expert interpretation was low, with Cohen’s kappa (κ) indicating poor-to-fair concordance (κ≤.37). Conclusions: VE-LLMs showed moderate overall accuracy but low sensitivity and limited agreement with expert ECG interpretation. Current performance is inconsistent and insufficient for clinical deployment. Future development should focus on domain-specific training and hybrid approaches combining LLM reasoning with established ECG algorithms before use in patient care.
Background: Health apps, which comprise both medical and wellness apps, hold potential to improve prevention, diagnosis, treatment, and management of disease. Yet, adoption by health care professional...
Background: Health apps, which comprise both medical and wellness apps, hold potential to improve prevention, diagnosis, treatment, and management of disease. Yet, adoption by health care professionals (HCP) including recommendation and prescription rates are low, even in countries where these apps are reimbursed. Professional guidelines from medical societies and trusted standardized health app quality assessment reports providing the information HCPs need to recommend or prescribe a specific app for an individual patient are crucial to enable this digital transformation of medicine. The CEN-ISO/TS 82304-2 (hereinafter “82304-2”) Technical Specification, an initiative of the European Commission, includes a research-based health app quality assessment framework comprising 81 quality requirements. Results of 82304-2 app assessments are summarized in a “health app quality label”. The 82304-2 label’s potential to increase willingness to recommend health apps was recently confirmed. However, to adequately inform HCPs for recommending and prescribing high-quality apps a more detailed “health app quality report” is required in addition to the 82304-2 label. Objective: To codesign the 82304-2 health app quality report by including the information detail that satisfies the information needs of individual HCPs in decision-making on a health app. Methods: A Participatory Design approach was applied to generate the report. In an 18-month process of participatory prototyping with 9 HCPs with digital health expertise the 82304-2 health app quality report was iteratively developed, designed, and validated. A convenience sample of 31 HCPs indicated the priority 82304-2 health app quality requirements that would inform their decision-making on recommending an app and as such need detailed quality information. Final feedback meetings with the 9 HCPs with digital health expertise and 8 medical societies were used to finalize the 82304-2 report design. Web-based questions in these meetings, as well as a comparative content analysis and the Health Education Materials Assessment Tool (HEMAT) were used to indicatively evaluate the 82304-2 report design. Results: In total 30/81 (37%) of the 82304-2 quality requirements were prioritized by >50% of the HCPs. The final 82304-2 report design provides detailed information for 27/30 (90%) of the prioritized 82304-2 quality requirements. The reporting detail for two 82304-2 quality requirements requires more research. The final feedback meetings, comparative content analysis and HEMAT provide indicative proof of the usefulness and usability of the 82304-2 report design. Conclusions: We succeeded in our aim to design the 82304-2 health app quality report and found promising potential for its distinctive usefulness for HCPs and medical societies. Further efforts are needed to test and fine-tune its multi-stakeholder and intercontinental usefulness and usability, to support medical societies in providing guidance and potentially training on recommending health apps, and to advance from the current design to a scalable fully-functional version of the 82304-2 report and associated open access database.
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.
Background: Cervical cancer (CC) is one of the leading causes of female mortality after breast cancer. CC accounts for more than 7.5% of female cancer deaths worldwide. Human papillomavirus (HPV) is t...
Background: Cervical cancer (CC) is one of the leading causes of female mortality after breast cancer. CC accounts for more than 7.5% of female cancer deaths worldwide. Human papillomavirus (HPV) is the most common sexually transmitted infection (STI) in women and the leading cause of cervical cancer in women for almost 99% of all CC cases. HPV vaccination could prevent up to 70% of HPV-related cervical cancer and 90% of genital warts. HPV vaccination is the bedrock of primary prevention and helps reduce the incidence and death rates of HPV-associated cervical cancer. This analysis examines each theme based on responses to questions about HPV and HPV vaccine knowledge and health beliefs among Kuwaiti parents. Objective: The study aims to understand the knowledge and health beliefs of 20 Kuwaiti parents regarding HPV vaccination, with the goal of developing a health promotion policy and introducing a national immunization program in Kuwait. Methods: The remaining 37 participants were then evaluated using purposive sampling to select 20 participants for the one-on-one semi-structured interviews. The researcher wanted to include both participants (male and female parents) with primary education (diploma and below) and those with secondary and above education (Bachelor's and above). the researcher had four categories with sufficient numbers (at least five in each category), which gave us at least 20. Semi-structured interviews were held with individual parents based on the Health Belief Model (HBM). The data were thematically analyzed using an inductive approach, generating themes through the theoretical framework of the HBM, and theme extraction analyses were managed on a semantic level. Results: There are seven main themes containing 20 sub-themes. Seven themes are: (1) Knowledge and awareness about HPV infection and vaccine. (2) Perceived susceptibility, which is explained by the HPV infection effect based on gender. (3) Perceived barriers to HPV vaccination with several sub-themes: stigma, social customs, negative vaccination uptake, and religious influences. (4) Perceived benefits: Parents emphasise the benefits of getting an HPV vaccine, such as protection against the virus and related cancer diseases. (5) Perceived severity, a fear of getting a severe disease. (6) Self-efficacy, with sub-themes of parent experiences decisions regarding HPV vaccination (7) Cues-to-action, the role of the Ministry of Health, and (HCPs). Conclusions: The HBM framework is beneficial for Kuwait's HPV vaccination campaign. The correlation between sexual intercourse and the HPV vaccine frequently adds complexity to the decision-making process about immunisation. This study demonstrates that positive cues to action from HCPs and educational vaccination benefits can overcome perceived barriers among parents related to stigma and religion. It is essential to conduct such research to guide the development of interventions aimed at promoting the adoption of the HPV vaccine uptake.
Background: With the growing ageing population, technology that supports independent living is increasingly important. Web search systems are well-established, whereas Generative AI (e.g., ChatGPT) re...
Background: With the growing ageing population, technology that supports independent living is increasingly important. Web search systems are well-established, whereas Generative AI (e.g., ChatGPT) represents a newer, adaptive tool that could offer personalised information access. However, little is known about how older adults, particularly those with Mild Cognitive Impairment (MCI) or mild dementia, perceive and engage with these systems. Objective: This study explored the use and perspectives of web search and Gen-AI in older adults with and without MCI. Methods: A UK-wide mixed-methods study was conducted with older adults, including those with MCI or mild dementia. An online survey captured technology use, Likert-scale ratings of web search and Generative AI, and reasons for non-use. Follow-up focus groups provided in-depth qualitative perspectives. Quantitative data were analysed using descriptive and comparative statistics, while qualitative data were thematically analysed. Results: Survey findings showed higher use of web search (98%) compared to Generative AI (14%) within these groups. Web search was rated positively across participants, although challenges were raised regarding the phrasing of queries and commercialisation. Gen-AI use was less common, but more than half of non-users expressed willingness to adopt it in future. Combined with focus group responses, themes exploring keyword searching, mistrust, lack of knowledge, and willingness to learn were established. Participants also suggested potential applications of Generative AI, such as supporting independent living through monitoring and simplifying complex searches. Conclusions: Web search remains the primary method, and participants highlighted both advantages and frustrations with current systems. Generative AI was underused but seen as promising, with its adoption mainly limited by mistrust and knowledge gaps. Our findings indicate that structured training, early introduction, and user-centred design could encourage adoption, enhance accessibility, and support independent living among older adults with and without MCI.
Background: Women with breast cancer face many barriers to return to work (RTW) after their treatment. The FAcilitating and SusTain the Return to work After breast Cancer (FASTRACS) intervention aims...
Background: Women with breast cancer face many barriers to return to work (RTW) after their treatment. The FAcilitating and SusTain the Return to work After breast Cancer (FASTRACS) intervention aims to facilitate and sustain functional RTW. The main objective of the RECOVA-FASTRACS study is to evaluate the processes of the using a realist approach that analyses what works, how, for whom and under what circumstances.
Methods: The RECOVA-FASTRACS study uses a mixed-methods design to assess the implementation, context, and impact mechanisms of the FASTRACS intervention. The qualitative analysis will include two main components: a trajectory analysis and a focus group assessment. The trajectory analysis will examine the experience of women who participated in the intervention and key individuals involved in their RTW process. We will use semi-structured interviews according to the multiple-case study method. Additionally, to explore organisational and professional practices, focus groups will be conducted with professionals who deliver the intervention. To analyse the trajectories, embedded and iterative integration will combine the qualitative findings, with relevant quantitative data from the FASTRACS randomised controlled trial for five domains (i.e. personal situation, professional situation, return to work, care pathway, intervention tool use and perceived usefulness).
Discussion: Our mixed-methods realist evaluation will provide a detailed analysis of the intervention processes, helping to identify impact mechanisms within specific contexts. This approach is meant to ensure a more informed and realist deployment of the intervention by professionals following the study.
Japan has universal health coverage and designated childhood cancer centers, but the national information environment still functions as a “black box”: incidence can be tracked, whereas treatment...
Japan has universal health coverage and designated childhood cancer centers, but the national information environment still functions as a “black box”: incidence can be tracked, whereas treatment exposure and long term follow up are not reliably linked across hospitals, registries, and survivorship services. In this viewpoint, we synthesize international guidance and Japanese policy to propose a formative design for digital health governance that connects clinical systems, registries, and survivorship. We specify a minimal pediatric dataset and an interoperability architecture based on the Health Level Seven Fast Healthcare Interoperability Resources (HL7 FHIR) standard. A national Pediatric Data Steward would govern standards and APIs, terminology and coding (eg, International Classification of Diseases for Oncology and International Classification of Childhood Cancer, with mappings to SNOMED CT and LOINC), privacy and consent aligned with the Act on the Protection of Personal Information and the Next Generation Medical Infrastructure Act, data use agreements, data quality, and audit. Validated data from electronic health records, laboratories, radiology, pathology, and cooperative group databases would flow through a FHIR gateway to the National and Hospital based Cancer Registries and to a patient facing digital survivorship passport, with bidirectional updates and linkage to resident registries and vital statistics. We also propose pediatric indicators and a staged roadmap to implementation. Transforming pediatric oncology information in Japan into a learning system is primarily a governance task; a Pediatric Data Steward, a harmonized pediatric data dictionary, and a portable, consent aware survivorship passport could improve timeliness, completeness, follow up, equity, and transparency.
Background: The growing need for remote patient monitoring, accelerated by the global pandemic and an aging population, necessitates the development of advanced non-contact technologies for measuring...
Background: The growing need for remote patient monitoring, accelerated by the global pandemic and an aging population, necessitates the development of advanced non-contact technologies for measuring vital signs. Conventional contact-based sensors pose risks of infection and cause discomfort during long-term use, while existing non-contact methods suffer from inaccuracies due to environmental factors and user motion. Heart rate (HR) and body temperature (Temp) are fundamental indicators of health, yet their reliable and convenient remote measurement remains a significant challenge. Objective: This study aims to develop and validate an integrated, non-contact system for accurately measuring HR and Temp. The proposed system leverages 60GHz RADAR and a high-performance infrared (IR) sensor, enhanced with advanced signal processing and an AI-based computer vision algorithm, to overcome the limitations of conventional methods. Methods: A novel system was designed, combining a 60GHz RADAR sensor for HR measurement and an IR sensor for Temp measurement. To enhance HR accuracy, a Window Filter and a Peak Uniformity algorithm were applied to the raw RADAR signal to mitigate noise and motion artifacts. For Temp measurement, an IR sensor with a narrow 5° Field of View (FOV) was integrated with a YOLO Pose-based tracking system, which uses a camera and servo motors to automatically orient the sensor towards the user's face. The system's performance was validated in a controlled laboratory setting with 30 healthy adult participants (mean age 40.8). The results were benchmarked against gold-standard devices: a MAX30102 PPG sensor for HR and a Braun ThermoScan 7 for Temp. Results: The advanced signal processing for the RADAR sensor significantly improved HR measurement accuracy, reducing the Mean Absolute Error (MAE) from 13.73 BPM to 5.28 BPM (p=0.002) and decreasing error variability. For temperature, the AI-guided IR sensor demonstrated superior performance, lowering the MAE from 4.10°C to 1.64°C (p < 0.001) compared to a fixed-angle sensor. Notably, the maximum error of the AI-guided system (2.2°C) was lower than the minimum error of the conventional method (2.6°C), indicating enhanced stability and reliability. Conclusions: The findings demonstrate that integrating 60GHz RADAR with advanced signal processing and an AI-driven tracking system provides a robust and accurate solution for non-contact vital sign monitoring. Clinical Trial: KWNUIRB-2025-07-007-001
Background: Austria has only recently established a legal framework for assisted dying. Individuals seeking assistance in suicide must navigate a multi-stage process that has been criticised for its c...
Background: Austria has only recently established a legal framework for assisted dying. Individuals seeking assistance in suicide must navigate a multi-stage process that has been criticised for its complexity, both for the individuals seeking assistance as well as the healthcare and legal professionals involved in the procedure. Objective: In order to address the critical gap in empirical research that sheds light onto the law’s implementation, we have designed a multi-perspective evaluation that examines the various perspectives of individuals seeking assisted suicide, their family members and other relatives, as well as of all professions specified in the legal framework – notably physicians, notaries and other relevant legal professionals, pharmacists, and psychologists. Methods: Our study employs an interdisciplinary approach that integrates theories, concepts, and methodologies from legal science, ethics and social science. Within the latter, we aim to provide a comprehensive account of stakeholder experiences through a series of semi-structured interviews and two online questionnaire surveys. Results: The initial research proposal received approval from the University of Vienna Ethics Committee in November 2023. The legal and ethical analyses are ongoing, with several key legal and ethical barriers already identified. Recruitment for interview participants began in January 2024. As of October 2025, the majority of interviews across all stakeholder groups have been conducted and transcribed. Given the novelty of the legislation and the limited practical experience to date, further interviews are scheduled until the end of 2025. Data collection for both questionnaire surveys took place from June until September 2024 with data analysis completed in May 2025. Conclusions: Our multi-perspective evaluation aims to assess the framework on assisted suicide in Austria. By evaluating the perspectives of relevant key stakeholders, we aim to provide a nuanced understanding of the law’s societal, legal, and ethical implications.
