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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: Cardiac rehabilitation (CR) improves patient quality of life, morbidity, and mortality. Unfortunately, it is underused by patients. Digital health interventions offer a solution to increase participation in CR. However, patients’ interest in virtual CR, especially among those in the inpatient setting, has not been fully explored. These benefits have been predominantly demonstrated in traditional, center-based CR programs. Objective: The objective of this prospective cross-sectional study was to explore inpatient interest in virtual cardiac rehabilitation among adult patients who were hospitalized with a cardiac rehabilitation-qualifying diagnosis. Methods: A Qualtrics survey, comprised of multiple-choice questions, was administered to cardiac inpatients at the progressive cardiac care unit at Johns Hopkins Hospital from January 2020 to March 2024. Sociodemographic and clinical characteristics were retrieved from the electronic medical record. The study included English-speaking patients over 18 years of age with a diagnosis eligible for CR. Results: A total of 150 patients were included (age 64 ± 13 years, 38% women, and 57% White). With respect to sociodemographic characteristics, 26% of the patients had a high school education or less, 47% were married, 26% were employed full-time, and 63% had private insurance. Participants with greater than high school education were more likely to perceive smartphones as beneficial for leading a healthier lifestyle (48.1% vs. 24.3%, p=0.01) and learning about illnesses (85.7% vs. 54.1%, p<0.001) than participants with a high school education or less. Participants across all sociodemographic factors expressed interest in virtual CR (overall 71.3%), with non-White participants being more interested than White participants (84.6% vs. 61.2%, p=0.002). Conclusions: The majority of cardiac inpatients expressed interest in home-based/virtual CR to alleviate barriers to in-person CR participation. Future work should emphasize digital equity and user support to optimize the widespread adoption of virtual CR.
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: Cardiac rehabilitation (CR) improves patient quality of life, morbidity, and mortality. Unfortunately, it is underused by patients. Digital health interventions offer a solution to increas...
Background: Cardiac rehabilitation (CR) improves patient quality of life, morbidity, and mortality. Unfortunately, it is underused by patients. Digital health interventions offer a solution to increase participation in CR. However, patients’ interest in virtual CR, especially among those in the inpatient setting, has not been fully explored. These benefits have been predominantly demonstrated in traditional, center-based CR programs. Objective: The objective of this prospective cross-sectional study was to explore inpatient interest in virtual cardiac rehabilitation among adult patients who were hospitalized with a cardiac rehabilitation-qualifying diagnosis. Methods: A Qualtrics survey, comprised of multiple-choice questions, was administered to cardiac inpatients at the progressive cardiac care unit at Johns Hopkins Hospital from January 2020 to March 2024. Sociodemographic and clinical characteristics were retrieved from the electronic medical record. The study included English-speaking patients over 18 years of age with a diagnosis eligible for CR. Results: A total of 150 patients were included (age 64 ± 13 years, 38% women, and 57% White). With respect to sociodemographic characteristics, 26% of the patients had a high school education or less, 47% were married, 26% were employed full-time, and 63% had private insurance. Participants with greater than high school education were more likely to perceive smartphones as beneficial for leading a healthier lifestyle (48.1% vs. 24.3%, p=0.01) and learning about illnesses (85.7% vs. 54.1%, p<0.001) than participants with a high school education or less. Participants across all sociodemographic factors expressed interest in virtual CR (overall 71.3%), with non-White participants being more interested than White participants (84.6% vs. 61.2%, p=0.002). Conclusions: The majority of cardiac inpatients expressed interest in home-based/virtual CR to alleviate barriers to in-person CR participation. Future work should emphasize digital equity and user support to optimize the widespread adoption of virtual CR.
Background: Post thoracotomy pain remains a major clinical challenge, with substantial impact on pulmonary function, postoperative recovery, and patient quality of life. Thoracic epidural analgesia is...
Background: Post thoracotomy pain remains a major clinical challenge, with substantial impact on pulmonary function, postoperative recovery, and patient quality of life. Thoracic epidural analgesia is widely regarded as the standard of care; however, it is associated with potential complications, including hypotension, urinary retention, and inadequate analgesia in a subset of patients. Intercostal cryoanalgesia, a peripheral nerve block technique that induces temporary axonal degeneration through controlled freezing, has emerged as a potential alternative for prolonged postoperative pain control. Objective: The primary objective of this study is to compare postoperative hospital length of stay between intercostal cryoanalgesia and thoracic epidural analgesia. Secondary objectives include the evaluation of postoperative pain intensity, opioid consumption, adverse effects, postoperative complications, quality of life, quality of recovery, and patient satisfaction. Methods: This is a single-center, prospective, randomized, parallel-group clinical trial comparing intercostal cryoanalgesia with thoracic epidural analgesia for postoperative pain control in patients undergoing thoracic surgery. Fifty adult patients (≥18 years) are randomized 1:1 to either epidural or cryoanalgesia groups. All perioperative and postoperative care is provided by the attending clinical teams according to routine institutional practice, with no influence from the research team beyond randomized allocation. The primary endpoint is postoperative hospital length of stay. Secondary outcomes include pain intensity (visual analogue scale), opioid consumption, incidence of adverse effects and complications, quality of life (WHOQOL-BREF), and quality of recovery (QoR-15). Data are collected up to 1 year postoperatively. Results: Approval from the Human Research Ethics Committee was obtained in November 2024, and participant recruitment began in July 2025. Data collection commenced in September 2026 and is expected to be completed by August 28, 2027. Data analysis will begin in September 2027, with results anticipated in the first quarter of 2028. Conclusions: This study protocol outlines a randomized clinical trial designed to assess clinical outcomes associated with intercostal cryoanalgesia compared with thoracic epidural analgesia following thoracic surgery. The findings are expected to contribute to the evidence base on postoperative pain management and inform the design of future comparative and implementation studies in this field. Clinical Trial: Brazilian Registry of Clinical Trials (ReBEC): identifier RBR-78zfpxd.
Background: Sub-Saharan Africa (SSA) has the most growing elderly population affected by both HIV and other comorbidities compared to other regions globally, highlights from (Mwangala et al.,2021). Un...
Background: Sub-Saharan Africa (SSA) has the most growing elderly population affected by both HIV and other comorbidities compared to other regions globally, highlights from (Mwangala et al.,2021). United Nations agrees to the use of age 60 years to refer to elderly population in Africa, even though 65yrs is the chronical age used to define elderly population in the high-income countries (Kowal, Wolf 2000).
In this scoping review “geriatric” or “elderly” population refers to those aged 60yrs and above.
Aboderin (2015) alludes that the elderly population living with both HIV and comorbidities in SSA is predicted to increase from 42.6 million to 160 million in 2050. Furthermore Aboderin (2010) point out that the healthcare services for this population are mostly delivered at Public Health system at primary care level.
Despite the expected increase in the number of elderly populations who are living with both HIV and comorbidities that will require primary care predominantly healthcare workers (HCWs) in SSA, including South Africa (SA) receive little or no training of elderly or geriatric patients (Butler, I., & Tipping, B. (2024). As a result, there is a little awareness of age-related health elderly generally and furthermore elderly population living with both HIV and comorbid.
The World Health Organization call for the alignment of the health system with the health needs of the elderly population in the Global Strategy Plan on Ageing and Health (Beard,2016). Furthermore, African Union in the African continent echoed African Union Policy Framework and Plan on Ageing (Union A. African Health Strategy 2016-2030).
There is limited research showcasing the development and implementation of the tailored specialised care for the elderly population in SSA and the public Health system in the African continent have been unable to plan adequately to cater for the health needs of the elderly population (Naidoo &van Wyk, 2019). Objective: To identify, explore and map out the literature on the development and implementation of tailored specialised care for the elderly population living with both HIV and comorbid and identify the best practice for South African setting. It is anticipated that the results of the scoping review will influence the government, ministry of Health and policy makers to prioritise the development and implementation of tailored specialised care for the geriatric /elderly population within the public health system in the three spheres of heath, which are Primary healthcare, secondary healthcare (regional hospital) and tertiary healthcare (Academic hospitals) spheres. Furthermore, the results will assist the academic experts to identify geriatric competencies and skills and to develop geriatric curricula, that will be able to close the gap and meets the health needs of the geriatric in generally and the elderly population living with both HIV and comorbidities in the health care system Methods: This Protocol is for the systematic review of the literature reporting a tailored specialised care for the elderly population living with both HIV and Non-communicable diseases in SSA, South Africa, Canada, USA and France.
The scoping review method was selected as an aim to outline different type of evidence, the gaps, describe and identify emerging knowledge on this area of interest in the Low and Middle-Income countries (LMIC)which is South Africa and other SSA and High-Income countries (HIC) which are Canada, USA and France.
The proposed review will be guided by the methodological framework of Arksey and O’Malley (Arksey, O’Malley 2005).
The steps that will be followed in this review are:
I. Identify the research question
II. Inclusion and exclusion criteria
III. Search Strategy
IV. Selection of eligible studies
V. Charting the data and
VI. Collation and summarising the results
VII. Reporting
VIII. Ethical Considerations
Quality appraisal will not be done as this review aims to map all research activities ni this field Results: The expected results of the scoping review will offer an in-depth overview of implementation of the tailored specialised chronic care for the elderly population living with both HIV and comorbidities of NCDs in SSA. Conclusions: Future research should concentrate on assessing the effectiveness of integrated models and investigating the role of community-based and social support systems in improving care outcomes. Also, by highlighting knowledge gaps the study will provide the researchers, policy maker, Healthcare experts and Academics with the new venture of research is needed.
This can incorporate exploration of innovative strategies, benchmarks of best practices of effective healthcare models, assessing long-term outcomes of existing programs and examining the role of other healthcare support systems that enhances the quality of life for the elderly population. Clinical Trial: Not applicable
Background: Fatigue is a common debilitating symptom of breast cancer, and its treatment may result in significant symptom burden and affect adherence to treatment. Graded Exercise Therapy (GET) and C...
Background: Fatigue is a common debilitating symptom of breast cancer, and its treatment may result in significant symptom burden and affect adherence to treatment. Graded Exercise Therapy (GET) and Cognitive Behavioral Therapy (CBT) have separately been shown in previous studies to be beneficial for the management of cancer-related fatigue (CRF). Objective: This study aims to assess the feasibility, acceptability and potential efficacy of combining GET and CBT for treatment of fatigue in breast cancer patients on treatment in Singapore. Methods: In this randomized controlled pilot study, a total of 100 female breast cancer patients, with self-reported rating of at least moderate fatigue (One-item fatigue scale score ≥4) will be recruited and randomized in a 1:1 ratio to undergo a combination of GET and CBT versus GET alone (standard of care). This will include a primary cohort of 90 patients with Stage I to III breast cancer who have completed surgery and adjuvant chemotherapy (if indicated), and an exploratory cohort of 10 patients with Stage IV breast cancer undergoing systemic therapy. Acceptability is measured using client satisfaction questionnaire including items on cultural sensitivity. Feasibility is measured by participant uptake, adherence to sessions and willingness to pay for therapy sessions. Efficacy is assessed based on quantitative measures of fatigue, quality of life, and physical and functional outcomes. Results: The recruitment of participants commenced on 14 July 2025 and is projected to be completed by 31 July 2026. Potential extension to this project would be the subsequent expansion of the current exploratory cohort of patients with metastatic breast cancer. Conclusions: The present study compares the use of a combination of GET and CBT against GET alone for management of fatigue in breast cancer survivors, applied to the Singaporean context. The primary aim is to establish feasibility and acceptability of GET and CBT interventions in the local context, with a secondary aim of evaluating efficacy in terms of fatigue, quality of life and functional outcomes. Clinical Trial: ClinicalTrials.gov ID: NCT07116161
Background: Existing research on the accuracy of self-assessment (SA) in health professions (HP) has shown poor accuracy of SA compared to external assessors. Objective: We systematically reviewed the...
Background: Existing research on the accuracy of self-assessment (SA) in health professions (HP) has shown poor accuracy of SA compared to external assessors. Objective: We systematically reviewed the evidence for educational interventions aimed at improving the accuracy of SA for technical (procedural) and non-technical (critical thinking, decision making and knowledge) Methods: We conducted this systematic review according to the PRISMA guidelines using Medline, Cochrane Library, Embase, CINAHL, AMED, ERIC, Education Source, Web of Science and Scopus databases. We included studies in English that reported on educational interventions aimed at improving the accuracy of SA versus external assessment across all health professions. A narrative synthesis of the extracted data was used using a convergent integrated approach, which reported both quantitative and qualitative data. We used the modified Medical Education Research Study Quality Instrument (MMERSQI) as the critical appraisal and bias tool to evaluate the methodological quality of included studies. Results: After abstract and full text screening of 7439 studies, we included 35 studies and 3127 participants, the majority of which were of good methodological quality. Twenty-four studies explored SA of non-technical competencies, while 11 studies explored SA of technical competencies. Health professions included medicine (n=16), dentistry (n=9), pharmacy (n=4), nursing (n=2), physiotherapy (n=2), midwifery (n=1) and occupational therapy (n=1). The accuracy of SA was improved with the use of self-assessment rubrics (11 out of 14 studies), video review for feedback (5 out of 12 studies), verbal feedback (2 of 2 studies), electronic portfolios (2 of 2 studies), simulation (2 of 2 studies), and coaching (1 of 1 study). The use of internet-based applications (1 of 1 study) and didactic learning (1 of 1 study) did not improve the accuracy of SA. Conclusions: The accuracy of self-assessment can be improved by using SA rubrics, video and verbal feedback, simulation, electronic portfolios and coaching. Limitations include a clear definition of self-assessment across research studies resulting in exclusion of systematic review. This information can be used by educators to improve the accuracy of SA within health professions education. Clinical Trial: PROSPERO (CRD42024586510)
Background: Academic staff and researchers experience high levels of stress, burnout, and psychological distress, driven by competitive work environments, job insecurity, heavy workloads, and performa...
Background: Academic staff and researchers experience high levels of stress, burnout, and psychological distress, driven by competitive work environments, job insecurity, heavy workloads, and performance pressures. These conditions have led to growing interest in interventions aimed at promoting mental health and well-being in higher education workplaces. Despite this expansion, existing evidence remains fragmented and has largely focused on students or healthcare professionals, with less attention to academic staff and researchers. Objective: This systematic review aimed to identify, evaluate, and synthesise interventions implemented to promote mental health and well-being among academic staff and researchers in higher education institutions. Specifically, the review examined which interventions have been applied, their effectiveness, and the opportunities and challenges associated with their implementation. Methods: Following PRISMA guidelines, systematic searches were conducted in PubMed/MEDLINE, Web of Science, and Scopus for peer-reviewed articles published between 1994 and 2024. Eligible studies targeted academic staff and/or researchers, implemented interventions aimed at improving mental health or well-being, and reported empirical outcomes. Studies focusing exclusively on students, editorials, reviews, and studies assessed as having low methodological quality using the Physiotherapy Evidence Database (PEDro) scale. Screening was conducted independently by two reviewers, with disagreements resolved by a third. The review protocol was registered in PROSPERO. Due to substantial heterogeneity in study designs, outcomes, and measurement tools, no meta-analysis was conducted; findings were synthesised narratively. Results: A total of fifty-three studies met inclusion criteria. Interventions were categorised into three groups: web-based programs (n = 14), hybrid formats combining digital and in-person components (n = 18), and in-person programs delivered on campus (n = 21). Web-based interventions commonly reported improvements in stress, anxiety, and coping but were frequently by challenges related to adherence and sustained engagement. Hybrid interventions demonstrated balanced benefits, combining flexibility with interpersonal support. In-person interventions reported more consistent improvements in stress reduction, well-being, and sense of community, although scalability and resource demands were commonly reported limitations. Across modalities, most studies reported at least one positive mental health or well-being outcome; however, the evidence base was constrained by small samples, short follow-up periods, single-site designs, and methodological heterogeneity. Conclusions: Interventions targeting mental health and well-being among academic staff and researchers show promise, with digital, hybrid, and in-person approaches each offering distinct strengths and limitations. Institutions should prioritise integrated, multimodal strategies that combine individual-level support with broader structural and cultural change. Future research should adopt more rigorous and longitudinal designs to strengthen the evidence based and clarify long-term effectiveness and sustainability. Clinical Trial: https://www.crd.york.ac.uk/PROSPERO/view/CRD420251025454
Background: Clinical natural language processing (NLP) refers to computational methods for extracting, processing, and analyzing unstructured clinical text data, and holds a huge potential to transfor...
Background: Clinical natural language processing (NLP) refers to computational methods for extracting, processing, and analyzing unstructured clinical text data, and holds a huge potential to transform healthcare. The advancement of deep learning, augmented by the recent emergence of transformers, has been pivotal to the success of NLP across various domains. This success is largely attributed to the end-to-end training capabilities of deep learning systems. Further, advances in instruction tuning have enabled Large Language Models (LLMs) like OpenAI’s GPT to perform tasks described in natural language. While these advancements have dramatically improved capabilities in processing languages like English, these benefits are not always equally transferable to under-resourced languages. In this regard, this review aims to provide a comprehensive assessment of the state-of-the-art NLP methods for the mainland Scandinavian clinical text, thereby providing an insightful overview of the landscape for clinical NLP within the region. Objective: The study aims to perform a systematic review to comprehensively assess and analyze the state-of-the-art NLP methods for the Scandinavian clinical domain, thereby providing an overview of the landscape for clinical language processing within the Scandinavian languages across Norway, Denmark, and Sweden. Generally, the review aims to provide a practical outline of various modeling options, opportunities, and challenges or limitations, thereby providing a clear overview of existing methodologies and potential avenues for future research and development. Methods: A literature search was conducted in various online databases, including PubMed, ScienceDirect, Google Scholar, ACM Digital Library, and IEEE Xplore between December 2022 and March 2024. The search considers peer-reviewed journal articles, preprints, and conference proceedings. Relevant articles were initially identified by scanning titles, abstracts, and keywords, which served as a preliminary filter in conjunction with inclusion and exclusion criteria, and were further screened through a full-text eligibility assessment. Data was extracted according to predefined categories, established from prior studies and further refined through brainstorming sessions among the authors. Results: The initial search yielded 217 articles. The full-text eligibility assessment was independently carried out by five of the authors and resulted in 118 studies, which were critically analyzed. Any disagreements among the authors were resolved through discussion. Out of the 118 articles, 17.9% (n=21) focus on Norwegian clinical text, 61% (n=72) on Swedish, 13.5% (n=16) on Danish, and 7.6% (n=9) focus on more than one language. Generally, the review identified positive developments across the region despite some observable gaps and disparities between the languages. There are substantial disparities in the level of adoption of transformer-based models. In essential tasks such as de-identification, there is significantly less research activity focusing on Norwegian and Danish compared to Swedish text. Further, the review identified a low level of sharing resources such as data, experimentation code, pre-trained models, and the rate of adaptation and transfer learning in the region. Conclusions: The review presented a comprehensive assessment of the state-of-the-art Clinical NLP in mainland Scandinavian languages and shed light on potential barriers and challenges. The review identified a lack of shared resources, e.g., datasets and pre-trained models, inadequate research infrastructure, and insufficient collaboration as the most significant barriers that require careful consideration in future research endeavors. The review highlights the need for future research in resource development, core NLP tasks, and de-identification. Generally, we foresee that the findings presented will help shape future research directions by shedding some light on areas that require further attention for the rapid advancement of the field in the region
Background: Adolescent anxiety is a growing public health concern and is associated with significant academic, social, and emotional impairment. Mindfulness-based interventions (MBIs) have shown promi...
Background: Adolescent anxiety is a growing public health concern and is associated with significant academic, social, and emotional impairment. Mindfulness-based interventions (MBIs) have shown promise in reducing anxiety and improving well-being; however, engagement and acceptability remain challenges. Virtual reality (VR)–based delivery may enhance immersion and attention, potentially addressing barriers associated with traditional mindfulness formats. To date, evidence on VR-based mindfulness interventions for adolescents, particularly in Hong Kong, remains limited. Objective: This study aimed to evaluate the feasibility and acceptability of a virtual reality mindfulness-based intervention (VR-MBI) delivered via a Cave system for adolescents with mild-to-moderate anxiety symptoms in Hong Kong. Secondary aims were to explore preliminary effects on psychological outcomes and physiological stress regulation, and to identify facilitators and barriers influencing engagement. Methods: A mixed-methods single group pre–post study was conducted with adolescents experiencing mild-to-moderate anxiety symptoms, recruited from secondary schools and youth service organizations in Hong Kong. Participants completed an 8-week group-based VR-MBI program. Feasibility and acceptability were assessed using recruitment, attendance, retention, homework practice frequency, dropouts, and adverse events. Psychological outcomes were measured using the Depression Anxiety Stress Scale–21 (DASS-21) and the Mindful Attention Awareness Scale (MAAS). Heart rate variability (HRV) indices (SDNN, RMSSD) were collected at baseline and post-intervention using a wearable device. Post-intervention focus group interviews explored participants’ experiences. Results: A total of 42 participants were enrolled and completed both baseline and post-intervention assessments. Attendance was high, with 73.8% of participants attending at least 80% of sessions, and participants engaged in regular homework practice. No dropouts or adverse events were reported. Quantitative analyses showed no significant pre–post changes in self-reported anxiety, depression, stress, or mindfulness. However, significant improvements were observed in HRV indices, indicating enhanced physiological stress regulation. Qualitative findings suggested perceived benefits in emotional regulation, stress reduction, focus, and sleep, with the immersive CAVE environment and group-based format identified as key facilitators of engagement. Conclusions: The CAVE-based VR-MBI was feasible and acceptable for adolescents with mild to moderate anxiety symptoms in Hong Kong. Although self-reported psychological outcomes did not show significant change, improvements in physiological indicators of stress regulation and positive qualitative feedback suggest early benefits not fully captured by self-report measures. These findings support further investigation of VR-delivered mindfulness interventions using controlled study designs and longer follow-up periods. Clinical Trial: n/a
Background: The CAPABLE (Cancer Patients Better Life Experience) project developed an application for remote monitoring and management of treatment-related symptoms, as well as for delivering a set of...
Background: The CAPABLE (Cancer Patients Better Life Experience) project developed an application for remote monitoring and management of treatment-related symptoms, as well as for delivering a set of supplementary nonpharmacological interventions, with the aim of improving patients’ quality of life. Clinical studies were conducted to evaluate the effectiveness of CAPABLE, yielding encouraging results. However, these studies did not explore individual patients’ perspectives. Objective: Following the evaluation of the CAPABLE intervention’s efficacy, this study aims at exploring end users’ overall experience with the telemonitoring system, identifying strengths and weaknesses in relation to users’ needs and expectations, in order to inform future developments. Methods: Toward the end of the clinical study, a focus group was conducted with a subset of enrolled patients. The discussion was led by a psycho-oncologist using a predefined framework of topic-related questions, which served as prompts to encourage open discussion. Patients freely shared their experiences, and a thematic analysis was performed on the collected statements. Results: The findings showed that the tool primarily served a dual function of support and reassurance. Patients reported psychological relief and a sense of security, driven by the perception of being closely monitored and supported by a multidisciplinary hospital team. CAPABLE was perceived as easy to use, effective, and useful. Nevertheless, several weaknesses also emerged. Suggestions for improvement focused on a closer alignment between CAPABLE functionalities and patients’ individual treatments and preferences, as well as concerns regarding application maintenance after the end of the project. Conclusions: The focus group provided valuable insights to inform the future development of telemonitoring applications for cancer patients.
Background: Knowledge graphs are increasingly important in radiology for representing factual clinical information and supporting downstream applications such as decision support, information retrieva...
Background: Knowledge graphs are increasingly important in radiology for representing factual clinical information and supporting downstream applications such as decision support, information retrieval, and structured reporting. However, generating radiology-specific knowledge graphs remains challenging due to the specialized vocabulary used in radiology reports, the scarcity of domain-annotated datasets, and the predominance of unimodal approaches that rely solely on text. Objective: To develop and evaluate a multimodal Vision-Language-Model (VLM) framework capable of generating radiology knowledge graphs using both radiographic images and the corresponding reports. Methods: We designed a VLM-based knowledge graph generation framework that integrates radiology images and free-text reports through instruction tuning and visual instruction tuning. The model is optimized for long-context radiology reports and structured triplet extraction. Its performance was compared with existing unimodal baselines on benchmark datasets. Results: Our multimodal VLM-KG (MIMIC) demonstrated the strongest overall performance across standard NLG metrics, achieving the highest BLEU scores (BLEU-1: 54.98, BLEU-2: 49.65, BLEU-3: 46.12, BLEU-4: 43.29), substantially outperforming all unimodal baselines, including the BERT-based Dygiee++ model. This improvement highlights the effectiveness of multimodal learning, where the integration of visual and linguistic information enhances contextual understanding in text generation. Although Dygiee++ achieved a comparable ROUGE-L score (56.49), VLM-KG (MIMIC) provided markedly higher BLEU scores, indicating stronger n-gram overlap and more accurate triplet generation. VLM-KG (MIMIC) also achieved a competitive ROUGE-L score of 54.69, slightly lower than LLM-KG (MIMIC) (56.53), suggesting that while multimodal features improve precision, they may introduce minor variability in generated outputs. Additionally, LLM-KG (MIMIC) consistently outperformed LLM-KG (IU) across all metrics (e.g., BLEU-3: 35.96 vs. 18.02), underscoring the advantages of training on a large-scale, domain-specific dataset. Conclusions: This study presents the first multimodal VLM-driven approach for radiology knowledge graph generation. By leveraging both images and reports, the framework overcomes limitations of previous text-only systems and provides a more comprehensive foundation for medical knowledge representation and downstream radiology informatics applications.Vision Language Models; Large Language Models; Knowledge Graph; Radiology; Multimodal AI; Medical NLP
Incorporating culturally relevant music can enhance awareness and control in hypertension management and stroke preparedness. The Music4Health initiative created music-driven campaigns focused on yout...
Incorporating culturally relevant music can enhance awareness and control in hypertension management and stroke preparedness. The Music4Health initiative created music-driven campaigns focused on youth and their caregivers. We outlined the components of songs developed through community participation to raise awareness about hypertension and stroke preparedness.
The project was conducted in three phases: an open call, a designathon, and a bootcamp. From October 2023 to July 2024, a crowdsourcing open call was launched online and in person. Teams and individuals submitted ideas for creatively disseminating evidence-based prevention strategies for hypertension and stroke through music. Fifteen participants were invited to a 3-day designathon to refine their songs with expert mentors. The final phase, a bootcamp, involved community assessment and intensive workshops with the top six teams to develop and record complete songs with experts and producers. The lyrics from the bootcamp were analyzed using rapid thematic analysis guided by the PEN-3 cultural model, focusing on Relationships and Expectations and Cultural Empowerment domains.
Thematic analysis of the seven finalist songs from the bootcamp identified themes using two PEN-3 model domains. The Relationship and Expectations domains included perceptions of hypertension severity, myths about hypertension (like the role of “juju”), and the necessity for healthy coping strategies. Enablers focused on the availability of hypertension prevention strategies, such as healthy diets, stress management, and avoidance of smoking. Nurturers emphasized raising awareness about hypertension among families, adopting healthy practices for loved ones, and the role of peers in promoting healthy habits. Unique cultural aspects included using Afrobeat and Fuji beats, pidgin English, and references to spirituality in adopting health practices.
Culturally centered music may be an appealing channel for promoting the uptake of evidence-based health interventions. This study highlights the feasibility of using participatory approaches to co-create health dissemination strategies, leveraging music's cultural relevance and appeal to engage youth and their caregivers in hypertension and stroke prevention.
Background: Photoplethysmography (PPG) is widely used in consumer and clinical devices for heart rate, rhythm, sleep, respiratory, and hemodynamic monitoring. However, rapid expansion of applications...
Background: Photoplethysmography (PPG) is widely used in consumer and clinical devices for heart rate, rhythm, sleep, respiratory, and hemodynamic monitoring. However, rapid expansion of applications has produced a fragmented evidence base with heterogeneous methods and variable validation quality. Objective: To synthesize and critically appraise systematic reviews evaluating PPG-based applications in healthcare, map major clinical domains and methodological practices, and identify limitations and priorities for future research. Methods: A protocolized umbrella review (PROSPERO CRD420251015845) was conducted across six databases. Systematic reviews and meta-analyses involving human PPG applications were included. Screening, extraction, and AMSTAR-2 quality assessment were performed in duplicate following PRISMA-S and PRIOR guidelines. Results: Fifty-nine systematic reviews were included. PPG showed consistent accuracy for resting heart-rate monitoring and strong performance for opportunistic atrial fibrillation screening when paired with confirmatory ECG. HRV estimation, stress monitoring, sleep assessment, neonatal and maternal monitoring, and metabolic applications showed emerging but heterogeneous evidence. Cuffless blood pressure estimation remains limited by calibration dependence, motion sensitivity, and poor generalizability. Remote PPG (rPPG) achieves good accuracy under controlled lighting but degrades with motion, light variability, and darker skin pigmentation. Across domains, performance was typically higher in controlled environments and attenuated in free-living settings. Common methodological limitations included small samples, inconsistent reporting of device and preprocessing details, lack of external validation, algorithm opacity, and underrepresentation of diverse populations. Conclusions: PPG is approaching clinical maturity for atrial fibrillation screening and resting heart-rate monitoring, while other applications remain earlier in development. Safe integration into practice requires confirmatory ECG for rhythm abnormalities, awareness of bias sources, and adherence to transparent reporting. Future progress depends on multicenter longitudinal studies, real-world validation, diverse benchmark datasets, standardized metrics, and improved reproducibility across devices and algorithms. PPG holds promise as a scalable component of digital health infrastructure when developed and evaluated with methodological rigor. Clinical Trial: PROSPERO Registration: CRD420251015845
Background: Digital parenting programs offer a scalable solution to improve early childhood development outcomes, especially in low- and middle-income countries like China, but face challenges in sust...
Background: Digital parenting programs offer a scalable solution to improve early childhood development outcomes, especially in low- and middle-income countries like China, but face challenges in sustaining user acceptability and engagement. The culturally specific factors that shape these processes are also not well understood. Objective: This study explored the lived experiences of caregivers and facilitators in a digital-human parenting program delivered within the preschool systems in a lower-middle-income city in China, with a particular focus on the determinants of acceptability, the facilitators and barriers to engagement, and the drivers of perceived changes. Methods: Embedded within a cluster randomized controlled trial in urban China, this qualitative study used semi-structured interviews and focus group discussions with 26 caregivers and 18 program facilitators. Data were analyzed using a thematic approach. Results: Findings demonstrated a virtuous cycle where acceptability (driven by content relevance and digital usability) fostered engagement, leading to perceived changes that reinforced the cycle. Engagement was shaped by intrinsic and extrinsic motivators. Cultural factors were critical: mismatched expectations from the blurred concepts of “parenting” and “education” hindered acceptance, and a "shame culture" inhibited open discussion. An anonymous “Tree-hole” feedback system emerged as a key culturally sensitive solution. Conclusions: The effectiveness of digital parenting interventions in collectivist contexts requires deep cultural adaptation. Interventions must move beyond one-size-fits-all models to incorporate user-centered design and culturally resonant features, such as anonymous feedback systems. A hybrid, family-centered model leveraging trusted human figures is essential for building trust and maximizing impact. Clinical Trial: ChiCTR2400081911
Background: Chronic obstructive pulmonary disease (COPD), emphysema, bronchiectasis, and cor pulmonale are chronic lung diseases (CLD) that pose a global public health challenge. However, there remain...