Background: While growing evidence suggests that music training supports child development, few long-term randomized controlled trials (RCTs) have rigorously tested these claims. Moreover, it remains...
Background: While growing evidence suggests that music training supports child development, few long-term randomized controlled trials (RCTs) have rigorously tested these claims. Moreover, it remains unclear whether the benefits are confined to music-specific domains or extend to higher-order cognitive functions such as inhibitory control (IC), a core executive function associated with long-term outcomes in academic achievement, career success, socio-emotional health, and physical well-being. Objective: This paper presents the protocol for the Extracurricular Activity and Child Early Learning and Development (EXCEL) trial, an RCT designed to assess the feasibility of a long-term music training program focusing on the brain and behavioral correlates of IC. Methods: A total of 126 children, aged 6 to 8 years and residing in neighborhoods with limited resources in Los Angeles, were individually randomized to either a music (intervention) or theatre (active control) after-school program. Both programs were delivered over 24 months by established community arts organizations. Eligibility criteria included: average intellectual functioning, no major medical or psychiatric conditions, and MRI eligibility. Children with prior formal music training exceeding six months or severe hearing impairment were excluded. Before the intervention began, all participants completed baseline behavioral and neuroimaging assessments. The primary trial aim was to assess the effects of extended music training, relative to theatre training, on changes in measures of IC (i.e., Go/No-Go task and delayed gratification) and related neural functional activation. A secondary interim aim of the trial was to evaluate the feasibility of conducting a long-term RCT of music education in a first cohort, measured by participant retention, adherence to the program, willingness to continue at the 12-month mark, and fidelity. Results: Recruitment, screening, baseline testing, randomization, and program enrollment began in August 2022, and after-school programming began in October 2022. The randomized interventions and all data for the first cohort (N = 42) have been collected. Intervention and active control programs for a second cohort are ongoing and will end in Fall 2026. Conclusions: This paper reports the EXCEL trial protocol and provides estimates of the feasibility of implementing a long-term randomized controlled trial of music training in real-world, community-based settings with children. While similar neuroimaging RCTs are currently underway in Europe, the EXCEL trial is among the first in the United States to integrate longitudinal neuroimaging with arts intervention. Findings will inform the viability of scaling such programs and contribute to our understanding of how sustained music engagement may influence the development of inhibitory control circuitry in childhood. Clinical Trial: The EXCEL Trial Registration: ClinicalTrials.gov NCT05502939
Background: Software-based interventions have emerged as effective tools for managing chronic diseases. Lifestyle intervention (LI) apps can promote weight loss, but many depend on human coaches or he...
Background: Software-based interventions have emerged as effective tools for managing chronic diseases. Lifestyle intervention (LI) apps can promote weight loss, but many depend on human coaches or healthcare providers, thus introducing higher costs and variability in feedback. Most existing apps rely on calorie-counting approaches and lack structured, individualized guidance, leaving users to determine how to translate general recommendations into daily nutrition and exercise practices. Moreover, many do not provide rapid, adaptive feedback to adjust meal or activity plans in real time. Objective: To compare the RightBMI App, an autonomous, protein-focused digital lifestyle intervention, with provider-delivered lifestyle intervention (LI) for weight loss and body composition changes in bariatric surgery candidates. Methods: A prospective randomized trial (April 2024 - April 2025, Holyoke Medical Center) enrolled 160 patients (App: n=80; no-App: n=80). RightBMI provided personalized nutrition and exercise plans. Outcomes (% total body weight loss [%TBWL], % fat mass loss [%FML], % visceral fat loss [%VFL], % muscle mass loss [%MML]) were assessed over 24 weeks using Generalized Estimating Equations (GEE) with multiple imputation, adjusting for age, BMI, and sex (no-App as reference). Results: GEE models showed significant time effects for %TBWL (week 8: coefficient 2.91, p<0.001; week 24: 18.12, p<0.001) and %FML (week 8: 5.30, p<0.001; week 24: 33.65, p<0.001) in the no-App group. The App group had greater %TBWL at weeks 12 (2.64%, p<0.001) and 16 (2.47%, p=0.027), and %FML at weeks 12 (2.94%, p=0.028) and 16 (4.20%, p=0.003). At week 24, outcomes were comparable: App group (%TBWL: 20.01%, %FML: 36.81%, %VFL: 31.99%, %MML: -0.09%) vs. no-App (%TBWL: 19.97%, %FML: 35.74%, %VFL: 32.97%, %MML: 0.70%; all p>0.89). Both groups maintained stable %MML with %TBWL ~20% and %FML >32%. User satisfaction was high (8.76/10). Conclusions: The RightBMI App outperformed provider-led LI at mid-study, achieving comparable 24-week weight loss (~20%) and muscle preservation. With 90% bariatric surgery clearance (vs. 71.25% no-App) and low dropout (3.75% App, 1.25% no-App), RightBMI is a scalable, effective tool for obesity management.
Background: Acute respiratory distress syndrome (ARDS) is caused by an acute pulmonary inflammation, resulting in significant hypoxia. Current treatment is symptomatic, using mechanical ventilation, a...
Background: Acute respiratory distress syndrome (ARDS) is caused by an acute pulmonary inflammation, resulting in significant hypoxia. Current treatment is symptomatic, using mechanical ventilation, and in extreme cases the initiation of extracorporeal membrane oxygenation (ECMO). Objective: Uncertainty exists regarding the optimal timing of initiation of veno-venous ECMO therapy. In the “Early versus Late Initiation of vvECMO” (ELIEO) trial, we aimed to investigate the impact of early ECMO initiation on outcome in ARDS patients. Methods: ELIEO is a prospective randomized multicenter trial assessing whether early initiation of vvECMO could result in improved outcomes in ARDS. Five hundred and eight patients suffering from severe ARDS will be divided into two groups: patients in Group A will begin ECMO therapy within 24 h after admission to the ICU of an ECMO center, while patients in Group B will receive standard treatment according to ARDS Network guidelines and ECMO therapy as a rescue therapy only. During follow-up visits (days 28 and 90), the functional status of patients will be evaluated. The primary endpoint is survival during the 90-d follow-up period. Secondary endpoints include SOFA scores, bleeding complications and ICU-related complications. Results: The results of the ELIEO trial will highlight the importance of the time point at which vvECMO therapy is initiated in patients with severe ARDS. Conclusions: Given the enormous challenge patients with ARDS pose to the health care system, the findings of this trial might have a significant impact on the structuring of intensive care units and on ARDS therapies. Clinical Trial: The ELIEO trial was first approved by the local ethics committee (University Tübingen, Germany) on December 18, 2020. The trial is registered at clinicaltrials.gov (NCT04208126).
Background: Introduction: Advances in cancer treatment have improved survival rates, however, patients continue to experience significant treatment-related side effects, leading to reduced quality-of-...
Background: Introduction: Advances in cancer treatment have improved survival rates, however, patients continue to experience significant treatment-related side effects, leading to reduced quality-of-life. Prehabilitation is an intervention that occurs before treatment and can improve patients’ functional capacity, recovery, and wellbeing through exercise, dietary, and psychological support. Typical hospital-based prehabilitation is not accessible to all patients due to geographical, socioeconomic, and time-related barriers. Technology-based approaches, including eHealth and mHealth interventions, may overcome these barriers by enabling remote, patient-centred delivery. However, the current evidence base is heterogeneous and lacks synthesis regarding feasibility, acceptability, outcomes, and equity. Objective: Objective: This protocol for a scoping review aims to outline how we will systematically map and synthesise the evidence on technology-based prehabilitation interventions for people with cancer, to identify intervention designs, assess feasibility and accessibility, and highlight knowledge gaps to guide future research and practice. Methods: Inclusion criteria: In the proposed scoping review, eligible studies will include adults (≥18 years) with a cancer diagnosis who are scheduled for elective treatment (surgery, radiotherapy, chemotherapy, immunotherapy, or hormone therapy). Interventions must involve eHealth or mHealth approaches supporting unimodal or multimodal prehabilitation activities such as exercise, nutrition, psychological support, or lifestyle modification. Outcomes of interest include functional fitness, quality of life, psychological wellbeing, treatment preparedness, recovery, adherence, and feasibility.
Method: The review will follow JBI methodology and PRISMA-ScR guidelines. A three-step search strategy will be applied across multiple databases and grey literature sources. Data will be charted and presented in tables, figures, and narrative synthesis. Critical appraisal using JBI tools will contextualise methodological quality but not exclude studies. Results: N/A Conclusions: N/A Clinical Trial: https://doi.org/10.17605/OSF.IO/VNWA7
Background: Labor pain is among the most intense forms of pain, and neuraxial anesthesia including epidural, spinal, and combined spinal-epidural techniques is considered the gold standard for its man...
Background: Labor pain is among the most intense forms of pain, and neuraxial anesthesia including epidural, spinal, and combined spinal-epidural techniques is considered the gold standard for its management. Despite its effectiveness, persistent misconceptions, cultural barriers, and disparities in awareness contribute to underutilization among certain populations. Educational interventions have been developed to address these gaps, yet a comprehensive synthesis of such efforts within the United States is lacking. Objective: This scoping review aims to map the extent, range, and nature of educational interventions designed to improve knowledge, awareness, and acceptance of neuraxial anesthesia during labor among pregnant women. Methods: Following the PRISMA-ScR framework, peer-reviewed studies published in English were identified through PubMed, Embase, and Scopus up to August 2025. Eligible studies included pregnant women eligible for neuraxial anesthesia, with interventions delivered during prenatal or perinatal care. Extracted data included study design, intervention type, delivery method, timing, and outcomes related to knowledge acquisition, awareness, acceptance, satisfaction, and uptake. Data will be summarized descriptively, supported by tables, charts, and narrative synthesis. Results: Findings are expected to demonstrate that culturally sensitive, multimodal interventions particularly when delivered prenatally enhance knowledge retention, reduce anxiety, and increase epidural uptake, especially in historically underserved populations. Digital interventions such as mobile apps and videos are emerging, though accessibility remains a challenge. Gaps in long-term outcome data and limited attention to diverse racial and rural populations are anticipated. Conclusions: This review will inform the development of standardized, scalable, and culturally relevant educational strategies to promote informed decision-making, patient autonomy, and equitable access to neuraxial labor analgesia.
Background: Antidepressant use and withdrawal are often accompanied by side effects such as dizziness, weight gain, and sexual dysfunction. Antidepressants and associated side effects are stigmatized...
Background: Antidepressant use and withdrawal are often accompanied by side effects such as dizziness, weight gain, and sexual dysfunction. Antidepressants and associated side effects are stigmatized topics. Social media platforms like Reddit are considered "safe spaces" by users because they can freely share their experiences and receive support. Objective: This study aimed to examine how users talk about antidepressant side effects, withdrawal symptoms, and related experiences of depression on the subreddit r/depression. Methods: We scraped 10 high-engagement threads from the r/depression subreddit using the Python wrapper for the Reddit API and conducted a two-step analysis. First, a pilot test was performed using Sertraline/Zoloft threads, followed by analysis of all antidepressant threads. A subset of data was hand-coded to create and validate regular expressions, which were then used to automatically code the remaining dataset. The resulting coded data were analyzed using Epistemic Network Analysis (ENA) and complemented with qualitative analysis, elements of semantic networks, and hypergraphs. Results: We found that posts are more likely to discuss emotional flattening, sleep, and memory or brain issues. Additionally, references to dizziness tended to occur with discussions of withdrawal and offers of empathy, while reports of dream-related side effects and requests for personal experiences co-occurred frequently. With the additions of elements of semantic networks and hypergraphs, we deduced that offers of empathy occurred when users said they experienced dizziness caused by withdrawal, while mentions of brain zaps from withdrawals received teaching support. Conclusions: Study findings highlight how individuals experiencing antidepressant side effects and withdrawal symptoms use online forums like Reddit to seek validation, share coping strategies, and provide emotional support to others. The nuanced discussions observed, particularly around empathy, symptom management, and shared learning, underscore the role of peer-to-peer networks in normalizing stigmatized experiences and mitigating isolation associated with antidepressant use. Clinicians and digital health practitioners can leverage these insights to better understand patient language, emotional framing, and informational needs outside clinical settings.
Background: Cognitive function, inhibitory control (IC), cognitive flexibility (CF), working memory (WM), and attention are higher-level executive control functions with a complex relationship. There...
Background: Cognitive function, inhibitory control (IC), cognitive flexibility (CF), working memory (WM), and attention are higher-level executive control functions with a complex relationship. There is only limited evidence on the effectiveness of electroacupuncture at acupoints GV20 and GV24 to improve executive control function. Objective: This study aimed to evaluate the clinical effectiveness of electroacupuncture at acupoints GV20 and GV24 to alleviate deficits in executive control. Methods: A single-blind randomized controlled trial was conducted at the First Affiliated Hospital of Henan University of Chinese Medicine involving 76 patients with post-stroke cognitive impairment (PSCI) aged 18 to 74 years. The participants were randomly assigned to either the electroacupuncture group or the conventional treatment group at a 1:1 ratio, with 38 individuals in each group. In addition to receiving conventional treatment, the electroacupuncture group underwent electroacupuncture at acupoints GV20 and GV24 for 30 minutes, 5 times a week, for 4 weeks. Participants were assessed using the Montreal Cognitive Assessment (MoCA), Trail Making Test (TMT), Stroop Color Word Test (SCWT), and Stroke Specific Quality of Life Scale (SSQOL) both before and after intervention. Results: Using an intention-to-treat (ITT) analysis, both the electroacupuncture and control groups demonstrated significant improvements in executive control function, overall cognitive function, and activities of daily living compared to baseline. Specifically, the electroacupuncture group exhibited highly significant improvements in TMT-A (Z=−4.859, OR −23, 95% CI −29.50 to −17.50, P <.001), TMT-B (Z=−5.316, OR −34, 95% CI −42.50 to −28.00, P <.001), SCWT-W (Z=−5.375, OR −28, 95% CI −34.00 to −22.50, P <.001), and SCWT-CW scores (Z=−5.178, OR −44, 95% CI −59.00 to −34.50, P <.001). In the control group, significant improvements were observed in TMT-A (Z=−3.785, OR −8, 95% CI −11.00 to −4.50, P <.001) and SCWT-W scores (T=3.893, OR 8.158, 95% CI 3.912 to 12.404, P <.001), while TMT-B (Z=−2.882, OR −11.50, 95% CI −21.00 to −4.00, P =.004) and SCWT-CW scores (Z=−2.448, OR −12, 95% CI −21.00 to −2.00, P =.025) showed significant enhancements. Both groups experienced significant improvements in MoCA, and SS-QOL scores (P <.001) compared to baseline. Notably, when comparing the electroacupuncture group to the control group, the electroacupuncture group exhibited highly significant improvements in SCWT-W scores (Z=−3.414, OR 24, 95% CI 10 to 36, P <.001) and significant enhancements in MoCA (t=−2.908, OR −2.396, 95% CI −4.038 to −0.754, P =.005), SSQOL (T=−2.104, OR −13.921, 95% CI −27.103 to −0.739, P =.039), and SCWT-CW scores (Z=−2.244, OR 45, 95% CI 6 to 83, P =.025). Conclusions: Electroacupuncture at acupoints GV20 and GV24 demonstrated significant efficacy in ameliorating executive control deficits in PSCI patients. This intervention effectively restored key executive control functions, such as IC, CF, attention, and WM, while also enhancing overall cognitive function and the activities of daily living following a stroke. Clinical Trial: ChiCTR2300074400
Background: Obesity is associated with numerous health issues, and early identification and intervention in high-risk individuals can improve prognosis. Objective: This study evaluated the impact and...
Background: Obesity is associated with numerous health issues, and early identification and intervention in high-risk individuals can improve prognosis. Objective: This study evaluated the impact and effectiveness of an integrated health management program on the weight of individuals with obesity. Methods: We retrospectively collected case management data from 97 individuals with obesity (≥20 years) who received weight management care between September 1, 2023, and April 30, 2024. Exclusions included weight-loss medications, surgery, and other weight-loss programs. Data comprised patient demographics, medical history, health behaviors, and physical examination results. Follow-up data were obtained from a health management platform and hospital database. Statistical analyses used paired t-tests, Wilcoxon signed-rank tests, and generalized estimating equations (GEE). Results: The 2-month completers achieved a greater weight loss (−0.87 ± 1.35 kg) than 1-month completers (−0.03 ± 0.75 kg; p < 0.001). Health behavior scores improved at both time points (all p < 0.001), and step counts increased 8.9-fold overall and 10.5-fold among 2-month completers (p < 0.001). Among 2-month completers, those engaging in ≥3 program components had a lower adjusted weight (86.0 ± 1.6 kg) than those engaging in <3 program components (89.3 ± 1.7 kg; F = 6.09, p = 0.017). GEE models showed that ≥3-component engagement increased odds of weight loss by 16.6-fold at 1 month and 23.2-fold at 2 months. Conclusions: Weight loss improved in a dose-dependent manner, with ≥3-component engagement showing the strongest effects. Long-term studies with booster contacts are needed to validate the sustained benefits of our integrated health management program. Clinical Trial: none
Background: AD is a chronic and relapsing inflammatory dermatologic condition marked by erythematous, pruritic lesions. Multiple factors like psychological stress, lifestyle factors, and socioeconomic...
Background: AD is a chronic and relapsing inflammatory dermatologic condition marked by erythematous, pruritic lesions. Multiple factors like psychological stress, lifestyle factors, and socioeconomic determinants influence disease progression and treatment outcomes globally. Clinical trials evaluating AD interventions face challenges in recruitment and retention of underserved patient populations, potentially compromising the generalizability of findings to wider populations. This study aims to investigate recruitment and retention strategies in Atopic Dermatitis (AD) clinical trials. Objective: This study aims to investigate recruitment and retention strategies in Atopic Dermatitis (AD) clinical trials. Methods: We conducted a cross-sectional analysis following PRISMA guidelines to assess recruitment and retention strategies in AD clinical trials. The relevant clinical trials were acquired in a comprehensive search of MEDLINE (PubMed) and Embase (Elsevier) on May 28, 2024. Data were extracted from trials published between January 1, 2013 and December 31, 2023. For statistical analysis, Stata 18 SE (StataCorp LLC, College Station, TX) was used to determine the frequencies of recruitment and retention strategies. Results: Of the 32 trials analyzed, only 4/32 (12.5%) integrated recruitment strategies to include underrepresented populations and only 4/32 (12.5%) had planned diversity goals to improve recruitment. Three out of the 32 (9.4%) studies reported challenges with recruitment. Conclusions: : Our study highlights the need for inclusive recruitment and retention strategies in AD clinical trials. The lack of diverse representation in clinical trials can contribute to obstacles in medical research and can eventually prevent advancements in treatment outcomes. It is essential to address these gaps to ensure the external validity of research findings and to improve treatment outcomes.
Background: Hansen’s Disease, a chronic infectious disease, presents with a variety of cutaneous lesions. Being the “great mimicker” that it is, patients may often be misdiagnosed initially, hen...
Background: Hansen’s Disease, a chronic infectious disease, presents with a variety of cutaneous lesions. Being the “great mimicker” that it is, patients may often be misdiagnosed initially, hence the delay in the initiation of the multidrug therapy. Dermoscopy offers an effective, efficient, operator-friendly and non-invasive adjunctive tool in the diagnosis of Hansen’s Disease. Objective: The general objective of the study is to describe the common dermoscopic features according to clinical and histologic findings among all newly diagnosed Hansen’s Disease patients in a tertiary institution within the study period of 6 months. Methods: Purposive sampling was applied to include all newly diagnosed and biopsy-proven Hansen’s Disease patients aged 18 years to 65 years. Participants were clinically examined and dermoscopy was performed on a representative lesion. Other data were collected from chart review, acid fast smear and histopathology reports. Results: The main dermoscopic feature of Hansen’s Disease is yellowish orange areas observed in all 23 cases studied regardless of the spectrum. This feature can be attributed well to the presence of granuloma formation and inflammation. Another common feature is the presence of white globules and dots which correlates to the presence of the grenz zone, while vascular structures correlate with dilated blood vessels on histopathology. Conclusions: The major dermoscopic features seen in the study may add to the clinical clues to arrive at a diagnosis of Hansen’s Disease. Although dermoscopy alone is insufficient for the confirmation of Hansen’s Disease, combining it with physical findings would provide additional basis for its clinical diagnosis.
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.
Background: Mpox was declared a public health emergency of international concern in 2022. Instagram is widely used by age groups and communities disproportionately affected by mpox, yet platform-speci...
Background: Mpox was declared a public health emergency of international concern in 2022. Instagram is widely used by age groups and communities disproportionately affected by mpox, yet platform-specific evidence on mpox information characteristics and engagement is limited. Objective: To characterize sources, content, and engagement features of mpox-related Instagram posts, to describe prevention and treatment framing, and to compare the top 10% most-liked posts with the remaining corpus. Methods: We retrieved English-language public Instagram posts via CrowdTangle containing “mpox” or “monkeypox” dated May 5, 2022 to January 17, 2023 (initial N=18,616). Using a pretested, deductive codebook adapted from prior Instagram health studies, two coders completed two pilot rounds; variables with low agreement were excluded. Interrater reliability across retained variables showed mean κ=0.70 (median κ=0.83). A randomized analytic sample of N=1,000 posts was coded for source type, content features, and prevention/treatment framing. Descriptive statistics were computed. For engagement contrasts, we compared the top 10% most-liked posts with the bottom 90% using tests of differences in independent proportions (mean differences [MD] with p-values). Results: Most posts originated from organizations (76.0%) versus individuals (24.0%). Organizational sources most commonly included businesses (57.4%) and news/media outlets (52.8%); government (22.9%), nonprofits (17.3%), and health-care organizations (9.2%) were less frequent. About one-third of posts cited a source (34.4%), most often the WHO and CDC/other federal entity. Posts predominantly used illustrated images/infographics (82.7%); photos appeared in 47.3% and videos in 12.4% of posts. Prevention content appeared in 38.4% of posts, most commonly vaccination (68.5% of prevention posts), followed by avoiding close contact (14.5%), avoiding contact with objects (8.3%), abstaining from sexual activity (7.6%), and condom use (1.3%); 28.9% of prevention posts noted barriers. Treatment mentions were uncommon (2.5% traditional biomedical; 0.2% alternative). Compared with the bottom 90%, the top 10% most-liked posts: 1) were more likely to originate from public figures/celebrities among individuals (MD=-0.591; p<.001) and from businesses (MD=-0.299; p<.001) or news/media (MD=-0.350; p<.001) among organizations; 2) were less likely to be from government, nonprofit, or health-care organizations (all p<.01); and more often included non-moving images (MD=-0.119; p<.05), visible lesion depictions (MD=-0.081; p<.05), prevalence mentions (MD=-0.180; p<.001), and citations (MD=-0.162; p<.01). Conclusions: During the initial outbreak period, highly engaged mpox content on Instagram skewed toward posts by public figures and news/business accounts and toward static, citation-bearing visuals that included prevalence context and occasionally lesion imagery. Public-health communicators seeking reach on Instagram should prioritize clear static infographics with explicit source citation and epidemiologic context and consider co-publishing with trusted creators and news outlets, while addressing access barriers highlighted in prevention posts.
Background: Atypical fibroxanthoma (AFX) is a rare mesenchymal neoplasm that is most commonly identified on sun-exposed areas of the head, neck, and dorsal extremities of elderly patients. Objective:...
Background: Atypical fibroxanthoma (AFX) is a rare mesenchymal neoplasm that is most commonly identified on sun-exposed areas of the head, neck, and dorsal extremities of elderly patients. Objective: To present a rare case of AFX, its clinical presentation, histopathology, treatment, and to discuss the use of immunohistochemistry as a method in diagnosing AFX. Methods: We evaluated a 67-year-old woman with a history of basal and squamous cell carcinomas who presented with a rapidly enlarging, tender papule on the dorsal left hand. Prior topical fluorouracil therapy was ineffective. Histopathologic examination confirmed the diagnosis, and treatment was initiated. A literature review was performed to place this case in context with other reported instances of AFX. Results: Clinical examination revealed a 1.2 cm dome-shaped, red-pink papule; shave biopsy demonstrated histopathologic features consistent with atypical fibroxanthoma (AFX). The patient was referred to Mohs micrographic surgery with a high cure rate, where the tumor was cleared in a single stage, with histologically negative margins. Conclusions: This case highlights the importance of clinicopathologic correlation, appropriate use of immunohistochemistry, and evidence-based surgical management in AFX.
Background: In recent years, digital mental health interventions have become increasingly important due to the rising demand for psychotherapy and the shortage of resources. Digital inpatient-like psy...
Background: In recent years, digital mental health interventions have become increasingly important due to the rising demand for psychotherapy and the shortage of resources. Digital inpatient-like psychotherapy (DIPT) is a therapeutic concept that allows patients to receive inpatient-like psychotherapy in a digital environment. Objective: This qualitative interview study sheds light on patients' attitudes and expectations towards digital inpatient-like psychotherapy in terms of acceptance, perceived benefits and barriers, as well as suggestions for initial implementation steps Methods: Semi-structured interviews were conducted with 20 patients receiving day-patient or inpatient psychotherapy in a university hospital. Iterative thematic analysis and inductive coding were conducted by three independent researchers following Braun and Clarke’s approach to thematic analysis. Three main categories emerged with multiple subsidiary themes and subthemes. Results: Interviewees included individuals with various mental health disorders. The analysis of the interviews revealed three categories ((1) requirements for effective implementation of DIPT, (2) influence of familiar environment and digital communication on the interpersonal level, and (3) patients’ benefits from DIPT) with seven themes. The overall attitude and acceptance towards digital inpatient-like psychotherapy was predominantly positive, with interviewees recognizing numerous benefits including location independency, time flexibility, and increased openness towards personal mental health issues. However, potential barriers such as lack of personal prioritization, tendencies for social withdrawal or insufficient relationship building must be addressed and considered. Conclusions: Interviewees included individuals with various mental health disorders. The analysis of the interviews revealed three categories ((1) requirements for effective implementation of DIPT, (2) influence of familiar environment and digital communication on the interpersonal level, and (3) patients’ benefits from DIPT) with seven themes. The overall attitude and acceptance towards digital inpatient-like psychotherapy was predominantly positive, with interviewees recognizing numerous benefits including location independency, time flexibility, and increased openness towards personal mental health issues. However, potential barriers such as lack of personal prioritization, tendencies for social withdrawal or insufficient relationship building must be addressed and considered.
Background: While healthcare providers are the most trusted sources of health information, service establishments within communities represent important yet underutilized sources of health information...
Background: While healthcare providers are the most trusted sources of health information, service establishments within communities represent important yet underutilized sources of health information. Specifically, food and beauty establishments can act as alternative settings for health communication, facilitating broader engagement with the general population. Objective: This study examined factors associated with willingness to receive health information from these non-healthcare services among community-dwelling adults in Singapore. Methods: A cross-sectional survey was conducted among residents in two neighbourhoods in Central Singapore between November 2024 and April 2025. Data on socio-demographics, trust in information from healthcare and non-healthcare sources, and willingness to receive health information were collected anonymously. Multivariable logistic regression identified factors independently associated with willingness to receive health information from non-healthcare sources. Results: Most of the 403 respondents were aged ≥50 years (55.3%), female (54.3%), Chinese (82.6%), and higher educated (74.9%). Of the 339 respondents with no prior exposure to health information from non-healthcare services, nearly one-in-three (31.3%) reported that they were willing to receive health information from these sources. In adjusted analysis, those who trusted health information (AOR 3.71 [95% CI: 1.50, 9.19]) and those with high health information orientation (AOR1.89 [95% CI: 1.11, 3.21]) were more willing to receive health information from non-healthcare services in the future. Trust in health information increased willingness among those aged 21-34 years (AOR 4.98 [95% CI: 1.35, 18.30]), those aged 35-49 years (AOR 8.02 [95% CI: 2.62, 24.59]), and males (AOR 6.22 [95% CI: 2.79, 3.89]) to receive health information from these sources, but not among those aged ≥50 years (AOR 1.92 [95% CI: 0.92, 4.02]) or females (AOR 1.85 [95% CI: 0.87, 3.96]). Conclusions: Nearly a third of community-dwelling adults expressed willingness to receive health information from non-healthcare services, highlighting the potential for leveraging these channels in health communication. Higher level of willingness was positively associated with higher health information orientation and greater trust. Additionally, trust in non-healthcare services enhanced the willingness among those aged 21-49 years and males. Building age- and gender-sensitive trust strategies could strengthen engagement through non-healthcare channels.