Background: Chronic obstructive pulmonary disease (COPD), emphysema, bronchiectasis, and cor pulmonale are chronic lung diseases (CLD) that pose a global public health challenge. However, there remains a lack of accurate assessment and predictive indicators. The triglyceride-glucose (TyG) index serves as a reliable indicator of insulin resistance (IR). IR is associated with an increased incidence, prevalence, or severity of CLD. Objective: This study aims to investigate the relationship between the TyG index and the risk of CLD, as well as to assess the predictive role of the TyG index in CLD. Methods: Based on data collected from the China Health and Aging Longitudinal Study (CHARLS) from 2011 to 2020, a total of 3,776 research subjects were included for data analysis. K-means clustering analysis was employed to categorize the subjects into three groups. The Kaplan-Meier curve was used to compare the survival rates of CLD events among the groups. Multivariate Cox proportional hazards regression analysis was conducted to examine the relationship between the TyG index and CLD events across the groups. A restricted cubic splines (RCS) regression model was utilized to explore potential linear associations between the TyG index and CLD events. The Receiver Operating Characteristic Curve (ROC) was used to evaluate the predictive value of the TyG index for CLD events. Results: During the follow-up period from 2013 to 2020, 940 subjects were diagnosed with CLD. Based on baseline characteristics, the K-means clustering analysis identified three groups of subjects. The Kaplan-Meier curve indicated statistically significant survival differences among the groups (p=0.0064). After a follow-up period exceeding 50 months, Group 1 exhibited the fastest decline and the lowest rate of disease-free survival. Multivariate Cox proportional hazards analysis revealed that in the unadjusted model, the TyG index of Group 1 was significantly associated with CLD events (HR, 1.58 [95% CI 1.18-2.13], p<0.05). This association remained significant in models adjusted for demographic factors (HR, 1.61 [95% CI 1.18-2.20], p<0.05) and in models adjusted for both demographic factors and disease status (HR, 1.64 [95% CI 1.19-2.26], p<0.05). Similarly, the TyG index in Group 3 showed a significant association with CLD events in both the unadjusted (HR, 1.62 [95% CI 1.12-2.32], p<0.05) and adjusted models (HR, 1.66 [95% CI 1.15-2.39], p<0.05; HR, 1.66 [95% CI 1.14-2.41], p<0.05). RCS curves demonstrated a positive association between the TyG index and CLD events in Groups 1 and 3. ROC curves indicated that the predictive value of the TyG index for CLD events was limited (AUC=0.511-0.548). Conclusions: Research indicates a positive association between the TyG index and CLD in specific populations, although it is not an independent predictor. The calculation and monitoring of the TyG index can aid in risk stratification and the development of intervention strategies for populations.
Background: Mental health conditions (MHC), in particular depression and anxiety are the leading contributors to youth disability globally. In the United States, there has been a steep increase in dia...
Background: Mental health conditions (MHC), in particular depression and anxiety are the leading contributors to youth disability globally. In the United States, there has been a steep increase in diagnosed MHC cases over the last decade. Adolescents in rural areas are often disproportionately affected due to a combination of limited access to mental health professionals, and stigma around seeking care. Untreated depression and anxiety can lead to an increased risk of substance use, academic struggles, and delinquency, making early intervention key to preventing such negative outcomes. Current treatment options, mainly psychotherapy and psychopharmacology, have shown modest effects. Prior research suggests associations between slow paced deep breathing and autonomic function, cerebral perfusion, and stress regulation, rendering structured breathing an under-utilized tool for MHC management. Objective: This project addresses two key priorities: reducing health disparities and enhancing population and value-based care in rural communities. This project is grounded in an equity-oriented approach to serving diverse and underserved youth populations by utilizing structured deep breathing, as an accessible and low-cost intervention. Methods: This study assesses the feasibility of collecting functional near-infrared spectroscopy (fNIRS) and data in the full sample and magnetic resonance imaging (MRI) in a subsample of 20 adolescents, without prespecifying neurobiological efficacy hypotheses. We aim to recruit approximately 40 adolescent patients receiving care through the Mayo Clinic Health System (MCHS) from rural communities in northwestern Wisconsin and southeastern Minnesota. All primary and secondary outcomes will be summarized using descriptive statistics, including means, standard deviations, medians, proportions, and 95% confidence intervals, as appropriate. Because this is a pilot feasibility study, the analytic focus is on estimation, variability, and data completeness, rather than hypothesis testing or formal statistical inference. Results: This study focuses on generating feasibility metrics and descriptive summaries of physiological and psychological data to inform future trial design. Conclusions: Adolescents with anxiety and depression are a particularly vulnerable group, often undertreated due to limited access to mental health care. The proposed breathing intervention offers an accessible and scalable tool that integrates multimodal brain physiology measures in rural youth populations.
Background: Artificial intelligence (AI) is rapidly integrating into health professions education (HPE) and clinical practice, creating significant opportunities alongside new ethical challenges. Alth...
Background: Artificial intelligence (AI) is rapidly integrating into health professions education (HPE) and clinical practice, creating significant opportunities alongside new ethical challenges. Although current international and professional guidance establishes essential values, it offers limited direction for how clinicians, educators, learners, and institutions should act in routine educational, research, and clinical contexts. The CARE-AI (Contextual, Accountable and Responsible Ethics for AI) project responds to this practice-level gap by articulating guidance that moves beyond values toward professional accountability and equity, with explicit attention to educational and clinical practice contexts. Objective: Our study objective was to develop and validate a consensus-based, actionable framework of principles to guide responsible AI use across health professions education, research, and clinical care. Methods: We conducted a three-phase modified Delphi consensus study, reported in accordance with the Accurate Consensus Reporting Document (ACCORD). Phase I involved two international professional meetings and three purposively sampled focus groups (AI/technology, HPE, ethics/professionalism) to adapt and refine draft principles using an exploratory qualitative approach. Phase II employed an online survey with a 5-point importance scale and prespecified consensus criteria (inclusion ≥70% high ratings; exclusion ≥70% low ratings). Phase III used include/exclude/undecided voting on revised principles. Quantitative thresholds determined consensus. Qualitative free-text comments informed iterative refinement. Results: Participants represented diverse communities of practice across health professions education, clinical care, ethics, and digital health, spanning multiple professional roles and training levels. Across all phases, 303 unique participants contributed to the study. Phase I focus groups (n=61) provided early insight and direction. In Phase II, Delphi survey round 1, 242 participants initiated the survey, with 120 completing it (49.6%). In Phase III, Delphi survey round 2, 103 participants were invited based on expressed interest at the end of Round 1; 78 initiated the survey and 75 completed it (96.2% of starters). In Phase II, 58 of 61 statements (95%) met inclusion, and participants submitted 1,887 comments (697 were content-rich), prompting clearer accountability language, stronger equity commitments, and more usable wording. In Phase III, all nine principles and their statements met inclusion. Participants contributed 224 comments (179 were content-rich) that informed final refinements. Endorsement was near-unanimous: 96% agreed or strongly agreed that the framework clearly defined professionalism expectations for AI to meet educational, technological, and ethical needs in the health professions. Conclusions: The Health CARE-AI Framework, with its preamble and nine principles, articulates actionable, consensus-validated guidance that moves from values to competence, into professional accountability, and toward structural commitments to equity. Paired with a companion implementation guide and toolkit, the framework is intended to support use across education, research, and clinical settings. Clinical Trial: Not applicable
Background: Acute flaccid paralysis is a clinical syndrome characterized by the rapid onset of lower motor neuron weakness and remains an important global public health concern. Objective: This study...
Background: Acute flaccid paralysis is a clinical syndrome characterized by the rapid onset of lower motor neuron weakness and remains an important global public health concern. Objective: This study assessed the epidemiological characteristics and environmental risk factors associated with acute flaccid paralysis in Taiwan from 2006 to 2020 using surveillance data from the Taiwan Centers for Disease Control (TCDC). Methods: Acute flaccid paralysis-related data reported to the National Infectious Diseases Statistics System between 1 January 2006 and 31 December 2020 were analyzed. Variables included age, sex, area of residence, and season of occurrence. Associations with meteorological and air pollution factors were evaluated. Results: A total of 719 cases with acute flaccid paralysis were identified, including 715 (99.4%) local and four (0.6%) imported cases. The average annual incidence rate ranged from 0.6 to 2.1 per 1,000,000 population. Significant differences were observed across age groups, seasonal patterns, and areas of residence (p < 0.001, p < 0.001, p = 0.032, respectively). The male-to-female ratio was approximately 1.5:1. Children younger than 5 years accounted for the largest proportion of cases. The cases occurred year-round, with peaks in winter and spring, and were most frequently reported in southern Taiwan. No significant differences by age group, season, or area of residence were observed when cases were stratified by sex. In contrast, significant differences by season and residence were identified across age groups (p = 0.004 and p = 0.003, respectively). Multiple linear regression analysis showed no significant associations between acute flaccid paralysis incidence and temperature, precipitation, relative humidity, or mean pressure. However, acute flaccid paralysis cases were positively associated with particulate matter ≤10 µm (B value = 0.177, p = 0.028) and negatively associated with SO2 concentration (B value = -3.092, p < 0.001). Conclusions: This study provides the first comprehensive analysis of laboratory-confirmed acute flaccid paralysis cases in Taiwan using long-term TCDC surveillance data. The findings highlight the importance of extended surveillance, environmental assessment, and geographically comprehensive analyses to better understand acute flaccid paralysis patterns and support prevention and control strategies in Taiwan.
Background: Loneliness is a prevalent and growing concern across the United Kingdom. While numerous validated scales exist to quantify the severity and prevalence of loneliness experiences across popu...
Background: Loneliness is a prevalent and growing concern across the United Kingdom. While numerous validated scales exist to quantify the severity and prevalence of loneliness experiences across populations (the University of California, Los Angeles Loneliness Scale and the DeJong Gierveld Loneliness Scale) (de Jong-Gierveld, 1987; Russell, 1996), there remains a gap in understanding how loneliness manifests and is addressed within therapeutic practice. Given the associated stigma and shame surrounding loneliness self-disclosure, practitioner perspectives offer crucial insights into how clients express loneliness concerns within digital therapeutic environments. Objective: The objectives of this study are to gather the practitioners' perspective of loneliness within a digital therapeutic context, and are defined as follows:
1. To understand how practitioners identify loneliness concerns
2. To identify how loneliness is elicited in digital mental health interventions
3. To identify co-occurring themes (such as grief, shame, and social disconnection) that signal loneliness concerns in client communications within digital therapeutic environments Methods: Semi-structured interviews were conducted with nine experienced practitioners (minimum one year of practice). Participants included specialists in grief counselling, LGBTQ+ support and digital mental health platform therapists. Interview transcripts were analysed using Braun and Clarke's six-phase thematic analysis approach, employing an inductive, data-driven methodology to allow themes to emerge from participant accounts rather than fitting data to pre-existing theoretical frameworks. Results: Four interconnected themes were identified: 1. Conceptualising Loneliness: practitioners distinguished between social contact and meaningful connection, identifying the experience of being “lonely in the crowd” where clients feel disconnected despite having social networks; 2. Contextual Causes: loneliness emerges from life transitions (university, grief, relationships change), stigmatised identities and cultural minorities (LGBTQ+, neurodiversity), and resource reduction (youth services closures and social support); 3. Expressions and Language: specifically that clients rarely expressed loneliness directly, instead using terms like “depressed” or “misunderstood”, with disclosure patterns varying by age and stigma experience; 4. Mental Health Co-occurrence: severe mental health conditions created bidirectional cycles where loneliness exacerbated symptoms, while mental health difficulties increased social isolation. Practitioners reported that 80-90% of their clients experienced loneliness concerns, yet direct disclosure was virtually absent across all participants' experiences. Conclusions: Practitioners identified multiple stigmatising experiences as contextual drivers of loneliness, highlighting how loneliness emerges not only from individual factors but from broader patterns of social exclusion and marginalisation. For therapeutic practice, these insights suggest that practitioners can use awareness of stigmatising experiences as potential indicators when assessing loneliness risk. The presence of these contextual patterns were consistent across digital practitioners’ experiences, providing a foundation to develop more targeted interventions that address both the emotional experience of loneliness and its underlying social drivers across therapeutic environments.
Background: Cerebral Palsy (CP) is the most frequent motor disability in childhood, with a higher prevalence in low- and middle-income countries where access to essential early rehabilitation is limit...
Background: Cerebral Palsy (CP) is the most frequent motor disability in childhood, with a higher prevalence in low- and middle-income countries where access to essential early rehabilitation is limited. Generative Artificial Intelligence (GenAI) emerges as a disruptive technology with potential to address these challenges. This scoping reviews maps the current landscape of GenAI applications in CP rehabilitation. Objective: To systematically review and synthesize literature on the use of GenAI in CP rehabilitation, analyzing its applications, reported benefits, technical/ethical challenges, and future research directions. Methods: A systematic search was conducted following PRISMA 2020 guidelines across five databases (PubMed/MEDLINE, Scopus, Web of Science, IEEE Xplore, Google Scholar) through October 2025. Studies utilizing generative models (LLMs, GANs, VAEs, diffusion models) for diagnosis, assessment, therapy planning, documentation, or education in CP were included. Screening and data extraction were performed independently by two reviewers. Results: From 487 initial records, 32 studies (2022-2025) were included, indicating a nascent field dominated by research in high-income countries. Large Language Models (LLMs) constituted 75% of applications. Four key application categories were identified:
1. Diagnosis/Assessment: LLMs enabled early CP detection from clinical notes (Sensitivity:82%); GANs synthesized movement data to improve GMFCS classification accuracy from 72% to 90%.
2. Therapy Planning: LLMs generated personalized exercise regimens (quality 7.8/10 vs. expert 8.9/10); AI-designed VR content increased therapy adherence by >40%.
3. Clinical Documentation: Automation reduced note-writing time by 55%; AI decision support showed 80% concordance with clinical guidelines.
4. Patient/Caregiver Education: Tailored educational materials significantly improved family knowledge scores.
Reported benefits included enhanced personalization, efficiency, and accessibility. Critical challenges included hallucinations/factual errors, data privacy concerns, algorithmic bias, a lack of interpretability, and risks of dehumanization. Conclusions: GenAI presents significant potential to augment CP rehabilitation by scaling personalization and improving efficiency. However, current evidence is primarily proof-of-concept. Responsible implementation necessitates: (1) robust clinical trials focusing on functional outcomes, (2) development of domain-specific models, (3) ethical frameworks addressing bias and accountability, (4) strategies for equitable global access, and (5) professional training for AI-augmented practice. GenAI should amplify, not replace, the therapist's expertise and the human therapeutic connection. Our collective choices will determine its ultimate impact on care.
Background: Smartphones play a central role in adolescents’ daily lives, making dietary mobile health (mHealth) apps—tools that provide nutrition education and tracking eating behaviors—a promis...
Background: Smartphones play a central role in adolescents’ daily lives, making dietary mobile health (mHealth) apps—tools that provide nutrition education and tracking eating behaviors—a promising avenue for influencing dietary habits. While numerous studies have examined the impact of mHealth apps on diet, few have investigated adolescents’ perspectives and experiences with these tools. Objective: This scoping review aimed to synthesize the evidence and map the research gaps on adolescents’ perspectives (positive or negative) and experiences (attitudes, barriers, and facilitators) of using dietary mHealth apps on their smartphones. Methods: A systematic scoping review was conducted according to the 5-stage framework by Arksey and O’Malley. Articles that included mixed-methods studies that focused on adolescents (10-19 years of age) reporting perspectives (positive or negative) and experiences (attitudes, barriers, and facilitators) related to dietary apps use were searched across: PsycINFO, Embase, Medline, Web of Science and CINAHL for studies that were published from 2012 until 2023. Articles that were not specific to diet, not research studies, and not written in English were omitted. Results: Of the 590 abstracts screened, 17 studies met the eligibility criteria. Ten studies assessed the usability, feasibility and acceptability of standalone or multi-component dietary mHealth apps, while nine examined app likability and effectiveness. Thematic analysis revealed seven overarching themes: (1) Technical Functionality and Usability; (2) Appreciation of Nutritional Education and Content Depth; 3) Importance of Social Connection, Feedback and Support; (4) Values of Entertainment and Gamification; (5) Significance of Personal Goals, Motivation and Tracking; (6) Interest for Simple Design and Interface; and (7) Perceived Effectiveness of Dietary mHealth Apps. Positively perceived features included food identification, tracking and gamification elements. Commonly barriers included technical difficulties, tracking inaccuracies, complex information delivery and limited social engagement. Facilitators to app use were ease of navigation, targeted information, social interaction, rewards and goal setting. Suggested improvements focused on tracking accuracy, interface design, feedback mechanisms and notification options. Overall, adolescents perceived effective apps to as those that raised awareness of eating habits and support improvements in dietary intake. Conclusions: This scoping review highlights that adolescents’ experiences with dietary mHealth apps are shaped by technical functionality, usability, social engagement, personalization, and gamification. While these features can enhance engagement, barriers such as tracking inaccuracies, technical issues, and limited social interaction reduce app effectiveness. Understanding these perspectives is critical for designing apps that are not only informative but also appealing and sustainable for adolescent users.
Background: Abstract
Digital transformation in healthcare, including electronic health records, telemedicine, data analytics, and mobile health applications, is reshaping service delivery and patient...
Background: Abstract
Digital transformation in healthcare, including electronic health records, telemedicine, data analytics, and mobile health applications, is reshaping service delivery and patient experience. However, evidence on how these technologies influence e-healthcare service quality within developing countries remains limited. This study aimed to examine the impact of digital transformation on e-healthcare service quality through the mediating role of clinical process change. A quantitative, cross-sectional survey design was conducted among healthcare users in Alexandria Egypt private sector data. Data were collected using validated instruments addressing electronic health services, telemedicine, data analytics, and mobile applications, with physician–patient communication. Responses were analyzed to assess perceptions of accessibility, security, usability, and service quality. Findings showed a predominance of neutral attitudes toward digital health technologies. Nearly half of respondents (45%) were neutral about accessibility, and only 32% strongly agreed that records were secure. Neutrality was also common regarding data analytics (33.8% awareness, 38.0% quality of care, 32.8% decision-making) and mobile applications (36.8% user-friendliness, 34.3% wait time reduction, 38.5% technical reliability). Communication indicators showed moderate ratings, with neutrality prevailing for physician listening (34.0%) and patient comfort (32.3%). Despite neutrality, around one-third agreed on the convenience of telemedicine and clarity of information provided (45.8%). The study demonstrates that digital transformation, mediated partly through clinical process change, enhances clinical workflows and perceived e-healthcare service quality. However, widespread neutrality indicates knowledge gaps, highlighting the need for user-centered design, digital literacy training, and improved communication to maximize the benefits of healthcare digitalization.
Keywords: Digital transformation; E-healthcare service quality; Clinical process change; Data analytics; Telemedicine. Objective: The study aims to achieve the following objectives:
1. To examine the scope and evolution of digital transformation in healthcare systems.
2. To identify the key enablers of successful digital transformation, including technological infrastructure and leadership.
3. To explore the major barriers to digital health implementation.
4. To assess the impact of DT on healthcare delivery, patient outcomes, and provider experience.
5. To develop a conceptual framework to guide future digital transformation efforts. Methods: 6.1 Research Design
A quantitative, cross-sectional survey design was employed to examine the relationships between digital transformation (DT), clinical process change (CPC), and healthcare e-service quality in private hospitals in Egypt. Structural Equation Modeling (SEM) using Partial Least Squares (PLS-SEM) was used to test the hypothesised mediation model.
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6.2 Target Population and Sampling Frame
The target population consisted of patients who received services from private hospitals in Egypt during the data collection period. Staff members or clinical professionals were not included in the sample to maintain conceptual consistency, because the dependent variable—e-service quality—is evaluated by patients, not employees.
The sampling frame covered adult patients (≥18 years old) who visited outpatient departments, emergency units, or utilised digital channels (e.g., mobile apps, portals) during the study period.
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6.3 Sampling Strategy and Justification
A convenience sampling approach was used due to practical constraints, including variable patient flow across hospitals and restricted access to patient records. Although probability sampling is ideal, convenience sampling is widely acceptable in healthcare service quality research when direct access to sampling lists is not feasible.
To mitigate limitations, recruitment occurred across multiple hospitals, different days of the week, and various service units to improve representativeness. Results: 7. Results
This study presented and analyzed the empirical findings of the study investigating the impact of Digital Transformation through the dimensions of E-health Records, Telemedicine Services, Data Analytics, and Mobile Applications on E-healthcare Service Quality within Egypt’s private healthcare sector, with Clinical Process Change acting as a mediating variable.
The descriptive analysis offered a clear understanding of the respondent demographics, suggesting a sample of digitally literate and experienced users.
The results revealed generally positive attitudes toward digital healthcare services, especially in areas related to telemedicine convenience, mobile app functionality, and perceived security.
Using Structural Equation Modelling (SEM), the study validated a strong model fit and confirmed the reliability and validity of the measurement constructs. The analysis demonstrated that Digital Transformation has a significant positive impact on both Clinical Process Change and E-healthcare Service Quality.
Furthermore, the results established that Clinical Process Change partially mediates the relationship between Digital Transformation and E-healthcare Service Quality (H4), reinforcing the importance of internal operational improvements in realizing the benefits of digital initiatives.
Overall, the findings confirm that successful digital transformation initiatives in healthcare not only require technological implementation but must be accompanied by clinical process enhancements to achieve higher service quality. These results have significant implications for healthcare decision-makers, emphasizing the need to invest in integrated digital and process change strategies to improve patient outcomes and service delivery in the digital age.
Figure 4 shows the measurement model which consists of 11 latent variables, namely, E-health records, Telemedicine services, Data analytics, Mobile App, Physician-Patient Interaction, Information Accessibility, Security, Responsiveness, Reliability, Ease of use and Loyalty. Conclusions: Conclusion
Our empirical results resonate strongly with the broader scholarly literature: digital transformation including E health records, telemedicine, data analytics, and mobile apps significantly enhances both clinical processes and perceived e healthcare service quality. The partial mediation through clinical process change further corroborates system-level frameworks and empirical studies describing how digital tools translate into quality improvements when embedded in improved clinical workflows. These results provide solid academic validation and practical guidance for implementing digital innovation in healthcare.
Background: Pediatric survivors of critical illness often face persistent psychosocial challenges after PICU (Pediatric Intensive Care Unit) discharge, but follow-up support across hospital, home, com...
Background: Pediatric survivors of critical illness often face persistent psychosocial challenges after PICU (Pediatric Intensive Care Unit) discharge, but follow-up support across hospital, home, community, and school settings remains inconsistent. Digital interventions could help bridge these gaps and support recovery. Objective: To systematically review the literature on digital psychosocial follow-up solutions for children who survived critical illness, describing target populations, intervention design, evaluation methods, and psychosocial effects. Methods: A systematic literature review was performed using the Scopus database, supplemented by backward citation searches and hand searches of related reviews. Eligible studies included children surviving medical conditions potentially requiring PICU care, implemented a digital intervention (excluding telephone-only), and evaluated psychological or social outcomes; studies published before 2010, in non-English languages, not peer-reviewed, lacking full text, not original research, involving mixed child-adult populations, or with unspecified participant age or diagnosis were excluded. The quality of the included studies was appraised with the MMAT (Mixed Methods Appraisal Tool) 2018. Owing to heterogeneity in populations, interventions, comparisons, outcomes, and study designs, a narrative synthesis was applied. Results: Thirty-three publications reporting on 31 unique studies (N=1,717 participants, ages 0–17) were included. The studies spanned North America, Europe, and Asia and were conducted in inpatient, outpatient, home, and school contexts. Interventions comprised web applications (n=9/31), mobile apps (n=7/31), social robots (n=6/31), video games (n=4/31), and mixed modalities (n=5/31). Many studies (n=18/31) engaged guardians as co-participants or co-developers along with children. Target conditions were predominantly cancer (n=11/31), type 1 diabetes (n=8/31), and asthma (n=7/31). Mixed methods designs were most common (n=11/31), followed by nonrandomized quantitative trials (n=7/31) and randomized controlled trials (n=6/31). Most studies reported positive psychosocial effects. Across outcomes, self-management (n=3/31) and quality of life (n=5/31) showed the most statistically significant (P<.05) benefits. Evidence for psychosocial outcomes was less consistent. The certainty of evidence was limited by a single-database search, single-reviewer screening, variable methodological quality, and heterogeneity. Conclusions: Digital psychosocial follow-up for childhood critical illness survivors appears feasible and promising, particularly for self-management and quality of life, but the evidence base is heterogeneous and methodologically constrained. To strengthen clinical translation, future work should prioritize rigorous trials, standardized and theory-informed pediatric psychosocial outcome sets, longer follow-up, transparent reporting, and equity-focused designs that integrate family-centered hybrid clinic-home pathways and, where feasible, predictive features. Clinical Trial: PROSPERO CRD42022364703; https://www.crd.york.ac.uk/PROSPERO/view/CRD42022364703
Background: The Ready-Made Garments (RMG) industry is a vital part of Bangladesh's economy and employing over 4 million workers from low-income backgrounds that generally neglects their healthcare asp...
Background: The Ready-Made Garments (RMG) industry is a vital part of Bangladesh's economy and employing over 4 million workers from low-income backgrounds that generally neglects their healthcare aspects. Historically, the sector is criticized for labor exploitation, unsafe working conditions, and rights violations since there has been massive loss of lives over accidents. While compliant factories adhere to better labor standards, many non-compliant factories expose workers to poor conditions, increasing their health risks. The COVID-19 pandemic exacerbated vulnerabilities within this workforce, resulting in widespread factory closures and massive job losses, heightened health risks, and leaving millions of workers without wages. Although the government provided some relief, it lacked policies for job security, social protection, health services, and emergency relief. Although technology has played a critical role in crisis response and healthcare, access to these technologies remains limited for them due to digital literacy gaps. Many RMG workers primarily use basic mobile phones for communication, not for accessing health or emergency services. Therefore, there is a need to develop a sustainable system that leverages their existing technological familiarity to ensure their voices are heard. Objective: Our aim was to gain a deeper understanding of RMG workers' experiences based on their existing work environments and interactions with technology, healthcare management, and the impact of COVID-19 on their circumstances. By understanding these aspects, we can recommend a technology-based framework design that serves as a sustainable and contextual model. Methods: We conducted in-person interviews with 55 RMG workers, comprising 32 female and 23 male participants from urban and suburban areas of Dhaka and suburban Gazipur, in Phase 1, before the pandemic. The participants were aged between 18-40. We reconnected with 12 participants from Phase 1 during the pandemic in Phase 2, in addition to three stakeholders from RMG factories via one-on-one phone conversations. Each interview was conducted in Bengali, and we obtained consent to record the audio. Overall, 846 minutes of discussion were translated and transcribed. The results were analyzed using thematic analysis. Results: We found insights into the working conditions, personal experiences, perceptions of healthcare, lifestyle choices, and technology use, all of which differed based on the type of factory which is yet not discussed together. Those employed at compliant factories enjoyed better healthcare support and utilized technology more effectively compared to their counterparts in non-compliant factories. Due to the pandemic, the situation for all workers changed dramatically, regardless of factory compliance, leading to major impacts on their daily lives, heightened health and safety worries, and a lack of emergency assistance. The RMG sector encountered a lot of challenges, underscoring the pressing need for targeted emergency relief and healthcare services for these workers. Conclusions: This research examined the workplace and daily lives of RMG workers, focusing on their challenges, healthcare perspectives, and technology use during the pandemic. Based on the findings, we proposed a technology-based framework design called VOICE, which connects workers to service providers through a straightforward interface. This would help reach marginalized communities during emergencies and provide essential support to improve their well-being.
Background: The fragmentation of electronic health records (EHRs) is a major barrier to integrated cancer care, negatively impacting diagnostic efficiency and treatment continuity. Blockchain technolo...
Background: The fragmentation of electronic health records (EHRs) is a major barrier to integrated cancer care, negatively impacting diagnostic efficiency and treatment continuity. Blockchain technology has emerged as a promising solution for secure health data sharing, with the potential to enhance interoperability, data governance, and traceability in complex clinical settings like oncology. However, the successful implementation of such technology is contingent upon patient acceptance and trust, which remain underexplored. Objective: This study aimed to investigate the perceptions of oncology patients regarding the use and control of their digital health data. We specifically assessed their willingness to share information, their level of trust in different stakeholders within the healthcare ecosystem, and the conditions under which they would find blockchain-based solutions acceptable. Methods: We conducted a cross-sectional, exploratory, quantitative study with 110 oncology patients at Hospital Santa Izabel in Salvador, Brazil. A structured questionnaire, validated by experts for clarity and relevance, was used. Data collection was managed via the REDCap platform. The instrument's internal consistency was assessed using the Cronbach's alpha coefficient. Descriptive, comparative, and correlational statistical analyses were performed to identify differences across sociodemographic groups. Results: A majority of participants demonstrated a high acceptance of digital tools for storing and sharing health data (86.4%), which increased significantly when security measures like anonymization and encryption were assured (83.6%). Trust in data sharing varied substantially by institution: it was highest for healthcare professionals (79.1%), moderate for hospitals (51.8%), and considerably lower for the government (10%) and the pharmaceutical industry (15.5%). A statistically significant difference was found in technology adherence by age, with younger patients (18-59 years) showing higher acceptance than older adults (p = 0.024). The survey domains—self-management, adherence, and governance—demonstrated satisfactory internal consistency (Cronbach's alpha ranging from 0.75 to 0.88). Conclusions: Our findings indicate a high willingness among oncology patients to adopt digital health tools for data management, provided that robust security, transparency, and patient empowerment are central to the design. The significant trust gap between clinicians and institutions like government and industry underscores the critical need for clear communication and trustworthy governance models. To foster confidence and promote equitable access, future digital health platforms must be designed to be accessible, reliable, and centered on patient autonomy. Clinical Trial: This was an observational, cross-sectional study and did not involve a clinical intervention. Therefore, registration in a clinical trials registry (such as ClinicalTrials.gov) was not applicable. The study was conducted with the approval of the Institutional Review Board (CAAE: 70726523.3.0000.5520). All study records, including de-identified raw data, the survey instrument, and consent forms, are securely archived by the authors in accordance with institutional and ethical guidelines.
Background: The COVID-19 pandemic gave rise to a global “infodemic” in which social media platforms amplified misinformation. Despite high social media adoption rates and heavy reliance on social...
Background: The COVID-19 pandemic gave rise to a global “infodemic” in which social media platforms amplified misinformation. Despite high social media adoption rates and heavy reliance on social media for pandemic news in Arab-speaking countries, relatively little is known about the prevalence and characteristics of online Arabic COVID-19 misinformation. Objective: To capture and analyze a snapshot of the COVID-19 misinformation ecosystem in Arabic, identifying characteristics and patterns to guide future research and interventions of particular benefit to this linguistic region. Methods: We compiled a database of 234 COVID-19 misinformation claims published online from March 2020 to March 2022, sourced from four International Fact-Checking Network (IFCN)-certified Arabic fact-checking organizations. Claims were coded inductively and deductively with high inter-rater reliability, to determine misinformation type (κ = 0.88), narrative typology (κ = 0.913), framing strategies (κ = 0.72), medical jargon usage (κ = 0.794), and societal implications (κ = 0.752). All Cohen's kappa coefficients were significant at p < 0.001. Results: Facebook was the most popular platform, followed by Twitter, with regular users being the primary source of debunked claims. The most prevalent narrative typologies were COVID-19 biological aspects (origins, existence, diagnosis, prevention, transmission, and cures) (47.2%) and vaccines (30%). Fabricated/manipulated (54.9%), followed by misleading content (36.9%), were the most common misinformation types. The most frequent framing strategy involved distortion of science and medicine (29.6%), followed by entertainment/satire (23.6%), political content (18.9%), and conspiracies (13.3%). Notably, 36.3% of claims were translated from English, and only 50% of the analyzed content was moderated by the original platforms. Conclusions: Fact-checked Arabic COVID-19 misinformation exhibited distinct patterns, including heavy reliance on translated content, manipulated content, and scientific distortion as a credibility strategy, and significant gaps in platform moderation. These findings highlight the need for enhanced Arabic-language content moderation, cross-linguistic fact-checking collaboration, culturally appropriate media and health literacy interventions, and rebuilding institutional trust to address misinformation in the Arab-world effectively. Clinical Trial: N/A
Background: Amidst the COVID-19 pandemic, Action4Diabetes (A4D), a non-profit organisation collaborating with local healthcare professionals across Southeast Asia (SEA), developed HelloType1 a digital...
Background: Amidst the COVID-19 pandemic, Action4Diabetes (A4D), a non-profit organisation collaborating with local healthcare professionals across Southeast Asia (SEA), developed HelloType1 a digital educational platform for Type 1 diabetes (T1D) in regional languages. Launched sequentially in Cambodia (2021), Vietnam (2022), Thailand (2022), and Malaysia (2023) through Memorandums of Understanding (MOUs), the digital platform aimed to improve diabetes awareness, education, and access to credible local-language resources. Objective: This study aims to evaluate the usability, reach and online engagement of HelloType1 from 2021 to 2024. Methods: Website traffic data from Google Analytics (GA4) and Facebook metrics were analysed to assess user growth, traffic sources, and engagement trends across countries. Results: Total users increased by 645% between 2021 and 2022 and a further 31% between 2022 and 2023. By 2024, 78% of visits originated from search engines, 13% from social media, and 9% from direct access. Pageviews rose from 4,644 (2021) to 82,689 (2024). Facebook followers grew from 940 to 4,553, with engagement rates increasing from 8% (2022) to 29% (2024). Cambodia achieved the highest reach, while Vietnam showed strong engagement among younger female caregivers. Conclusions: HelloType1 demonstrates a scalable, low-cost digital model for delivering culturally adapted T1D education in resource-limited SEA settings. Clinical Trial: NA
Background
Cancer predisposition syndromes (CPS) are identified in approximately 10% of pediatric cancer patients, with an increasing number of affected families each year. Despite the known psychoso...
Background
Cancer predisposition syndromes (CPS) are identified in approximately 10% of pediatric cancer patients, with an increasing number of affected families each year. Despite the known psychosocial challenges faced by these families, including uncertainty in communication, genetic risk implications, and lifelong surveillance, there is limited data on the specific support needs of families in Germany.
Objective
The KiTDS-Care study aims to: (1) Conduct a comprehensive analysis of the current care landscape, psychosocial stressors, psychosocial burden and support needs of families with children/ adolescents diagnosed with CPS in Germany; and (2) Develop recommendations for improving psychosocial care based on these findings.
Methods
A mixed-methods approach will be employed. The first phase involves a systematic review to systematically gather existing literature on the psychosocial situation and support needs of CPS families. In the second phase, a cross-sectional survey of families (parents and children/ adolescents aged ≥7 years) will assess e.g. psychosocial well-being, quality of life, support needs, and care utilization. Additionally, qualitative interviews will be conducted with families and healthcare providers to explore deeper psychosocial experiences, service and care gaps. Data will be analyzed using descriptive and inferential statistics, while qualitative data will be processed through content analysis. Recommendations for psychosocial care will be derived and validated through feedback from both families and healthcare professionals.
Discussion
The study results will provide a comprehensive overview of the psychosocial situation and supportive care needs of families affected by CPS of a child/ adolescent. The results will help to improve family-centered care and psychosocial support systems. It will help identify gaps in current care practices and inform more effective approaches.
Trial registration
German Clinical Trials Register, ID: DRKS00035594, Registered on 9th December 2024
Background: Caregiving for patients with chronic mental illnesses like schizophrenia and major depressive disorder (MDD) places a significant burden on families, often leading to financial strain, dom...
Background: Caregiving for patients with chronic mental illnesses like schizophrenia and major depressive disorder (MDD) places a significant burden on families, often leading to financial strain, domestic disruption, and a decline in the caregiver's own physical and mental health. In the Indian context, family members are often the primary "natural" caregivers due to cultural expectations and limited state resources. While schizophrenia is generally a continuous illness and MDD is episodic, both require long-term emotional and financial support from carers. There is currently a relative dearth of Indian research directly comparing the specific perceived stress, burden, and quality of life (QoL) between these two distinct caregiver groups. Objective: The primary aim of this study is to compare the caregiver burden, perceived stress, and quality of life between caregivers of patients with schizophrenia and those with major depressive disorder. Specific objectives include:
1)Evaluating and comparing the absolute levels of burden, perceived stress, and QoL in both groups.
2)Analyzing these factors in relation to the socio-demographic characteristics of the caregivers, such as age, gender, and educational level Methods: This is a cross-sectional, hospital-based study conducted at the Department of Psychiatry, Acharya Vinoba Bhave Rural Hospital in Sawangi, Wardha. The study will include a total sample of 290 participants, consisting of 145 caregivers for each patient group (schizophrenia and MDD). Inclusion criteria for patients include a diagnosis based on ICD-11, a duration of illness ≥ 2 years, and living with the caregiver for ≥ 3 years. Caregivers must be aged 18–60 and provide active daily support. After obtaining informed consent, data will be collected using a semi-structured sociodemographic proforma and three standardized tools: the Perceived Stress Scale (PSS), the short version of the Zarit Burden Interview (ZBI), and the WHO Quality of Life BREF Scale (WHOQOL-BREF). Statistical analysis will be performed using SPSS version 28.0, employing Student’s t-tests or Mann-Whitney U tests for mean comparisons and Chi-square tests for categorical variables Results: Yet to be analyzed as study is underway, sample collection has been completed and analysis is underway Conclusions: Yet to be stated as study is underway Clinical Trial: Clinical Trials Registry of India (CTRI)
Registration Number: CTRI/2024/05/066968
Background: Due to demographic change the number of older people is increasing. Older age is often accompanied by limitations in terms of mobility, nutrition and independence. Routine, preventive moni...
Background: Due to demographic change the number of older people is increasing. Older age is often accompanied by limitations in terms of mobility, nutrition and independence. Routine, preventive monitoring of these areas is rare, as care systems struggle with staff shortages and limited resources. Technical assistance systems offer a way to support older people (≥70 years) in self-assessing their health parameters and in consequence keep independence. We developed the AS-Tra system which combines an app with a measurement and training station (MuTS) to identify deficits and risks in the areas of nutrition and mobility in older adults at an early stage. Objective: This paper presents the pilot study of the AS-Tra system with the aim of evaluating its usability and testing the feasibility of collecting health-related data of older adults (70+) with early / mild deficiencies in nutritional state and physical functionality in preparation for a future randomized controlled trial (RCT). Methods: The system was developed as a complex intervention in accordance with the Medical Research Council (MRC) framework. In this pilot study, the participants used the system four weeks. The assessments (grip strength, Timed ‘Up and Go’, 5-Time Chair Rise) were conducted at the baseline (BL) as well as after one, two and four weeks (T0, T1, T2). At BL, inclusion criteria, baseline characteristics, MNA-SF and SPPB were recorded. Participants received a tablet containing the app and an activity sensor to measure physical activity for seven days. At T0, next to the assessments, the training exercises were introduced and carried out. At T1 the assessments were repeated, along with registering a 3-day food diary in the tablet app and the activity sensor data was evaluated. At T2, the final assessments, including MNA-SF, SPPB, SUS, and feedback questionnaires as well as the ‘Evaluation Overall System’-questionnaire (EOS) (evaluation of all subcomponents on a scale of 1 to 5) were collected. Throughout the entire period of use, participants were asked to train independently in MuTS at least once a week. They regularly kept a food diary using the tablet app and were asked to provide feedback on the app and MuTS in form of an ‘Experience Report’-questionnaire (ER), in which it is asked which elements caused problems and which were particularly easy. Results: Ten participants (80 ± 5 years, 50% female) participated in this study, of which one droped-out between T0 and T1. The SUS score was good (79 ± 13.4). The MuTS devices had minor technical problems (in <17% of the usage) according to the ER, while 57% of the users experienced instability issues with the food diary in the tablet app. Overall, ratings of the system were very good with good with slightly lower ratings (2–3 out of 5) for the tablet app and regular use. Conclusions: The usability of the technical assistance system used in this study was rated as good. The data collection with questionnaires, sensors, and automated assessments proved feasible. The biggest challenge was the tablet-based food diary which still needs improvement, before the effectiveness of the AS-Tra system regarding mobility and nutritional status will be evaluated in a RCT.
Background: Continuing Medical Education (CME) is a legal and ethical obligation for physicians in Germany. The rapid rise of large language models (LLMs) such as ChatGPT, Gemini, Claude, and Grok rai...
Background: Continuing Medical Education (CME) is a legal and ethical obligation for physicians in Germany. The rapid rise of large language models (LLMs) such as ChatGPT, Gemini, Claude, and Grok raises concerns about the integrity of CME assessments, as LLMs can already pass German CME tests. Objective: To determine whether the choice of document format (searchable PDF, raster PDF, vector PDF) and LLM can influence the solvability of CME test questions by LLMs above the passing threshold specified for each CME module (typically 70%). Methods: In a fully crossed within-subjects repeated-measures structure, 18 expired CME articles from three major German publishers across six specialties will be converted into three PDF formats and processed by four current LLMs (ChatGPT-5, Mistral 3.1 small, Claude Sonnet 4, Grok-4) and two predecessor versions (ChatGPT-4o and Grok-3). Each model will answer every article once per file-format condition. This results in 18 experimental conditions. The primary outcome is the proportion of correctly answered questions; secondary outcomes are pass/fail rate and efficiency. The study has been approved by the University of Witten/Herdecke Ethics Committee (reference number S-260/2025, dated 08.10.2025) and is preregistered at the Open Science Framework (DOI: 10.17605/OSF.IO/V96R5). Results: Data collection will start in January 2026 and will last approximately 4 weeks. As of December 2025, the study has been preregistered, and no results are available yet. The analyses will quantify performance differences across document formats and model generations; these findings may inform the feasibility of non-searchable document formats as a temporary measure to reduce AI-enabled cheating risks in CME contexts. Conclusions: By quantifying how document format constrains LLM performance, this study aims to evaluate simple technical safeguards that may reduce AI-assisted manipulation of CME tests and inform regulators and CME providers on balancing assessment validity, accessibility, and responsible LLM integration into postgraduate medical education. Clinical Trial: Open Science Framework DOI: 10.17605/OSF.IO/V96R5.
Background: Atopic dermatitis (AD) affects 10–20% of children and 5–10% of adults, with approximately 89% of cases being diagnosed as mild to moderate. AD influences over 200 million individuals a...
Background: Atopic dermatitis (AD) affects 10–20% of children and 5–10% of adults, with approximately 89% of cases being diagnosed as mild to moderate. AD influences over 200 million individuals around the world and is viewed as an important health problem due to its elevated prevalence, long course of disease, and heavy disease burden. Qi Wei Antipruritic Lotion is an empirical prescription formula composed of eight Chinese herbs, with purported effects of clearing heat, drying dampness, detoxification, and alleviating pruritus. While it is employed in clinical settings for pruritic dermatoses, robust evidence from high-quality clinical trials is still lacking. This study will evaluate the efficacy and safety of QW Antipruritic Lotion for the treatment of AD. Objective: This study will evaluate the efficacy and safety of QW Antipruritic Lotion for the treatment of AD. Methods: Methods and analysis: This single-center, randomized, double-blind, placebo-controlled trial will enroll 154 patients with mild-to-moderate AD from the Hospital of Chengdu University of TCM. Participants will be randomly assigned (1:1) to either the treatment group (QW Antipruritic Lotion) or the placebo control group. The trial comprises an 8-week treatment period followed by a 12-week follow-up. Efficacy will be assessed using several endpoints to measure Improvement in clinical severity. The primary outcome is the reduction in the SCORAD (Scoring Atopic Dermatitis) index. Secondary outcomes include the Eczema Area and Severity Index (EASI) scores, the Patient Self-Assessment Questionnaire (DQLI, NRS), as well as safety outcomes. A clinical dermatologist will perform assessments at baseline (week 0), weeks 4, 8, 12, 16, and 20. Results: This study will evaluate the efficacy and safety of QW Antipruritic Lotion for the treatment of AD. Conclusions: This study will evaluate the efficacy and safety of QW Antipruritic Lotion for the treatment of AD.
Background: In a significant proportion of carotid interventions, carotid graft replacement is required to achieve a successful outcome both as primary method or as bail-out solution. An exhaustive m...
Background: In a significant proportion of carotid interventions, carotid graft replacement is required to achieve a successful outcome both as primary method or as bail-out solution. An exhaustive mapping of the sparse and heterogeneous evidence available in the literature may provide a more comprehensive understanding of this topic. Objective: This scoping review aims to examine and summarize the evidence from scientific literature concerning the role of graft interposition during elective and emergent carotid interventions. Methods: This scoping review will be conducted following recommendations outlined by Levac et al and will adhere to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews) guidelines for reporting. Peer-reviewed papers written in English will be searched in the following databases: PubMed/MEDLINE, Embase, Scopus, and Web of Science. The web-based systematic review platform Rayyan will be used to create a data extraction template. It will cover the following items: elective carotid endarterectomy, emergent carotid endarterectomy, carotid artery restenosis, carotid artery trauma, carotid artery aneurysm, carotid artery dissection, carotid patch infection, internal carotid artery fibrosis, carotid artery tumour. All study designs (RCTs, observational, case series) will be considered. Non-English studies, animal studies, cadaveric/anatomical-only reports, purely technical notes without clinical data. An data regarding extracranial-to-intracranial bypass will be excluded. Study selection based on title and abstract screening (first stage), full-text review (second stage), and data extraction (third stage) will be performed by a group of researchers, whereby each paper will be reviewed by at least 2 people. Any conflict regarding the inclusion or exclusion of a study and the data extraction will be resolved by discussion between the researchers who evaluated the papers; a third researcher will be involved if consensus is not reached. Results: A preliminary search of PubMed/MEDLINE, Embase, Scopus, and Web of Science was conducted, and no current or ongoing systematic reviews or scoping reviews on the topic were identified. The results of the study are expected in July 2026 Conclusions: Our scoping review will seek to provide an overview of the available evidence and identify research gaps regarding the role of graft interposition during elective and emergent carotid interventions.
Background: Obesity remains a pressing global health issue. Research suggests that better health literacy can support obesity management. This study tested digital interventions combining healthy eati...
Background: Obesity remains a pressing global health issue. Research suggests that better health literacy can support obesity management. This study tested digital interventions combining healthy eating guidelines with AI and mobile tools, including a ChatGPT-powered Line chatbot for daily education and an AI food plate recognition system for calorie tracking and meal suggestions. Objective: This study aims to evaluate the efficacy of an integrated digital intervention, combining YOLOv5-based AI food plate recognition and a ChatGPT-powered LINE chatbot, on weight reduction (BMI) and health literacy among overweight and obese adults. Methods: The study used a quasi-experimental design-intervention case-control design. Both the case and intervention groups received basic health education through app notifications and used an AI food plate recognition tool to estimate their nutritional intake. Only the intervention group could access an AI weight-loss chatbot for timely suggestions. Questionnaire data were collected from users at several points during the intervention. Results: Eighty participants were enrolled. The intervention group demonstrated significantly greater reductions in BMI (β = −1.32; 95% CI, −1.56 to −1.09; P < .001) and improvements in health literacy (β = 4.71; 95% CI, 3.86 to 5.56; P < .001) versus controls. Physical activity (step count β = 1,926.5; 95% CI, 1,209.3 to 2,643.7; P < .001) and weekly exercise time (β = 0.56; 95% CI, 0.21 to 0.92; P = .002) also increased, while late-night snacking decreased (β = −0.45; 95% CI, −0.81 to −0.08; P = .017). The intervention group consistently outperformed the control group across key health measures. However, the AI chatbot alone lacked significant effects on primary outcomes. Conclusions: This integrated digital intervention effectively promotes weight loss and health literacy. Given the strong short-term efficacy, future research should employ randomized designs, larger sample sizes, and longer follow-ups to establish long-term weight maintenance and address potential influences such as the Hawthorne effect. It also highlights the need to further develop interactive, personalized health education tools and optimize AI food plate recognition systems to improve health literacy and weight management.
Background: In Bangladesh, infertility is an increasing concern, influenced by cultural, social, and economic factors. One of the leading contributors to female infertility is Polycystic Ovarian Syndr...
Background: In Bangladesh, infertility is an increasing concern, influenced by cultural, social, and economic factors. One of the leading contributors to female infertility is Polycystic Ovarian Syndrome (PCOS), a common endocrine disorder that affects women of reproductive age. Characterized by elevated androgen levels, PCOS results in the development of multiple fluid-filled cysts on the ovaries, disrupting normal ovulation. The current fertility rate in Bangladesh stands at 1.93 births per woman as of 2023, reflecting a decline in recent years. Objective: This study aimed to identify risk factors associated with infertility in women and to explore potential prevention and treatment strategies. Methods: Conducted as a cross-sectional study at two tertiary hospitals, 189 women participated, with 163 diagnosed with PCOS and facing prolonged difficulties in conceiving. The data were analyzed using SPSS software, employing descriptive statistics, comparative analysis, and multivariate logistic regression. Results: The results showed that the average age of the participants was 26.96 ± 4.88 years, with an average infertility duration of 5.03 ± 2.80 years. The highest prevalence of PCOS was observed in women aged 19-25 (40.2%), followed by those aged 26-30 (31.8%) and 31-35 (15.1%). A smaller percentage (3.9%) were aged 36-40. The findings indicate that most PCOS-related infertility cases occur in women in their early 20s. Conclusions: Despite its prevalence, PCOS poses significant health risks, including type 2 diabetes and hypertension. Effective management of PCOS is essential for reducing its long-term health impacts and improving reproductive outcomes for women in Bangladesh.
Background: Depression is the most common mental health disorder worldwide and frequently leads to workplace absences. As face-to-face treatment can be difficult to access app-based interventions are...
Background: Depression is the most common mental health disorder worldwide and frequently leads to workplace absences. As face-to-face treatment can be difficult to access app-based interventions are a popular solution, although their effectiveness in working populations and mechanisms of action are unclear. Deficits in executive functioning (EF) may contribute to the onset and maintenance of depression, and EF training is proposed to improve symptoms by enhancing EF. Responders to cognitive behavioural therapy (CBT) show improvements in EF, suggesting this may be one mechanism of action. Objective: This study investigated the effectiveness of app-based interventions (EF- or CBT-based) in reducing depressive and anxious symptoms, and improving workplace wellbeing, and whether changes in EF mediated improvements. Methods: 228 participants (147 female) with mild to moderate depression and anxiety were randomly assigned to either a waitlist control group, or to use an EF training app or a self-paced CBT app. Participants completed measures of depressive symptoms, anxious symptoms and workplace wellbeing at baseline, after the 4-week intervention period, and at 12-week follow-up. Results: EF training reduced anxiety and depressive symptoms at follow-up, but not at post-intervention, and did not affect workplace wellbeing. There were no reductions in depressive or anxiety symptoms in the self-guided CBT group, though workplace wellbeing was improved post-intervention and at follow-up. Improvements in EF did not mediate intervention-related changes in symptoms or workplace wellbeing. Conclusions: These results suggest app-based EF training may be effective at managing symptoms of anxiety and depression in a working population, whilst using self-guided CBT apps may improve workplace wellbeing. However, EF did not appear to be a mechanism of action of either intervention. Clinical Trial: The study was pre-registered on the Open Science Framework: https://osf.io/zsncj
Background: Autism Spectrum Disorder (ASD) is characterized by persistent difficulties in social communication, restricted interests, and sensory challenges. Although Applied Behavior Analysis (ABA) i...
Background: Autism Spectrum Disorder (ASD) is characterized by persistent difficulties in social communication, restricted interests, and sensory challenges. Although Applied Behavior Analysis (ABA) is widely used, traditional interventions often face challenges, such as high costs, limited access to qualified therapists, and balancing structured therapy with individual needs. Recent advances in consumer-grade virtual reality (VR) and artificial intelligence (AI) offer opportunities to design personalized, immersive interventions aligned with naturalistic developmental behavioral intervention (NDBI) principles. Objective: This study aimed to design, develop, and evaluate an immersive VR game, the “Elevator Game” for verbal requesting and social initiation, to determine its feasibility, acceptability, and preliminary behavioral impact on children with ASD. Methods: Three children with autism and limited verbal skills participated in home-based VR sessions consisting of 10-15 minutes of gameplay followed by breaks. Results: Results suggest the intervention is feasible, well tolerated, and associated with increased spontaneous verbal requesting. Conclusions: AI-assisted VR interventions integrating ABA and NDBI principles are feasible, engaging, and potentially effective for children with ASD, including those with limited progress in traditional therapy. Personalized reinforcers, immersive engagement, and sensory-adaptive environments appear critical for success. Findings support further development and evaluation in larger trials.
Background: The growth of patient-facing health technology has the potential to transform the delivery and receipt of patient-centered primary care. However, successful integration of data from these...
Background: The growth of patient-facing health technology has the potential to transform the delivery and receipt of patient-centered primary care. However, successful integration of data from these digital tools into clinical workflows depends not only on technical efficacy, but also on usability across diverse patient populations. To ensure the successful integration of digital tools, Tech Testing Panels (TTPs) can assess usability and provide feedback. Objective: This study aimed to assess technology usage and literacy among adult primary care patients that opted in a TTP and compare these measures between English-preferred and Chinese-preferred speaking patients. Methods: We conducted a cross-sectional online survey from April to July 2024 at an urban academic primary care based TTP composed of adult patients that use the patient portal and spoke English and/or Chinese. The survey assessed socio-demographic characteristics and technology usage and literacy, including comfort with app installation, video chat setup, and problem-solving tech issues. Respondents received a $5 online gift card for completion. Bivariate analyses were conducted using Pearson’s chi-squared and Fisher’s exact tests to compare responses by preferred language. Results: Of the surveys distributed, the response rate for surveys in English was 53.7%, while the response rate for surveys in Chinese was approximately 27.0% with a total sample size of 222 respondents. Respondents had a mean age of 61.6 years, with nearly half aged 65 or older. A majority had high educational attainment and household incomes. Most respondents strongly agreed that they could install applications (85.5%) and able to initiate video chats independently (82.4%). Internet access was nearly universal (99.1%), and patient portal usage was high (99.1%) with most accessing the portal via smartphones or tablets (54.8%). However, Chinese-preferring respondents reported significantly lower technology literacy across multiple domains compared to English-preferring respondents, including lower confidence in using applications (64.5% vs 89.0%, P=.001) and resolving technical issues (38.7% vs 60.0%, P<.001). Conclusions: While technology usage was high in this sample of adult primary care patients in a TTP, disparities by preferred language in technology literacy persist. Chinese-preferring patients were less confident in navigating digital tools, despite similar technology usage. These findings underscore the importance of TTPs with diversity in technology literacy to support inclusive development of culturally and linguistically responsive patient-facing digital tools. Addressing barriers identified among end users with different degrees of technology literacy will be essential to ensuring equitable adoption of digital health tools and supporting inclusive innovation in primary care.
Background: Large language models (LLMs) are increasingly used and evaluated in health professions education, including studies assessing model performance on healthcare examination questions. The rap...
Background: Large language models (LLMs) are increasingly used and evaluated in health professions education, including studies assessing model performance on healthcare examination questions. The rapid growth and heterogeneity of this literature make it difficult to track research concentration, collaboration patterns, and emerging themes. Objective: To map publication trends, key contributors, collaboration networks, and thematic hotspots in research on LLM-supported exam solving in healthcare education. Methods: We conducted a bibliometric analysis of publications from 2023–2025. Searches were performed in PubMed, Scopus, CINAHL Ultimate (EBSCOhost), and Web of Science using structured terms for AI/LLMs (eg, ChatGPT, generative AI, large language models) combined with healthcare education and training concepts. Eligible studies addressed AI-based technologies within healthcare education or training contexts; studies focused solely on clinical practice or non-educational applications were excluded. Bibliographic metadata from PubMed (TXT) and Scopus (BIB) were merged and analyzed using bibliometrix/Biblioshiny (R) and VOSviewer to quantify productivity, collaboration (including international co-authorship), and keyword co-occurrence patterns. Results: The dataset comprised 262 documents from 158 sources, with an annual publication growth rate of 36.58% and a mean document age of 1.83 years. A total of 1,351 authors contributed (mean 5.97 co-authors per document); international co-authored publications accounted for 13.36%. Most records were journal articles (253/262), followed by letters (8/262) and one conference paper. Annual output rose from 52 (2023) to 113 (2024; +117.3%), then decreased to 97 (2025; −14.2% vs 2024) while remaining above 2023 levels. JMIR Medical Education published the most articles on this topic (34/262), followed by Scientific Reports (9/262) and BMC Medical Education (7/262). Frequent keywords included “humans” (n=144), “artificial intelligence” (n=82), “generative AI” (n=30), and “large language models” (n=20); education-focused terms such as “educational measurement/methods” were also prominent (n=76). Conclusions: Research on LLMs and exam performance in healthcare education expanded rapidly from 2023–2025, with publication activity concentrated in a limited set of journals and relatively low international collaboration. Thematic patterns emphasize assessment-related outcomes and LLM/ChatGPT performance, supporting the need for more comparable, transparent reporting (eg, prompts and model versions) and education-centered outcomes beyond accuracy in future studies. Clinical Trial: /
Background: Dentistry is a multifaceted field within healthcare, encompassing a wide range of clinical and administrative activities. In recent years, the adoption of electronic health records has fac...
Background: Dentistry is a multifaceted field within healthcare, encompassing a wide range of clinical and administrative activities. In recent years, the adoption of electronic health records has facilitated the integration of artificial intelligence tools into dental practice. Among these, Process Mining (PM), a business process management technique that analyzes and optimizes complex workflows using real-world data, has emerged as a promising approach to improving efficiency and outcomes across various areas of dentistry. Objective: This scoping review aims to map the existing techniques and challenges related to the application of Process Mining across all domains of dentistry. Specifically, it seeks to identify the databases used, contexts of application, research objectives, limitations, and future opportunities for PM in dental research and practice. Methods: This review will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines to address the central research question: “What does the existing literature report about the applications, contributions, techniques, limitations, and challenges of Process Mining in clinical and administrative processes within dentistry?” Results: The further research aims to outline the applications and further opportunities of Process Mining in all of the fields of dentistry. Conclusions: Conclusion: The integration of PM with dental management systems represents a possibility to optimize workflows, reduce costs, and enhance the quality of patient care, so then promoting more efficient decision-making. Clinical Trial: https://osf.io/rb4cy
Background: Accurate assessment of surgical margins is essential in the treatment of squamous cell carcinoma (SCC) of the upper aerodigestive tract or cutaneous origin, as well as basal cell carcinoma...
Background: Accurate assessment of surgical margins is essential in the treatment of squamous cell carcinoma (SCC) of the upper aerodigestive tract or cutaneous origin, as well as basal cell carcinoma (BCC). Intraoperative frozen-section analysis is the current standard but is time-consuming and requires coordination among surgical and pathology teams. Reflectance confocal microscopy offers rapid, real-time evaluation of surgical margins and may provide diagnostic information comparable to frozen-section analysis, while enabling the development of a reference atlas for tumor visualization. Objective: The HISTOBLOC study aims to evaluate the concordance between confocal microscopy and intraoperative frozen-section examination for assessing surgical margins in SCC and BCC. A secondary objective is to compile a confocal imaging reference atlas to document tumor features and support consistent interpretation Methods: HISTOBLOC is a prospective, monocentric, randomized pilot study conducted at the Institut de Cancérologie de Lorraine, a nonprofit comprehensive cancer institute. Patients undergoing surgical excision for SCC or BCC have their margins assessed using both confocal microscopy and frozen-section analysis. The study measure concordance between the two methods and the time required for intraoperative margin assessment. Results: Patient recruitment for the study began on July 26, 2023, and was completed in June 4, 2025. All patients were enrolled according to the approved study protocol. Experimental procedures have been conducted in all recruited participants, and data collection has been completed. The results are currently undergoing statistical analysis and interpretation. Conclusions: This protocol describes a study designed to determine whether confocal microscopy can provide rapid, reliable intraoperative margin assessment comparable to frozen-section analysis, and to generate a reference atlas for clinical and research use. Clinical Trial: ClinicalTrials.gov; NCT05935995; https://clinicaltrials.gov/study/NCT05935995
Background: During crisis, individuals increasingly rely on digital platforms for information, communication, and emotional support. Cyber behavior - which encompasses online engagement, security prac...
Background: During crisis, individuals increasingly rely on digital platforms for information, communication, and emotional support. Cyber behavior - which encompasses online engagement, security practices, and information sharing is shaped by cognitive and emotional factors such as awareness, knowledge, and anxiety. Understanding these relationships is crucial for promoting digital resilience and well-being during wartime and other large-scale emergencies. Objective: This study sought to examine how cybersecurity awareness, knowledge, and crisis-related anxiety influence cyber behavior and well-being during a national crisis. Drawing on the Protection Motivation Theory (PMT), the study further explored how cognitive and affective responses interact to shape individuals’ online engagement patterns and subsequent psychological outcomes. Methods: A cross-sectional online survey was conducted among 512 Israeli adults aged 18-65 during the ongoing war period (January 2024). Standardized psychometric instruments were used, including the WHO Well-Being Index, DASS-21 Stress subscale, and the Connor-Davidson Resilience Scale (CD-RISC-10). Media engagement was assessed across ten distinct digital activities. Data analysis employed a comprehensive approach, including cluster analysis, exploratory factor analysis (EFA), regression modeling, and path analysis. Results: Cluster analysis yielded two distinct segments: a high media engagement cluster and a low media engagement cluster. Participants in the high-engagement group reported significantly higher stress levels and greater utilization of digital media for news consumption, social networking, and charitable donations (p < .001). Furthermore, exploratory factor analysis revealed three salient dimensions of media usage: active, passive, and institutional. Path analysis indicated that stress was a positive predictor of all forms of media engagement. In predicting well-being, active media use (β = .12, p = .006) and resilience (β = .30, p < .001) were positively associated, whereas passive media use demonstrated a marginally negative association (β = -.08, p = .078). Conclusions: Cyber behavior during wartime is demonstrably influenced by both cognitive awareness and emotional stress. Specifically, while anxiety and stress tend to increase online engagement, overexposure to digital media may simultaneously well-being. Therefore, enhancing cyber literacy, cultivating emotional resilience, and promoting balanced media consumption are crucial strategies that can mitigate psychological distress and significantly strengthen digital resilience during crises.
Ambient AI technologies are increasingly marketed as solutions to reduce clinician burden and
improve care efficiency, yet real-world performance varies widely across clinical settings.
Healthcare p...
Ambient AI technologies are increasingly marketed as solutions to reduce clinician burden and
improve care efficiency, yet real-world performance varies widely across clinical settings.
Healthcare provider organizations face challenges in determining which aspects of ambient AI
performance matter most and how to obtain meaningful information about those aspects from
vendors or through internal evaluation. This article presents a shared mental model to guide
health system leaders in conceptualizing ambient AI performance across three interdependent
dimensions: technical, interface, and system-level. For each dimension, we outline the types of
information relevant to assessment, what vendors should reasonably be expected to provide, and
how healthcare provider organizations can conduct their own evaluations to contextualize,
verify, or supplement vendor claims. By integrating both vendor and health-system perspectives,
this work offers a grounded, practical structure to support organizations of all sizes in
understanding and making informed decisions about ambient AI technologies.
Scientific writing is a core competency in medical education and academic medicine, yet it remains a major barrier for early-career clinicians and researchers, particularly in resource-limited setting...
Scientific writing is a core competency in medical education and academic medicine, yet it remains a major barrier for early-career clinicians and researchers, particularly in resource-limited settings. Common challenges include limited formal training in scientific writing, heavy clinical workloads, restricted access to journals and editorial support, and difficulties writing in English as a non-native language. Recent advances in artificial intelligence (AI) have generated widespread interest as potential tools to support academic writing. However, most available guidance focuses on proprietary platforms or presents overly generic advice generated by large language models, offering limited practical value for trainees and educators working under real-world constraints.
In this Viewpoint, we present a practice-informed, tool-agnostic workflow illustrating how freely accessible or freemium AI tools may be used to support scientific writing in medical research and education. Rather than claiming empirical validation or comparative performance, we offer a scholarly perspective grounded in the lived experience of medical educators and researchers who routinely supervise early-career authors. We argue that the educational value of AI lies not in content generation, but in supporting core academic skills such as literature navigation, structured reading, drafting clarity, and iterative revision.
We outline key functional categories of free AI tools relevant to scientific writing, including literature discovery, reference management, PDF-based summarization, drafting and editing support, and table or figure preparation. We also address important limitations, including learning curves, internet connectivity requirements, data privacy concerns, disciplinary variability, and the risk of over-reliance on AI at the expense of critical thinking. Ethical considerations and transparency in AI use are emphasized in line with current editorial guidance.
We conclude that, when used deliberately and ethically, free AI tools may help to lower barriers to scientific writing for medical trainees and early-career researchers. Their greatest educational value lies in complementing—not replacing—foundational research skills, thereby supporting more equitable
Background: Despite several regional registries existing in the UK, gaps in geographic coverage have limited the ability to produce accurate national estimates of incidence, prevalence, and regional v...
Background: Despite several regional registries existing in the UK, gaps in geographic coverage have limited the ability to produce accurate national estimates of incidence, prevalence, and regional variation for Motor Neuron Disease (MND). To address these challenges, a comprehensive national register encompassing England, Wales and Northern Ireland was established to support epidemiological studies, healthcare planning, and clinical research. Objective: The primary objective of the MND Register is to provide a centralized research database aggregating clinical and demographic data from across the UK to facilitate high-quality research. Secondary objectives include estimating disease incidence and prevalence, identifying regional differences in care and survival, evaluating potential disease clustering, and supporting data linkage and clinical trial recruitment. Methods: Eligible patients are those aged ≥16 years with a confirmed MND diagnosis made by a consultant neurologist. Data are collected prospectively and retrospectively through standardized templates, available via MS Access, Excel, or the REDCap web platform, and include up to 34 demographic and clinical variables. Additional self-reported data can be contributed through the Telehealth in MND–Research (TiM-R) platform. All data are securely stored in the King’s College London Trusted Research Environment, undergo standardized preprocessing, and may be linked to NHS and national datasets for epidemiological analyses. Results: The Register includes data on over 11,000 individuals with MND, of whom nearly 7,000 are currently alive. Postcode data are available for more than 4,300 patients, enabling future geospatial analyses. By October 2025, 60 clinical sites were participating in the Register, with around 50 actively submitting data. Conclusions: The MND Register represents one of the largest national registries for MND worldwide, providing a robust foundation for epidemiological modelling, clinical research, and healthcare planning. Ongoing efforts to expand prospective data collection, improve completeness, and integrate digital tools will further enhance its impact and support national and international MND research collaborations. Clinical Trial: This is not a Clinical Trial but a Research Register. The MND Register has undergone ethical review by the London - South East Research Ethics Committee (REC reference: 25/LO/0371) and has been in operation since 2015.