Background: Mental health problems among university students are a growing global concern, yet limited resources and inadequate understanding of counseling procedures often delay support. Informed con...
Background: Mental health problems among university students are a growing global concern, yet limited resources and inadequate understanding of counseling procedures often delay support. Informed consent forms (ICFs) are vital for protecting rights and autonomy, but many are incomplete, ambiguous, or overly technical, and few institutions can effectively optimize them. Large language models (LLMs) offer scalable, low-cost solutions to enhance clarity and accessibility. Objective: This study aimed to evaluate whether LLM-based optimization could improve the structure, readability, content quality, and comprehensibility of university counseling ICFs, and to compare the performance of two advanced models—ChatGPT-5 and Grok-4. Methods: Counseling ICFs from 33 Chinese universities were collected and optimized using two advanced LLMs, ChatGPT-5 and Grok-4. A multidimensional framework assessed textual structure and readability, content quality from counselors’ perspectives, and comprehension from clients’ perspectives. Evaluations were conducted by mental health professionals and student volunteers. Wilcoxon signed-rank tests and linear mixed-effects models were applied for comparison and validation. Results: Compared with the originals, LLM-optimized ICFs demonstrated significant gains across all dimensions. The Lee–Yang readability index decreased from 28.68(5.69) to 22.39(2.13) with ChatGPT-5 and 24.37(2.32) with Grok-4 (both P<.001), while tone friendliness increased from 2.57(0.29) to 2.67(0.12) and 2.67(0.13), respectively. Expert-rated content quality improved from 45.33(8.74) to 52.54(7.92) and 55.49(7.81) (P<.001), primarily through enhanced specificity and existence of key items. Client comprehension scores rose from 19.02(1.32) to 22.33(0.81) and 22.05(0.90) (P<.001), reflecting higher clarity, readability, and acceptability. Linear mixed-effects models confirmed these findings. Conclusions: LLM-based rewriting markedly improved the clarity, completeness, and readability of counseling consent forms. By enhancing linguistic accessibility and professional precision, these models can support clearer communication and stronger counselor–client understanding. For universities with limited counseling resources, integrating LLM-assisted optimization may represent a practical step toward standardized, comprehensible, and client-centered counseling documentation. Clinical Trial: Not applicable.
Purpose: We aim to create and compare two digital-health pipelines for voice recording and speech analyses based on smartphones and professional recording equipment. We benchmark the quality of the sm...
Purpose: We aim to create and compare two digital-health pipelines for voice recording and speech analyses based on smartphones and professional recording equipment. We benchmark the quality of the smartphone recordings on speech features previously shown to be predictive of neurological impairment.
Approach: A mobile phone app that records a user performing three speech tasks on the smartphone’s built-in microphone is developed. Simultaneously they are recorded using professional recording equipment. Speech features are extracted and compared across each recording pair. Speakers performed three speech tasks – reading a text, repeating syllables for 20 seconds, and sustaining a vowel. Bland-Altman visualization, Deming regression, correlation tests (Spearman, Intraclass) and Kolmogorov-Smirnov tests were utilized to assess concordance degree across devices. Dotplots and standard deviation across speaker and across time were used to assess longitudinal stability. Signal-to-Noise ratios were utilized to assess inter-and-within-rater reproducibility. The robustness of the features over time was assessed by the relative size of differences between speakers and measurement error and profiling the reproducibility of the measurements in a test-retest scenario.
Results: A corpus of 20 recording sessions was collected from 3 speakers, with one speaker recording 6 sessions spanning 5 months. Twenty-three speech features served as a basis for assessing concordance between recording pairs. 19/23 (80%) features were found to be significantly correlated across recording methods; 10/23 (43%) features have statistically-significant p-values. The smartphone’s noise-cancellation seems to be affecting some features. Most features (14/23, 63%) are longitudinally unstable.
Conclusions: A voice recording and speech analyses pipeline using a smartphone appears potentially capable of capturing the essential variability of the speech features used for establishing and validating speech biomarkers. Device-dependent noise-reduciton algorithms deployed in modern smartphones need to be handled with care. Much-larger-N Case-Control studies are needed to validate and extend these preliminary findings to clinical setting and across specific neurological disease states.
Background: The COVID-19 pandemic highlighted the importance of public health communication while exposing its shortcomings, particularly among African American communities disproportionately affected...
Background: The COVID-19 pandemic highlighted the importance of public health communication while exposing its shortcomings, particularly among African American communities disproportionately affected by the virus. Traditional expert-driven communication campaigns often failed to resonate with this population due to pandemic fatigue, misinformation, and longstanding mistrust. Culturally grounded and co-created communication strategies are increasingly recognized as critical to improving the effectiveness of public health messaging. However, limited experimental evidence exists to evaluate whether community such messages outperform traditional expert-developed messages in promoting engagement and behavioral intent. Objective: This study aimed to evaluate the effectiveness of community co-created messages, developed through participatory design methods and supported by generative artificial intelligence tools, in promoting COVID-19 preventive behaviors among African American adults in North Carolina. Specifically, we sought to determine whether participatory messages outperformed standard public health messages in enhancing message credibility, emotional engagement, and behavioral intent. Methods: We employed a multi-phase crowdsourcing approach, including two open calls and a designathon, to develop a culturally grounded, community-driven video promoting COVID-19 prevention. A key innovation was the integration of generative AI tools enabling designers to translate ideas into digital outputs without requiring technical expertise. The community-driven video was compared with an expert-designed video widely circulated during the pandemic. African American adults (N = 546) recruited online were randomly assigned to view one of the two videos and then completed a survey assessing narrator relatability, youth versus adult appeal, motivation, attention, and perceived behavioral impact. Results: Regression analyses controlling for demographics indicated that participants rated the community-driven video as significantly more relatable, motivating, attention-grabbing, and behaviorally impactful than the expert-designed video. The community-driven video was also perceived as more appealing to youth, while the expert-designed video was seen as more appealing to adults. No significant differences emerged for message agreement or likelihood to recommend. Conclusions: Findings provide empirical evidence that community-driven communication can increase message appeal among African American audiences. Incorporating participatory design and creative co-production processes into public health campaigns may increase their resonance, strengthen trust, and improve equity in health communication. These results provide experimental support for embedding community perspectives at the center of message development in future public health initiatives.
Background: Neighborhood disinvestment, characterized by built environment disrepair and deterioration, has been linked to health behaviors and outcomes, including cancer survival. While prior studies...
Background: Neighborhood disinvestment, characterized by built environment disrepair and deterioration, has been linked to health behaviors and outcomes, including cancer survival. While prior studies have assessed spatial variation, temporal dynamics of disinvestment remain underexplored, despite potential relevance for long-latency outcomes such as colon and rectum cancer (CRC). Objective: We describe and validate a spatio-temporal neighborhood audit protocol using Google Street View (GSV) imagery, develop and compare predictive spatio-temporal models of neighborhood disinvestment and examine time-lagged associations between disinvestment and CRC survival. Methods: We conducted 8,256 virtual audits across Franklin County, Ohio (2009–2022) using seven disinvestment indicators (e.g., garbage, graffiti, abandoned buildings). After excluding ineligible images, 5,751 audits of 2,214 unique locations were included. A neighborhood disinvestment score (NDS) was derived using item response theory and adjusted for rater bias. We fit mean trend and spatio-temporal residual processes using universal kriging with a simplified sum-metric covariance structure, comparing two candidate mean trend models via out-of-sample root mean square prediction error (RMSPE). One model accounted for discrete physical barriers to define neighborhood clusters, the other was traditional kriging where NDS was allowed to vary continuously across space and time. For exposure time validation, predictions were linked to 2,727 CRC cases (2012–2019) from the Ohio Cancer Incidence Surveillance System (OCISS). We use accelerated failure time (AFT) models to estimate associations between NDS averaged over 0, 3, 6, 12, 18, and 24 months prior to diagnosis and CRC survival, adjusting for age and stage at diagnosis and demographic covariates. We test stage-specific associations between NDS and survival. Results: Spatio-temporal modeling indicated substantial spatial but modest temporal variation in disinvestment, with small-scale spatial correlation measurable within 2.8 km and temporal correlation up to 270 days. The traditional spatio-temporal kriging model (RMSPE = 0.651) outperformed alternate models including neighborhood cluster fixed (RMSPE = 0.671) and random (RMSPE = 0.662) effect models. Seasonal effects were significant, with lower disinvestment scores in spring and summer compared to fall. The AFT model fit shows higher pre-diagnosis NDS was significantly associated with shorter survival among patients with stage 2 cancer (acceleration factor < 1 across all lag windows, P value < .05), but not among patients with stage 1 or stage 3. Associations were consistent across time lags, with slightly stronger effects for averages over 6–24 months prior to diagnosis. Conclusions: Neighborhood disinvestment exhibits substantial spatial and moderate temporal variation. Traditional spatio-temporal universal kriging provides most accurate prediction compared to models accounting for discrete neighborhood boundaries. Neighborhood disinvestment is associated with reduced CRC survival time among cases diagnosed at regional stage, highlighting the utility of built environment spatio-temporal assessments within CRC 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...
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: Leisure-time physical activity (LTPA) is a well-established contributor to physical, psychological, and social well-being worldwide. Human touch also plays a vital role in life course heal...
Background: Leisure-time physical activity (LTPA) is a well-established contributor to physical, psychological, and social well-being worldwide. Human touch also plays a vital role in life course health, yet opportunities for safe, consensual touch are often limited, particularly in LTPA settings. For gay, bisexual, transgender, and queer (GBTQ) men, barriers to affirming LTPA spaces can make it particularly difficult to access such benefits. In response, community-based approaches that integrate touch are needed, alongside systematic evaluations of such strategies. “The Studio” (pseudonym), a membership-based wellness community, addresses this gap by offering touch-centered partner yoga and bodywork programs designed to support the holistic health of GBTQ men. Objective: This protocol describes a mixed-methods evaluation of the Studio’s touch-based yoga programming in New York City. The evaluation aims to (1) assess individual health benefits (i.e., physical, emotional, and psychological) of partner-based yoga participation and (2) examine interpersonal outcomes of intentional touch in a GBTQ wellness community, including social connection, trust, and belonging. Methods: The evaluation employs a pre- and post-test mixed-methods design. A total of 40–50 participants will be recruited from new Studio members. Quantitative measures will include flexibility (sit-and-reach, goniometry), stress (Perceived Stress Scale), body awareness (Multidimensional Assessment of Interoceptive Awareness), and resilience (Brief Resilience Scale). Social network analysis will map participant connections before and after program participation. Qualitative data will be collected through semi-structured interviews with 15–20 participants, or until saturation is reached, focusing on comfort with touch, emotional regulation, and experiences of community connectedness. Survey and interview guides will be co-developed with a Community Advisory Group to ensure cultural responsiveness and relevance. Findings will be integrated using triangulation methods to explore convergence across data sources. Results: As of October 2025, this study has not yet begun. Pending funding opportunities, Institutional Review Board submission is planned for Fall 2026. Afterward, study instruments will be finalized and pilot-tested with Studio teachers. Participant recruitment is projected to begin in Summer 2027, and data collection will include three time points (baseline, post-intervention, 4–6 week follow-up). Data analysis and dissemination of findings are expected in 2028. Preliminary pilot testing of the survey instruments with Studio employees and community advisory group members will indicate feasibility and cultural fit. Conclusions: This evaluation will be among the first to systematically examine touch-focused partner yoga for GBTQ men in a community wellness setting. Findings are expected to provide novel insights into the role of intentional touch in LTPA spaces, support trauma-informed and inclusive wellness practices, and contribute to broader discourse on GBTQ health promotion and intervention. Results will be disseminated to the Studio employees, members, and LGBTQ+-focused wellness organizations, as well as through peer-reviewed publications and conferences.
Background: Globally 537 million persons lives with diabetes, with a lifetime risk of up to 34% of developing diabetic foot ulcers (DFUs), drives preventative initiatives. Objective: The aim was to de...
Background: Globally 537 million persons lives with diabetes, with a lifetime risk of up to 34% of developing diabetic foot ulcers (DFUs), drives preventative initiatives. Objective: The aim was to develop and evaluate a clinical decision support system (CDSS) to be used by healthcare professionals (HCPs) in foot assessment and risk stratification, as a base for prevention. Methods: Based on human interaction design, the CDSS was developed for DFU. Users, HCPs from Region Västra Götaland in Sweden evaluated the functions regarding effectiveness, efficiency and satisfaction. Expectations and experiences of using the CDSS was evaluated with the System Usability Scale (SUS). Results: User expectations of the CDSS, measured by SUS, averaged 77.2±14.6. Post-test SUS scores were 68.9±14.3, with a mean difference of 8.3 (P=.071), a non-significant reduction of usability after test. The effectiveness of the CDSS in supporting users to complete nine clinical tasks, showed that for seven out of nine tasks (78%), at least five of the nine testers (56%) successfully achieved the intended goals. Tasks involving the identification of ingrown toenails and the confirmation of foot status, including the risk stratification for the patient, were completed by fewer testers. Efficiency, measured as mean task completion time, ranged from 7 seconds to 9 minutes and 20 seconds. The users found that a structured CDSS has the potential to contribute to a digital health service leading to an equal, good, and person-centred DFU prevention. Conclusions: A digital health service for DFU risk stratification, was developed based on national and international guidelines. Although the users’ expectations of the usability were higher compared to how they experienced the CDSS, the SUS test was near a threshold of 70, indicating that the system being tested was above average in usability. Further development, national and international, where the users’ needs and preferences are considered, is recommended. Clinical Trial: ClincalTrials.gov ID: NCT05692778
Background: Health-data interoperability in post-transition systems depends on enforceable standards and clear governance. Affinity domains, as defined in the IHE XDS framework, offer a combined organ...