Background: Inclusive physical education (PE) plays an important role in promoting participation and development among students with different abilities. However, many teachers do not have adequate to...
Background: Inclusive physical education (PE) plays an important role in promoting participation and development among students with different abilities. However, many teachers do not have adequate tools to modify PE activities to meet these diverse needs. In addition, parents are essential partners, as their involvement helps to reinforce strategies and provide useful information about their children. While online platforms provide a practical way to deliver such solutions, only a few are intentionally created to support both teachers and parents in implementing inclusive PE learning. Objective: This study aimed to develop an online platform that provides inclusion strategies for PE teachers and to examine how teachers and parents perceived its usability, acceptability, and overall usefulness using a mixed methods approach. Methods: A mixed methods research design was adopted in two phases. Phase 1 involved the development of the platform through expert consultation and literature review with feedback from educators. Phase 2 focused on user evaluation and involved usability testing using the System Usability Scale (SUS) and the Questionnaire for User Interaction Satisfaction (QUIS), alongside task performance metrics. Semi-structured interviews were also conducted with PE teachers (n=8) and parents (n = 8). Quantitative data were analyzed descriptively and with inferential statistics, while qualitative responses were coded thematically and the results were integrated using joint display. Results: All participants successfully completed the assigned tasks except few instances of minor difficulty during task completion (14 total errors across 136 task attempts). The Platform satisfaction scores were good as reported by PE teachers (8.03±1.59) and parents (8.13±1.06). QUIS scores were high among PE teachers (overall reaction: 8.03 ± 1.59; learning: 9.69 ± 0.40) and parents (overall reaction: 8.13 ± 1.06; learning: 8.63 ± 1.57). Mixed-methods integration showed strong convergence between high satisfaction scores and positive professional value quotes. However, divergence was noted in the learning domain, as high scores contrasted with reported uncertainty among new users. Lower system capability scores from parents (6.69 ± 2.25) were consistent with qualitative concerns about navigation inefficiencies and slow platform response. Desktop design was praised, while the mobile view was considered visually dense. Conclusions: The online platform provides strong usability and satisfaction among PE teachers and parents. Future work will involve improved implementation and evaluation of its impact on students’ participation outcomes.
Background: Young people increasingly experience mental health challenges and often turn to the internet for support. Self-guided digital mental health promotion services have become widely used resou...
Background: Young people increasingly experience mental health challenges and often turn to the internet for support. Self-guided digital mental health promotion services have become widely used resources for youth seeking help and guidance. These platforms offer accessible, anonymous support, yet little is known about the concerns young people articulate when engaging with them. Objective: This study examines inquiries submitted to a digital letterbox on one of Denmark’s most widely used digital mental health promotion services, Mindhelper.dk, to identify recurring themes in young people's inquiries about mental health and well-being. In addition, it explores how gender influences these experiences in the context of engagement with a self-guided digital platform. Methods: Employing an inductive analysis strategy and a grounded theory–inspired coding framework, this study analyzes a dataset of 2,523 inquiries submitted to the Mindhelper letterbox between March 2016 and August 2023. The archive provides rare, unsolicited first-person accounts from young people in moments of emotional vulnerability, providing immediate and authentic insights into their mental health concerns. Results: The analysis identifies 17 recurring themes that reflect the mental health challenges young people seek help for. These themes are grouped into three overarching analytical categories: Social Relations and Social Contexts, Emotional Life, and Body and Illness, with the first two dominating the material. The most prominent themes include Sociality, Love Life, Unease, Self-Criticism and Insecurity, and Communication and Reaching Out for Support. The intersection of themes underscores the central role of social relationships in young people's mental health and well-being, with frequent co-occurrence of inquiries addressing both Love Life and Sociality. Regardless of gender, users frequently inquire about Sociality and Love Life, indicating shared concerns related to social relationships. However, girls were markedly overrepresented among inquirers, highlighting potential gender differences in help-seeking behavior. Conclusions: Social relationships play a central role in young people's lives, yet many also face emotional struggles, particularly related to anxiety, self-esteem, and despair. The letterbox serves as an important help-seeking channel for youth who may lack access to support elsewhere, with a marked overrepresentation of girls, indicating gender patterns in help-seeking behavior. This study provides novel insights into the mental health challenges Danish youth face and their engagement with digital support services, informing the design of targeted, gender-sensitive self-help content and guiding future efforts to promote well-being and reduce barriers to help-seeking.
Background: Using mobile in healthcare is modernizing Patient-Reported Outcomes (PRO) for patient-centered approach. Our study introduces a mobile application that combines IoT devices as a remote pat...
Background: Using mobile in healthcare is modernizing Patient-Reported Outcomes (PRO) for patient-centered approach. Our study introduces a mobile application that combines IoT devices as a remote patient monitoring to enhance real-time communication and management between solid malignancy and healthcare providers. Objective: To evaluate the effectiveness of this mobile application on quality of life and compare emergency room visits in solid malignancy. Methods: A pilot randomized controlled trial was conducted on 30 patients with solid malignancies, recruited from an outpatient oncology clinic. The study compared remote monitoring via a mobile application and smartwatch plus conventional care with a physician as the care provider, to conventional care alone. The primary outcome was quality of life, assessed using the Functional Assessment of Cancer Therapy – General (FACT-G). The secondary outcome was the cumulative number of emergency room visits. An additional finding was literacy of side effects. Quality of life and emergency room visits were collected and analyzed at 1, 3, and 6 months, while literacy of side effects was assessed at 3 months. Results: Of the 30 participants, 26 completed all 6 months of follow-up assessments (Intervention: 13/15, 86.6%; control: 13/15, 86.6%). At 6 months, the intervention group had higher total quality of life scores (84.23 ± 12.128) compared to the control group (77.15 ± 14.002), though this was not statistically significant (P=.073). Notably, statistically significant improvements were observed in the intervention group in physical well-being at 1 to 3 months within group (P=.010) and at 6 months between groups (P=.048), and in emotional well-being at 1 to 6 months (P=.032). Functional well-being was preserved in the intervention group, while a decline was observed in the control group, with a significant within-group decline from baseline to 3 months (P=.033). Social and family well-being did not differ between groups across time. The intervention group had no emergency room visits, compared to three in the control group (P=.070). The literacy of side effects was not significantly different (P=.318). Conclusions: This study suggests that a smartphone application with wearable IoT support has the potential to improve quality of life in cancer patients. A clinically meaningful trend toward better outcomes was observed, with significant improvements in physical and emotional well-being, along with the prevention of functional deterioration. Fewer emergency room visits in the intervention group suggest effectiveness in remote patient monitoring (RPM) for the early detection of adverse clinical outcomes, supporting a more proactive approach to cancer care. These findings warrant further evaluation in larger, adequately powered trials. Clinical Trial: Thai Clinical Trials Registry TCTR20230331002; https://www.thaiclinicaltrials.org/show/TCTR20230331002
Background: The NHS 10 Year Health Plan emphasises an increasing shift towards digital healthcare delivery. However, there is limited research on how best to support, engage, and include individuals w...
Background: The NHS 10 Year Health Plan emphasises an increasing shift towards digital healthcare delivery. However, there is limited research on how best to support, engage, and include individuals who are digitally excluded. As healthcare services become more digitally driven, evidence-based interventions are needed to address digital exclusion and ensure equitable access to care, particularly for people living with long-term conditions. Objective: This study aimed to evaluate the feasibility and acceptability of providing digital literacy training alongside a digital health intervention (DHI), compared with a DHI alone. Kidney Beam, a DHI designed to promote physical activity and improve quality of life in people living with chronic kidney disease (CKD), was used as an exemplar intervention. Methods: A mixed-methods, single-site pilot randomised controlled trial recruited 40 adults with CKD who were digitally excluded. Digital exclusion was defined as lacking access to a Wi-Fi–enabled digital device or scoring <7 on a Digital Health Literacy Screening tool (DHLS). Participants were randomised 1:1 to receive either the Ex-Tab digital inclusion intervention alongside Kidney Beam or Kidney Beam alone. Participants in the intervention group received a Wi-Fi–enabled iPad with Kidney Beam pre-installed, digital literacy training, and ongoing support to access the 12-week Kidney Beam programme, which included twice-weekly live exercise and education sessions. The control group received sign-up instructions for Kidney Beam only.
Feasibility outcomes were assessed against a priori progression criteria and included screening, recruitment, retention, adherence, safety, and acceptability. Secondary outcomes included the Kidney Disease Quality of Life questionnaire, Chalder Fatigue Questionnaire, and Patient Health Questionnaire-4 (PHQ-4). Outcomes were measured at baseline and 12 weeks. Acceptability and user experience were explored through semi-structured interviews with participants from both groups at 12 weeks (n=25). Results: Between September 2023 and September 2024, 169 individuals were screened and 40 enrolled (median age 66.5 years; 50% male; median DHLS score 4). Twenty-one participants were randomised to the Ex-Tab group and 19 to the control group. Thirty-five participants (88%) completed the 12-week follow-up (Ex-Tab n=18; control n=17).
All pre-specified feasibility criteria for recruitment, retention, adherence, and safety were met. Qualitative findings indicated that the tablet loan and digital literacy training were acceptable and highly valued, enhancing confidence, motivation, and engagement with the DHI. Providing loaned devices was particularly important for overcoming access barriers, especially for participants unable to afford their own. Conclusions: Providing Wi-Fi–enabled devices and digital literacy training alongside a DHI was feasible and acceptable for people with lower levels of digital literacy. Findings support progression to a future definitive multicentre trial or implementation study and offer transferable insights for the design of digital inclusion strategies across other long-term health conditions. Clinical Trial: The study was approved by the Bromley NHS Research Ethics Committee (Ref: 21/LO/0243) and registered on ClinicalTrials.gov (NCT04872933).
Background: The nursing field is facing unprecedented challenges driven by an explosion of heterogeneous data, persistent data silos, and increasing complexity in clinical decision-making. These issu...
Background: The nursing field is facing unprecedented challenges driven by an explosion of heterogeneous data, persistent data silos, and increasing complexity in clinical decision-making. These issues underscore the urgent need for a systematic, integrative framework to organize and leverage nursing information effectively. Objective: This paper aims to conceptualize “Nursing Omics” a novel, multi-omics inspired integrative framework for future-oriented nursing informatics. Methods: Using a theoretical development approach, we draw on paradigms from genomics, proteomics, and other omics disciplines, integrating core principles from nursing informatics, systems science, and data science to construct a coherent conceptual architecture. Results: We propose a formal definition of Nursing-Omics and introduce a multidimensional integrative framework comprising the Intervenomics, Responsomics, Behaviomics, Exposomics, Experienomics. The framework is grounded in four foundational principles: holism, dynamism, data-driven insight, and individualization. Conclusions: Nursing-Omics offers a transformative paradigm for the systematic integration of nursing data, enabling precision decision-making, accelerating knowledge generation, and advancing intelligent, person-centered care. It represents a critical direction for the evolution of nursing informatics in the era of digital health. Clinical Trial: NO
Introduction: Large Language Models (LLMs) are increasingly applied in medical contexts, offering benefits for clinical decision-making, education, and patient communication. However, bias in LLM outp...
Introduction: Large Language Models (LLMs) are increasingly applied in medical contexts, offering benefits for clinical decision-making, education, and patient communication. However, bias in LLM outputs may exacerbate healthcare disparities and compromise trust. This systematic review will examine how bias is identified, measured, and mitigated in healthcare use cases of medical LLMs.
Methods and Analysis: A systematic search will be conducted in EMBASE, MEDLINE, PsycINFO, PubMed, ACL Anthology, ACM Digital Library, ArXiv, MedRxiv, and BioRxiv. Studies will be included if they investigate bias in LLM applications within healthcare, report experimental findings, and are published in English from 2017 onwards. Grey literature with adequate methodological detail will also be considered. Findings will be synthesised using a narrative approach due to anticipated methodological heterogeneity.
Ethics and Dissemination: As a secondary analysis of published literature, ethical approval is not required. Results will be disseminated through peer-reviewed publications, academic conferences, and open-access repositories to inform responsible LLM deployment in healthcare.
Registration Details: This protocol has been registered in PROSPERO (ID: 638943) https://www.crd.york.ac.uk/PROSPERO/view/CRD420250638943 and OSF.
Background: After non-curative resection for early gastric cancer (EGC) with endoscopic submucosal dissection (ESD), gastrectomy with lymphadenectomy is generally recommended. However, most patients a...
Background: After non-curative resection for early gastric cancer (EGC) with endoscopic submucosal dissection (ESD), gastrectomy with lymphadenectomy is generally recommended. However, most patients are found to have no residual cancer in the stomach or regional lymph nodes, while surgery carries a considerable risk of postoperative complications. In Western settings, patients with EGC are often elderly and have concomitant comorbidities. Objective: In this study, we aim to assess the feasibility and safety of indocyanine-green (ICG) - guided lymphadenectomy with or without laparoscopic and endoscopic cooperative surgery (LECS) following non-curative ESD for EGC. Methods: A single-center phase 1 prospective trial. Patients with EGC treated with ESD within the expanded criteria will be considered for inclusion, provided the resection was non-curative (eCuraC2). For patients with radically resected EGC, ICG-guided lymphadenectomy alone will be performed. In those with a non-radically resected EGC, ICG-guided lymphadenectomy and LECS will be performed. The primary objective is to evaluate the safety of the procedure, defined as Clavien-Dindo grade III or more. The secondary endpoints include other complications, operation time, number of positive lymph nodes, short-term mortality, and health-related quality of life. Results: As of January 9th, 2026, no patients have yet been recruited to the trial. Conclusions: ICG-guided lymphadenectomy with or without LECS is an appealing and potentially promising treatment strategy following non-curative ESD for EGC. To the best of our knowledge, no previous studies from the Western world have been conducted on this subject. Clinical Trial: ClinicalTrials.gov identifier: NCT07295002 Registered December 18th, 2025. URL: https://clinicaltrials.gov/study/NCT07295002?term=NCT07295002&rank=1
Background:
Malaysia is a multicultural country with the main ethnic groups being Bumiputra, Chinese, and Indian. This creates a rich food culture with distinct dishes, cooking styles, and portion si...
Background:
Malaysia is a multicultural country with the main ethnic groups being Bumiputra, Chinese, and Indian. This creates a rich food culture with distinct dishes, cooking styles, and portion sizes, making dietary assessment challenging. Intake24 is a web-based 24-hour dietary recall system that automates data collection, reduces recall bias, and saves time.
Objective:
This paper describes a protocol for the development and relative validation of Intake24 Malaysia (Intake24-MY) for the Malaysian population.
Methods
This paper describes two phases in adapting Intake24-MY: (1) the development process and (2) the validation study. Phase 1 consists of the following components: (1a) system translation, (1b) food list development, (1c) portion-size estimation, (1d) food-composition data, (1e) small-scale and pilot testing, and (1f) user guide development. Phase (2a) single-meal validation study that will be conducted among 100 adults, comparing Intake24-MY with observed intake. Phase 2b) cross-sectional study conducted among 482 Malaysian adults to compare 4 days of dietary intake using Intake24-MY against an interviewer-led 24-hour dietary recall. A structured questionnaire will be used to assess the feedback on the usability of Intake24-MY. The Bland-Altman method will be used to determine the agreement between these methods.
Results:
Recruitment for the pilot study began in September 2025. The single-meal validation study has been ongoing and is scheduled for completion by March 2026. Recruitment for the relative validation is scheduled to begin in May 2026, following institutional review board approval from the Monash University Human Research Ethics Committee (MUHREC ID: 41337).
Conclusion
Intake24-MY is a comprehensive digital dietary assessment tool for Malaysia and will contribute to improving dietary assessment for the multi-ethnic population in Malaysia.
Background: Cognitive reappraisal is a widely studied emotion regulation strategy that helps individuals reinterpret stressful situations in ways that reduce their emotional impact. Digital mental hea...
Background: Cognitive reappraisal is a widely studied emotion regulation strategy that helps individuals reinterpret stressful situations in ways that reduce their emotional impact. Digital mental health (DMH) tools often struggle to support this process because scripted templates fail to adapt to the varied and incomplete ways users describe their stressors. Large language models (LLMs) offer opportunities to increase conversational flexibility while preserving structured intervention steps. Objective: This study examined the feasibility of an LLM-based single-session intervention for workplace stress reappraisal. We aimed to assess whether the activity would be associated with short-term improvements in stress-related outcomes, and what design tensions arise during user interaction. Methods: We conducted a feasibility study with 100 employees from a large US technology company. Participants completed a structured cognitive reappraisal session delivered by a GPT-4o–based chatbot within Qualtrics. Pre–post measures included perceived stress intensity, stress mindset, perceived demand, and perceived resources (all 5-point scales). Paired Wilcoxon signed-rank tests were used with Benjamini-Hochberg correction. To complement self-reports, we analyzed sentiment and stress trajectories across conversation quartiles using a RoBERTa sentiment classifier, a RoBERTa stress classifier, and an LLM-based stress rater. Open-ended responses were analyzed using thematic analysis. Results: Participants wrote an average of 12.81 ± 1.66 messages, contributed 283.74 ± 243.16 words, and spent 23.09 ± 23.99 minutes engaging with the chatbot. Significant reductions were observed in perceived stress intensity (Δ = 0.29 ± 0.83, p = 0.002, r_rb = 0.54) and significant improvements in stress mindset (Δ = 1.70 ± 4.37, p = 0.002, r_rb = 0.44). Perceived resources increased (Δ = 0.17 ± 0.83, p = 0.07, r_rb = 0.32), and perceived demand decreased (Δ = 0.12 ± 0.83, p = 0.17, r_rb = 0.22) though neither reached significance. Sentiment and stress classifiers showed consistent declines in negative sentiment and stress from conversation start to end (all omnibus Friedman tests p < 0.001; Q1 to Q3 differences significant across all models). Qualitative analysis showed that participants valued the structured prompts for organizing thoughts, gaining perspective, and feeling validated. Reported design tensions included perceived scriptedness, variable preferences for conversation length, and mixed reactions to AI-driven empathy. Conclusions: An LLM-enhanced cognitive reappraisal activity showed promise to be delivered as a brief digital intervention and is associated with short-term improvements in perceived stress and stress mindset. Participants appreciated the clarity and reflection supported by the structured sequence, while noting important design challenges in balancing structure with conversational naturalness and contextual depth. These findings highlight both the promise and the design constraints of integrating LLMs into DMH interventions for workplace settings.
Background: The anesthesiology healthcare workers across various hospital levels in China were invited to participate in an electronic survey. Objective: The study aimed to assess the prevalence and i...
Background: The anesthesiology healthcare workers across various hospital levels in China were invited to participate in an electronic survey. Objective: The study aimed to assess the prevalence and impact of occupational burnout among anesthesiologists and anesthetic nurses in China, identifying key factors and providing a scientific basis for intervention strategies. The importance of this research lies in addressing the critical shortage of medical personnel in anesthesiology and its impact on healthcare quality. Methods: The primary goal was to provide a comprehensive analysis of occupational burnout among anesthesiologists and nurses in China using an electronic questionnaire. The questionnaire included assessments of occupational burnout, demographic and work-related information, work stress, interpersonal relationships, and health status. Results: A total of 1,465 participants were included across China. The response rate was 96.30%, with an overall burnout rate of 79.52%. Anesthesiologists had a burnout rate of 82.51%, and anesthetic nurses had a rate of 72.85%, showing a significant difference (P = 0.000). The prevalence of high emotional exhaustion and depersonalization was 45.80%, with anesthesiologists at 50.30% and nurses at 35.76%. Multivariable logistic regression analysis identified independent risk factors associated with burnout, including work environment, colleague relationships, and sleep quality for anesthesiologists, and experience, hospital level, and work intensity for anesthetic nurses. Conclusions: Occupational burnout is prevalent among anesthesiology professionals in China, with significant implications for individual well-being and patient care. The study's findings call for targeted interventions, such as improving work environments, enhancing education and training, and establishing support systems to mitigate burnout and promote work-life balance. Future research should focus on developing and evaluating effective intervention measures to ensure the well-being of medical professionals and the quality of healthcare services.
Background: Artificial intelligence (AI) is transforming medicine by enhancing care and reducing administrative tasks, and facilitating research. AI also raises many concerns, including a lack of clin...
Background: Artificial intelligence (AI) is transforming medicine by enhancing care and reducing administrative tasks, and facilitating research. AI also raises many concerns, including a lack of clinical context awareness, data dependence, and the absence of ethical judgment. As future practitioners, medical students must be prepared for these changes. Most studies assessing students' attitudes and knowledge were conducted before artificial intelligence became accessible and tailored to the needs of the population. Therefore, how medical students actually use AI remains largely unexplored. Objective: This study explores French medical students' knowledge and attitudes toward AI. Methods: A mixed-methods study was conducted in 2025 among French medical students in their 4th to 6th year of school, corresponding to the clerkship year. An online survey adapted from Ten et al. 2025 included open-ended questions about AI definition and feelings toward AI, a Likert scale item to assess specific attitudes, and multiple-choice questions about the characteristics of the student. Quantitative analysis was performed using non-parametric tests (Kruskal-Wallis) to compare attitudes by AI knowledge level, academic years, career aspirations, and ranking within the class. Qualitative analysis was performed inductively. Results: Of 1,377 responses received, 1,342 were included. Students had a mean age of 23.1 years and were predominantly in their 5th year. Only 6% provided a correct definition of AI, while 51% gave incorrect responses. Attitudes toward AI were generally positive, with a mean score of 6.85, with significant differences by correct response to the definition (p <0.01; Unknown: 6.12, Incorrect: 6.84, Partially correct: 6.94, Correct: 6.88) and by career goals (p<0.01; clinical: 6.58; research: 6.83; private practice: 7.19). Regarding learning, 49% of students think that AI learning should be outside the curriculum, compared to 44%. Most of the students suggested AI training through multiple workshops
Qualitative analysis revealed five themes: Representation, Nuanced Optimism, Critical Consideration, Replacement, and AI Use. Students represent AI as a robot, as an improved search engine, or as an unlimited data source. Their nuanced optimism blends enthusiasm for efficient patient care and provides an opportunity to focus more on the patient relationship, with fears of dehumanization, energy costs, and skill regression. Critical consideration underscores distrust in ethical dilemmas and data security risks. Replacement concerns arise over shifting professional roles, though many believe human empathy remains irreplaceable. For AI use, students highlight administrative aid, personalized training, and clinical support. Conclusions: There is growing interest in AI among medical students, accompanied by new ecological concerns and fears of skill loss. Students seem to have learned to use AI on their own for learning. These results highlight the need to adapt training programs to include the responsible use of these technologies and how to use AI to its fullest potential.
Background: Background: Adolescence is a critical period for spinal and neuromuscular development, during which abnormal spinal curvature may progress rapidly and lead to long-term musculoskeletal dys...
Background: Background: Adolescence is a critical period for spinal and neuromuscular development, during which abnormal spinal curvature may progress rapidly and lead to long-term musculoskeletal dysfunction. Exercise therapy is widely recommended as a non-surgical intervention; however, substantial individual variability in treatment response limits its clinical effectiveness. Although multidimensional data on body composition and spinal function are routinely collected in schools and rehabilitation clinics, these data are rarely integrated into intervention decision-making. Current screening and treatment selection still rely largely on visual assessment and simple angular measurements, and validated tools for identifying adolescents most likely to benefit from specific exercise therapies are lacking. Objective: Objective: This study aimed to evaluate the effects of a 12-week spiral muscle chain training (SPS) and combined exercise therapy incorporating proprioceptive neuromuscular facilitation (PNF), and to develop an interpretable machine learning–based predictive model to support personalized exercise therapy planning for adolescents with abnormal spinal curvature. Methods: Methods: The data for this study were derived from a 12-week randomized controlled trial of exercise therapy. A total of 125 middle and high school students with abnormal spinal curvature were recruited from schools and randomly assigned to a spiral muscle chain training group (n = 61) or a combined exercise therapy group (n = 64). All interventions were conducted offline. Baseline and post-intervention assessments of body composition and spinal health were performed using standardized clinical measurements. Singular value decomposition–based principal component analysis (SVD-PCA) was applied to extract principal components representing spinal mobility and balance. These components, together with demographic and clinical indicators, were used to construct predictive models using four machine learning algorithms: K-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost). Model performance was evaluated, and SHapley Additive exPlanations (SHAP) were used to interpret the optimal model. Results: Results: Both exercise therapies significantly improved spinal curvature, spinal mobility, and head, shoulder, and pelvic balance, with combined exercise therapy demonstrating superior efficacy. The reduction in angle of trunk inclination (ATI) was greater in the combined therapy group(P<0.001). SVD-PCA extracted three mobility-related principal components and one balance-related component from 21 spinal indicators, explaining 86.37% of the total variance. Among all models, the RF model achieved the best predictive performance (AUC=0.950, F1=0.857, BS=0.120). SHAP analysis identified exercise therapy type, kyphotic angle (KA), ATI, and spinal function–related principal components as the most influential predictors. Conclusions: Conclusions: Both SPS and combined exercise therapy effectively improve adolescent spinal curvature abnormalities, with SPS showing particular value for mild to moderate cases. Machine learning–based predictive models can integrate multidimensional spinal health data to provide interpretable and individualized predictions, supporting precision assessment and personalized intervention strategies for adolescents with abnormal spinal curvature. Clinical Trial: Trial Registration:
ClinicalTrials.gov NCT07319702; https://clinicaltrials.gov/ct2/show/NCT07319702
Background: The global prevalence of pressure injuries is high and can cause severe infections, or death. Accurate staging is vital for effective intervention. Deep learning streamlines pressure injur...
Background: The global prevalence of pressure injuries is high and can cause severe infections, or death. Accurate staging is vital for effective intervention. Deep learning streamlines pressure injury assessment, enhances efficiency, and yields practical, accurate results. This scoping review summarized research on multi-modal deep learning for intelligent pressure ulcer recognition. Objective: It systematized models, training methods, and outcomes to identify the best systems for rapid detection and automated staging of pressure ulcers. Enhancing the timeliness, accuracy, and objectivity of diagnosis is the goal. Methods: We searched the following databases and sources: PubMed, the Cochrane Library, IEEE Xplore, and Web of Science. The scoping review was conducted in accordance with the JBI Scoping Review Methodology Group’s guidance and reported following Preferred Reporting Items for Systematic Reviews and Meta-Analyses—Extension for Scoping Reviews guidelines. The study protocol was registered with the International Prospective Registry of Systematic Reviews (PROSPERO) on 12 December 2025 (registration number: CRD420251251573). Results: 15 articles were included: 26 models were involved, including AlexNet; VGG16; ResNet18; DenseNet121; SE-Swin Transformer; Cascade R-CNN; vision transformer (ViT); ConvNextV2; EfficientNetV2; Meta Former; TinyViT; CCM; BCM; ResNext + wFPN; SE-Inception; Mask-R-CNN; SE-ResNext101; Faster R-CNN; ResNet50; ResNet152; DenseNet201; EfficientNet-B4; YOLOv5; Inception-ResNet-v2; InceptionV3; MobilNetV2. The training methodology for intelligent pressure ulcer recognition models involves establishing an image database, processing images, and constructing the recognition model. Different models exhibit varying accuracy rates in staging pressure ulcers, with overall accuracy fluctuating between 54.84% and 93.71%. The DenseNet121 model achieved the highest recognition accuracy of 93.71%, while VGG16 was the most widely applied. The same model demonstrated significant variations in recognition accuracy across different studies. Conclusions: The multi-modal and deep learning-based intelligent recognition model for pressure injuries demonstrates high overall accuracy, enabling rapid automated staging of such injuries. Future research may explore optimized intelligent assistance systems to enhance the accuracy, objectivity, and efficiency of pressure injury diagnosis.
Background: Prolonged exposure to computer screens has been associated with visual fatigue and reduced visual comfort, which may in turn affect cognitive performance and concentration. While blue-enri...
Background: Prolonged exposure to computer screens has been associated with visual fatigue and reduced visual comfort, which may in turn affect cognitive performance and concentration. While blue-enriched screen light and display settings are known to influence visual strain, their impact on short-term task performance under different backlight configurations remains insufficiently quantified from a human factors perspective. Objective: This study aimed to evaluate the effects of different computer screen backlight settings on user concentration, using typing speed as a quantitative proxy for task performance. Methods: A total of 22 adult participants performed standardized reading and typing tasks under different screen backlight conditions, including black text on a white background and white or orange text on a dark background. Screen illuminance and spectral characteristics were measured using a calibrated spectrometer. Typing speed was recorded after controlled reading periods, and statistical analyses were conducted to assess changes in performance across conditions. Results: Typing speed decreased significantly after 30 minutes of reading under a traditional black text on white background. In contrast, switching to a dark background with white text resulted in a significant increase in typing speed. Further improvement was observed when orange text was used on a dark background. Myopic diopter showed no significant correlation with changes in typing performance. Conclusions: Lower screen illuminance achieved through dark background display settings was associated with improved short-term task performance. These findings suggest that display configurations emphasizing reduced luminance may help maintain concentration during computer-based tasks and have implications for visual ergonomics and human-centered display design. Clinical Trial: Not applicable.
Background: Background Heart failure (HF) is a refractory disease with a global public health issue that is continuously increasing. Metabolic syndrome plays a crucial role in prevalence and mortality...
Background: Background Heart failure (HF) is a refractory disease with a global public health issue that is continuously increasing. Metabolic syndrome plays a crucial role in prevalence and mortality of HF. The triglyceride-glucose (TyG)-related obesity indices, such as body mass index (BMI), a body shape index (ABSI), and waist-to-height ratio (WHtR), have been recognized as a significant predictor of cardiovascular disease risk. Nevertheless, the predictive value of these makers for HF prevalence and their association between all-cause mortality in general populations remains unclear. Objective: in this study, we aimed to evaluate their association with prevalence and all-cause mortality among HF patients using machine learning techniques. Methods: The U.S. National Health and Nutrition Examination Survey (NHANES) (2001-2018) database provided all the data for this study. The status of the participants was followed through December 31, 2019. Participants were categorized into a non-HF group and a HF group. Weighted binary logistic regression was performed to evaluate the independent associations between the TyG-related obesity indices and HF. Meanwhile, subgroup analysis was performed to confirm the reliability of the associations observed among different population. Restricted cubic spline (RCS) models were utilized to delineate whether the relationship is non-linear. Random forest analysis and Boruta algorithm were adopted to assess the predictive value of each biomarker for the prevalence of HF. Receiver operating characteristic (ROC) curves were generated to assess the predictive performance. Additionally, those biomarkers were categorized into two groups based on threshold derived from the maximally selected rank statistics (MSRS). Kaplan-Meier survival analysis and weighted Cox regression models were employed to explore the association between each TyG-related obesity indices and all-cause mortality among HF patients. Results: 40,908 participants (1,174 HF patients) were encompassed in this retrospective study. In the fully adjusted model, TyG-BMI, TyG-ABSI, and TyG-WHtR exhibited higher odds ratio (OR) than TyG alone. TyG-ABSI exhibited the strongest association both as a continuous variable and across quartiles, demonstrating a significant near-linear positive dose-response relationship with HF risk. RCS analysis further confirmed a linear relationship between TyG-related obesity indices and HF risk. The ROC curve analysis demonstrated that TyG-ABSI had the best predictive performance for HF risk (AUC: 0.721, 95% CI: 0.690–0.736). Random forest analyses and Boruta algorithm identified those biomarkers as an important clinical feature. Subgroup analysis revealed no significant interactions across all subgroups, except for age. During a median follow-up of 9 years, a total of 566 deaths were documented, when stratified by the MSRS-derived optimal cutoff value, Kaplan-Meier survival analysis and Cox regression model demonstrated significantly worse overall survival for the higher TyG-ABSI group (HR:1.44, 95% CI=1.11-1.86, P=0.006), each standard deviation increment in TyG-ABSI was associated with an 11% increment all-cause mortality risk among HF patients. Conclusions: Our study suggests that TyG-BMI, TyG-ABSI and TyG-WHtR are associated with increased odds of HF in the U.S. TyG-ABSI demonstrate the best predicted performance and expect to become more effective metrics for improving risk stratification. TyG-ABSI is independently associated with increased all-cause mortality risk in HF patients, highlighting its potential as a useful tool in aiding personalized management.
Background: Parkinson’s disease (PD) is a progressive neurodegenerative disorder that poses complex challenges for persons with Parkinson’s (PwP), informal caregivers, and healthcare professionals...
Background: Parkinson’s disease (PD) is a progressive neurodegenerative disorder that poses complex challenges for persons with Parkinson’s (PwP), informal caregivers, and healthcare professionals. With growing interest in digital and predictive Artificial Intelligence (AI) tools for disease management, understanding the needs and digital readiness of these stakeholder groups is crucial. Objective: This work aims to (1) identify digital practices for PD management among PwP, at‑risk individuals, caregivers, and healthcare professionals; (2) compare these practices across groups; (3) explore stakeholder desires for AI-based tools; and (4) assess alignments and gaps to inform tailored AI solutions. Methods: An anonymous cross-sectional online survey of exploratory nature was distributed (from Dec. 2024 to Oct. 2025) in five languages. It was completed by 255 respondents. Descriptive statistics summarized responses to 41 questions, including stakeholder-specific items. Chi-square tests were performed to examine stakeholder differences in desired AI-features. Results: : Interest in predictive AI was high across stakeholder groups. Symptom-tracking was the most desired feature (selected by >76% of respondents); however, stakeholder priorities diverged in other areas. Healthcare professionals rated improving patient and informal caregiver engagement as significantly more important than PwP did, χ²(1, N=205)=34.78, p<.001, Cramer’s V=0.41. Despite considerable interest, the reported use of digital tools was limited, as most PwP did not use symptom-tracking apps or wearables, nor were they currently monitoring their condition, although many expressed intentions to begin. Conclusions: While AI tools were viewed positively across groups, there were significant gaps in current usage. Stakeholder-specific preferences, including informal caregiver engagement and preventive lifestyle guidance, highlight the importance of tailored design. These findings offer early-stage insight to guide development of future AI-based solutions for PD.
Background: : Stroke remains a leading cause of motor disability globally. Functional electrical stimulation (FES) has emerged as a promising neurorehabilitation modality, but its comparative efficacy...
Background: : Stroke remains a leading cause of motor disability globally. Functional electrical stimulation (FES) has emerged as a promising neurorehabilitation modality, but its comparative efficacy, optimal application parameters, and long-term sustainability remain incompletely characterized. Objective: To synthesize evidence from randomized controlled trials and systematic reviews published between 2021 and 2025 regarding the effectiveness of FES interventions for upper and lower limb motor recovery in post-stroke populations. Methods: A comprehensive literature search was conducted across PubMed, Scopus, Web of Science, and Cochrane Library databases. Studies were selected based on PRISMA 2020 criteria. Quality appraisal was performed using the Physiotherapy Evidence Database (PEDro) scale and Cochrane Risk of Bias 2 tool. Quantitative synthesis was conducted using random-effects meta-analyses. Results: Twenty-seven studies (n=2,309 stroke participants) were included, encompassing diverse FES modalities: manually controlled, electromyography-triggered, brain-computer interface-controlled, and hybrid systems. Meta-analytic findings demonstrated that FES combined with occupational therapy produced significantly greater improvements in upper limb motor function (Fugl-Meyer Assessment: mean difference [MD] = 5.08, 95% confidence interval [CI] 2.46-7.71) compared to standard care alone. Brain-computer interface-controlled FES achieved superior outcomes (standardized mean difference [SMD] = 0.73, 95% CI 0.26-1.20) particularly when paired with action observation tasks. For lower limb recovery, FES reduced foot drop severity and enhanced gait parameters, with 52% of participants achieving independent walking. Cost-effectiveness analysis demonstrated long-term value (£15,406 per quality-adjusted life year). Adverse events were minimal, primarily limited to temporary skin irritation. Conclusions: FES represents a viable, evidence-supported adjunctive intervention for post-stroke motor recovery across subacute and chronic phases. Emerging technologies integrating brain-computer interfaces and artificial intelligence offer enhanced personalization and efficacy. Future research should prioritize real-world implementation trials, long-term follow-up protocols, and mechanisms underlying neuroplastic adaptations.
Background: Medical and welfare facilities in the Noto region of Japan were severely affected by the 2024 Noto Peninsula earthquake and the subsequent torrential rains. Staff members working in these...
Background: Medical and welfare facilities in the Noto region of Japan were severely affected by the 2024 Noto Peninsula earthquake and the subsequent torrential rains. Staff members working in these facilities have been disaster victims and frontline caregivers and face prolonged restoration work with limited psychological support. Nonverbal social robots have been designed to provide companionship and emotional comfort. However, their effects on health-related quality of life (QoL) and well-being among care staff in disaster-affected settings are unknown. Objective: This study aimed to investigate whether introducing a nonverbal artificial intelligence (AI) communication robot can improve QoL and subjective well‑being in care facility staff working under disaster conditions. The secondary objective was to assess the safety, acceptability, and intention to continue using the robot. Methods: An ABAB intervention design was implemented between February and June 2025. After a 2‑week baseline, staff in dementia care, general care, and short‑stay units received the robot intervention for 2 weeks (A1), followed by a 2‑week withdrawal (B1), re‑intervention (A2), and final withdrawal (B2). The questionnaires were administered at the end of each phase. Primary outcomes were health‑related QoL (EQ‑5D‑5L), well‑being (WHO‑5 Well‑Being Index), and mental health continuum (MHC‑SF). Secondary outcomes included safety (three Likert‑scale items), acceptability (17 semantic‑differential items), and interaction frequency. Friedman tests were used to compare outcomes across phases, with Wilcoxon signed-rank tests and Bonferroni correction for post-hoc comparisons. Only participants with complete data across all phases were analyzed. Results: Of the 58 staff completing baseline assessments, 49 provided complete data (25 dementia care, 12 general care, 12 short‑stay). The participants were predominantly female, with a median age in the fifth decade; 75.7% reported personal disaster damage. The median baseline EQ‑5D‑5L utility, WHO‑5 percentage, and MHC‑SF scores were approximately 0.93, 60%, and 35 points, respectively. Interaction frequency with the robot significantly increased during the intervention phases, but Friedman tests showed no significant differences in EQ‑5D‑5L, WHO‑5, or MHC‑SF scores across the ABAB phases within or across units. Safety outcomes and the intention to continue use did not differ between the intervention and withdrawal phases, and no adverse events were reported. Acceptability improved for items, such as “felt calm,” “liked,” and “felt peaceful” in the dementia care unit and for “competent” and “peaceful” in the pooled analysis. However, these effects were insignificant after Bonferroni correction. Conclusions: In this study, the short-term use of a nonverbal AI communication robot did not lead to measurable improvements in QoL or well-being. Nonetheless, the increased interaction and positive acceptability ratings suggest that the robot was well-received and could be safely and feasibly deployed in disaster settings. Long-term studies with larger samples are required to determine whether such robots can provide meaningful mental health support to healthcare workers. Clinical Trial: Not applicable.
Background: Mentalization is a core human capacity involving the interpretation of one’s own and others’ behavior in terms of underlying mental states. Within the Mentalization-Based Treatment (MB...
Background: Mentalization is a core human capacity involving the interpretation of one’s own and others’ behavior in terms of underlying mental states. Within the Mentalization-Based Treatment (MBT) framework, this capacity is described along multiple dimensions integrating cognitive, affective, relational, and regulatory processes. Large language models (LLMs) have recently shown an ability to generate linguistically reflective discourse, raising questions about whether the formal linguistic structure of mentalization can be reproduced independently of experiential and affective processes. This study investigates whether LLM outputs can be systematically evaluated as reflecting the linguistic structure of mentalization without implying psychological mentalization or theory of mind. Objective: The aim of this study is to assess whether a large language model can generate outputs that are structurally coherent with established MBT dimensions, and to determine the extent to which such outputs are recognizable as formally mentalizing by expert clinicians. The study introduces and operationalizes the concept of algorithmic reflectivity, defined as a formally coherent but non-experiential linguistic phenomenon. Methods: A comparative, descriptive methodological design was adopted. Fifty dialogic interactions between a large language model and human participants were generated under standardized conditions. At the end of each interaction, the model produced a narrative mentalization profile structured along MBT dimensions. Five psychiatrists with formal MBT training independently and blindly evaluated all profiles. Evaluations used 5-point Likert scales assessing (1) evaluative coherence, (2) argumentative coherence, and (3) global quality across the MBT dimensions. Interrater reliability was estimated using the intraclass correlation coefficient (ICC[3,1]). Descriptive statistics were used to summarize score distributions and variability. Results: Across all dimensions, mean scores ranged from 3.63 to 3.98, indicating moderate to high structural coherence. Interrater reliability was substantial to high, with ICC values ranging from 0.60 to 0.84. The highest scores were observed for dimensions related to explicitness, synthesis, and self–other differentiation, while lower scores were observed for integration between internal states and external context. Qualitative comments consistently described the outputs as linguistically organized and clinically interpretable, but affectively neutral and weakly contextualized. No evidence of experiential grounding, affective modulation, or intentional agency was observed. Conclusions: The findings indicate that LLMs can reliably reproduce the formal linguistic structure associated with mentalization as defined by MBT, generating outputs that expert clinicians recognize as structurally coherent. However, this capacity reflects algorithmic reflectivity rather than psychological mentalization: a form of linguistic coherence without experiential, affective, or relational grounding. The study supports a clear conceptual distinction between mentalization as a psychological function and its discursive structure as a linguistic phenomenon. These results suggest that LLMs may serve as methodological tools for research and training on reflective language, while remaining unsuitable for unsupervised clinical application.
Background: Self-assessment is a key requirement for lifelong learning in medicine. Evidence from gender-related research indicates that important moderators affecting self-assessment are influenced b...
Background: Self-assessment is a key requirement for lifelong learning in medicine. Evidence from gender-related research indicates that important moderators affecting self-assessment are influenced by gender. Therefore, systematic gender differences in the accuracy of self-assessment may be assumed. Objective: The present study aims to examine gender differences in medical students’ self-assessment. Specifically, this study addresses two research questions: (1) Are there systematic gender differences in medical students' self-assessment accuracy? (2) What is the magnitude of these gender differences when accounting for academic progress and knowledge? Methods: Medical students from 3 cohorts at the Medical School OWL were surveyed in 3 waves between April 2023 and April 2024 during the Progress Test Medicine (PTM). Prior to answering the test, students were asked to indicate the percentage of the PTM questions they expected to answer correctly in five knowledge areas. Self-assessment accuracy was calculated as the difference between the subjective self-assessment and the objective test score. Linear mixed models (LMMs) were used to analyze the influence of gender on students’ self-assessment accuracy while accounting for academic progress and knowledge. Results: A total of 165 students participated in this study (66.58% women, 33.42% men; age: M=21.96 years, SD=3.61). Across all models, female students rated themselves significantly less accurately than their male peers. The observed gender effect ranged from -3.74 to -6.08 percentage points. Conclusions: The results indicated systematic gender differences in medical students’ self-assessment, in favor of male students, with a magnitude comparable to the average knowledge acquired in an entire semester of study. In view of the potentially negative consequences of inaccurate self-assessment, targeted support for developing realistic self-assessment during medical studies may be particularly beneficial for female students.
Background: Mobile health (mHealth) and online video are increasingly central to cardiology education and point-of-care decision support. However, little is known about how simple design choices—suc...
Background: Mobile health (mHealth) and online video are increasingly central to cardiology education and point-of-care decision support. However, little is known about how simple design choices—such as mobile-first web layouts and captioned video—function as equity enablers across income settings when examined with multi-country learning analytics. Objective: This exploratory ecological study used real-world, cross-platform learning analytics from a French-language cardiology mHealth education initiative to quantify how mobile web access and captioned YouTube viewing varied across World Bank income groups and assess whether greater reliance on these access enablers was associated with poorer engagement. Methods: We analyzed country-level analytics from the École Numérique de Cardiologie (ENC) mobile-optimized website and companion YouTube channel over a 2-year period. Countries were grouped as high-, middle-, or low-income. Primary access indicators were the share of website sessions from mobile devices and the share of YouTube watch time with subtitles enabled (any language). Engagement outcomes included website bounce rate and time on page and YouTube average view duration, audience retention, and intentional views. We summarized medians by income group and explored associations using nonparametric tests, Spearman correlations, and median quantile regression. Results: Thirty-four countries contributed data (13 high-income, 14 middle-income, 7 low-income). Caption-enabled watch time showed a marked income gradient, increasing from 18.8% in high-income to 38.7% in middle-income and 60.9% in low-income groups, a caption equity gap of 42.1 percentage points between low- and high-income settings. Median mobile share of website sessions also rose with decreasing income (36.5%, 63.3%, and 81.4%, respectively). Income groups with higher caption use also had a higher share of intentional views and younger audiences. Greater reliance on mobile access was not independently associated with higher bounce rate or shorter time on page in quantile regression models. Conclusions: In this multi-country mHealth learning analytics case study, mobile-first web access and captioned video were used most intensively in lower-income settings and were not associated with penalties in basic engagement metrics. These findings support treating mobile-optimized design and systematic captioning, including non-French subtitles, as core, low-cost components of equitable digital cardiology and mHealth education, and suggest that simple analytics indicators can serve as equity-focused monitoring tools for global mHealth initiatives.
Background: While Artificial Intelligence (AI) is increasingly adopted in healthcare, clinicians face barriers including insufficient understanding, limited trust, and interpretation challenges. Exist...
Background: While Artificial Intelligence (AI) is increasingly adopted in healthcare, clinicians face barriers including insufficient understanding, limited trust, and interpretation challenges. Existing frameworks, such as the UNESCO AI Competency Framework, lack clinical specificity. Additionally, there remains limited evidence on structured, framework-based training programs designed to advance AI literacy among medical professionals. Objective: This study aimed to (1) develop and validate a Medical AI Competency Framework and (2) demonstrate the framework’s practical application through the design and pilot implementation of an AI training program. Methods: We first drafted a Medical AI Competency Framework by integrating the UNESCO AI framework with Miller’s pyramid model. Expert feedback and validation involved 24 stakeholders (six hospital administrators, eight medical professionals, and ten university instructors). A five-module AI training program was designed incorporating problem-based learning (PBL) and flipped classroom methodology. A two-round Delphi process with nine educators in instructional design, medical education, and AI validated the program design using consensus criteria (Round 1: IQR≤1, AS≥75%, FS > threshold; Round 2: AS≥80%). A pilot mini-workshop with 28 participants and 4 instructors assessed the feasibility of the training program by measuring participants’ satisfaction, engagement, and self-confidence. Results: A six-dimension, four-level Medical AI Competency Framework was developed. Expert validation showed strong emphasis on AI foundations (79.17%) and application skills (95.83%) of the framework. Based on the framework, a five-module AI training program was designed. Each module included five elements: content, learning goals, teaching activities, learning resources, and assessment. The Delphi process achieved complete consensus across all 25 elements of the training program. Pilot implementation surveys suggested participants’ high satisfaction (Mean = 4.00), strong engagement across behavioral, emotional, and cognitive dimensions (Mean = 3.80–4.05), and positive self-confidence in applying AI in medical contexts (Mean = 3.63). Conclusions: This study presents an empirically informed framework and demonstrates its practical value through a structured training program. It provides a scalable model for integrating AI into medical curricula, enhancing medical professionals’ readiness for AI-driven healthcare. Future work should expand the framework and training program to new regions and delivery formats (e.g., semester-long courses and continuing medical education) and evaluate their long-term impact through longitudinal, multi-institutional studies.
Background: Over the past decade, Europe has expanded school-based mental health prevention programs, yet the prevalence of mental disorders among children and adolescents remains high and has risen f...
Background: Over the past decade, Europe has expanded school-based mental health prevention programs, yet the prevalence of mental disorders among children and adolescents remains high and has risen further since the COVID-19 pandemic. Digital interventions have proliferated, yet implementation gaps persist, limiting their impact. Objective: To synthesize quantitative, qualitative, and mixed-methods evidence on the facilitators and barriers to implementing digital and analog universal school-based mental health promotion programs for children and adolescents (ages 5–19) in European primary and secondary schools, and to examine how implementation quality is assessed and the role of the digital environment. Methods: A three-step search will be conducted across the interfaces PubMed, EBSCO, Clarivate Analytics, PubPsych, Fachportal Pädagogik, Google Scholar, relevant preprint servers, and the reference lists of all included sources of evidence. A first systematic search was completed in January 2026. Titles/abstracts and full texts will be screened independently by two reviewers, with disagreements resolved through discussion or a third reviewer. Methodological quality will be appraised by assessing the trustworthiness, relevance, and results of published papers. Data will be extracted using standardized JBI forms and analyzed separately into quantitative (descriptive statistics, possible meta-analysis) and qualitative (meta-aggregation) components, followed by a convergent, segregated synthesis to integrate findings. No deviations from the JBI mixed-methods systematic review methodology are anticipated. Results: A comprehensive PubMed search was conducted on January 6, 2026, and 614 records were retrieved after applying filters. Results are expected to be published by December 2026. Conclusions: By integrating quantitative and qualitative findings, this review will identify the key facilitators and barriers influencing the real‑world uptake of digital and analog school‑based mental‑health programs across Europe. Mapping these determinants onto implementation frameworks such as CFIR and RE‑AIM and linking them to program outcomes will yield actionable recommendations that can close the implementation gap, bolster sustainability, and improve mental‑health outcomes for children and adolescents in the post‑COVID era.
Objective: The exponential expansion of biomedical literature has created an urgent need for efficient methods to recognize and extract PICO (Population, Intervention, Comparison, Outcome) - the found...
Objective: The exponential expansion of biomedical literature has created an urgent need for efficient methods to recognize and extract PICO (Population, Intervention, Comparison, Outcome) - the foundational elements of evidence-based medicine (EBM). This study systematically evaluates two complementary approaches for automating PICO recognition and extraction in medical literature: prompt engineering optimization and parameter-efficient Fine-Tuning of large language models (LLMs).
Methods: We developed a dual-phase methodological framework: (1) systematic prompt optimization incorporating In-Context Learning (ICL), Chain-of-Thought (COT), and Tree-of-Thought (TOT) reasoning strategies; and (2) parameter-efficient fine-tuning (PEFT) of the LLM architecture using Low-Rank Adaptation (LoRA), Quantized LoRA (QLoRA), and Freeze techniques. PubMed-PICO and NICTA-PIBOSO benchmark datasets are used for recognition tasks while EBM-NLP is applied for extraction tasks. Performance metrics includes precision, recall, and F1-score . F1 is adopted as the major metric as it balances precision and recall.
Results: COT prompting demonstrated superior recognition accuracy, achieving F1-scores of 77.1% (Population) and 84.5% (Outcome) on PubMed-PICO. In PEFT implementations, LoRA achieved peak classification performance (91.7% F1 for Population), while QLoRA showed best ex-traction capability (79.3% F1 for Intervention). Fine-tuned models established new benchmarks across all datasets, attaining SOTA results on NICTA-PIBOSO and EBM-NLP. PEFT demonstrated marked improvements over prompt engineering.
Conclusion: Our findings indicate that large language models (LLMs) can effectively automate PICO recognition and extraction through two complementary approaches. First, prompt engineering allows the model to perform tasks directly without altering its internal settings. Second, the PEFT method further unlocks their maximum performance potential by incorporating additional fine-tuning based on prompt engineering. This work made significantly advances and provides critical insights for optimizing methodological approaches in clinical applications related to or comprised of PICO extraction and recognition tasks.
Background: Traditional conversational agents have emerged as potential psychological tools in mental health field. While the text-only interactions limit compliance and efficacy. Virtual agents (VAs)...
Background: Traditional conversational agents have emerged as potential psychological tools in mental health field. While the text-only interactions limit compliance and efficacy. Virtual agents (VAs) show great potential to solve this problem. Objective: This study aimed to assess whether the combination of Echo appV2.0, a VA-based digital psychological intervention and TAU (Treatment as usual) yield greater efficacy compared to TAU alone. Methods: 93 participants were randomized to 4-week Echo-app-v2.0 intervention combined with TAU compared to TAU alone. The primary outcome was change of craving. Secondary outcomes were relapse and change of emotional state, sleep quality, and treatment motivation. Results: The intervention group showed significant lower craving at week 8(β = -1.81, 95% CI: [-3.45, -0.16], p = 0.03) and week 16 (β = -1.97, 95% CI: [-3.61, -0.32], p = 0.02). A significant difference in relapse between the two groups at the week 8 follow-up (χ2=4.09, P=0.04). Statistically significant larger improvement in sleep quality was found in the intervention group (β = -3.28, 95% CI: [-5.08, -1.49], p <0.001). Perceived stress decreased significantly over time (β = -2.91, 95% CI: [-5.27, -0.56], p = 0.02). Linear regression showed that group and change in PSS significantly predicted Craving change at week 8(intervention: β = -2.13, 95% CI: [-4.03, -0.24], p = 0.03 ;PSS: β = 0.15, 95% CI: [0.03, 0.26],p = 0.02) and week 16(intervention:β= -2.24, 95% CI: [-4.07, -0.42], p = 0.02；PSS: p = 0.03).Both group (β = 0.12, 95% CI: [0.01, 0.24], p = 0.03) and abstinence frequency (β = 0.27, OR = 1.31, 95% CI: [1.06, 1.63], p = 0.01) significantly predict relapse at week 8. Conclusions: Echo app V2.0 has certain therapeutic potential in treating AUD, but further adjustments to the intervention are needed to enhance its long-term efficacy. Clinical Trial: ClinicalTrials.gov Identifier: NCT05675553
Background: Despite the high potential of artificial intelligence (AI) in diagnosing Alzheimer's disease, a profound gap exists between reported accuracy in ideal conditions and models' reliable perfo...
Background: Despite the high potential of artificial intelligence (AI) in diagnosing Alzheimer's disease, a profound gap exists between reported accuracy in ideal conditions and models' reliable performance in real-world clinical settings. Objective: This systematic analysis aimed to identify the root causes of this gap and propose practical solutions. Methods: We conducted a systematic analysis in accordance with PRISMA 2020, analyzing 56 studies (2013-2023). A qualitative content analysis was performed around four pillars: 1) Data repository characteristics, 2) Data preprocessing and model design, 3) Technical implementation frameworks, and 4) Performance evaluation protocols. Results: Results indicate a methodological transition towards standardized data repositories and modern AI frameworks. However, rapid algorithm development has outpaced the maturity required for clinical generalizability. Four key deficits were identified:
1. Data limitations due to reliance on restricted, low-diversity datasets (63% of studies used ADNI exclusively).
2. Insufficient standardization in preprocessing and modeling, prioritizing 'convenience' over 'generalizability'.
3. A disconnect between technical capabilities and critical clinical needs (only 7% focused on the crucial sMCI/pMCI distinction).
4. Deficiencies in evaluation protocols, notably scarce multi-center validation (only 7%) and inadequate reporting of comprehensive metrics (96% relied solely on Accuracy).
Practical solutions to address these deficits across data, modeling, and evaluation domains are prop osed. Conclusions: Transitioning from 'accuracy under ideal conditions' to 'reliability in real-world settings' is an unavoidable necessity. This requires investment in multi-center data repositories, alignment of models with clinical needs, and institutionalizing comprehensive evaluations. The findings and recommendations are generalizable to other domains of AI-based disease diagnosis.
Background: Digital technologies have the potential to support physical, cognitive, and social activity among older adults, but many small and medium-sized enterprises (SMEs) lack the resources to con...
Background: Digital technologies have the potential to support physical, cognitive, and social activity among older adults, but many small and medium-sized enterprises (SMEs) lack the resources to conduct meaningful codesign with end-users. Toolkits derived from rigorous codesign processes may offer a scalable mechanism for translating end-user priorities into real-world product development. Objective: This study aimed to (1) engage older adults in an extensive codesign process to identify priorities for digital technologies that support physical activity and reminiscence, (2) translate these findings into a practical developer-facing toolkit, and (3) evaluate the toolkit’s perceived utility and influence among digital technology SMEs. Methods: 157 participants (120 older, 7 younger, and 30 staff) across 15 community and care settings in England and Scotland engaged in 106 technology interaction sessions, 22 evaluation focus groups and 10 codesign workshops involving more than 20 digital technologies. Thematic analysis and structured card-ranking tasks were used to derive end-user priorities. Preliminary toolkits were created and provided to 10 UK-based SMEs who received small grants to apply the toolkit to active development projects. Developer reports and follow-up interviews were analysed thematically to identify perceived impacts on design decisions, product adaptations, and business outcomes. Results: Codesign activities generated seven cross-cutting themes: motivation, content, barriers, design and inclusivity, suitability, acceptability, and motivations to use. These were organised into three toolkit sections: general design principles, online physical-activity platforms, and virtual reality. Developers reported that the toolkits enhanced understanding of older adults’ needs, validated design decisions, and inspired new features. Reported impacts included improved usability, expanded accessibility options, increased content variety, clearer instructional design, enhanced social components, and reduced operational costs. SMEs also reported business benefits, including strengthened cases for investment and increased product uptake. Conclusions: Codesign-derived toolkits offer a scalable and cost-effective mechanism for translating older adults’ priorities into digital product development. SMEs perceived the toolkit as practical, relevant, and impactful for informing design choices. This approach complements, but does not replace, direct user involvement and may help accelerate inclusive digital-health innovation for ageing populations. Clinical Trial: n/a
Background: Drug information apps are widely used clinical decision support tools that improve prescribing accuracy, yet in low- and middle-income countries such as Cameroon they remain unregulated, r...
Background: Drug information apps are widely used clinical decision support tools that improve prescribing accuracy, yet in low- and middle-income countries such as Cameroon they remain unregulated, raising safety concerns. Despite high smartphone penetration among doctors, no studies have assessed whether available apps meet local needs or regulatory standards. Objective: This study aimed to evaluate whether drug information apps available in Cameroon met doctors’ clinical information needs by assessing content completeness and usability, using criteria that combine national regulatory standards with breadth of clinically relevant information. Methods: We systematically evaluated drug information apps from the Apple App Store and Google Play Store in Cameroon (March–June 2025). Of 193 eligible apps, 100 were selected through stratified sampling. A framework of 33 drug characteristics grouped into six macro-types was developed and applied based on the Ministry of Public Health standards and clinical needs. Completeness was measured through breadth coverage and Ministry of Public Health compliance; usability was assessed by two independent clinical assessors using the Mobile App Rating Scale (MARS). Results: Nineteen percent of the apps were developed in Africa, with only one from Cameroon. Just three offered bilingual content, while 39% required paid subscription averaging USD $37 annually. Most apps had low completeness with major gaps in safety (contraindications, drug interactions), and quality assurance information (references and author credentials). Usability was limited, with only 15% rated as good quality. Conclusions: Since most drug information apps did not meet Ministry of Public Health standards or core clinical decision-making requirements, there is an urgent need for regulatory oversight and the development of safer, locally adapted prescribing tools. The framework introduced in this study offers a scalable, evidence-based approach that can be adopted across low- and middle-income countries to guide regulation, strengthen quality assurance, and establish globally relevant benchmarks for evaluating drug information apps.
Background: Medical graduate education increasingly uses blended and online delivery, although students' academic self-regulation may be shaped by different motivational and cognitive processes across...
Background: Medical graduate education increasingly uses blended and online delivery, although students' academic self-regulation may be shaped by different motivational and cognitive processes across learning contexts, with emotional factors potentially playing a complementary role. Understanding how these mechanisms operate and whether their structural relationships differ between online/blended and face-to-face formats can inform targeted educational supports. Objective: The present investigation developed and tested a comparative causal model of academic self-regulation among medical graduate students in online/blended versus face-to-face programs. We examined how key motivational constructs (eg, academic self-efficacy, task value, future orientation, perfectionism, and academic help-seeking), positive achievement emotions, and cognitive factors (cognitive academic engagement and need for closure) relate to academic self-regulation, and whether these relationships differ by learning context. Methods: The design was cross-sectional, comparative causal modeling. Participants were master’s-level students at Shahid Beheshti University of Medical Sciences enrolled in either face-to-face (population n=1554; sample n=310) or blended/online (population n=449; sample n=205) programs selected using cluster sampling. Data were collected using validated instruments measuring academic self-regulation (Bouffard scale), academic self-efficacy (Midgley et al), academic engagement (Schaufeli & Bakker), multidimensional perfectionism (Frost), academic help-seeking (Ryan & Pintrich), task value (Pintrich), future orientation (Seginer), need for closure (DeBacker & Crowson), and achievement emotions (AEQ; Pekrun et al). Data were analyzed using path analysis/structural equation modeling. Model fit was evaluated using χ²/df, CFI, GFI, AGFI, and RMSEA. Direct, indirect, and total effects were estimated for each group, and comparative interpretation focused on effect patterns and explained variance. Results: The hypothesized causal model reached an acceptable fit in both face-to-face and blended/online groups (χ²/df approximately <3; CFI/GFI/AGFI in the acceptable range; RMSEA approximately 0.02–0.05). In both groups, most of the specified direct effects reached statistical significance, while the indirect effects of exogenous variables on academic self-regulation through intermediate constructs were supported overall. Cognitive academic engagement and academic self-efficacy were important proximal predictors of academic self-regulation. The need for closure had a negative direct effect with regard to academic self-regulation. However, a previously specified direct effect from need for closure to self-regulated learning strategies could not be retained in the final revised model. In both cohorts, the indirect pathway from positive achievement emotions to academic self-regulation via cognitive engagement was not supported, indicating that positive emotions alone were insufficient to increase self-regulation through cognitive engagement. The model explained a substantial proportion of variance in academic self-regulation in both groups—being approximately 0.44 in face-to-face and 0.46 in blended/online students—indicating comparable overall explanatory power across learning contexts. Conclusions: A comparative causal model integrating motivational, emotional, and cognitive pathways provided an adequate explanation of academic self-regulation among medical graduate students in both face-to-face and blended/online formats. Findings highlight the central role of cognitive engagement and academic self-efficacy as proximal levers for supporting self-regulation across contexts. The lack of a supported indirect effect from positive emotions to self-regulation via cognitive engagement suggests that emotional experiences may not be enough unless they are accompanied by cognitively engaged learning behaviors. Considering motivational and cognitive mechanisms that together shape self-regulation within different delivery modes, educational interventions in medical graduate programs should focus on strengthening self-efficacy beliefs and cognitively engaged learning practices.
Background: Total Hip Arthroplasty (THA) is a common surgical procedure, and an increasing number of patients are turning to short-video platforms for information. Although Douyin and TikTok belong to...
Background: Total Hip Arthroplasty (THA) is a common surgical procedure, and an increasing number of patients are turning to short-video platforms for information. Although Douyin and TikTok belong to the same parent company, they cater to distinct sociocultural environments. Objective: To compare the quality, content, and user engagement of THA-related videos, and to explore the different attitudes of medical professionals and patients on these two platforms. Methods: We systematically searched and analyzed 265 THA-related videos and 600 highly liked comments. Video quality was evaluated using the JAMA (Journal of the American Medical Association) benchmark, GQS (Global Quality Score), and DISCERN tools. The content and comment themes were categorized. Chi-square test with effect size analysis was used to compare categorical variables, while Mann–Whitney U test and Kruskal–Wallis H test were applied to compare differences in scores. Results: The majority of authors on Douyin were medical staff (97.71%), whereas on TikTok, the proportions of science communicators and patients/caregivers were higher (41.04% and 20.15%, respectively). There were significant differences in author backgrounds and content types between the two platforms (p<0.01). Douyin had higher median scores for JAMA and DISCERN (p<0.01), while no significant difference was found in GQS. Significant differences in comment sentiment and themes were observed across platforms, author identities, and content types (p<0.01). Conclusion: Despite technical similarities, Douyin and TikTok exhibit distinct ecosystems: Douyin maintains a doctor-centered, authority-driven model with higher content quality and greater user engagement; TikTok fosters a patient-centered, community-driven model that provides more abundant experience sharing. Conclusions: These differences reflect varying cultural attitudes toward medical authority and shared decision-making.
Background: Erectile dysfunction (ED) is strongly influenced by persistent misconceptions that delay help-seeking and limit engagement with effective care. Patient-centered digital strategies, includi...