Background: Health-data interoperability in post-transition systems depends on enforceable standards and clear governance. Affinity domains, as defined in the IHE XDS framework, offer a combined organisational-technical model for cross-enterprise data exchange. Evidence from Central Europe remains scarce. Objective: This study aimed to examine stakeholder perceptions of prerequisites, opportunities, and barriers to implementing affinity domains in the Czech healthcare system, and to contextualise these findings in relation to international experiences to inform policy development. Methods: We conducted 18 semi-structured interviews (January–April 2025) with policymakers, regional authorities, providers, insurers, IT vendors, and consultants. Participants were selected through purposive and snowball sampling to capture diverse perspectives. Data were analysed inductively using thematic analysis in MAXQDA24, with dual coding, consensus resolution, and reflexive memos to ensure rigour. Results: Thematic analysis yielded five categories: (A) roles and responsibilities, characterised by ambiguous mandates, regional fragmentation, vendor dominance, and exclusion of clinicians; (B) perceived risks, including institutional distrust from past failures, legal uncertainty regarding liability, technical inconsistency, and vendor lock-in; (C) system-level prerequisites, such as the need for an empowered governance body, sustainable funding, adoption of common standards, and adequate human resources; (D) perceived benefits and opportunities, notably improved continuity of care, reduced diagnostic redundancy, and enhanced accountability; and (E) implementation barriers, including legal ambiguity, political discontinuity, vendor resistance, and limited stakeholder engagement. Stakeholders also emphasised the need for an independent governance body, binding interoperability standards (IHE XDS, HL7 FHIR), and pilot affinity domains with certification mechanisms. Conclusions: To advance affinity domain implementation in Czechia, we recommend establishing an empowered coordinating authority, launching pilot projects with contractual enforcement and audit trails, and aligning national efforts with EHDS requirements (metadata catalogues, access bodies). These steps can reduce duplication, improve continuity of care, and strengthen cross-sectoral trust. Clinical Trial: n/a
Background: Thyroid dysfunction is a prevalent endocrine disorder that often remains underdiagnosed due to non-specific symptoms and the absence of routine thyroid testing in standard health checkups....
Background: Thyroid dysfunction is a prevalent endocrine disorder that often remains underdiagnosed due to non-specific symptoms and the absence of routine thyroid testing in standard health checkups. Although thyroid hormone assays are the diagnostic standard, routine blood tests may already contain early biochemical signals associated with thyroid imbalance. Leveraging these widely available markers with machine learning can enable earlier, low-cost detection. Objective: This study aims to develop and validate machine learning models to screen thyroid dysfunction using only routine blood test results, without including thyroid-specific hormones such as TSH or FT4. The study also investigates key laboratory predictors that may serve as early indicators of thyroid disorders. Methods: This retrospective study uses de-identified data from the Taipei Medical University Clinical Research Database (TMUCRD). Patients are categorized as hypothyroid, hyperthyroid, or euthyroid based on diagnostic codes, prescriptions, and thyroid hormone results. Thirty-nine routine laboratory features, including liver, kidney, lipid, hematologic, and metabolic markers that are used as predictors. Machine learning algorithms (logistic regression, random forest, and XGBoost) are trained on stratified datasets. Model performance is assessed using AUROC, precision, recall, specificity, and F1-score. Ethical approval was obtained from the Taipei Medical University Joint Institutional Review Board (TMU-JIRB). Results: Model development and validation are ongoing. Preliminary analyses using pilot data indicate that routine blood test features can distinguish thyroid dysfunction with high accuracy (AUROC range 0.85–0.90). Important predictors include hemoglobin, creatinine, and lipid markers. Final model validation and subgroup performance (by sex and age) will be presented in the completed study. Conclusions: This protocol outlines a machine learning–based framework to identify thyroid dysfunction using only routine laboratory data. Early results suggest that non-thyroid-specific biomarkers may provide reliable signals for preliminary screening, enabling broader and cost-effective approaches to thyroid dysfunction detection in primary care settings.
Background: Physical inactivity remains a public health concern with 42% of women and 34% of men in the United Kingdom (UK), for example, failing to meet moderate-to-vigorous physical activity (MVPA)...
Background: Physical inactivity remains a public health concern with 42% of women and 34% of men in the United Kingdom (UK), for example, failing to meet moderate-to-vigorous physical activity (MVPA) guidelines. To promote physical activity (PA) at scale, smartphone-based mobile health software applications (mHealth apps) offer a promising solution. Objective: To evaluate the feasibility of implementing an mHealth app offering very small (‘micro’) financial incentives (FI) for PA in Leeds, UK. Methods: A five-week single-arm proof-of-concept study was conducted with rolling recruitment among Caterpillar Health app users between September 12 and December 12, 2022 (ORBIT model, Phase IIa). Users earned FI in the form of ‘points’, redeemable for consumer rewards (e.g., movie tickets, gym passes), for meeting personalized daily step goals ($0.13 USD per goal achieved; set using data from 5-day baseline) and completing educational quizzes ($0.33 USD per quiz). Descriptive statistics assessed feasibility outcomes (i.e., reach, recruitment, retention, engagement, acceptability) and preliminary effectiveness. Paired-samples t-tests (p<0.05) examined changes in weekly mean daily step count (from baseline) and step goal achievement over five weeks. Results: Of 285 app downloads, 46 users consented to participate (recruitment rate: 16.1%). Participants (mean age: 39.9±11.1 years; 71.1% female) had a baseline step count of 5598±2664 steps/day. Twenty-five remained engaged (i.e., completed at least one quiz) at Study Week 5 (retention rate: 54.3%). Acceptability was high, with 75% of respondents (12/16) indicating they would recommend the app. Weekly mean daily step count did not significantly increase from baseline (mean difference±standard deviation: 317±2273, p=0.533). Weekly daily step goal achievement rate (%) decreased from Study Week 1 to 5 (-23.23±22.85, p=0.024). Conclusions: Despite lower-than-expected recruitment and no statistically significant PA increase, relatively high engagement and acceptability suggest future pilot testing (ORBIT model, Phase IIb) of a refined intervention (e.g., wider selection of loyalty reward partners) and modified study protocol (e.g., simplified consent process) is warranted. Clinical Trial: ClinicalTrials.gov NCT05294692; https://clinicaltrials.gov/study/NCT05294692
Background: Access to health education remains limited for women in underserved regions, particularly in rural Colombia. The Urabá subregion, characterized by high social vulnerability and limited ac...
Background: Access to health education remains limited for women in underserved regions, particularly in rural Colombia. The Urabá subregion, characterized by high social vulnerability and limited access to health and educational services, faces persistent barriers to information on reproductive and maternal health. Objective: To evaluate the usability and user satisfaction of HADA Educación, a mobile application designed to provide digital health education through the Mujer Saludable (Healthy Woman) program for women of reproductive age, during pregnancy, and postpartum. Methods: A prospective observational study was conducted between May and November 2024 among 89 women from the Urabá subregion of Antioquia, Colombia. Participants received tablets preloaded with HADA Educación, which delivered tailored educational content according to each participant’s reproductive stage. Usability was assessed using the System Usability Scale (SUS), and satisfaction was evaluated through a custom Likert-scale survey. Results: Of the 89 participants, 77 completed post-trial surveys. The average SUS score was 82.4, indicating high usability; 44% of users rated the app as excellent and 48% as good. Participants reported strong agreement on ease of use, clarity of content, and confidence in using the app. Overall satisfaction was high, with average ratings above 4.5 out of 5 for content relevance, usefulness, and willingness to recommend the program. The perceived need for prior technical knowledge was low, indicating accessibility across educational levels. Conclusions: HADA Educación demonstrated high usability and user satisfaction among women in a socially vulnerable region, confirming its potential as an accessible digital health education tool. The findings support its broader implementation in low-resource settings to promote women’s health and reduce information and access barriers.
Background: To address care delivery gaps, the healthcare system must embrace innovative digital solutions. Additionally, the rising integration of digitalization as a topic into medical education is...
Background: To address care delivery gaps, the healthcare system must embrace innovative digital solutions. Additionally, the rising integration of digitalization as a topic into medical education is providing students with broader opportunities to engage with digitalization overall. As digital health becomes an increasingly integral component of medical education and healthcare practice, digital affine recent medical graduates constitute a vital resource for advancing digitalization within the healthcare sector. Objective: This study examines how recent medical graduates acquire digital knowledge, apply it across diverse practical contexts – ranging from startups and corporate environments to traditional clinical settings – and how they contribute to the advancement of digitalization within the entire healthcare sector. Methods: Using a qualitative approach, 19 interviews with medical graduates from the last 15 years, educated by a university in Germany with a strong focus on digitalization, were conducted. Subsequently, the interviews were transcribed and analyzed using a deductive-inductive approach, following the qualitative content analysis method by Kuckartz (2012). Results: The findings reveal that medical graduates often acquire digital skills through an intensive self-study and learning on the job, integrating them in various ways into their professional life. Moreover, while graduates recognize their high potential to make on own contribution to the advancement of digitalization, they also face significant barriers such as knowledge gaps, limited resources, and complex regulations, which hinder their ability to contribute to push digitalization in a variety of professional settings. Medical graduates report that they face a pressing need for enhanced knowledge access, improved institutional frameworks, and supportive policy measures to maximize their potential in advancing digitalization initiatives. Conclusions: Recent medical graduates represent an underutilized resource for healthcare digitalization. Unlocking this potential requires coordinated action across medical education, healthcare institutions, and policymaking to create appropriate conditions for graduates to actively drive digitalization.
Background: Personal digital health technologies (DHTs) enable real-time monitoring of physiological metrics and behavioral data, including HRV, supporting early detection of pregnancy-related conditi...
Background: Personal digital health technologies (DHTs) enable real-time monitoring of physiological metrics and behavioral data, including HRV, supporting early detection of pregnancy-related conditions and personalized care throughout the perinatal period. While recent studies demonstrate the utility of personal DHTs in tracking pregnancy-related symptoms, they often rely on aggregate statistical methods that overlook individual variability. Objective: To compare aggregate and individual-level analyses of digital health technology (DHT) data for early detection of pregnancy-related conditions, using the comprehensive BUMP dataset to highlight the importance of individual variability and data heterogeneity. Methods: This BUMP study (Jan 2021 – May 2022) analyzed physiological and behavioral metrics, such as heart rate variability (HRV), sleep, and fatigue, in 256 individuals using Oura rings and self-reported surveys. Individual-level (N-of-1) trajectories were evaluated and compared with aggregate results to uncover personal and collective trends. A statistical method was developed to assess the influence of adverse events and severe symptoms, while case studies explored confounding and modifying factors underlying heterogeneity. Comprehensive statistical analysis included the coefficient of determination, Kolmogorov-Smirnov tests, likelihood ratio tests, and Welch’s t-tests, with inter-individual variability flagged based on high-variability thresholds. Results: Results revealed significant variability in HRV, sleep, and fatigue throughout pregnancy. For instance, only 4.76% of individuals had HRV inflection points at the aggregate week 33 inflection, with a 14.24% coefficient of variation. Our analysis found no significant p-values for demographic or pregnancy complication-based subgrouping, suggesting these factors alone do not drive the observed variability. Case studies further highlighted both intra- and inter-individual differences, emphasizing the importance of considering external factors like adverse events and severe symptoms. Conclusions: Our findings show that aggregate wearable data often fails to generalize across populations, oversimplifying pregnancy-related physiological and subjective changes. This simplification can obscure individual trajectories, leading to generalized insights that may not reflect many pregnant women's experiences. Our results highlight the impact of heterogeneity on pregnancy outcomes, emphasizing the need to move beyond one-size-fits-all models and leverage DHT for personalized care.
Background: Family caregivers of children and youth with neurodevelopmental disabilities report higher levels of stress, anxiety, and depression than other caregivers, yet few evidence-based mental he...
Background: Family caregivers of children and youth with neurodevelopmental disabilities report higher levels of stress, anxiety, and depression than other caregivers, yet few evidence-based mental health services are available to them. Our prior research demonstrated that caregivers benefitted from an in-person group-based Acceptance and Commitment Training (ACT) workshop, which increased their psychological flexibility and improved their mental wellbeing. During the pandemic, we adapted this intervention to be delivered virtually and evaluated its impact on caregivers’ psychological flexibility and mental wellbeing. Objective: The objective of this study was to examine the preliminary effectiveness and acceptability of a virtual adaptation of a group-based ACT intervention across different sites in Canada. Methods: Two hundred and five family caregivers of neurodivergent people or people with disabilities who registered for a virtual, group-based ACT intervention (10-12 hours across 5 or 6 weeks) from one of 27 teams across Canada participated in this research. ACT process (measured via Cognitive Fusion Questionnaire [CFQ], Acceptance and Action Questionnaire [AAQ], Self Compassion Scale - Short Form [SCS-SF] and Valued Living Questionnaire [VLQ]) and mental well-being outcomes (measured through Patient Health Questionnaire [PHQ-4], Parenting Stress Index (PSI-4), Multi-System Model of Resilience Inventory [MSMR-I] and Short Warwick–Edinburgh Mental Wellbeing Scale [SWEMWBS]) were measured before and after the intervention, and again at 8 week follow-up. An acceptability survey was completed post-intervention including items on satisfaction with virtual aspects of the intervention Results: Significant improvements were found for most ACT processes and mental wellbeing measures from pre- to post - intervention and these improvements were maintained (psychological flexibility, values, self-compassion) or continued to improve (cognitive defusion) at follow-up. There was high intervention acceptability related to the virtual format (>80% satisfaction) despite variability in individual preferences. Direct content analysis of participants qualitative survey responses revealed that cognitive defusion, mindfulness and acceptance were the most helpful ACT processes. Conclusions: A virtual, group-based ACT intervention was acceptable and led to positive changes in psychological flexibility and mental wellbeing among family caregivers of people with disabilities or neurodivergent individuals, similar to in-person delivery of the intervention. Further exploration of accessible and sustainable ways to deliver this type of intervention to a diversity caregivers is important. This is an important first step to facilitate the dissemination of such services to the community. Clinical Trial: N/A
Background: Exclusive breastfeeding (EBF) is the optimal feeding practice for infant health, yet rates remain low worldwide, including in Saudi Arabia Objective: This study aimed to identify the modif...