Background: Erectile dysfunction (ED) is strongly influenced by persistent misconceptions that delay help-seeking and limit engagement with effective care. Patient-centered digital strategies, including generative–artificial intelligence (AI) microlearning, may improve sexual-health literacy; however, real-world evidence in urological practice remains sparse. Objective: To evaluate whether a clinician-supervised generative-AI microlearning video improves ED-related knowledge in adult men attending routine outpatient care. Methods: This single-center pre–post study included 200 adult men in a university urology clinic. Participants completed an 8-item ED-myth questionnaire immediately before and after watching a 3-minute educational video. The narration script was drafted using a large-language model (ChatGPT-5) and iteratively reviewed by urologists for accuracy and cultural appropriateness. The primary outcome was the within-participant change in total correct responses (0–8). Subgroup analyses assessed effects across age (<40 vs ≥40), education level, and self-reported ED. Paired analyses and multivariable logistic regression were used (α=.05). Results: All participants completed the intervention (mean age 44.0, SD 11.6 years). Total correct responses increased from 3.77 to 6.56 (mean Δ=2.79; P<.001), indicating a large effect (Cohen’s d >1.0). Knowledge gains were consistent across subgroups, with greater improvements among those with lower education. Self-reported ED was independently associated with lower odds of achieving ≥2-point improvement (odds ratio 0.46, 95% CI 0.26–0.81; P=.01). No adverse events or technical difficulties occurred. Conclusions: A brief generative-AI microlearning video, when supervised by clinicians, substantially reduced ED-related misconceptions in routine care. AI-assisted microlearning may serve as a scalable, low-burden adjunct to enhance sexual-health literacy during urological consultations. Long-term retention and behavioral outcomes should be evaluated in future trials. Clinical Trial: Not applicable.
Background: Quality dashboards are essential tools in healthcare, providing integrated and visualized data to support decision-making. Continuous monitoring of key indicators is crucial for managing c...
Background: Quality dashboards are essential tools in healthcare, providing integrated and visualized data to support decision-making. Continuous monitoring of key indicators is crucial for managing chronic diseases such as diabetes mellitus and improving outcomes Objective: The aim of the study was to develop a web-based quality control dashboard to improve diabetes care in the public primary care setting in Tandil, Argentina Methods: The dashboard was embedded into the web-based EHR system of Tandil's Health Information System through a multidisciplinary approach, including literature review, stakeholder consultation, SQL database queries, and data visualization using Apache Superset. Key quality indicators were developed, including patient demographics, comorbidities, and clinical outcomes. Usability was evaluated using the SUS. Results: The interactive dashboard enables efficient monitoring of diabetes care through 29 process and outcome indicators. It facilitates real-time tracking of key metrics such as HbA1c testing rates, blood pressure control, and medication withdrawal, allowing for the identification of care gaps and disparities. The dashboard provides intuitive visualizations that support resource allocation, quality of care, and evidence-based policy development, with a mean SUS score of 78.7, suggesting high usability. Conclusions: This dashboard is a development in harnessing health information for the advancement of diabetes management in resource-stressed settings.
Background: Smoking is common in Saudi Arabia, particularly among men. Religion plays a protective role against smoking and serves as a motivator for quitting. Faith-based interventions have shown pos...
Background: Smoking is common in Saudi Arabia, particularly among men. Religion plays a protective role against smoking and serves as a motivator for quitting. Faith-based interventions have shown positive effects in supporting smoking cessation among Muslims who smoke, but few studies have tested their acceptability when delivered via mobile phone messaging. Objective: The aim of this pilot randomized controlled trial (RCT) was to evaluate the feasibility and acceptability of an Islamic-based smoking cessation intervention delivered via WhatsApp messaging in Saudi Arabia. Methods: This study was a two-arm RCT involving adult Muslim smokers who used cigarettes, waterpipe, or both, with participants randomized in a 1:1 ratio to either the intervention or control group. The intervention messages were co-designed with religious leaders and informed by formative research with Muslim current and former smokers. The intervention group received both religious and smoking-related health messages, while the control group received smoking-related health messages only. The intervention lasted for 21 days, during which participants received two messages per day, one in the morning and one in the evening. The primary outcomes were the feasibility and acceptability of the intervention. Results: A total of 34 participants were recruited, Two-thirds of whom remained in the study for the full study period. Both groups received 44 messages over 21 days. Message engagement was high, with most participants reading the daily messages (88.2% in the intervention group and 82.4% in the control group). The intervention was acceptable and helpful in supporting smoking reduction or cessation. Abstinence from all tobacco products was 20.6%, with a slightly higher rate in the intervention group (23.5%) than in the control group (17.6%). Cigarette abstinence was 35.3% in the intervention group and 23.5% in the control group, while waterpipe abstinence was much higher but equal in both groups (76.5%). Among those who continued smoking, modest reductions in cigarette and waterpipe consumption were reported in both groups. Conclusions: This Islamic faith-based intervention, delivered via WhatsApp messaging, was feasible and highly acceptable, showing promising effects on smoking cessation and reduction. However, these findings require verification in a larger, fully powered effectiveness trial. Clinical Trial: Australian and New Zealand Clinical Trials Registry
Trial Registration No. ACTRN12625001413415
https://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=390880&showOriginal=true&isReview=true
Background: Mobile health (mHealth) applications hold significant potential for improving healthcare, yet their adoption in developing countries like Egypt remains low. While most research focuses on...
Background: Mobile health (mHealth) applications hold significant potential for improving healthcare, yet their adoption in developing countries like Egypt remains low. While most research focuses on patient acceptance, physicians' adoption is crucial for success. This study investigates the factors influencing Egyptian physicians' acceptance of the AFib mHealth app for managing Atrial Fibrillation, a common and serious heart condition. Objective: The primary objective was to identify the key factors affecting Egyptian physicians' behavioral intention and actual use of the AFib mobile application, using the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) as a theoretical framework. Methods: A cross-sectional online survey was distributed via convenience sampling to 35 cardiologists in Alexandria, Egypt. The survey measured their perceptions based on four key variables: Perceived Usefulness, Perceived Ease of Use, Social Influence, and Trust, and their link to Behavioral Intention and Actual Use. Data were analyzed using SPSS to perform descriptive statistics and test five research hypotheses. Results: Descriptive results showed high scores for Perceived Ease of Use (4.07), Perceived Usefulness (4.04), and Behavioral Intention (4.11). Trust (3.44) and Social Influence (3.33) received more moderate scores. Hypothesis testing revealed that Perceived Usefulness and Trust were the only factors with a statistically significant positive effect on Behavioral Intention. Surprisingly, Perceived Ease of Use and Social Influence did not significantly influence intention. Finally, a strong, significant link was confirmed between Behavioral Intention and Actual Use. Conclusions: The study concludes that for Egyptian physicians, the decision to adopt the AFib app is driven primarily by its perceived clinical utility and their trust in its reliability and security, not merely its ease of use or peer influence. Therefore, to enhance mHealth adoption, developers and policymakers should focus on demonstrating tangible benefits to patient care and ensuring robust data security and accuracy. These findings provide a valuable guide for implementing mHealth solutions in Egypt and similar developing contexts.
Background: Migraine is a common and disabling neurological disorder characterized by recurrent headaches and associated symptoms that significantly affect quality of life. Conventional physiotherapy...
Background: Migraine is a common and disabling neurological disorder characterized by recurrent headaches and associated symptoms that significantly affect quality of life. Conventional physiotherapy plays a supportive role in migraine management; however, it may not adequately address central sensitization and altered pain modulation. Non-invasive neuromodulation techniques such as transcutaneous auricular vagal nerve stimulation (ta-VNS) and transcutaneous supraorbital nerve stimulation (t-SNS) have shown potential in modulating central pain pathways and reducing migraine burden. Objective: The primary objective of this study is to evaluate and compare the effectiveness of ta-VNS and t-SNS, each combined with conventional physiotherapy, in reducing pain intensity and migraine frequency, incidence in individuals with migraine. Secondary objectives include assessing their effects on migraine disability, neck disability, and cervical range of motion. Methods: This randomized controlled trial will include individuals clinically diagnosed with migraine. Participants will be randomly allocated into two groups: Group A will receive ta-VNS along with conventional physiotherapy, and Group B will receive t-SNS along with conventional physiotherapy. Both interventions will be administered for a defined treatment period. Outcome measures will be recorded at baseline, immediately post-intervention, and during follow-up periods to evaluate short- and long-term effects. Results: It is anticipated that both ta-VNS and t-SNS, when combined with conventional physiotherapy, will lead to significant improvements in pain intensity, migraine frequency, and functional outcomes. One neuromodulation technique may demonstrate superior or more sustained benefits over the other. Conclusions: This study is expected to provide comparative evidence on the effectiveness of ta-VNS and t-SNS as adjuncts to conventional physiotherapy in migraine management. The findings may support the inclusion of targeted non-invasive neuromodulation techniques in physiotherapy-based treatment protocols for migraine. Clinical Trial: Ctri registration- CTRI/2026/01/100045
Background: Vessels encapsulating tumor clusters (VETC) are a distinct vascular pattern associated with aggressive behavior and poor prognosis in hepatocellular carcinoma (HCC). Preoperative identific...
Background: Vessels encapsulating tumor clusters (VETC) are a distinct vascular pattern associated with aggressive behavior and poor prognosis in hepatocellular carcinoma (HCC). Preoperative identification of VETC is crucial for treatment planning but currently relies on invasive pathological examination. Radiomics-based artificial intelligence (AI) offers a potential noninvasive solution, yet evidence regarding its diagnostic and prognostic accuracy remains synthesized. Objective: We aimed to systematically evaluate the diagnostic performance and prognostic value of radiomics-based AI models for noninvasively predicting VETC status in patients with HCC. Methods: We systematically searched PubMed, Embase, Web of Science, and the Cochrane Library for studies published up to July 11, 2025. Studies developing or validating AI models using medical imaging (contrast-enhanced MRI [CEMRI], contrast-enhanced CT [CECT], contrast-enhanced ultrasound [CEUS], or [18F]FDG PET/CT) to predict pathologically confirmed VETC status in HCC patients were included. Study quality was assessed using the PROBAST+AI tool. Diagnostic accuracy (sensitivity, specificity, AUC) and prognostic value for early recurrence (hazard ratio [HR]) were pooled using random-effects models. Results: Fourteen studies involving 729 patients in internal and 581 in external validation cohorts were analyzed. AI models based on CEMRI demonstrated the highest diagnostic accuracy, with a pooled AUC of 0.87 (95% CI 0.84-0.90), sensitivity of 0.82 (95% CI 0.75-0.88), and specificity of 0.77 (95% CI 0.71-0.82). Models using other modalities (CECT, PET/CT, CEUS) showed moderate to good performance. Prognostically, HCC patients classified as VETC-positive by AI had a significantly higher risk of early recurrence (pooled HR 2.34, 95% CI 1.93-2.84). Conclusions: Radiomics-based AI models, particularly those using CEMRI, are promising for the noninvasive prediction of VETC and offer valuable prognostic stratification for early recurrence risk in HCC. However, significant heterogeneity and the retrospective nature of current studies limit the strength of evidence. Prospective, multicenter validation is required to confirm clinical utility. Clinical Trial: PROSPERO CRD420251167155
Purpose
Despite attempts to ban the platform, TikTok is an increasingly influential resource for adolescents, serving as a major platform for acne advice and discourse and shaping users’ awareness...
Purpose
Despite attempts to ban the platform, TikTok is an increasingly influential resource for adolescents, serving as a major platform for acne advice and discourse and shaping users’ awareness of acne treatments. We surveyed adolescent patients to assess perspectives on TikTok acne content and examine associations between acne severity and engagement behaviors, including trying products, discussing videos with dermatologists, and posting acne content.
Methods
A 15-item questionnaire was administered to patients aged ≥ 14 with acne presenting to the pediatric dermatology clinic at an urban, safety-net hospital. Investigator’s Global Assessment scores were assigned by a board-certified dermatologist. Relationships between metrics of acne experience and TikTok engagement behaviors were examined using Fisher’s exact test (R Studio version 4.4.2, α=.05 level of significance).
Results
Most participants self-identified as Black and/or Hispanic. While no statistically significant associations were observed, patients with longer acne duration (5+ years) were more likely to post about acne, and those on systemic therapies were more likely to view acne videos. Median IGA scores were higher among viewers (2.5; interquartile range 2, 3.8) and posters (3; 1, 3). Spironolactone, oral contraceptives, and topical antibiotics were treatments less represented on TikTok.
Conclusions
Nearly all adolescents in our cohort who engage with TikTok reported viewing acne-related videos, demonstrating the substantial impact of the application, particularly among those with longer-standing and more severe acne. These trends indicate a tendency towards self-education and information-seeking among pediatric patients, highlighting opportunities for dermatologists to engage patients in discussion, tailor counseling, and address informational gaps.
Background: Baseline data from our survey of 527 self-referred users of the mental health chat- and voice bot Clare® indicate high psychological distress and barriers to accessing face-to-face care s...
Background: Baseline data from our survey of 527 self-referred users of the mental health chat- and voice bot Clare® indicate high psychological distress and barriers to accessing face-to-face care strong working alliance was established within 3–5 days (Working Alliance Inventory-Short Report, M = 3.76, SD = .72). The feasibility of sustained engagement and therapeutic bonding with Clare® in real-world use remains underexplored. Objective: This exploratory feasibility study evaluated engagement patterns, sustained therapeutic bonding, and preliminary mental health outcomes during 4- and 8-week use of the LLM-enabled voice and text chatbot Clare®. Methods: A single-group pre-post feasibility study was conducted with the English-speaking general population that self-referred and interacted with the voice- and chatbot Clare® for 4 weeks (n=53) or 8 weeks (n=21). Usage patterns, modes of engagement (hybrid, text-only, call-only), message volume, and call duration were examined. Users were further assessed for working alliance and changes in loneliness, depression, anxiety, distress, and life satisfaction at baseline, week 4 (t1), and week 8 (t2). Results: A total of 53 participants (73.6% women) engaged with the Clare® over 4 weeks (sample 1) and 21 participants (71.4% women) completed all assessments (sample 2), with both samples showing comparable demographic profiles. At baseline, participants reported moderate depression and anxiety, elevated social anxiety and high loneliness. Initial engagement peaked in the first week, with participants initiating an average of 1.77 calls and sending 10.02 messages, before declining steadily. On average participants initiated an average of 3.34 calls, sent 23.65 messages, and spent a total of 8.07 minutes in voice calls in 4 weeks. A comparable pattern was observed in the 8-week completer sample (n = 21). Working alliance increased over time, rising from 2.91 (SD 0.88) at baseline to 3.21 (SD 1.17) at mid-assessment and 3.34 (SD 1.03) at post-assessment (t3). There was no significant association with engagement intensity. Higher baseline distress was associated with fewer messages and lower alliance. Higher depressive symptoms were linked to fewer early calls and lower overall call frequency. Modest improvements were observed across loneliness, depression, anxiety, and distress. This was a feasibility study with substantial attrition and results should be interpreted with caution. Conclusions: Clare® appears feasible and acceptable for short-term community use, with early and stable bonding and preliminary signals of emotional and mental-health improvement. Declining engagement over time and the weak association between communication volume and alliance highlight the need for technology improvement and individualized symptom-oriented design strategies that support sustained and meaningful interaction.
Background: Child-centered care (CCC) is standard practice in pediatrics, emphasizing the child as an individual with rights while acknowledging the child's role within the family. A key aspect of CCC...
Background: Child-centered care (CCC) is standard practice in pediatrics, emphasizing the child as an individual with rights while acknowledging the child's role within the family. A key aspect of CCC is the involvement of the child in health care decisions alongside parents and professionals. Although this is a right recognized by the United Nations Convention on the Rights of the Child (UNCRC) it may not always be applied in practice. Objective: The aim of this study is to explore the preferences of 3- to 5-year-old children for participation in health care from both the child's perspective as well as the child perspective, i.e., to ask their parents and health professionals about their understanding of children's preferences. Methods: Preferences were studied using Q-methodology, comparing responses from twelve children, fourteen parents, and twelve health professionals who ranked twenty-five statements. Factor analysis identified shared perspectives on participation preferences. Children’s rankings were also analyzed separately for comparison. Results: Three perspectives presenting different preferences were identified: direct communication between the child and healthcare professionals; understanding and shared decision-making; and responsive and child-led participation. A separate analysis of children’s rankings resulted in three perspectives: included in and setting their own terms for participation; small choices, meaningful outcomes; and trust through familiarity and shared decision-making. Conclusions: This study suggests that children value shared decision-making and situational control but prefer to leave major decisions to adults. It affirms that pre-school-aged children can meaningfully participate in healthcare when given age-appropriate choices, support, and tools. Children’s perspectives must be acknowledged directly rather than adults assuming their views. The findings support child-centered care (CCC) principles and reinforce the UNCRC mandate to respect children’s views regarding all issues relevant to them.
Background: Frontline workers across multiple occupations operate in high-stress, trauma-exposed environments, facing chronic demands, and irregular schedules that increase risk of burnout, depression...
Background: Frontline workers across multiple occupations operate in high-stress, trauma-exposed environments, facing chronic demands, and irregular schedules that increase risk of burnout, depression, and poor sleep. Emerging evidence highlights the role of 24-hour movement behaviours together with psychological health. Despite growing attention, research remains fragmented, often focusing on single elements. This protocol outlines a scoping review to map evidence, identify gaps, and inform future interventions. Objective: The primary aim is to map research on the relationships between 24-hour movement behaviours and mental health outcomes in frontline workers. Objectives include examining measurement approaches, associations, methodological gaps, and exploring monitoring and interventions. Methods: This scoping review will follow Joanna Briggs Institute methodology and PRISMA-ScR guidelines. Eligible studies include English-language research (2000–2025) on adult frontline workers across multiple occupations, addressing movement behaviours and mental health outcomes. Stakeholder consultation will inform research questions, interpretation, and dissemination. A three-step search will be conducted across various databases with targeted grey literature searches while screening and data extraction will be conducted independently by reviewers. Findings will be reported in tabular and narrative formats, integrating empirical and descriptive findings Results: Preliminary searches and pilot testing were completed in October 2025. Full searches, data charting, and synthesis are planned for December 2025–early 2026, and final synthesis expected by April 2026. Conclusions: This review will provide a comprehensive overview of research linking 24-hour movement behaviours with mental health, highlighting methodological diversity, underrepresented sectors, and gaps. Findings will guide assessment frameworks, wearable monitoring, and interventions to enhance wellbeing, resilience, and recovery.
Background: The incidence of type 2 diabetes (T2D) continues to increase, and the lack of individualized therapy strategies hinders patient engagement with and commitment to a healthy lifestyle. The P...
Background: The incidence of type 2 diabetes (T2D) continues to increase, and the lack of individualized therapy strategies hinders patient engagement with and commitment to a healthy lifestyle. The PROTEIN project aimed to facilitate users to choose healthy living, thereby improving their metabolism and T2D management. Objective: To assess the efficacy of a personalized mobile application to achieve a 5% time in range (TIR) improvement over a 12-week intervention in adults with prediabetes or T2DM. Methods: We conducted a randomized controlled trial (RCT) with 21 individuals with T2D or prediabetes who used a continuous glucose monitoring (CGM) system and the PROTEIN mobile application (PROTEIN app) for personalized meals and exercise recommendations based on their glucose levels and physical activity. Results: The TIR of the participants increased (p<0.05; from 71.8% ± 27.3% to 76.0% ± 28.1%) with individual use of the PROTEIN app but did not achieve a 5% improvement overall. Glycated hemoglobin, fasting blood glucose, and body weight did not fluctuate throughout the 12-week intervention. The dropout rate was high and the average duration of use of the PROTEIN app was 42 days (range 5 to 84). Conclusions: Our results showed an improvement in TIR with the use of the PROTEIN-app. Integrating wearables and automated personalization for wellbeing is an innovative approach that must keep pace with the accelerated development of ever-evolving technologies. Clinical Trial: ClinicalTrials.gov: registration no. NCT05951140
https://clinicaltrials.gov/study/NCT05951140
Background: Refugees commonly encounter barriers when accessing and navigating healthcare. While many educational interventions have been implemented to improve health literacy, the evidence is scatte...
Background: Refugees commonly encounter barriers when accessing and navigating healthcare. While many educational interventions have been implemented to improve health literacy, the evidence is scattered. This emphases the need for a consolidation and synthesis of educational interventions. Objective: The purpose of this scoping review is to map and critically synthesise healthcare-related educational interventions designed for refugee populations. Specifically, it examines (1) the knowledge themes and topics of the healthcare educational interventions reported, (2) the pedagogical approaches, delivery formats, and educational tools employed and 3) the evaluation strategies and outcomes reported. Methods: A scoping search was conducted in four major databases (PubMed, CINAHL, EMBASE, and ERIC) for studies published between 2018 and 2024 that implemented and assessed a health or health-education intervention for refugee populations using the Joanna Briggs Institute (JBI) approach. Results: Forty-two studies satisfied the inclusion criteria. A wide range of health-related themes were identified but Diseases and Conditions, Mental Health, and Nutrition were identified as the most common knowledge themes across the included interventions reflecting refugee needs. Interventions used a variety of delivery methods, such as in-person, online, and mixed formats, although fully online interventions occurred less frequently in the studies explored. Didactic, lecture-style approaches were mainly adopted and applied across many interventions, however interactive and peer-led models were reported in some studies. Different studies varied in how they involved refugees and community members in the design and delivery of educational interventions. Approximately, one-third of the interventions actively included refugees in the design and development of the interventions. Outcomes of the educational interventions explored, were mainly measured in terms of knowledge gain, with fewer studies assessing behavioural change, health outcomes, or long-term impact. Where behavioural and health outcomes were reported, results were mixed. Finally, no relationship between educational approaches and outcomes could be conclusively discerned. Conclusions: Overall, this review provides a practical evidence basis for researchers, policymakers, and practitioners seeking to design and implement educational initiatives that improve health literacy and healthcare integration for refugee populations.
The shortcomings of digital technologies and artificial intelligence have transformed the landscape of health education, creating opportunities for better health education techniques and accessibility...
The shortcomings of digital technologies and artificial intelligence have transformed the landscape of health education, creating opportunities for better health education techniques and accessibility to information. These developments have accelerated the spread of a one-size-fits-all approaches which may risk disengagement, and poor adherence to care plans and may compromise the irreplaceable role of human connection in facilitating behavior change. This paper introduces a human-centered framework for patient health education that integrates theoretical insights and empirical evidence to counter the limitations of AI-driven and generalized approaches. Specifically, it presents two innovative tools—the Empathy Map and the Persuasive Pattern framework.
As a theoretical paper, proposes a structured framework to align with patient-centered care principles within a proper use of technology that integrates the humanistic approach.
The proposed framework is built around three pillars: (1) empathy-driven needs assessment, operationalized through the Empathy Map to capture patient perspectives, barriers, and motivations; (2) metacognitive empowerment to build reflective, self-directed learning skills; and (3) persuasive psychological strategies, organized into a Persuasive Pattern framework that enhances motivation, sustains engagement, and supports long-term behavior change.. This model reframes health education as a collaborative and empowering process rather than a passive transfer of information.
A human-centered framework—with its Empathy Map and Persuasive Pattern model—offers a pathway to more effective, ethical, and equitable patient education. Integrating the framework components will ensure that Artificial IntelligenceI tools are applied as supportive complements rather than replacements for human empathy and relational care.
Background: Stress is widespread and carries substantial mental health, social, and economic burdens. Yet access to clinician-led stress management remains constrained by service capacity, cost, and s...
Background: Stress is widespread and carries substantial mental health, social, and economic burdens. Yet access to clinician-led stress management remains constrained by service capacity, cost, and stigma. In response, artificial intelligence (AI)–enabled tools have rapidly proliferated as scalable, self-directed options. However, evidence on how these systems support stress management outside formal clinical settings remains fragmented. Objective: This systematic review synthesises empirical evidence on how AI-enabled technologies are used for self-directed stress management. We map the emerging functions of these tools, the psychological frameworks informing their design, the populations and settings studied, and the outcomes reported. Methods: We conducted a PRISMA-compliant systematic review of English-language studies published between 2000 and 2025. Six databases were searched (APA PsycINFO, PubMed/MEDLINE, Scopus, Web of Science Core Collection, ProQuest, and Google Scholar). Results: Out of 3,008 records identified, 35 studies met the inclusion criteria. AI-supported stress management operates through five core functions, including psychological intervention, behavioural support, psychoeducation, emotional companionship, and stress monitoring and triage, collectively enabling users to identify stress, regulate responses, and engage in self-directed coping outside formal clinical care. Conclusions: AI-enabled systems show preliminary promise for supporting self-directed stress management through multiple user-facing functions grounded in established psychological frameworks.
Background: The Swiss Personalized Health Network facilitates the interoperability and secure sharing of health-related data for research in Switzerland, in line with the FAIR principles. Since medica...
Background: The Swiss Personalized Health Network facilitates the interoperability and secure sharing of health-related data for research in Switzerland, in line with the FAIR principles. Since medical datasets can be highly sensitive, access is often governed by complex legal and regulatory requirements. Enabling researchers to discover, understand, and evaluate datasets through rich, well-structured metadata is therefore essential to support informed decisions about data suitability and reuse. Objective: This study describes the design and functionality of the SPHN Metadata Catalog and its role in supporting the discovery, exploration, and reuse assessment of health-related datasets. Methods: The SPHN Metadata Catalog is a FAIR Data Point-compliant infrastructure that provides rich, structured metadata in both human and machine-readable form. Dataset descriptions are based on HealthDCAT, ensuring a standardized representation of health data catalogs. Beyond the descriptive metadata typically offered by other catalogs, the SPHN Metadata Catalog includes extensive dataset-level statistics expressed using the Vocabulary of Interlinked Datasets. An interactive visualization component further enables users to explore graph-based schemas and datasets, including entities, attributes, relationships, and their relative abundances. Results: The SPHN Metadata Catalog enables users to explore the semantic structure of graph schemas and statistics of datasets prior to requesting access. Researchers can examine data structures, relationships, attributes, and the abundances of individual data elements. This functionality supports feasibility assessments and informed evaluations of dataset suitability and reuse conditions. Conclusions: By combining HealthDCAT-based descriptions with rich statistical metadata and interactive exploration capabilities, the SPHN Metadata Catalog enhances dataset discoverability and supports FAIR-compliant data reuse. As a key component of Switzerland’s health data research infrastructure, the SPHN Metadata Catalog provides a foundation for future interoperability initiatives, including potential alignment with emerging frameworks such as the European Health Data Space.
Background: Clinical Temporal Relation Extraction (CTRE) is essential for reconstructing patient timelines from unstructured Electronic Health Records (EHRs). However, the linguistic complexity of cli...
Background: Clinical Temporal Relation Extraction (CTRE) is essential for reconstructing patient timelines from unstructured Electronic Health Records (EHRs). However, the linguistic complexity of clinical notes and the high cost of expert annotation impede the development of large-scale training corpora. While Large Language Models (LLMs) have transformed general Natural Language Processing, their application to CTRE remains underexplored. Objective: This study aims to determine the optimal adaptation strategy for CTRE by conducting a comprehensive benchmarking of LLM architectures and fine-tuning methodologies in both data-rich and limited-data regimes. Methods: We evaluated four LLMs representing two distinct architectures: Transformer Encoders (GatorTron-Base, GatorTron-Large) and Transformer Decoders (LLaMA 3.1-8B, MeLLaMA-13B). We compared four adaptation strategies: (1) Standard Fine-Tuning, (2) Hard-Prompting, (3) Soft-Prompting, and (4) Low-Rank Adaptation (LoRA). Experiments were conducted on the 2012 i2b2 CTRE benchmark in both full-supervision and 1-shot scenarios. Results: We achieved results that exceed the current state-of-the-art (SOTA) on the 2012 i2b2 dataset. Comparative analysis reveals that hard-prompting consistently yields superior efficacy compared to standard fine-tuning. Regarding Parameter-Efficient Fine-Tuning (PEFT) strategies, Low-Rank Adaptation (LoRA) targeting query and value layers emerged as the optimal configuration. Conversely, soft-prompting demonstrated suboptimal performance, likely due to constraints on representational capacity. Architecturally, we observed a performance dichotomy based on data availability: Encoder-based models (GatorTron) exhibited superior stability and accuracy in few-shot scenarios, whereas Decoder-based models (LLaMA 3.1, MeLLaMA) demonstrated dominant performance in data-rich regimes. Conclusions: This study provides a rigorous roadmap for adapting LLMs to clinical extraction tasks. Based on our empirical findings, we recommend hard-prompting to maximize predictive accuracy and identify specific LoRA configurations (targeting query and value layers) as the preferred approach when computational efficiency is paramount. Furthermore, our findings suggest that while generative Decoders excel with abundant data, domain-specific Encoders remain the robust choice for few-shot clinical applications.
Background: Gamification has been increasingly integrated into mobile health (mHealth) applications to enhance user engagement and support mental health outcomes. However, empirical evidence explainin...
Background: Gamification has been increasingly integrated into mobile health (mHealth) applications to enhance user engagement and support mental health outcomes. However, empirical evidence explaining how gamified mHealth experiences contribute to users’ psychological well-being remains limited, particularly with respect to the underlying psychological mechanisms. Objective: This study aimed to examine the relationship between gamified mHealth experiences and psychological well-being and to investigate the mediating role of positive psychological capital (PsyCap) in this relationship. Methods: A cross-sectional survey was conducted among users of gamified mHealth applications. Gamified experience, PsyCap (hope, self-efficacy, resilience, and optimism), and psychological well-being were measured using validated scales. Structural equation modeling was employed to test the hypothesized mediation model. Results: Data from 483 active users of mobile health applications were analyzed. Gamification affordances (GA) were positively associated with psychological well-being (PWB) (β = 0.54, P < .001) and positive psychological capital (β = 0.61, P < .001). Positive psychological capital was also positively related to psychological well-being (β = 0.54, P < .001). Bootstrapping analysis (5,000 resamples) indicated a significant indirect effect of GA on psychological well-being via positive psychological capital (indirect effect = 0.32; 95% CI 0.21–0.43), supporting partial mediation. Conclusions: This study highlights positive psychological capital as a key psychological mechanism linking gamified mHealth experiences to psychological well-being. The findings extend gamification research beyond engagement-focused outcomes and underscore the importance of designing mHealth interventions that support psychological empowerment and long-term well-being.
Background: Non-Small Cell Lung Cancer (NSCLC) remains the leading cause of cancer-related mortality worldwide. The identification and prioritization of molecular biomarkers involved in NSCLC pathogen...
Background: Non-Small Cell Lung Cancer (NSCLC) remains the leading cause of cancer-related mortality worldwide. The identification and prioritization of molecular biomarkers involved in NSCLC pathogenesis are essential for advancing early diagnostic strategies and optimizing therapeutic interventions. Objective: This study aimed to utilize genomic network approaches and bioinformatics tools to prioritize clinically relevant biomarkers associated with NSCLC. Methods: Non-Small Cell Lung Cancer (NSCLC) remains the leading cause of cancer-related mortality worldwide. The identification and prioritization of molecular biomarkers involved in NSCLC pathogenesis are essential for advancing early diagnostic strategies and optimizing therapeutic interventions. This study aimed to utilize genomic network approaches and bioinformatics tools to prioritize clinically relevant biomarkers associated with NSCLC. Results: Data integration from three major genomic repositories DisGeNET, GWAS Catalog, and cBioPortal 1,317 NSCLC associated genes. Subsequent analyses included gene ontology enrichment, pathway enrichment, and protein–protein interaction (PPI) network construction. Network-based prioritization identified ten key hub genes: TP53, MYC, PTEN, CTNNB1, ACBT, STAT3, CCND1, AKT1, ESR1, and HIP1A, with
TP53, MYC, PTEN, CTNNB1 as the most prominent biomarkers according to CytoHubba scoring. Conclusions: This study presents a genomic network-based framework for identifying and prioritizing potential NSCLC biomarkers, offering critical insights into the molecular underpinnings of NSCLC pathogenesis.
Background: The global increase in the older adult population has led to a rising prevalence of cognitive impairment and dementia. Non-pharmacological interventions, particularly engaging activities l...