Background: Exclusive breastfeeding (EBF) is the optimal feeding practice for infant health, yet rates remain low worldwide, including in Saudi Arabia Objective: This study aimed to identify the modifiable factors influencing exclusive breastfeeding among mothers in Al-Kharj, Saudi Arabia. Methods: This study aimed to identify the modifiable factors influencing exclusive breastfeeding among mothers in Al-Kharj, Saudi Arabia. Results: The prevalence of EBF was 38%. Strong positive predictors were >3 antenatal visits (AOR 23.8, 95% CI 18–41), previous breastfeeding experience (AOR 16.6, 95% CI 7.0–21.1), colostrum feeding (AOR 7.8, 95% CI 3.8–16.1), husband’s support (AOR 6.4, 95% CI 2.6–15.9), rooming-in (AOR 6.2, 95% CI 2.7–14.4), and antenatal breastfeeding education (AOR 6.1, 95% CI 2.8–13.3). Key barriers included perceived insufficient milk (AOR 0.08, 95% CI 0.05–0.13), delayed initiation beyond three days (AOR 0.11, 95% CI 0.04–0.33), returning to work (AOR 0.15, 95% CI 0.08–0.28), prelacteal fluids (AOR 0.28, 95% CI 0.14–0.56), high income (AOR 0.33, 95% CI 0.10–1.09), late baby holding (AOR 0.35, 95% CI 0.13–0.94), and restrictive traditions (AOR 0.37, 95% CI 0.21–0.65). Conclusions: Modifiable factors such as education, family support, postnatal care, and supportive maternity policies are critical to improve EBF. Findings provide important evidence for policymakers in Saudi Arabia and similar contexts, highlighting the need for healthcare worker training, , early initiation and six months of paid maternity leave.
Background: Drug adherence is crucial for chronic disease management, yet treatment discontinuation remains common due to factors such as side effects, inefficacy, or cost. These reasons are often rec...
Background: Drug adherence is crucial for chronic disease management, yet treatment discontinuation remains common due to factors such as side effects, inefficacy, or cost. These reasons are often recorded only in free-text clinical notes, making large-scale analysis difficult. While large language models (LLMs) can interpret such unstructured data more effectively than traditional natural language processing methods, few studies have systematically categorized reasons for discontinuation or identified whether the decision was initiated by the patient or the clinician, especially in low-resource languages like Estonian. Objective: To assess the ability of LLMs to extract and classify reasons for drug discontinuation and identify who initiated it using Estonian electronic health records, and to characterize the observed discontinuation patterns and initiators for statins and antidiabetic medications. Methods: We combined prescription data with free-text anamneses from a 10% sample of the Estonian population (2012–2019). LLMs (Llama-3.1-70B and GPT-4o) were applied to extract discontinuation phrases and reasons, classify them into a clinician-developed taxonomy, and identify who discontinued the treatment. Performance was evaluated on randomly chosen 100 cases per drug group. Results: Extraction yielded 625 antidiabetic drug and 233 statin discontinuation cases. Validation confirmed high accuracy, with 93–98% of extracted phrases and 95–96% of extracted reasons judged correct. Classification of discontinuation reasons achieved weighted F1 scores of 0.808–0.836, while classification of who initiated discontinuation achieved weighted F1 scores of 0.645–0.774.
Adverse reactions were the most frequent reason overall, accounting for ~70% of discontinuations for statins and ~44% for antidiabetic drugs. For antidiabetic drugs, treatment inefficacy and contraindications were more common. Patients more often stopped due to adverse reactions or non-medical reasons, while physicians more often initiated discontinuation for contraindications. Conclusions: LLMs can accurately extract and classify medication discontinuation reasons and initiators from Estonian clinical narratives. Both local and proprietary models performed well, enabling scalable analyses that complement structured health records. This demonstrates the potential of LLMs to unlock information from clinical notes, turning this underutilized EHR component into a valuable resource for monitoring treatment patterns and detecting adverse event signals.
Background: Young to middle-aged breast cancer patients face significant psychosocial challenges. Existing interventions often lack comprehensiveness, timely initiation, and specific tailoring to this...
Background: Young to middle-aged breast cancer patients face significant psychosocial challenges. Existing interventions often lack comprehensiveness, timely initiation, and specific tailoring to this population's unique needs. Objective: To evaluate the impact of a peer-led, nurse-involved, blended online and offline peer support intervention program (PNO2PSP) on psychosocial adjustment in young to middle-aged breast cancer patients. Methods: The PNO2PSP effectiveness was validated through a single-center cluster randomized controlled trial involving 70 newly diagnosed young to middle-aged breast cancer patients (35 in each group). The intervention group received an 8-week PNO2PSP in addition to routine care. Psychosocial adjustment, self-efficacy, social support, and coping modes were assessed pre-surgery and at 4, 8, and 12 weeks post-surgery. Generalized Estimating Equations (GEE) were used for intention-to-treat analysis. In-depth interviews with 9 participants explored their experiences. Results: Compared to the control group, the intervention group demonstrated significantly lower psychosocial adjustment scores at 4 weeks (T1) (Wald χ² = 6.466, P = 0.011) and 12 weeks (T3) (Wald χ² = 4.395, P = 0.036); Social support was higher at 8 weeks (T2) (Wald χ² = 8.175, P = 0.004). Confrontation coping scores were higher at T3 (Wald χ² = 4.189, P = 0.041), while avoidance coping scores were lower at T1 (Wald χ² = 7.051, P = 0.008), T2 (Wald χ² = 7.346, P = 0.007), and T3 (Wald χ² = 5.062, P = 0.024). Qualitative findings supported these quantitative results, highlighting the program's role in facilitating psychosocial adjustment, providing vital support, boosting treatment confidence, and fostering positive coping. Conclusions: The PNO2PSP effectively enhances psychosocial adjustment, social support, and positive coping in young to middle-aged breast cancer patients. Its scientifically validated, feasible, and patient-centered design supports its recommendation for wider clinical implementation, with continued training for peer supporters and sustained delivery of peer support. Clinical Trial: Registry: Chinese Clinical Trial Registry, TRN: ChiCTR2300076471, Registered 10/10/2023
Background: Humanism is central to healthcare professionalism but remains poorly defined and inconsistently applied. Amid growing system pressures and dehumanization, this study aims to develop consen...
Background: Humanism is central to healthcare professionalism but remains poorly defined and inconsistently applied. Amid growing system pressures and dehumanization, this study aims to develop consensus-based conceptual and operational definitions of humanism to support education, evaluation, practice, and policy. Objective: This study aims to formulate consensus-based conceptual and operational definitions of humanism in healthcare, based on the combination of theorical perspectives and experience and expertise of clinician-educators, students, and members of the public. Methods: A two-phase mixed-methods design will be implemented to ensure both methodological rigor and analytical depth. Phase I will involve a critical review of the literature, systematically analyzed using content analysis. Concurrently, focus groups will be conducted with learners, clinician educators, and members of the public. The qualitative data generated will undergo thematic analysis to elucidate both theoretical and experiential dimensions of humanism in healthcare. To enhance transparency and epistemological reflexivity, a reflective journal will be maintained throughout the research process. Phase II will employ a modified e-Delphi technique, engaging a panel of interdisciplinary experts in iterative rounds to refine and validate key statements. This approach will reinforce construct validity and facilitate consensus-building. Results: This project is funded by the Research Chair in Compassion Science at the Université de Sherbrooke and supported by the Office of Social Accountability. Scientific evaluation and ethical approval were obtained on May 12, 2025 (Project #2025-4885). As of September 30, the selection of papers for the critical literature review is currently underway, results will be submitted for publication in Winter 2026. Recruitment for the focus groups is scheduled to begin in October 2025. A modified e-Delphi study is planned to begin in Spring 2026, and the results will be submitted for publication in Open Access journals.This project is funded by the Research Chair in Compassion Science at the Université de Sherbrooke and supported by the Office of Social Accountability. Scientific evaluation and ethical approval were obtained on May 12, 2025 (Project #2025-4885). As of September 30, the selection of papers for the critical literature review is currently underway, results will be submitted for publication in Winter 2026. Recruitment for the focus groups is scheduled to begin in October 2025. A modified e-Delphi study is planned to begin in Spring 2026, and the results will be submitted for publication in Open Access journals. Conclusions: This study will produce a validated conceptual and operational definition of humanism in healthcare, grounded in both theory and lived experience. These definitions will support the harmonization of educational practices, the development of assessment tools, and the integration of humanistic values into health policy.
Background: Predictive medicine relies on algorithms to determine clinical treatments tailored to each patient’s individual characteristics. Predictive models based on AI have shown promise in ident...
Background: Predictive medicine relies on algorithms to determine clinical treatments tailored to each patient’s individual characteristics. Predictive models based on AI have shown promise in identifying Atrial Fibrillation (AF) episodes; however, they rarely focus on short-term dynamic prediction. Objective: This study aims to evaluate the use of an AI model and remote monitoring data extracted from pacemaker devices to predict the onset or worsening of arrhythmias in the short term. Methods: This was an observational, prospective, multicenter study in which data from 314 patients were analyzed. A total of 65,243 data sequences were collected, of which 55,532 were used to train the algorithm. This model used 31-day records to predict whether the number of arrhythmic episodes increased, decreased, or remained the same in the following 14 days. Results: The sensitivity and specificity of the generated predictions were calculated from 9,711 prediction/observation pairs. The global sensitivity was 66.4% and specificity was 77.4%; with a sensitivity of 76.8% and specificity of 39.6% in patients with baseline arrhythmia; and a sensitivity of 39% and a specificity of 81% in patients without baseline arrhythmia. The analysis for the patient subgroup without prior history of AF yielded a 69% sensitivity and an 80% specificity. Conclusions: This model was capable of predicting short-term increases or decreases in arrhythmic episodes with reasonable sensitivity and specificity, using data collected through remote monitoring of implantable devices. The model’s performance is expected to improve progressively as more data samples become available, including demographic and clinical records. Clinical Trial: Trial code: IA-Pacing
Spain
Fundación FFDIS in collaboration with Arrhythmia Network Technology SL.
Background: The management of type 2 diabetes mellitus (T2DM) remains a complex clinical challenge, particularly for patients requiring multiple daily insulin injections (MDI). Advances in precision m...
Background: The management of type 2 diabetes mellitus (T2DM) remains a complex clinical challenge, particularly for patients requiring multiple daily insulin injections (MDI). Advances in precision medicine and continuous glucose monitoring (CGM) have created opportunities to personalize treatment and potentially reduce the therapeutic burden on people with T2DM. Assessing beta-cell function and autoimmunity could help identify patients with T2DM eligible for simplified regimens without compromising glycemic control. Objective: The aim of this study was to test a simple personalized medicine protocol in routine clinical practice for people with T2DM treated with MDI. The intervention is based on the evaluation of C-peptide and glutamic acid decarboxylase autoantibody (GADAb) status, with the goal of improving diagnostic accuracy and optimizing treatment. Methods: This is a pragmatic, before-and-after intervention study involving people with T2DM on current MDI across primary care centers and a referral hospital in the Lleida health care region, Catalonia (Spain). Eligible participants will undergo clinical and laboratory assessment, including C-peptide and GADAb testing, and wear a CGM device. Based on a predefined algorithm, patients may either continue or discontinue prandial insulin.
The primary outcome is the proportion of patients in whom prandial insulin is discontinued and remains discontinued over 6 months. Secondary outcomes include changes in glycated haemoglobin (HbA1c), CGM metrics variables, quality of life, adherence, and treatment satisfaction. Results: Recruitment was completed on March 31, 2025. The follow-up phase is ongoing and expected to conclude by September 30, 2025. Data analysis will begin thereafter. Conclusions: This study will evaluate the feasibility and impact of implementing a personalized therapeutic approach for persons with T2DM on MDI in real-world clinical settings. If effective, this strategy could contribute to safer, simpler, and more individualized diabetes care. Clinical Trial: ClinicalTrials.gov NCT06148376; https://clinicaltrials.gov/ct2/show/NCT06148376
Background: Approximately 27% of United States adults live with a disability, yet they face persistent disparities in health outcomes and access to care. The systematic collection of disability status...
Background: Approximately 27% of United States adults live with a disability, yet they face persistent disparities in health outcomes and access to care. The systematic collection of disability status and accommodation needs data in electronic health records (EHRs) can support more equitable access to care, help ensure that patients with disabilities receive appropriate, person-centered care, and bolster efforts to monitor and address health disparities for people with disabilities. However, data collection remains limited in the health care setting. Objective: This qualitative study aimed to examine current practices for collecting, documenting, and exchanging disability-related data in EHRs. This study identifies the current state of disability-related data collection by health care organizations; describes how these data are used by health care organizations and researchers; presents challenges to data collection; and offers opportunities to advance the standardized collection and use of disability-related data. Methods: A qualitative, two-pronged approach was employed, consisting of a literature scan and 13 key informant interviews with stakeholders from health systems, research institutions, and policymaking and advocacy organizations. Data were analyzed using a structured abstraction matrix to identify themes related to data collection practices, use cases, challenges, and opportunities to improve standardization and interoperability. Results: We identified three use cases for collecting, documenting, and exchanging disability-related data: (1) preparing for patient visits, (2) improving care quality, (3) facilitating care transitions, and (4) advancing equity research. However, findings from the literature scan and key informant interviews revealed that most health care organizations do not routinely collect disability status or accommodation needs data. Among those that do, they employ varied and non-standardized approaches, hindering the ability of health care organizations to provide legally mandated accommodations and deliver equitable, patient-centered care. Conclusions: Conclusions: Standardized and systematic collection of disability status and accommodation needs data is critical to advancing health equity, improving care quality, and supporting patient-centered care for people with disabilities. The inclusion of “disability status” as a requirement for certified health information technology, including electronic health records (EHR), beginning in 2026 represents a critical step toward more standardized data collection. Efforts to strengthen data collection practices should include workflows for documenting a patient’s self-reported disability and requested accommodations, enhancing health information technology systems, engaging stakeholders across health care settings, and promoting adoption of national standards to ensure disability-related data are accurate, actionable, and interoperable.