Background: The global increase in the older adult population has led to a rising prevalence of cognitive impairment and dementia. Non-pharmacological interventions, particularly engaging activities like tabletop games, are crucial for cognitive maintenance and well-being. However, existing commercial cognitive assistive tools often fail due to two main issues: a disconnect from the cultural and life experiences of the users, and an overly high cognitive load that hinders engagement and efficacy in clinical settings. There is an urgent need for an intervention tool designed specifically for this population, integrating principles of cultural relevance and neural adaptability to maximize therapeutic outcomes. Objective: This study aimed to develop a user experience-oriented, modular card-based assistive tool as an effective non-pharmacological intervention for older adults with mild cognitive impairment and dementia. The primary goal was to construct a robust cognitive intervention framework that enhances user motivation and improves neural feedback efficiency by integrating both cultural adaptability and neuroplasticity principles. Methods: The research utilized a multi-stage mixed-methods approach, grounded in User Experience Innovation Design methodology. The study combined literature analysis, structured expert interviews, ethnographic participatory observation, and preliminary prototype testing. The work was conducted across long-term care centers, dementia care centers, and day care centers in a county in southern Taiwan. Seventeen participants, including healthcare professionals, caregivers, administrative staff, and healthy older adults, were involved in the data collection and co-creation process to ensure the practical and cultural relevance of the design. Results: Results: The findings confirmed that cultural symbol misalignment and excessive cognitive demand were the main barriers to using current assistive tools, accounting for approximately 73% of reported usage difficulties. The newly developed tool, through the embedding of localized cultural contexts and a dynamic staged design, significantly enhanced participant motivation. Crucially, preliminary testing indicated effective enhancement of neural feedback efficiency. Conclusions: The study successfully designed and validated a modular cognitive assistive tool that overcomes common barriers by prioritizing cultural embedding and dynamic cognitive pacing. We propose a "Cultural-Cognitive Embedding Model" as a guiding framework, emphasizing that assistive tool design must integrate local life history and dynamically adjust cognitive difficulty to effectively promote neuroplasticity and sustained engagement in dementia care.
Background: Sleep is a core component of psychiatric assessment, yet inpatient monitoring typically relies on brief observational checks that are subjective, variable, and sometimes disruptive. Wearab...
Background: Sleep is a core component of psychiatric assessment, yet inpatient monitoring typically relies on brief observational checks that are subjective, variable, and sometimes disruptive. Wearable devices offer a means of capturing continuous, objective sleep and activity data without disturbing patients. Although digital health technologies are increasingly used in psychiatric research, little is known about how wearable-derived data can be integrated into routine inpatient workflows or used meaningfully by clinicians. Objective: This implementation aimed to evaluate the feasibility, usability, and workflow integration of a wearable-derived sleep and activity reporting system within an adult psychiatric inpatient unit. Methods: The implementation unfolded in two phases on a 21-bed inpatient unit at a psychiatric hospital in Massachusetts, USA. Patients were offered a wrist-worn GENEActiv actigraphy device upon admission. Raw accelerometry data were processed using the DPSleep pipeline to derive daily sleep and activity metrics for patients participating in the implementation. Sleep and activity reports combining graphical summaries and natural language summaries of sleep, activity, and medication data were iteratively refined and delivered to psychiatrists providing patient care. Semi-structured qualitative interviews were conducted with clinicians and unit staff to gather feedback on the sleep and activity report prototype and discuss barriers and facilitators to implementation. Interview data were coded and analyzed by a team of two. Results: During the first phase of the implementation, 155 patients were admitted, 88 (56% of admits) were offered a device, and 68 (77% of admits offered a device) accepted. Sleep and activity reports were generated for 42 patients (62% of patients wearing a device) during this phase. During the second phase of the implementation, automation reduced report generation time from approximately five days to under 24 hours. Only one of the three psychiatrists assigned to the unit regularly used the reports in routine care. Reports were most useful for reconciling discrepancies between patient and nursing sleep estimates and for supporting clinical conversations about sleep patterns and medication adherence between clinician and patient. Clinicians who had not yet used the reports expressed conceptual interest but emphasized the need for integration in the electronic medical record, reliably available “last-night” sleep data, and simplified design. Barriers included challenges in the speed, reliability, and clarity of data; variable staff buy-in; and disconnects between the research and clinical teams running the implementation. Conclusions: Wearable-derived sleep and activity data reporting is feasible in inpatient psychiatry and offers clinically meaningful insights, particularly when patient and staff sleep reports conflict. Sustainable use is more likely with near-instantaneous data transfer, electronic medical record integration, and shared implementation ownership across staff levels. Clinical Trial: Not applicable
Background: In heart failure patients, cardiac rehabilitation(CR) is recommended. However, center-based cardiac rehabilitation (CBCR) experiences low referral rates, accessibility barriers, and econom...
Background: In heart failure patients, cardiac rehabilitation(CR) is recommended. However, center-based cardiac rehabilitation (CBCR) experiences low referral rates, accessibility barriers, and economic constraints, leading to low usage rate. Mobile health offers a potential solution to these limitations through the remote delivery of home-based cardiac rehabilitation (HBCR). Objective: The objective of this systematic review and meta-analysis was to evaluate the comparative effectiveness of mobile health (mHealth) HBCR interventions versus usual care and CBCR among heart failure patients. Methods: Four electronic databases (MEDLINE, PubMed, Cochrane Library, and Embase) were searched from inception to October 27, 2025, without restrictions on language or publication type. Eligible studies comprised randomized controlled trials enrolling heart failure patients aged 18 years and older, with comparisons between mHealth HBCR interventions and usual care or CBCR. The primary outcome of interest was aerobic exercise capacity, as assessed by peak oxygen consumption (VO2 peak) or the 6-minute walk test (6MWT). Secondary outcomes included health-related quality of life. This review was registered in PROSPERO (CRD420251162078). Results: A total of 4,540 records were identified, and 62 underwent full-text assessment. Seven randomized controlled trials that met the inclusion criteria were included in the systematic review, encompassing 1,307 patients with heart failure. Intervention durations ranged from 8 to 12 weeks, and exercise frequencies varied from daily to five times per week. A random-effects meta-analysis demonstrated that mHealth HBCR significantly improved VO2 peak(SMD 0.36, 95% CI 0.11 to 0.62; p = 0.01) and the SF-36 score (SMD 0.16, 95% CI 0.03 to 0.28; p = 0.01). Compared with usual care, mHealth HBCR was associated with significant improvements in the 6MWD(SMD 0.81, 95% CI 0.23 to 1.39; p = 0.01) and MLHFQ score (SMD -0.57, 95% CI -0.98 to -0.17; p < 0.01). No significant differences were observed between mHealth HBCR and CBCR. Conclusions: MHealth HBCR significantly enhances aerobic exercise capacity and quality of life among heart failure patients. However, further large-scale randomized controlled trials are warranted to elucidate the impact of mHealth HBCR on all-cause mortality, major adverse cardiovascular events, and rehospitalization rates among heart failure patients. Clinical Trial: The protocol was registered in PROSPERO with ID CRD420251162078. https://www.crd.york.ac.uk/PROSPERO/view/CRD420251162078
Background: Prolonged residence in post-disaster container settlements may adversely affect respiratory health through environmental, functional, and psychosocial pathways. However, population-based e...
Background: Prolonged residence in post-disaster container settlements may adversely affect respiratory health through environmental, functional, and psychosocial pathways. However, population-based evidence incorporating objective pulmonary and functional indicators remains limited. Objective: This study aimed to quantify pulmonary function, dyspnea, fatigue-related functional capacity, and health-related quality of life among adults living in container settlements after the 2023 Kahramanmaraş earthquakes and to identify key sociodemographic and functional determinants. Methods: This cross-sectional field study included 360 adults (mean age 41.2±9.3 years; 53.6% female) residing in three container settlements in Malatya, Türkiye. Pulmonary function (FVC, FEV₁, FEV₁/FVC) was assessed using spirometry according to ATS/ERS standards. Dyspnea (mMRC), sleep quality (PSQI), muscle strength (handgrip dynamometry), and quality of life (SF-36) were evaluated. Group comparisons, correlation analyses, and multiple linear regression models were applied.
Results
Median FVC and FEV₁ were 2.85 L (IQR 2.30–3.40) and 2.32 L (IQR 1.85–2.85), respectively, while the mean FEV₁/FVC ratio remained within normal limits (80.1%±6.2). Significant differences in FVC and FEV₁ were observed by sex (p<.001; r=0.82) and employment status (p<.001; r=0.56). Handgrip strength showed strong positive correlations with FVC (r=0.74) and FEV₁ (r=0.77, both p<.001), whereas sleep quality demonstrated small but significant associations (p=.021; ε²=0.031). In multivariable analysis, age, sex, body mass index, employment status, and handgrip strength independently predicted FVC (adjusted R²=0.61; p<.001). Results: Respiratory impairment among adults living in post-disaster container settlements primarily reflects reduced lung volume and functional capacity rather than obstructive airway disease. Functional and social determinants, particularly muscle strength and employment status, play a central role, underscoring the need for integrated post-disaster respiratory surveillance and rehabilitation strategies. Conclusions: Respiratory impairment among adults living in post-disaster container settlements primarily reflects reduced lung volume and functional capacity rather than obstructive airway disease. Functional and social determinants, particularly muscle strength and employment status, play a central role, underscoring the need for integrated post-disaster respiratory surveillance and rehabilitation strategies.
Background: This study aims to assess the robustness of randomized control trials (RCTs) in the dental field by analyzing the fragility index (FI). The FI is a statistical measure defined as the small...
Background: This study aims to assess the robustness of randomized control trials (RCTs) in the dental field by analyzing the fragility index (FI). The FI is a statistical measure defined as the smallest number of event changes needed to convert statistical significance (P< 0.05), of a binary outcome, to a not significant result. Previous studies have found that the results of many RCTs in medical disciplines are very fragile. However, there is limited literature examining the robustness of trials in dentistry. Objective: The primary objective of this study is to evaluate the fragility of RCTs in top dental journals using the FI. The secondary objective is to explore factors associated with fragility. Methods: We will identify RCTs from five high-impact dental journals namely, Periodontology 2000, International Journal of Oral Science, Journal of Clinical Periodontology, Journal of Dental Research, and Journal of Dentistry published between January 2019 and December 2024 reporting at least one primary binary outcome. We will estimate the FI and factors associated with FI will be assessed using regression analysis. Results: Screening and data extraction began in August 2025 and are expected to conclude by December 2025. Data analysis will be conducted in January 2026, and we anticipate submitting the results for publication by March–April 2026. Conclusions: Dental practitioners rely on RCTs to guide patient care and treatment planning. Assessing the FI of trials allows us to determine the robustness of their results. By evaluating fragility, dental practitioners and policymakers can make more informed decisions on evidence based care and identify areas for further research.
Following its strategy to eliminate malaria by 2030, the Government of Tanzania implemented the nationwide biolarviciding in urban and rural areas in 2017 to supplement the core vector control interve...
Following its strategy to eliminate malaria by 2030, the Government of Tanzania implemented the nationwide biolarviciding in urban and rural areas in 2017 to supplement the core vector control interventions: insecticidal treated nets (ITNs) and indoor residual spraying (IRS). The need for biolarviciding in Tanzania stems from its potential to enhance integrated vector management strategies, address the challenge of outdoor biting and help mitigate the effects of insecticide resistance. It provides a sustainable, eco-friendly approach to controlling mosquito populations and reducing malaria transmission. However, substandard implementation of biolarviciding has been documented across several District Councils, most likely due to inadequate skills and financial resource to implement biolarviciding according to guidelines and standard operating procedures. Therefore, this protocol describes a large-scale pilot study aiming at generating evidence for guiding future upscaling of biolarviciding by providing guidance on quality implementation as per standard operating procedures. It emphasizes government leadership, community participation and engagement, as well as local capacity building.
The present biolarviciding protocol has been developed to be piloted in the three councils in Tanga Region. It describes steps for site selection, identification and characterization of breeding sites, preparation for and application of biolarvicides, mode of implementation, monitoring outcome on larvae and adult mosquitoes, as well as malaria disease incidence.
The present protocol will guide regions and councils and other stakeholders in the effective implementation of biolarviciding intervention, contributing to the malaria control and elimination efforts in Tanzania Mainland.
Background: Large language models (LLMs) have shown growing potential for clinical decision support. However, effectively integrating domain-specific medical knowledge into LLMs while maintaining accu...
Background: Large language models (LLMs) have shown growing potential for clinical decision support. However, effectively integrating domain-specific medical knowledge into LLMs while maintaining accuracy, safety, and interpretability remains a key challenge for postoperative discharge instructions and patient education. Fine-tuning (FT), retrieval-augmented generation (RAG), and hybrid FT+RAG approaches represent three prominent strategies for knowledge integration, yet their comparative performance in postoperative clinical contexts has not been systematically evaluated. Objective: We aimed to compare the clinical performance, reliability, and safety characteristics of baseline, fine-tuned, retrieval-augmented, and hybrid FT+RAG LLM configurations for postoperative clinical decision support. Methods: We conducted a controlled comparative evaluation of four LLM configurations using Google Gemini 2.5 Flash. A total of 600 postoperative question–answer pairs were used for model adaptation and validation, while 150 queries were reserved for final evaluation. Queries included routine postoperative care questions, emergency escalation scenarios, and deliberately out-of-scope questions. Model outputs were independently assessed by three blinded clinical experts for accuracy, completeness, and relevance. Automated metrics were used to evaluate readability, faithfulness, and hallucination propensity. Results: All knowledge-enhanced models significantly outperformed the baseline model in clinical accuracy (baseline 68.0% vs FT 92.7%, RAG 91.3%, FT+RAG 97.3%; p<.001). The hybrid FT+RAG model achieved the highest overall performance, including 100% precision, 96.7% recall, and the lowest hallucination rate. FT and RAG alone yielded comparable gains across accuracy, completeness, relevance, faithfulness, and hallucination reduction, with no statistically significant differences between them. While enhanced models produced shorter and more concise responses, they demonstrated reduced readability compared with the baseline model. Conclusions: Incorporating domain knowledge substantially improves the clinical performance of LLMs for postoperative decision support. Hybrid FT+RAG approaches provide the strongest overall accuracy and safety profile, although trade-offs in readability, interpretability, and rater variability remain. These findings support the use of knowledge-augmented LLMs in postoperative care while underscoring the need for careful governance, transparency, and human oversight prior to clinical deployment. Clinical Trial: Not applicable
Background: Large language models (LLMs) such as ChatGPT and Google Gemini have demonstrated promising capabilities in medical reasoning and clinical decision support. However, their comparative perfo...
Background: Large language models (LLMs) such as ChatGPT and Google Gemini have demonstrated promising capabilities in medical reasoning and clinical decision support. However, their comparative performance against human specialists in critical care scenarios, particularly acid-base disorder interpretation and sepsis management, remains inadequately characterized. Objective: This study aimed to compare the diagnostic and therapeutic decision-making performance of advanced AI models (ChatGPT-4 and Google Gemini), a consensus-based ensemble AI approach, and human medical specialists in acid-base disorder interpretation and sepsis management scenarios using validated clinical vignettes. Methods: A total of 45 clinical case vignettes (20 acid-base disorder cases and 25 sepsis management cases) were developed by an expert panel. Cases were independently evaluated by 20 human specialists (10 emergency medicine physicians and 10 anesthesiologists), ChatGPT-4, Google Gemini, and a simple majority-voting ensemble model. Blinded evaluation was ensured throughout. Performance metrics included diagnostic accuracy, treatment recommendation appropriateness, and Surviving Sepsis Campaign (SSC) bundle compliance rates. Results: For acid-base disorder interpretation, the ensemble AI model achieved the highest overall accuracy (86.0%), followed by anesthesiologists (84.5%), ChatGPT-4 (83.7%), emergency physicians (83.2%), and Google Gemini (79.5%). In simple metabolic and respiratory disorders, AI models demonstrated comparable or superior performance to human experts (>90% accuracy). However, human specialists outperformed individual AI models in mixed acid-base disorders (humans: 75.5% vs ChatGPT: 68.5%, Gemini: 65.3%, P<.05). For sepsis management, SSC hour-1 bundle compliance was highest in the ensemble model (95.8%), followed by ChatGPT-4 (94.2%), human experts (91.5%), and Gemini (89.7%). Conclusions: Advanced LLMs demonstrate comparable performance to human specialists in straightforward acid-base and sepsis scenarios, with ensemble approaches showing potential for improved accuracy. However, human expertise remains superior in complex, atypical presentations requiring nuanced clinical judgment. These findings are limited to text-based simulations and require validation in real-world clinical environments.
Background: Traditional journal clubs are a staple of graduate medical education (GME) designed to foster critical appraisal skills. However, learner engagement is often hindered by time constraints a...
Background: Traditional journal clubs are a staple of graduate medical education (GME) designed to foster critical appraisal skills. However, learner engagement is often hindered by time constraints and low reading compliance. Generative Artificial Intelligence (AI) offers novel modalities for content delivery, yet its utility in journal club preparation remains under-explored. Objective: This study aims to evaluate the feasibility, learner satisfaction, and knowledge acquisition of an AI-generated interactive audio overview (NotebookLM) compared to traditional reading-based preparation for resident journal clubs. Methods: We conducted a prospective, randomized crossover trial involving 60 participants from General Surgery and Internal Medicine. While the initial cohort included attendings and fellows, the primary paired analysis was restricted to the 28 residents who completed both arms of the study to focus on the primary learner group. Participants were randomized to two sequences: the traditional format (reading the full text) and the AI-assisted format (using NotebookLM to generate an interactive audio podcast and briefing document). Knowledge acquisition was assessed via a 5-point multiple-choice quiz. Learner satisfaction and perceptions of utility were measured using a 5-point Likert scale. Results: Sixty participants completed the baseline survey. Twenty-eight residents completed both arms of the crossover study. In this primary paired analysis, there were no significant differences in objective comprehension scores between the Traditional and NotebookLM arms (Traditional: 3.9 ± 1.1 vs. NotebookLM: 4.2 ± 1.0; P=.28). Participants reported comparable engagement (P=.97) and likelihood of applying findings (P=.33). Regarding critical appraisal, participants rated the traditional format numerically higher for understanding "limitations and biases" (Traditional: 3.4 ± 1.0 vs. NotebookLM: 3.1 ± 1.0), though this difference was not statistically significant in the paired cohort (P=.31). Notably, 89.2% of participants indicated they would use the NotebookLM tool for future journal clubs, and 54% found the AI-generated briefing document "very" or "extremely" helpful. Conclusions: The AI-generated NotebookLM interactive podcast proved to be a feasible and highly acceptable alternative to traditional reading, demonstrating non-inferiority in factual knowledge acquisition. While traditional methods may offer advantages for identifying study nuances, the high user acceptance of the AI tool suggests it is an effective "primer" to enhance efficiency for time-constrained trainees. Clinical Trial: Not Applicable
Background: Artificial intelligence (AI), including large language models (LLMs), is increasingly integrated into systematic review (SR) workflows. AI tools may accelerate searching, screening, data e...
Background: Artificial intelligence (AI), including large language models (LLMs), is increasingly integrated into systematic review (SR) workflows. AI tools may accelerate searching, screening, data extraction, and reporting, but their effects on methodological quality, reporting completeness, transparency, and reproducibility remain uncertain. Existing evaluations largely examine isolated tasks, and inconsistent disclosure of AI use limits reproducibility and oversight. Objective: This four-phase mixed-methods meta-research study will: (1) compare the methodological quality of AI-assisted versus traditional SRs; (2) refine, finalize, and apply a preliminary AI Transparency and Disclosure Index (AITDI); (3) evaluate reproducibility by comparing outputs across repeated runs of the same AI model, across different AI models, and between AI models and human reviewers at multiple SR stages; and (4) explore knowledge user perspectives on rigor, transparency, and trust in AI-assisted SR. Methods: We will conduct a matched cohort analysis of SRs published from 2023–2025 in biomedical journals. Each AI-assisted SR will be matched 1:2 with traditional SRs by publication year, clinical domain, review type, and meta-analysis status. Two independent reviewers will apply AMSTAR-2 (methodological quality), PRISMA 2020 (reporting completeness), and, when applicable, ROBIS (risk-of-bias rigor). A preliminary AITDI will be refined and then applied to all AI-assisted SRs. Reproducibility will be assessed using SR-derived tasksets to compare outputs across repeated runs of the same model, across different models, and between AI and human reviewers at key SR stages. Semi-structured interviews with authors, editors, clinicians, policymakers, and patient partners will be analyzed using reflexive thematic analysis. Results: As of December 2025, the study has been preregistered on OSF (DOI: 10.17605/OSF.IO/Q5JRW), the search strategy has been finalized, and title/abstract screening has begun. Data extraction is planned for March–May 2026, followed by AITDI refinement and reproducibility testing from May–October 2026. Qualitative interviews are anticipated from October 2026–February 2027, with final analyses by April 2027 and dissemination planned for mid-2027. Conclusions: This study will provide one of the first empirical comparisons of methodological quality, transparency, and reproducibility of AI-assisted versus traditional SRs in the LLM era. Findings will inform expectations for responsible AI integration and support refinement of reporting and methodological best practices, including future development of AI-specific reporting and appraisal extensions (e.g., PRISMA-LLM, AMSTAR-LLM). Clinical Trial: N/A
Background: ostoperative pain following total knee arthroplasty (TKA) is a significant challenge for both physicians and patients, adversely impacting patients' quality of life and the rehabilitation...
Background: ostoperative pain following total knee arthroplasty (TKA) is a significant challenge for both physicians and patients, adversely impacting patients' quality of life and the rehabilitation of joint function post-surgery. Exploring efficient and safe therapy approaches to alleviate pain and enhance joint function recovery is of paramount importance. Transcutaneous electrical acupoint stimulation (TEAS) demonstrates significant efficacy in alleviating postoperative pain. Nevertheless, there is a paucity of studies regarding its application following total knee arthroplasty. Objective: This study is to assess the effectiveness of TEAS in conjunction with multimodal analgesia for reducing postoperative pain and enhancing the quality of joint function rehabilitation following TKA. The impact of TEAS in conjunction with multimodal analgesia on opioid dosage and associated adverse responses, as well as the mechanism by which it enhances postoperative analgesic efficacy, was investigated. Methods: This article outlines a randomized controlled clinical trial that was blinded solely to evaluators. 154 participants were randomly allocated to an experimental group and a control group, with 77 cases in each group. This experiment will consist of a baseline phase, a 1-week treatment period, and a 12-week follow-up period. The control group patients who received TKA were administered the normal multimodal analgesic protocol. Patients in the experimental group received TEAS for 30 minutes, twice daily, from postoperative days 1 to 7, alongside normal multimodal analgesic treatment. The primary outcomes included Visual Analog Scale (VAS) at 3 and 7 days after surgery and the Western Ontario and McMaster Universities (WOMAC) score at 28 days after surgery. Secondary outcomes included VAS score at baseline and 1 day after surgery, WOMAC score at baseline, 1 week, 2 weeks and 12 weeks after surgery, and Short-Form 12-Item Health Survey (SF-12) at baseline, 4 weeks and 12 weeks after surgery. Other exploratory outcome measures included: knee pain threshold, Laser speckle imaging (LSI), quadriceps femoris motor unit stability index, knee skin temperature with infrared thermography (IRT), and plasma β-endorphin (β-EP) concentration. The knee pain threshold was evaluated at baseline, 1 day, 3 days, 7 days after surgery, and the plasma β-EP concentration was evaluated at baseline, 1 day, 7 days, 14 days after surgery. Other parameters were evaluated at baseline and 2 weeks after surgery. The use of postoperative analgesics was also recorded. Results: Recruitment for this clinical study began in July 2022, and we enrolled the first subject on July 21, 2022. As of January 1, 2025, all 154 planned participants had been enrolled. The study data are currently being collated and analyzed, and the results are expected to be completed and reported in the first quarter of 2026. Conclusions: The results of this study can provide a basis for TEAS to relieve pain after TKA and accelerate joint function recovery. Clinical Trial: Chictr.org.cn identifier: ChiCTR2200063897. Registered on 20 September 2022.
Background: Digital therapeutics (DTx) are evidence-based software interventions with the potential to treat health conditions. However, uptake remains limited by low public awareness and overly compl...
Background: Digital therapeutics (DTx) are evidence-based software interventions with the potential to treat health conditions. However, uptake remains limited by low public awareness and overly complex patient education materials that exceed recommended readability levels. Large language models (LLMs) may simplify such content; however, their effect on actual comprehension has not been empirically demonstrated. Objective: To examine whether LLM-based simplification of DTx explanatory materials enhances public comprehension and subjective evaluations of readability, clarity, and comprehensibility compared with manufacturer-provided documents. Methods: We developed a simplification tool using the GPT-4o API, configured for deterministic outputs and guided by structured readability instructions. Original DTx explanatory materials about insomnia and nicotine dependence were obtained from manufacturers and transformed into simplified versions. Two randomized, between-subject online experiments were conducted (N = 1,000; 500 per condition). Participants were stratified by age and sex and screened for relevance (Insomnia Severity Index ≥8 for the insomnia experiment; smoking ≥5 cigarettes/day for the nicotine dependence experiment). Within each experiment, participants were randomly assigned to review either the original or the LLM-simplified explanation. Perceived understanding and post-exposure evaluations of ease, clarity, and comprehensibility were assessed pre- and post-exposure. Results: Repeated-measures analysis of variance revealed significant Group × Time interaction effects on perceived understanding in both experiments: insomnia (F₁,₄₉₈ = 24.8; P <.001) and nicotine dependence (F₁,₄₉₈ = 14.1; P < .001), with greater improvements in the LLM-simplified groups. Mann–Whitney U tests further showed that LLM-simplified explanations were rated as significantly easier, clearer, and more comprehensible than the original versions in both experiments (all P < .05). Conclusions: Compared with manufacturer-provided original materials, LLM-simplified DTx explanations led to greater improvements in perceived understanding and subjective readability among lay audiences, even after a single exposure. This finding highlights the scalability of LLM-based simplification as a strategy to address health literacy barriers. Integrating such tools into patient education may enhance access to digital therapeutic information and support broader societal diffusion. Clinical Trial: Clinical Research Information Service (CRIS), Republic of Korea.
Registration number: KCT0011459.
Available at: https://cris.nih.go.kr
This research letter summarizes the development and deployment of a data analytics dashboard that uses natural language processing to streamline pre-MRI safety screening for implantable medical device...
This research letter summarizes the development and deployment of a data analytics dashboard that uses natural language processing to streamline pre-MRI safety screening for implantable medical devices, resulting in a 98% reduction in manual screening workload while maintaining high diagnostic accuracy.
Background: Mobile health (mHealth) interventions that integrate psychoeducation with structured problem-solving training (PST) hold strong potential for improving self-management of chronic condition...
Background: Mobile health (mHealth) interventions that integrate psychoeducation with structured problem-solving training (PST) hold strong potential for improving self-management of chronic conditions. Evaluating the usability of these interventions requires assessing technological, pedagogical, and sociocultural fit. However, most usability evaluations remain narrowly technocentric, focusing on interface-level metrics while neglecting pedagogical coherence, cultural responsiveness, and patient learning needs. Objective: This study aimed to characterize usability challenges and facilitators across three psychoeducational mHealth Problem Solving Training interventions and to identify technological, pedagogical, and sociocultural design features that can improve engagement, accessibility, and implementation for diverse users. Methods: A multi-method, multicase study was conducted with a total of n=14 participants who completed think-aloud usability sessions while interacting with one of three different mHealth PST interventions designed for persons with neurodevelopmental and/or neurological disability: (1) Epilepsy Journey 2.0, (2) Survivor’s Journey, and (3) Electronic Problem-Solving Training (ePST). Participants completed a presession technology comfort survey and the Comprehensive Assessment of Usability for Learning Technologies (CAUSLT) postsession. All sessions were recorded, transcribed, and analyzed thematically. CAUSLT data were analyzed using descriptive quantitative methods. Results: ePST demonstrated the highest usability (x̄ 88 out of 100, 95 percent CI 71.8 to 104.2), followed by Survivor’s Journey (x̄ 83 out of 100, 95 percent CI 57.1 to 108.9) and then Epilepsy Journey 2.0 (x̄ 79 out of 100, 95 percent CI 65.3 to 92.7). Findings revealed that usability in healthcare learning design is shaped by how effectively the technology, learning content, and contextual factors align with patients’ needs. Recurring challenges across interventions included unclear navigation, poor mobile responsiveness, instructional ambiguity, insufficient feedback, potential for greater inclusivity, and limited error recovery. Twelve cross-case design principles were derived, emphasizing mobile-first accessibility, cognitive load reduction, context-sensitive feedback, and empathetic, inclusive design. Conclusions: Usability challenges in mHealth PST interventions arise not only from interface level issues but also from how effectively the intervention supports users’ understanding, decision making, and real world application demands. This extends prior mHealth usability research by demonstrating that user difficulties often reflect misalignments between technological features, instructional structure, and the everyday contexts in which individuals engage with PST. Resulting design principles highlight specific, actionable priorities for developers, including mobile first optimization, clearer task scaffolding, and better feedback and error recovery. Future work should evaluate these principles in larger samples and clinical settings to determine their impact on engagement, adherence, and downstream health outcomes.
The healthcare digital transformation is gaining increasing notoriety, despite the observed challenges in its implementation. The envisioned benefits together with the growing need for better healthca...
The healthcare digital transformation is gaining increasing notoriety, despite the observed challenges in its implementation. The envisioned benefits together with the growing need for better healthcare are motivating academia, organizations, regulatory agencies, and governments to develop more effective digital healthcare solutions. Through extensive debates among the authors and supported by a narrative literature review, this paper discusses how digital transformation is being conducted in the healthcare sector. Our discussion relies on the concepts from the sociotechnical systems theory categorizing it according to three social (people, culture, and goals) and three technical (processes/procedures, infrastructure, and technology) dimensions. Overall, we argue that both social and technical dimensions present elements that have been either encouraging or discouraging the progress of healthcare digital transformation. The identification of current trends on such (on- and off-track) elements allowed the formulation of propositions for future testing and validation. This approach can help the establishment of better government policies, foster private initiatives, and shift regulatory guidelines to support a successful digital transformation in health systems. Lastly, from a research perspective, we outline some opportunities for further interdisciplinary investigation in the field, promoting advances in the understanding of healthcare digital transformation.
Background: Pediatric tuberculosis (TB) remains a significant public health concern in the Democratic Republic of Congo (DRC), particularly in rural areas where diagnostic capacity and community aware...
Background: Pediatric tuberculosis (TB) remains a significant public health concern in the Democratic Republic of Congo (DRC), particularly in rural areas where diagnostic capacity and community awareness are limited. Despite national efforts to reduce TB-related morbidity and mortality, the detection and management of pediatric TB are hindered by low levels of knowledge among caregivers, misconceptions about transmission, and systemic constraints within health facilities. Objective: This study aimed to assess the knowledge, perceptions, and practices of caregivers and health care providers regarding pediatric tuberculosis in the Kabondo Dianda health zone. It sought to identify behavioral and structural factors that contribute to delayed diagnosis and suboptimal management of TB in children. Methods: A descriptive cross-sectional study was conducted from January 2020 to December 2022 in five diagnostic and treatment centers (CSDTs) within the Kabondo Dianda health zone. Data were collected using KoboCollect and analyzed with SPSS. The study included 163 caregivers of children with TB and 27 health care providers. Quantitative variables were summarized using medians and interquartile ranges, while categorical variables were presented as frequencies and proportions. A scoring system was developed to assess knowledge levels. Results: Among caregivers, 95.1% demonstrated low knowledge of pediatric TB, and 45% believed TB was not transmissible. Over one-quarter attributed TB to supernatural causes. Only 3.7% of pediatric TB cases received HIV screening, and just 18.5% of providers routinely sent samples for GeneXpert testing. Although 59.3% of providers had high knowledge scores, 92.7% were unaware of latent TB forms. Nearly two-thirds of caregivers sought care only after disease progression or treatment failure. Conclusions: The study revealed critical gaps in community knowledge and provider practices regarding pediatric TB. Misconceptions, delayed care-seeking, and limited diagnostic efforts contribute to underreporting and poor outcomes. Strengthening community education, provider training, and diagnostic infrastructure is essential to reduce pediatric TB morbidity and mortality in rural Congolese settings.
Background: Internet search engines serve as primary gateways for cancer information, yet the commercialization of health content within organic search results remains understudied. While covert promo...