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.
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
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.
Background: Artificial intelligence (AI) systems are increasingly deployed in digital mental health platforms for early detection of suicide risk and severe psychological distress. Current “responsi...
Background: Artificial intelligence (AI) systems are increasingly deployed in digital mental health platforms for early detection of suicide risk and severe psychological distress. Current “responsible AI” approaches often prioritise precision and minimising false positives through human-in-the-loop (HITL) verification. While this can reduce operational strain and perceived liability, it delays interventions in time-critical crises, potentially increasing risk. This trade-off, where greater procedural safety paradoxically increases danger, is termed the Safety Paradox. Objective: To introduce the Rapid Intervention Safety Enhancement (RISE) protocol, a framework designed to reduce mean time to intervention (MTI) while maintaining safeguards, and to outline a proposed methodology for its evaluation. Methods: The RISE Protocol was developed through iterative design workshops, expert consultations, and review of mental health AI safety literature. It comprises four stages: Rapid Detection, Immediate Triage, Staged Intervention, and Evidence Logging. Each stage includes defined operational targets, intervention pathways, and accountability measures. Key operational metrics are proposed to evaluate system performance. Results: As the RISE Protocol has not yet undergone empirical trials, this paper presents it as a conceptual model for future evaluation. An illustrative use case and a comparative analysis against current industry approaches suggest that RISE could enable faster interventions without increasing liability risk, by automating detection and triage to reduce delays from human verification bottlenecks. Conclusions: The RISE Protocol reframes mental health AI safety as a function of responsiveness rather than precision alone. By establishing operational standards for mean time to intervention (MTI), cultural adaptation, and accountable automation, it aims to shift the industry toward proactive, life-saving interventions. Future research should focus on empirical validation of the framework and its impact on user outcomes.
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.
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.
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.
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.
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.
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.
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.
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
Background: An estimated 18 million people are living with Rheumatoid Arthritis (RA) in the world (1). The disease comes with significant morbidity for patients, including the increased risk of fractu...
Background: An estimated 18 million people are living with Rheumatoid Arthritis (RA) in the world (1). The disease comes with significant morbidity for patients, including the increased risk of fractures compared to the general population due to the chronic inflammation associated with RA, which can lead to reduced bone mineral density (2).
Presently, disease severity and progression have been helped by the rapid evolution of anti-rheumatic medications. These medications are broadly categorized into two main types: non-steroidal anti-inflammatory drugs (NSAIDs) and disease-modifying antirheumatic drugs (DMARDs) (3).
Patients with RA have an increased risk of post-operative complications of orthopaedic surgery because of its chronic impact on bone as well as the use of immunomodulatory medications that may interfere with bone healing (4). Drugs with immunosuppressive action can lead to potential complications with both wound healing and bone union during elective osteotomy. This is particularly important in foot and ankle surgery, where corrective osteotomies are commonly performed, and the risk of wound breakdown is high. Decisions to continue or suspend taking these medications need to be based on evidence weighing the potential for post-operative complications versus potentiating a disease flare.
There is an abundance of literature that highlights the adverse effects of NSAIDs on bone healing and fracture union, but there is little robust evidence surrounding the use of DMARDs in orthopaedic surgery (3, 5). Patients requiring surgery in trauma or elective settings will often be on one or more of these medications. Therefore, it is vital to understand the effect of DMARDs on postoperative outcomes to improve their recovery and rehabilitation, and if required, to suspend the medications in the perioperative period (6). Current guidance published by the American College of Rheumatology has been formulated for elective hip and knee surgery with a focus on preventing wound complications, typically restarting DMARDs at the 14-day mark when the wound has healed. These guidelines do not consider the time to union of bone, which is typically 6 weeks.
The currently available research has been in vivo or in vitro studies, with little to few studies assessing the clinical implications of DMARDs on bone healing in the rheumatoid patient. Objective: This literature review aims to synthesise and evaluate current evidence on the impact of DMARDs on bone healing. Methods: A literature search was conducted on PubMed, Embase, and Medline. An initial search was conducted looking at the effect of DMARDs or anti-rheumatic medications on bone healing in foot and ankle osteotomies. We used the keywords ‘DMARDs OR anti-rheumatic medications’, ‘foot and ankle surgery OR osteotomy’, AND ‘Bone healing OR bone union’. It yielded only four original papers for review after removing duplicates, case reports, conference abstracts, and non-English Language material. Due to the limited data in this field, we expanded our search and question to look at the effects of DMARDs on bone union in elective and trauma patients.
The keywords were subsequently refined to ‘rheumatic disease OR rheumatoid arthritis’ AND ‘anti-rheumatic medication OR disease-modifying anti-rheumatic medications OR DMARDs’ AND ‘fracture healing OR bony union OR malunion or non-union’. As there was still a limited number of original studies on this theme, we decided to include any study design apart from case studies. These were excluded due to their potential bias and limited generalisability. We also only included papers written in the English language and within the last 50 years. We selected papers that looked specifically at either DMARDs or methotrexate and their effect on bone healing, fracture union, or bone metabolism. The search resulted in 80 papers for review. After applying the above inclusion and exclusion criteria with two independent reviewers, a total of 9 papers were included for a narrative analysis. Results: The effect of methotrexate (MTX) on bone appears to be dose-dependent. Satoh et al showed that new bone formation in a fracture gap in rats did not differ significantly between low-dose MTX and control groups (7). However, there was a marked reduction in bone formation in the high-dose MTX group, particularly periosteal bone formation de novo in the fracture gap site in the first week. The study showed no difference between the three groups for intramedullary bone formation or chondroid tissue formation. A key limitation of this study was that it only looked at bone formation rather than bone strength or mineral density. Several other animal studies support the finding that high-dose MTX has a greater adverse effect on bone metabolism than low-dose MTX (8, 9, 10).
Pountos et al’s systematic review analysed 70 papers of in vivo and animal studies on the effect of MTX on fracture healing (11). The review gave rise to contradictory evidence. Some in vitro studies concluded that MTX reduces mitochondrial activity, bone cell metabolism, and turnover. Other studies showed no effect on osteoblast proliferation, which is a crucial step in bone healing (3). Some studies also showed there was a reduction in biochemical markers of osteogenesis, such as ALP (alkaline phosphatase), while in others, ALP increased (12).
In clinical studies, the impact of DMARDs on bone healing has been studied for patients undergoing elective spinal surgery (13). One study looked at bone fusion rates after craniovertebral junction surgery and found that those who continued DMARDs showed higher radiographic fusion outcomes than those who discontinued (92.8% vs 75%, P value = 0.276). However, the study was not statistically significant due to its small sample size of 30 patients in total (14). Guadiani et al studied revision spinal surgery rates for patients using DMARDs and TNF-alpha inhibitors compared to a control group not on either medication. The reoperation rate within 1 year was 19% for the TNF-alpha inhibitor group and 11% for the DMARD group compared to 6% for the control group. According to the Cox proportional hazard model they used the TNF-alpha group had a 3.1-fold increased risk compared to the control group (95% CI 1.4-7.0), while the DMARD group showed a 2.2-fold increase (95% CI: 0.96-5.3). The reasons for revision surgery were due to infection (40%) or other causes (60%), such as failure to fuse in the DMARDs group, while in the TNF-alpha inhibitor group, it was 47% for infection and 53% for other causes (15). This implies there is a higher rate of infection for the TNF-alpha inhibitor cohort. The authors concluded that continuing DMARDs, especially TNF-alpha inhibitors, 90 days before surgery, appeared to have a higher rate of revision spinal surgery than those who discontinued.
In 2017, the American College of Rheumatology and American Association of Hip and Knee Surgery (ACR/AAHK) performed an extensive meta-analysis of the literature around the use of DMARDs in orthopaedic surgery (10). They advised that conventional DMARDs, which include methotrexate, leflunomide, hydroxychloroquine, and sulfasalazine, can be continued in orthopaedic surgery as they did not lead to adverse post-operative outcomes. However, they recommended holding biologics for two weeks before surgery as there was an increased risk of poor wound healing. The effect on bone healing itself was not studied in this review. There is, in fact, very limited data on the effect of biologic DMARDs on bone healing, but in an in vivo study, they have shown an inhibitory effect on osteoblast proliferation (3). This is particularly true of TNF-alpha inhibitors such as infliximab, which showed a reduction in overall osteoblast cell numbers. This suggests it could interfere with the bone repair and remodelling (3).
Furthermore, the 2021 critical analysis review by Saunders et al on the perioperative management of antirheumatic drugs in foot and ankle surgery also concluded that conventional DMARDS are generally safe to use throughout the perioperative period, while biologics should be held typically before surgery (16). Conclusions: Our narrative review has highlighted an important literature gap within the field of DMARDs and bone healing, whether in a traumatic or elective setting. Much of the original research is in vivo or animal studies, and although they show statistically significant results, they cannot accurately predict human outcomes due to significant differences in physiology and biology (3,8,9,12). Clinical studies are even fewer, and the ones conducted so far have included small study populations. Moreover, they are antiquated and often do not examine the latest anti-rheumatic drugs. For instance, an important study we included, Elia et al, included only 30 patients, which reduced the statistical power of the results (14). All the clinical studies we have included so far are for elective procedures such as spinal surgery or foot and ankle surgery (13,14, 16, 17). To our knowledge, there are currently no randomised controlled trials that study the effect of DMARDs on bone healing, either in a trauma or elective setting.
Nevertheless, there is a greater number of publications available to consider for the effect of DMARDs on wound healing in orthopaedic surgery. This is of important consequence as surgical site infections, especially when involving the bone, can lead to impaired fracture healing, causing malunion or non-union (18). Current evidence suggests MTX has no adverse effect on wound healing in orthopaedic surgery and can be safely continued pre- and postoperatively (10,11). However, biologics are recommended to be held perioperatively due to the increased risk of surgical site infections and impaired wound healing. The current guidance is to schedule surgery at the end of their dosing cycle (10). Some hospital trusts have advised only restarting when most of the wound has healed (19). Considering a wider evidence base, biologics have shown an increased risk of serious infection, so there is certainly a research gap to explore on how these medications impact patient outcomes in orthopaedic surgery (20).
Any decision to stop anti-rheumatic medications in the preoperative period should be a carefully considered decision, with patients fully informed as to the risks and benefits of stopping such therapy. Patients on DMARDs tend to have more severe disease, and withholding them may result in disease flares, which can cause significant morbidity. Flares may lead to joint swelling, stiffness, pain, and increased cardiovascular risk (21). This can ultimately impair rehabilitation following major surgery, which predisposes the patient to further post-operative complications such as venous thromboembolism, hospital-acquired infections, or reduced functional baseline from a prolonged hospital stay (22).
Grennan et al found that those who discontinued MTX two weeks before and after surgery showed a higher rate of flare-ups than those who continued their medication. Patients who continued MTX before surgery had even fewer post-operative complications than the control group that was not on any MTX (23). The 2017 American College of Rheumatology study also concluded that continuing glucocorticoids and DMARDs perioperatively for hip and knee arthroplasty resulted in better function, a greater range of motion, and improved post-operative pain (10).
Therefore, we advise that the decisions around anti-rheumatic medications in patients undergoing orthopaedic surgery should be determined on an individual basis, with consideration given to their disease severity, functional baseline, and risk factors for poor bone healing, as we currently do not have enough evidence to suggest that they should be held.
Our literature review, however, has some limitations. Firstly, we used specific terminology to capture the effect of anti-rheumatic medications on bone healing, so we may have missed articles that contain this information, which did not include our keywords. Secondly, there is such little data available for our topic that the papers we have selected for review have small study populations or no controls. None of the studies we included showed any randomisation. Therefore, results were interpreted with caution as there is a potential for bias and reduced generalisability. Finally, many papers we included were animal studies, so their findings cannot be applied directly to humans.
In conclusion, the effect of DMARDs on bone union remains largely unstudied, especially considering human studies and large randomised controlled trials. Our literature review has identified that MTX may be safe to continue before orthopaedic surgery, as it does not appear to affect bone union at low doses that are used in RA. However, biologics should be withheld as there is evidence to suggest they can cause an increased risk of infection or wound breakdown. The effect of biologics specifically on bone healing, has not been studied to our knowledge. Given that millions of patients suffer from rheumatoid arthritis, and many will at one point undergo a joint procedure, it is important to further understand the clinical impact of DMARDs on bone so we can recommend evidence-based guidance. Until then, we advise a multi-disciplinary approach in determining which anti-rheumatic medications to withhold before any orthopaedic surgery.
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.
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.
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.
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.
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.
Background: Successful Research and MedTech collaborations depend on six key components: talent and workforce development, innovative solutions, robust research infrastructure, regulatory compliance,...
Background: Successful Research and MedTech collaborations depend on six key components: talent and workforce development, innovative solutions, robust research infrastructure, regulatory compliance, patient-centered care, and rigorous evaluation.
Institutional leaders frequently navigate multiple professional identities; simultaneously serving as educators, researchers, clinicians, and innovators; creating bridges between academic rigor and practical application that accelerate the translation of research into meaningful solutions. Institutions and organizations may also need to broaden their identities.
The contemporary landscape presents significant challenges as institutions balance the pursuit of academic excellence with the need for rapid responsiveness to technological and commercial innovation. Traditional research processes, while ensuring quality, often impede the pace of advancement necessary in today's rapidly evolving environment. This tension necessitates structural reforms across multiple dimensions of institutional operation.
To cultivate a thriving research and innovation ecosystem, several essential components must be established:First, institutions require agile research infrastructure with cutting-edge laboratories and collaboration spaces, specialized equipment, and certified research professionals specifically trained in device development and regulatory compliance. Robust clinical management platforms can expedite trials and streamline data extraction for publication and dissemination. Objective: The Orange County (OC) Impact Conference, held in November 2024, convened 180 key stakeholders from the life sciences, technology, medical device, and healthcare sectors. CHOC Research in collaboration with University Lab Partners (ULP) and the University of California, Irvine, provided this platform for leaders, decision-makers, and experts to discuss the intersection of innovation in research, healthcare, biotechnology, and data science. Methods: We convened a multidisciplinary symposium (180 participants) to examine advancements in life sciences and medical device research development. The structured forum incorporated moderated panel discussions and a keynote speaker. Participants represented diverse stakeholder categories including research scientists, clinicians, investors and financiers, and executive research and healthcare leadership. The event design facilitated both structured knowledge exchange and strategic networking opportunities aimed at identifying implementation pathways to enhance clinical impact. Results: The 2024 OC Impact Conference Proceedings outline a strategy for healthcare innovation, demonstrating how targeted collaboration between patients, families, researchers, clinicians, engineers, data scientists, and industry is reshaping the healthcare innovation ecosystem. This integrated approach ensures every stakeholder's voice contributes to meaningful advancement, guiding resource allocation and partnership development across the life science and medical device sectors. Our findings demonstrate that success requires moving beyond traditional approaches to patient-driven research priorities, augmented design principles for medical device development, and direct engagement between innovators, research participants, industry and healthcare centers throughout the research development cycle. Conclusions: The insights gained through participation in the OC Impact Conference contribute to the ongoing discourse in these fields, emphasizing collaborative efforts to enhance pediatric and adult healthcare outcomes. Clinical Trial: N/A
Background: Nigeria faces severe economic losses ($14 billion annually) and high youth unemployment (33.3%) due to persistent skills gaps, exacerbated by sectoral disparities (e.g., 68% ICT shortages...
Background: Nigeria faces severe economic losses ($14 billion annually) and high youth unemployment (33.3%) due to persistent skills gaps, exacerbated by sectoral disparities (e.g., 68% ICT shortages vs. 63% agricultural deficits) and systemic inequities in education and vocational access. Despite growing HRM interventions, empirical evidence on their efficacy remains limited, necessitating a comprehensive review to guide policy. Objective: This study analyzes Nigeria’s sector-specific skills gaps, evaluates the effectiveness of HRM interventions (apprenticeships, digital upskilling, PPPs), and proposes actionable frameworks to align workforce development with labor market demands. Methods: A narrative review of peer-reviewed literature (2015–2023), institutional reports (World Bank, PwC, NBS), and case studies (e.g., Andela’s model) was conducted. Data were synthesized to compare regional benchmarks (Kenya’s TVET, South Africa’s HRM reforms) and Nigeria’s performance (talent readiness score: 42/100). Results: Key findings include: (1) Vocational training (60% readiness) outperforms tertiary education (40%); (2) Apprenticeships and PPPs show high impact (30% job placement increase); (3) Urban-rural and gender disparities persist (women 30% less likely to access training). Private-sector models demonstrate scalability but require policy support. Conclusions: Nigeria’s skills crisis demands urgent, context-sensitive interventions. Blended strategies (e.g., industry-aligned curricula, gender-inclusive vocational programs) could unlock 5% annual GDP growth. Prioritize: (1) National skills councils to standardize certifications; (2) Tax incentives for employer-led training; (3) Digital infrastructure for rural upskilling. Closing Nigeria’s skills gaps would mitigate economic losses, reduce inequality, and enhance global competitiveness, transforming its youth bulge into a sustainable demographic dividend.
Background: Central venous catheterization (CVC) is a very common procedure performed across medical and surgical wards as well as intensive care units. It provides relatively extended vascular access...
Background: Central venous catheterization (CVC) is a very common procedure performed across medical and surgical wards as well as intensive care units. It provides relatively extended vascular access for critically ill patients, in order to the administer intricate life-saving medications, blood products and parenteral nutrition.
Major vascular catheterization provides a risk of easy accessibility and dissemination of catheter related infections as well as venous thromboembolism. Therefore, its crucial to ensure following standardized practices while insertion and management of CVC in order to minimize the infection risks and procedural complications. The aim of these central line insertion guidelines is to address the primary concerns related to predisposition of Central line associated blood stream infections (CLABSI). These guidelines are evidence based and gathered from pre-existing data associated with CVC insertion.
The most common used sites for central venous catheterization are internal jugular and subclavian veins as compared to femoral veins. Catheterization of these vessels enables healthcare professionals to monitor hemodynamic parameters while ensuring lower risks of CLABSI and thromboembolism. Femoral vein is less preferred due to advantage of invasive hemodynamic monitoring and low risk of local infection and thromboembolic phenomena.
CVC can be inserted using Landmark guided technique and ultrasound guided techniques. Following informed consent, the aseptic technique for CVC insertion includes performing appropriate hand hygiene and ensuring personal protective measures, establishing and maintaining sterile field, preparation of the site using chlorhexidine, and draping the patient in a sterile manner from head to toe. Additionally, the catheter is prepared by pre-flushing and clamping all unused lumens, and the patient is placed in the Trendelenburg position. Throughout the procedure, maintaining a firm grasp on the guide wire is essential, which is subsequently removed post-procedure. It is followed by flushing and aspirating blood from all lumens, applying sterile caps, and confirming venous placement. Procedure is ended with cleaning the catheter site with chlorhexidine, and application of a sterile dressing.
Hence, formal training and knowledge of standardized practices of CVC insertion is essential for health care professionals in order to prevent CLABSI. Our audit assesses the current practices of doctors working at a tertiary care hospital to analyze their background knowledge of standard practices to prevent CLABSI during insertion of CVC. Objective: This study was aimed to audit and re-audit residents’ practices of central venous line insertion in medical and nephrology units of A Tertiary Care Hospital of Rawalpindi, Pakistan and to assess the adherence of residents to checklist and practice guidelines of CVC insertion implemented by John Hopkins Hospital and American Society of Anesthesiologists. Methods: This audit was conducted as a cross sectional direct observational study and two-phase quality improvement project in the Medical and Nephrology Units of a Tertiary Care Hospital of Rawalpindi from December 2023 to February 2024.
After taking informed consent from patients and residents, CVC insertion in 34 patients by 34 individual residents was observed. Observers were given a purposely designed observational tool made from John Hopkins Medicine checklist and ASA practice guidelines for central line insertion, for assessment of residents’ practices.
First part contained questions regarding the demographic details of residents such as age, gender, year of post graduate training, and parent department, and data related to the procedure such as date and time of procedure, need of CVC discussion during rounds, site of CVC insertion, catheter type and type of procedure (Landmark guided CVC or Ultrasound guided CVC insertion). Second part included direct observational checklist based on checklist provided for prevention of intravascular catheter-associated bloodstream infections to audit the practices of residents during CVC insertion that included: adequate hand hygiene before insertion, adherence to aseptic techniques, using sterile personal protective equipment and sterile full body drape of patient, choosing the best insertion site to minimize infections based on patient characteristics.
The parameters observed to be done completely were scored "1" and the items not done were scored "0". The cumulative percentage of performed practices according to checklist, was satisfactory if it was 80% or more and unsatisfactory if it was less than 80%.
After initial audit, participants were given pamphlets with checklist incorporating John Hopkins Medicine checklist and ASA practice guidelines for CVC insertion. Re audit was performed one month after the audit, including same participants who participated in initial audit. The results of audit and re-audit were analyzed using SPSS version 25. Mean +/- SD was calculated for quantitative variables and Number (N) percentage was calculated for qualitative variables. Z- Test was applied on proportions of parameters and test scores to calculate Z –score and P value (<0.05 was significant). Results: Among the 34 participants, 44% of the participants belonged to Nephrology Department and 56% of participants belonged to Department of Internal Medicine.
32.3% residents were in their first year, 14.7% in second, 14.7 in third year, 17.6% in fourth year and 17.6% in 5th/Final year of training.
47% of the participants were male and 53% were female. Participants were aged between 27 and 34 years old, the median age at the time of audit was 29 years.
Landmark guided CVC insertion was performed in Subclavian Vein (73.5%) and Internal Jugular Vein (26.5%).
Post audit practices were improved from 73.5% to 94%. Conclusions: Our audit found that many of the residents adopted inadequate practices because of lack of proper training and institutional guidelines for CVC insertion. Our re-audit elaborated an improvement in the practices of residents following intervention with educational material. Our study underscores the importance of structured quality improvement initiatives in enhancing clinical practices and patient outcomes.
Background: Social media has profoundly transformed consumer behavior and marketing practices within the hospitality industry. Understanding how these changes influence hotel selection and booking dec...
Background: Social media has profoundly transformed consumer behavior and marketing practices within the hospitality industry. Understanding how these changes influence hotel selection and booking decisions, the effectiveness of social media strategies, and shifts in reputation management practices is crucial for hotels aiming to enhance their digital presence and customer engagement. Objective: The study aims to analyze the influence of social media on consumer behavior, audience engagement, and reputation management in hotel selection and booking decisions as well as compare pre- and post-social media reputation management practices. Methods: Data was collected through surveys and interviews with hotel guests and marketing professionals. The analysis included descriptive statistics and comparative assessments of pre- and post-social media reputation management practices. The effectiveness of various social media strategies was evaluated based on respondent feedback. Results: The findings indicate that promotional offers, user reviews, and visual content significantly influence consumer behavior in hotel selection and booking decisions. Collaboration with influencers, user-generated content, live video content, and social media advertising are the most effective strategies for audience engagement and brand building, each with a 100% effectiveness rate. There is a notable shift in reputation management practices, with a decrease in promptly addressing issues and providing compensation, and an increase in seeking private resolutions through direct messages post-social media. Conclusions: Social media plays a critical role in shaping consumer behavior and brand perception in the hotel industry. Effective social media strategies, particularly those involving influencers and user-generated content, are essential for engaging audiences and building brand identity. The transition to social media has also led to changes in reputation management, emphasizing the importance of balancing transparency with discreet conflict resolution. Hotels should prioritize comprehensive social media strategies that include collaboration with influencers, regular updates, and engaging content. Encouraging positive user-generated content and implementing robust monitoring and response systems are essential. Training staff on social media engagement and conflict resolution can further improve reputation management. Ongoing adaptation to emerging social media trends is crucial for maintaining effectiveness. This study provides valuable insights into the impact of social media on consumer behavior and marketing in the hospitality industry. By identifying effective social media strategies and examining changes in reputation management, it offers practical guidance for hotels seeking to enhance their digital presence and customer engagement. The findings underscore the importance of leveraging social media to achieve greater business success and maintain a positive brand reputation.
Background: Noncommunicable diseases (NCDs) pose a significant burden in the Philippines, with cardiovascular and cerebrovascular diseases among the leading causes of mortality. The Department of Heal...
Background: Noncommunicable diseases (NCDs) pose a significant burden in the Philippines, with cardiovascular and cerebrovascular diseases among the leading causes of mortality. The Department of Health implemented the Philippine Package of Essential Non-Communicable Disease Interventions (Phil PEN) to address this issue. However, healthcare professionals faced challenges in implementing the program due to the cumbersome nature of the multiple forms required for patient risk assessment. To address this, a mobile medical app, the PhilPEN Risk Stratification app, was developed for community health workers (CHWs) using the extreme prototyping framework. Objective: This study aimed to assess the usability of the PhilPEN Risk Stratification app using the (User Version) Mobile App Rating Scale (uMARS) and to determine the utility of uMARS in app development. The secondary objective was to achieve an acceptable (>3 rating) score for the app in uMARS, highlighting the significance of quality monitoring through validated metrics in improving the adoption and continuous iterative development of medical mobile apps. Methods: The study employed a qualitative research methodology, including key informant interviews, linguistic validation, and cognitive debriefing. The extreme prototyping framework was used for app development, involving iterative refinement through progressively functional prototypes. CHWs from a designated health center participated in the app development and evaluation process – providing feedback, using the app to collect data from patients, and rating it through uMARS. Results: The uMARS scores for the PhilPEN Risk Stratification app were above average, with an Objective Quality rating of 4.05 and a Personal Opinion/Subjective Quality rating of 3.25. The mobile app also garnered a 3.88-star rating. Under Objective Quality, the app scored well in Functionality (4.19), Aesthetics (4.08), and Information (4.41), indicating its accuracy, ease of use, and provision of high-quality information. The Engagement score (3.53) was lower due to the app's primary focus on healthcare rather than entertainment. Conclusions: The study demonstrated the effectiveness of the extreme prototyping framework in developing a medical mobile app and the utility of uMARS not only as a metric, but also as a guide for authoring high-quality mobile health apps. The uMARS metrics were beneficial in setting developer expectations, identifying strengths and weaknesses, and guiding the iterative improvement of the app. Further assessment with more CHWs and patients is recommended. Clinical Trial: N/A
This study investigates the behavioral dynamics of sociopaths, focusing on their reliance on glibness (superficial charm) as a primary manipulation tactic and aggressiveness as a secondary strategy wh...
This study investigates the behavioral dynamics of sociopaths, focusing on their reliance on glibness (superficial charm) as a primary manipulation tactic and aggressiveness as a secondary strategy when charm fails. Sociopathy, characterized by manipulative tendencies and a lack of empathy, often manifests in adaptive yet harmful behaviors aimed at maintaining control and dominance.
Using the Deenz Antisocial Personality Scale (DAPS-24) to collect data from 34 participants, this study examines the prevalence and interplay of these dual strategies. Findings reveal that sociopaths employ glibness to disarm and manipulate, transitioning to aggressiveness in response to resistance. The implications for understanding sociopathic manipulation are discussed, emphasizing the importance of early detection and intervention in both clinical and social contexts.