Background: Internet search engines serve as primary gateways for cancer information, yet the commercialization of health content within organic search results remains understudied. While covert promotional content—such as native advertising and stealth marketing—has been documented in various contexts, systematic comparisons across structurally divergent search platforms are lacking. Objective: This study examined the prevalence, distribution, and information quality characteristics of covert promotional cancer-related content across Naver and Google, South Korea's two dominant search engines, which have fundamentally different platform architectures. Methods: A two-phase cross-sectional content analysis was conducted. Phase 1 employed natural language processing to identify 33 cancer-related keywords from 1,400 preliminary posts. Phase 2 systematically collected 5,848 posts in October 2023, yielding 919 unique posts (598 from Naver and 321 from Google) that covered seven major cancer types, representing over 70% of Korean cancer incidence. Two trained coders analyzed promotional status, intensity, institutional sources, and information quality indicators (citation practices, information depth, and source attribution), with inter-coder reliability exceeding κ=.80. Chi-square tests examined the associations between platform and cancer type. Results: Covert promotional content appeared in 48.6% (447/919) of analyzed posts, with significantly higher prevalence on Google (54.2%, 174/321) than Naver (45.7%, 273/598; χ²₁=5.78, p=.016). Platform differences were pronounced: Naver promotional posts predominantly originated from blogs (96.0%, 262/273) and exhibited full promotional intensity (52.1%, 126/242), while Google posts primarily came from hospital websites (81.0%, 141/174) with simple institutional identification (57.8%, 52/90). Institutional source distribution varied significantly by platform (χ²₅=215.714, P<.001): traditional medicine institutions dominated Naver (99.2%, 119/120), whereas university-affiliated hospitals predominated on Google (85.0%, 96/113). Information quality differed substantially: indirect citation was more common on Google (81.6%, 142/174) than Naver (58.6%, 160/273; χ²₁=25.653, P<.001), while comparative informational depth was higher on Google (55.7%, 97/174) versus Naver (19.4%, 53/273; χ²₂=64.683, P<.001). Conclusions: Covert promotional cancer content is pervasive in Korean search results, with platform architecture systematically shaping promotional patterns, institutional sources, and information quality rather than reflecting deliberate marketing strategies. These findings underscore the need for platform-sensitive regulation and enhanced digital health literacy to protect vulnerable cancer information seekers from commercial exploitation embedded within ostensibly neutral search environments.
Background: The gut microbiota plays a crucial role in infant nutrition through its effects on energy metabolism, nutrient absorption, and immune regulation. However, evidence from Indonesian infants...
Background: The gut microbiota plays a crucial role in infant nutrition through its effects on energy metabolism, nutrient absorption, and immune regulation. However, evidence from Indonesian infants remains limited. Objective: This study aimed to examine the association between gut microbiota composition and underweight among infants in coastal areas of Central Sulawesi, Indonesia. Methods: A follow-up observational study was conducted among 88 six-month-old infants in coasta areas of Banggai, Central Sulawesi. Maternal and infant characteristics were collected through structured interviews and anthropometric assessments. Weight-for-age Z-scores (WAZ) were calculated based on WHO growth standards, and underweight was defined as WAZ < −2 SD. Fecal samples were analyzed by quantitative PCR to quantify the bacterial genera Bifidobacterium, Lactobacillus, Bacteroides, Clostridium, and Escherichia coli. Group differences were assessed using chi-square, Mann–Whitney U, and Wilcoxon signed-rank tests. Associations between bacterial abundance and WAZ were evaluated using multivariable linear regression, adjusted for relevant maternal, environmental, and infant factors. Results: The mean WAZ was −0.47 ± 1.09, and 8.0 % of infants were classified as underweight. Beneficial genera (Bifidobacterium, Lactobacillus) predominated over opportunistic bacteria (Wilcoxon signed-rank, p = 0.0017). Higher Clostridium abundance was inversely associated with WAZ (unadjusted β = −0.094, 95 % CI –0.173 to –0.015; p = 0.021; adjusted β = −0.089, 95 % CI –0.166 to –0.014; p = 0.030). No significant associations were observed for other bacterial genera. Conclusions: An increased abundance of Clostridium was independently associated with underweight status among infants in coastal Central Sulawesi. These findings highlight the potential role of gut microbiota imbalance in early growth faltering and support the need for longitudinal studies to clarify causal mechanisms and inform microbiota-targeted nutritional interventions in coastal Indonesian populations.
Background: Outdoor secondhand smoke (SHS) remains a public health concern, particularly around designated outdoor smoking areas where non-smokers may pass through or linger nearby. While previous stu...
Background: Outdoor secondhand smoke (SHS) remains a public health concern, particularly around designated outdoor smoking areas where non-smokers may pass through or linger nearby. While previous studies have quantified outdoor SHS concentrations, fewer have examined the number of people potentially exposed in real-world settings. Estimating exposure opportunity at the population level requires methods that are feasible, scalable, and minimally intrusive. Objective: This study aimed to evaluate the feasibility of using passive Wi-Fi packet sensing, calibrated with brief on-site observation, to quantify the number of smokers and passersby within a plausible SHS exposure range at a public outdoor smoking area in Japan. Methods: We conducted a formative field study at a designated outdoor smoking area of the Asia Pacific Trade Center (ATC), Osaka, Japan. A passive Wi-Fi packet sensor was installed adjacent to the smoking area, collecting timestamps, anonymized device identifiers (hashed MAC addresses), organizationally unique identifiers (OUIs), and received signal strength indicator (RSSI) values from October 13 to 29, 2023. On October 28, a high-traffic event day, a 30-minute manual count (15:00–15:30) of smokers and passersby was conducted within a 25-m radius to calibrate sensor-derived estimates. Records outside business hours were excluded, and devices transmitting outside business hours were treated as fixed devices and removed. Detected signals were aggregated into presence episodes, screened by dwell time, and classified as likely smokers or passersby using empirically derived RSSI thresholds. Calibration ratios from the observation window were applied to estimate hourly and daily counts during business hours. Results: During the 30-minute observation period, 14 smokers and 207 passersby were visually counted within the 25-m radius. On the same day, sensor logs yielded 659 eligible presence episodes during business hours. Applying classification rules and calibration ratios, we estimated that 262 smokers and 3,907 passersby were present within the plausible SHS exposure range over the course of the day. Temporal patterns indicated bimodal peaks in smoker presence and a midday peak in passerby traffic, corresponding to event-related footfall. Conclusions: This formative study demonstrates the feasibility of combining passive Wi-Fi packet sensing with brief manual validation to quantify population-level exposure opportunities to outdoor SHS in a real-world setting. The approach offers a low-cost and privacy-preserving method for assessing outdoor SHS exposure and may inform the design, placement, and management of smoking areas in public spaces. Further multi-site studies are warranted to refine exposure estimation and support evidence-based tobacco control strategies.
Background: Disparities in access to dermatologic care in medically underserved and rural communities within Northeast Ohio are reflective of national trends. MetroHealth’s teledermoscopy tool, Snap...
Background: Disparities in access to dermatologic care in medically underserved and rural communities within Northeast Ohio are reflective of national trends. MetroHealth’s teledermoscopy tool, Snapshot, is intended to streamline triage for potentially cancerous skin lesions. However, its utilization patterns and ability to expand access to care have not been studied. Objective: This study aimed to identify demographic and geographic trends in Snapshot utilization and assess its capacity to reach populations that may lack access to dermatologic care. Methods: County-level data on dermatologist density was extracted from records obtained from the American Academy of Dermatology (AAD). Spearman correlations were used to examine the relationship between Snapshot utilization and dermatologist density at the county level. A retrospective analysis of all Snapshot encounters from 2018 to 2025 was performed to identify demographic characteristics and clinical outcomes of this patient population. A ZIP code-level analysis was performed to identify areas with the greatest Snapshot encounters. Results: A total of 1,274 patients used Snapshot between 2018 and 2025, with 2,016 total Snapshot encounters. At the county level, dermatologist density was strongly positively correlated with Snapshot utilization (Pearson r=0.968, P<0.001; Spearman ρ=0.709, P=.028). A ZIP code level analysis demonstrated that the highest rates of utilization clustered around ZIP codes containing MetroHealth clinics offering Snapshot due to its walk-in design. However, 58% of Snapshot users were new patients with no prior dermatology encounters, indicating its potential role as an entry point into specialty care. Conclusions: Snapshot utilization appears to be strongly driven by dermatologist density geographic proximity to a MetroHealth clinic, suggesting that it is not bridging geographical gaps in access to dermatological care but is likely acting as a triage tool for patients with potentially cancerous skin lesions. However, the high proportion of new users suggests that it is acting as an entry point for patients who were not previously connected to dermatological care. Further work comparing Snapshot users with the broader MetroHealth dermatology population is needed to elucidate the characteristics of those who are currently benefiting from Snapshot and identify the ways it can be implemented to reach those who remain disconnected from care.
Background: Person-centred care (PCC) is a foundational principle of nursing practice, emphasising dignity, compassion, and respect for individuals receiving care. While PCC is commonly framed in rela...
Background: Person-centred care (PCC) is a foundational principle of nursing practice, emphasising dignity, compassion, and respect for individuals receiving care. While PCC is commonly framed in relation to physical, psychological, and social dimensions of health, the spiritual dimension becomes particularly salient in contexts of long-term illness, disability, and end-of-life care. Patients whose bodies no longer conform to normative expectations of health and independence are at risk of being reduced to diagnoses or prognoses, undermining person-centred practice. Objective: This paper aims to strengthen conceptual foundations for person-centred nursing care by articulating a relational account of personhood that affirms the dignity and wholeness of sick and dying bodies, particularly in contexts where cure is no longer possible. Methods: This is a conceptual and interdisciplinary analysis drawing on nursing ethics, disability theology, and bioethics. Ezekiel 37 is used as a theologically informed interpretive resource to explore relational understandings of personhood and healing, without presupposing religious belief. Results: Relational accounts of personhood challenge functional and capacity-based models that implicitly link human worth to autonomy, cognition, or productivity. Interpreted conceptually, Ezekiel 37 offers a framework in which personhood is grounded in relational address rather than bodily integrity or responsiveness. This perspective reframes healing as the alleviation of relational and existential suffering through presence, language, and compassionate care, even in the absence of physical recovery. Conclusions: A relational understanding of personhood supports person-centred nursing practice by resisting dehumanising narratives of decline and sustaining dignity at the end of life. This framework offers ethically robust conceptual resources for spiritual care that are attentive to patients’ sources of meaning while remaining compatible with pluralistic healthcare contexts. Clinical Trial: n/a
Background: Cancer research literature is often riddled with technical jargon that is not digestible to the average person. Individuals interested in research studies may want to contribute through pa...
Background: Cancer research literature is often riddled with technical jargon that is not digestible to the average person. Individuals interested in research studies may want to contribute through patient partner engagement or sample donation but find the relevant literature overwhelming. Through the generation of lay summaries, previously inaccessible research papers become easier to comprehend, especially for patient partners or data donors. With large language models (LLMs) continuing to advance, so does their capability to summarize large texts. Objective: In this study, we examined whether LLMs can produce lay summaries of scientific literature at-scale, while maintaining readability and accuracy to their source texts. Methods: We developed a tool to generate lay summaries of open-access article abstracts and their full texts with GPT-4-Turbo. Prompt development aimed for a target 8th grade reading level assessed with Flesch-Kincaid Grade Level. Human-review metrics were used to evaluate readability and accuracy when generated using abstracts versus full text articles. Results: The average Flesch-Kincaid Grade Level Score was 7.13 for abstract-based summaries and 7.39 for full text-based summaries, indicating summaries at around 7th grade reading level. Human-review metrics showed these summaries were of similar readability and accuracy when generated using abstracts versus full text articles, with mean accuracy scores from human review of 7.09 vs 7.42 out of 10 respectively. Additionally, qualitative patient-based assessment indicated these summaries would encourage participation in research studies. Conclusions: By generating lay summaries for complex and lengthy research papers, their scientific information becomes accessible to a larger audience, including patient partners interested in contributing to cancer research. Summaries that are easy to understand will allow participants to make informed decisions about their involvement and appreciate the impact of their contributions if and when their results are published.
Background: Environmental factors account for 23% of global deaths and 25% of chronic diseases. In France, the 4th National Health and Environment Plan prioritizes training health professionals in env...
Background: Environmental factors account for 23% of global deaths and 25% of chronic diseases. In France, the 4th National Health and Environment Plan prioritizes training health professionals in environmental health. Endocrine disruptors (EDCs) are chemical substances that interfere with hormonal systems, contributing to a range of health effects. In 2024, the Primary Care Environment and Health (PCEH) program at the University of Montpellier–Nimes introduced an innovative e-learning module on EDCs for first-year family medicine residents. Objective: To evaluate the impact of the PCEH e-learning module on participants’ satisfaction, knowledge, and self-reported behaviors regarding EDCs in household environments. Methods: This monocentric, matched before–after cohort study included all first-year family medicine residents. The module, developed collaboratively by clinicians and educators, integrated interactive images, AI-generated virtual rooms, short educational videos, games, and flashcards. Participants were assessed using pre- and post-training questionnaires, administered immediately before and after the training. These questionnaires evaluated satisfaction (using a 5-Likert scale), knowledge (with binary yes/no questions), and behaviors (using a 5-point Likert scale). Statistical analyses used McNemar’s test for qualitative variables and paired t-tests for quantitative variables (p < .05). Results: Of 148 eligible residents, 78 (52.7%) completed both assessments over a 17-day period. Overall satisfaction was high (mean 4.0/5, SD 0.9), with positive ratings for the e-learning format (4.1/5, SD 1.0) and duration (4.2/5, SD 1.0). Knowledge improved significantly, with a mean 56% increase in correct identification of EDCs across all substances (p < .001). Self-reported behaviors improved by 2.13 points (95% CI 1.71–2.56) on the 5-point scale (p < .001), exceeding gains reported in previous PCEH modules. Secondary outcomes showed high post-training identification of at-risk populations and exposure locations, though recognition of some substances (e.g., alkylphenols, phenoxyethanol) remained lower. Conclusions: This innovative e-learning module significantly improved residents’ knowledge and preventive behaviors related to EDCs. Findings support integration into curricula and potential replication in other health professions.
Background: The COVID-19 pandemic disrupted the delivery of occupational therapy (OT) services in-person on a global scale, accelerating the adoption of telehealth. During this time, there was a surge...
Background: The COVID-19 pandemic disrupted the delivery of occupational therapy (OT) services in-person on a global scale, accelerating the adoption of telehealth. During this time, there was a surge of OT focussed research on the use of telehealth. Synthesising this literature can be helpful to inform routine practice and to prepare for future disruptions to in-person care, including natural disasters, severe weather, and pandemics. Objective: This scoping review maps the literature on telehealth in OT during COVID-19, focusing on setting, study design, participants, clinical fields, modalities, interventions, outcomes, benefits, barriers, and facilitators. Methods: Using Arksey and O’Malley’s framework and Joanna Briggs Institute guidelines, we searched seven databases and Google Scholar for peer-reviewed articles. Eligibility criteria included: English and French papers reporting on telehealth-delivered OT services during COVID-19, across all ages, conditions, settings, and participant groups. Results: From 4,810 records screened, 43 articles were included. Most articles originated from high-income economies and were small in scale (mean=136; median=15). Most were descriptive (e.g., cross-sectional surveys, qualitative studies, and experiential reports). Participant groups were diverse, including OTs, clients, caregivers, and others (e.g., teachers). Telehealth in OT was most reported in pediatric neurodevelopmental and mental health fields, followed by adult mental health. Most articles described synchronous telehealth and the remaining a mixed approach. Only 40% reported on measurable outcomes, with most of these demonstrating statistically significant results. Reported benefits included improved accessibility, personalization, continuity of care, safety in terms of infection prevention, family engagement, and social support. Perceived barriers included technology access and literacy, lack of physical presence, limitations of the home environment, client and caregiver factors, and organizational challenges. Facilitators included home and intervention adaptations, digital skills and training, caregiver involvement, communication strategies, and organizational and system-level support. Conclusions: Telehealth helps to increase access to OT; however, therapists face barriers in using this approach especially for some interventions and populations. More research is needed on how best to implement telehealth across different populations and contexts. Clinical Trial: n.a.
Background: Nurse turnover is a major challenge for health systems, and workload is a key driver. Traditional workload measures rely on surveys or staffing ratios and may not reflect nurses’ real-ti...
Background: Nurse turnover is a major challenge for health systems, and workload is a key driver. Traditional workload measures rely on surveys or staffing ratios and may not reflect nurses’ real-time clinical demands. EHR audit logs provide granular measures of clinical activity and cognitive burden, but their relationship with nurse turnover has not been well characterized. Objective: To evaluate whether electronic health record (EHR)–derived measures of nursing workload
are associated with turnover among inpatient nurses. Methods: We analyzed staff nurses working on medical and surgical inpatient units at a large academic
medical center from January 1 to December 31, 2022. Data included de-identified demographics (age,
sex, years since licensure, service group), shift characteristics (number of shifts worked, night-shift
proportion, timing, location), and EHR audit logs. Audit logs captured (a) EHR activity patterns
(information review, medication administration, alert management, navigation, documentation,
communication), (b) patient load, and (c) cognitive load (patient switching). Associations between
workload measures and turnover were assessed using mixed-effects logistic regression adjusting for
demographics and shift characteristics. Results: Among 432 nurses (84% female; median age 27 [IQR 23–36]) contributing 6,812 shifts and 13
million EHR actions, 84 (19%) left the institution in 2022. A higher proportion of medication
administration actions was associated with increased odds of turnover (OR 2.20; 95% CI 1.36–3.54),
whereas greater alert engagement (OR 0.48; 95% CI 0.32–0.72) and more years since licensure (OR 0.57;
95% CI 0.38–0.84) were associated with lower turnover odds. Conclusions: EHR-derived workload measures—particularly greater medication administration burden and
lower alert engagement—were independently associated with turnover risk. Patterns of EHR use may
help identify nurses at elevated risk of leaving and inform targeted workforce retention strategies.
Background: The growing complexity of colorectal cancer (CRC) management requires advanced tools for integrating multimodal data and clinical knowledge. Large language models (LLMs) offer a promising...
Background: The growing complexity of colorectal cancer (CRC) management requires advanced tools for integrating multimodal data and clinical knowledge. Large language models (LLMs) offer a promising approach to address these challenges through sophisticated natural language processing and reasoning capabilities.
Objective: This systematic review evaluates the current applications, performance, and practical implications of LLMs across the continuum of CRC care, from screening to treatment decision support. Objective: This systematic review evaluates the current applications, performance, and practical implications of LLMs across the continuum of CRC care, from screening to treatment decision support. Methods: We searched six databases (PubMed, Embase, Web of Science, Scopus, CINAHL, Cochrane) up to November 1, 2025, following PRISMA guidelines. Included studies were original research investigating LLM applications specific to CRC, with extractable outcome data. Quality was assessed using QUADAS-2, PROBAST, and ROBINS-I tools by two independent reviewers. Results: Following the screening of 1,261 records, 34 studies met the inclusion criteria, all published between 2023 and 2025. The synthesis highlighted the utility of LLMs in automating data extraction from clinical texts, supporting patient education, aiding diagnostic processes, and assisting in clinical decision-making, with growing evidence of their emerging visual interpretation and multimodal capacities. The effectiveness of these models was significantly influenced by prompt design, which varied from basic zero-shot queries to specialized fine-tuning techniques. While the overall methodological quality of the included studies was deemed adequate, assessments identified recurring concerns regarding insufficient control of biases and inadequate reporting on data security measures. Conclusions: LLMs demonstrate tangible potential to augment CRC care, particularly in structuring unstructured data and providing clinical decision support. However, translating this potential into practice requires solutions for domain adaptation, multimodal integration, and rigorous prospective validation to ensure reliability and safety in real-world settings. Clinical Trial: PROSPERO CRD420251248261; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251248261.
Background: Large-language models (LLMs) have emerging applications in clinical decision-making and medical education, but prospective evaluations in hematology are limited. Objective: We conducted a...
Background: Large-language models (LLMs) have emerging applications in clinical decision-making and medical education, but prospective evaluations in hematology are limited. Objective: We conducted a prospective feasibility study examining the integration of two LLM-based models into a weekly classical hematology case conference. Methods: Over 8 consecutive sessions, ChatGPT and Open Evidence AI were incorporated into real-time case discussions. Presenters used structured prompts to obtain differential diagnoses, diagnostic pathways, guideline-supported management options, and reference retrieval. AI outputs were displayed during the conference and discussed alongside clinical reasoning by hematology faculty. After the intervention, 25 attendees completed a structured survey assessing changes in familiarity and use of AI, perceived value, observed limitations, and preferred implementation strategies. Results: Participants included 16 attending hematologists (64%) and 7 trainees (28%). Familiarity with AI increased from 16% “very familiar” prior to the intervention to 36% “a lot of familiarity” afterward. Frequent or occasional AI use increased from 44% to 68%. Most respondents (84%) rated AI as “very” or “somewhat valuable.” AI was most often perceived as helpful for suggesting alternative diagnoses (80%) and retrieving relevant references (92%). Limitations included prompt dependency (60%), insufficient personalization (52%), and occasional irrelevant or incomplete recommendations (52%). Nearly all respondents (92%) favored an adjunctive rather than self-supervised role for AI. Conclusions: Prospective integration of LLM tools into a classical hematology challenging cases conference was feasible, increased clinician familiarity and interest, and was perceived as diagnostically and educationally valuable. Future investigations should evaluate accuracy, reliability, and optimal frameworks for structured, supervised AI use in hematology education. Clinical Trial: Not applicable
Background: Anxiety disorders are highly prevalent among autistic adults, with 20%-65% experiencing at least one diagnosable anxiety disorder. While mindfulness-based interventions have demonstrated e...
Background: Anxiety disorders are highly prevalent among autistic adults, with 20%-65% experiencing at least one diagnosable anxiety disorder. While mindfulness-based interventions have demonstrated efficacy for anxiety reduction, treatment response varies considerably across individuals. Machine learning approaches offer potential for identifying who is most likely to benefit from smartphone-based mindfulness interventions, enabling more personalized treatment recommendations. Objective: This study aimed to develop and evaluate machine learning models to predict individual treatment response, in the form of reduced anxiety symptoms, to a smartphone-based mindfulness intervention for autistic adults. We sought to identify baseline characteristics that distinguish responders from non-responders, explore few-shot learning with large language models as a complementary approach for low-data clinical prediction, and implement a Personalized Advantage Index approach for individualized treatment recommendations. Methods: We conducted a secondary analysis of data from a randomized controlled trial comparing a 6-week smartphone-based mindfulness intervention (Healthy Minds Program) with a waitlist control condition in autistic adults. Among 73 participants who completed the intervention, we defined responders as those achieving ≥7-point reduction in State-Trait Anxiety Inventory state anxiety scores. Baseline predictors included demographic variables, autism trait measures, and self-report questionnaires assessing anxiety symptoms, perceived stress, affect, and mindfulness. We trained six machine learning models (logistic regression, Random Forest, XGBoost, TabNet, Tab-ICL, and TabPFN) using nested 10-fold cross-validation with inner 5-fold cross-validation for hyperparameter tuning. Additionally, we evaluated few-shot learning using GPT-4o models with tokenized baseline features at varying shot counts (20-70 examples). Model performance was evaluated using area under the receiver operating characteristic curve (AUC) for machine learning model and classification accuracy for few-shot learning. We examined feature importance and implemented Personalized Advantage Index analysis to estimate individualized treatment benefit. Results: Random Forest achieved the highest predictive performance for state anxiety response (AUC 0.79, 95% CI 0.66-0.91), followed by TabPFN (AUC 0.78, 95% CI 0.64-0.94) and logistic regression (AUC 0.77, 95% CI 0.73-0.81). Higher baseline state anxiety (coefficient 1.20, P<.001) predicted better treatment response, while higher trait anxiety (coefficient -0.17, P=.001), older age (coefficient -0.18, P=.02), and lower childhood pretend play scores (coefficient -0.93, P=.007) were associated with poorer response. Few-shot learning with 7-feature tokenization achieved accuracy of 0.867 (95% CI 0.81-0.92) at 70 shots, significantly outperforming Random Forest baseline (0.733, p<.001). Prediction of trait anxiety changes was substantially weaker (AUCs 0.57-0.68), likely reflecting the inherent stability of this personality dimension. The Personalized Advantage Index demonstrated significant moderation of treatment group differences (adjusted R²=0.29), with 75% of participants predicted to benefit more from the mindfulness intervention than the waitlist control. Conclusions: Machine learning models successfully identified baseline characteristics predicting treatment response to a smartphone-based mindfulness intervention in autistic adults. Few-shot learning with large language models demonstrated superior performance to traditional machine learning when provided with compact, high-signal feature representations, offering a promising approach for clinical prediction in small-sample settings. These findings demonstrate the feasibility of precision psychiatry approaches in digital mental health interventions for autistic adults. While modest sample size and limited demographic diversity warrant cautious interpretation, the stable cross-validation performance suggests robust predictive patterns within similar populations. Future research should validate these models in larger, more diverse samples and explore whether algorithm-guided treatment recommendations improve outcomes compared to standard care, through prospective randomized trials.
Background: The effectiveness of ST-elevation myocardial infarction (STEMI) treatment is highly time-dependent, and the information barrier between prehospital and in-hospital settings remains a key f...
Background: The effectiveness of ST-elevation myocardial infarction (STEMI) treatment is highly time-dependent, and the information barrier between prehospital and in-hospital settings remains a key factor leading to treatment delays. Existing digital coordination tools either have a single function or lack long-term real-world evidence support, making it difficult to meet clinical needs. This study adopts a self-developed prehospital chest pain alert app (hereafter referred to as the App) by Fengxian District Medical Emergency Center. Mediated through a WeChat-based chest pain center group, the App enables prehospital information synchronization, real-time alerts, multidisciplinary coordination, and feedback on treatment outcome parameters to form a closed-loop communication model, providing a solution to break the information barrier. Objective: To evaluate the impact of the App-mediated prehospital-in-hospital coordination model on treatment delays (e.g., time from first ECG to catheterization laboratory preactivation, door-to-wire time) and clinical outcomes (e.g., 30-day major adverse cardiovascular events, 1-year and 4-year all-cause mortality) in STEMI patients, and to assess its generalizability in high-risk subgroups. Methods: This is a single-center retrospective cohort study. STEMI patients admitted to Fengxian District Central Hospital from January 1, 2019, to December 31, 2024, will be enrolled and categorized into three groups: baseline group (January 1, 2019-December 31, 2020, without App use), intervention group (January 1, 2021-December 31, 2024, with App-mediated coordination), and concurrent control group (STEMI patients who came to the hospital independently without calling an ambulance or were transported by ambulance but not reported via the App during the same period). The primary outcome is door-to-wire time (D2W). Secondary outcomes include other treatment delay indicators, clinical prognosis, and App operational efficiency. We will use propensity score matching (PSM) to control for baseline confounding, segmented linear regression to analyze intervention trend effects, and subgroup analysis to assess generalizability in high-risk populations. Results: This study is based on four-year real-world data from the Department of Cardiology and STEMI database of Fengxian District Central Hospital. Baseline data and intervention-related data are derived from the hospital’s electronic medical record system and App backend logs. A total sample size of ≥944 is expected. Data extraction and statistical analysis are scheduled from January to April 2026. Results will focus on the App-mediated model’s effect on reducing treatment delays and improving clinical outcomes. Conclusions: Using four-year real-world data combined with PSM and interrupted time series analysis, this study will provide high-quality evidence for the App-mediated coordination model, which is expected to optimize the regional STEMI care system and offer references for the application of digital health technologies in acute coronary syndrome treatment. Clinical Trial: Planned registration; https://www.chictr.org.cn/
Background: The COVID-19 pandemic, with its unprecedented global scale and intensive public health interventions, functioned as a unique, large-scale "natural experiment" in population health educatio...
Background: The COVID-19 pandemic, with its unprecedented global scale and intensive public health interventions, functioned as a unique, large-scale "natural experiment" in population health education. It remains unclear whether this event led to sustained, broad improvements in public literacy regarding infectious diseases beyond COVID-19 itself. Objective: This study aimed to evaluate the longitudinal trends in infectious disease-specific health literacy (IDSHL) among residents of Zhejiang Province, China, from 2019 (pre-pandemic) to 2024 (post-pandemic), to assess the lasting impact of this "natural experiment." Methods: Six annual cross-sectional surveys were conducted from 2019 to 2024 across 30 counties in Zhejiang Province, using consistent multistage stratified random sampling and a validated 12-item IDSHL questionnaire assessing knowledge, behavior, and skills. Annual sample sizes ranged from 17,131 to 19,257 (total N=112,917). Joinpoint regression and multivariate logistic regression were used to analyze trends and identify associations. Results: Population-weighted overall IDSHL scores increased significantly from 2019 (mean=10.46, SD=3.09) to 2024 (mean=11.40, SD=2.88) (P<.001). All subscale scores (knowledge, behavior, skills) showed significant upward trends (all P<.001). Joinpoint regression revealed the most rapid annual increase in the proportion of residents with adequate IDSHL occurred during the acute pandemic phase (2019-2021: APC=9.42%, P<.001), which slowed significantly post-2021 (2021-2024: APC=2.28%, P=.074). Correct response rates surged for items directly related to pandemic messaging (e.g., respiratory etiquette increased from 28.44% to 39.67%), but knowledge on non-respiratory diseases like hepatitis B fluctuated without a clear sustained gain. Adjusted analysis, using 2021 as the reference year, confirmed the lowest odds of adequate IDSHL in 2019 (OR=0.741) and the highest in 2024 (OR=1.098). Conclusions: The COVID-19 pandemic served as a potent natural experiment, catalyzing a significant and sustained improvement in population-level IDSHL, particularly in pandemic-relevant knowledge and behaviors. However, the effect was time-sensitive, with accelerated gains during the acute crisis decelerating afterward, and was not uniform across all disease domains or demographic groups. Post-pandemic health strategies must reinforce comprehensive IDSHL through sustained education and address the digital information landscape to bridge persistent equity gaps.
Background: Clinicians spend a substantial share of their working hours on documentation, contributing to workflow inefficiencies, reduced patient-facing time, and increased burnout. AI medical scribe...
Background: Clinicians spend a substantial share of their working hours on documentation, contributing to workflow inefficiencies, reduced patient-facing time, and increased burnout. AI medical scribes have emerged as a promising solution to reduce this burden, yet real-world evidence remains limited and heterogeneous. Data from European health systems are especially scarce, despite growing interest in AI-enabled documentation support. Reducing clinicians’ documentation burden is a critical priority in modern health care, as excessive administrative work consumes substantial clinician time, contributes to burnout, and limits time available for direct patient care. Objective: To quantify the impact of an AI medical scribe on documentation time and clinician experience. Methods: This observational real-world evaluation was conducted between April 26th 2024 and October 27th 2025 to assess the impact of an AI medical scribe on documentation time and clinician experience using retrospective paired ratings. The study was carried out across multiple specialties in primary, secondary and hospital care within Capio Ramsay Santé, a large integrated health care provider operating in Sweden.
The target population consisted of licensed clinicians actively using the AI medical scribe in routine clinical practice. Eligibility was limited to “fully onboarded” users, defined as clinicians who had used the scribe for at least 3 months, created more than 100 notes, generated at least one document or certificate, and used the conversational edit (“Add or adjust”) feature at least once. Results: With the introduction of the AI medical scribe, the estimated time spent on documentation per note decreased from 6.69 minutes to 4.72 minutes (-29%, p = 1.70e-11). On a five-point Likert scale, the ability to work without stress related to administrative tasks increased from a mean of 2.41 to 3.14 (p = 2.46e-8), and perceived presence with patients increased from 3.73 to 4.33 (p = 2.47e-8). The median editing time was 93 seconds, and it did not decrease significantly over continued use. Conclusions: This study shows that the clinician time savings and reductions in cognitive load and stress reported in prior US-based studies can also be achieved in a European health care system using an AI scribe. Clinical Trial: The study adhered to the Standards for Quality Improvement Reporting Excellence (SQUIRE) guideline and was preregistered on the Open Science Framework on 7 October 2025 (DOI: 10.17605/OSF.IO/YPD9E)
Background: Psychiatry needs objective technological tools to address global staffing shortages, stigma, and other systemic challenges. A long-term, naturalistic study using AI to effectively detect c...
Background: Psychiatry needs objective technological tools to address global staffing shortages, stigma, and other systemic challenges. A long-term, naturalistic study using AI to effectively detect changes in mental state in major depressive disorder (MDD) and bipolar disorder (BD) based on physical characteristics of the voice represents a breakthrough in biomarker validation. The MoodMon system was developed along with a mobile application for smartphones. Objective: The aim of the study was to determine whether physical voice parameters would be effective as biomarkers of mental status changes in affective disorders and whether they would be useful in remote clinical monitoring of patients by psychiatrists. Methods: To evaluate the effectiveness of artificial intelligence (AI) algorithms in detecting changes in mental state based on physical voice parameters, data from 75 patients diagnosed with bipolar disorder (BD) and 25 patients with major depressive disorder (MDD) for 944 days were used. This makes this the longest analysis in the world covering two of the most common mental disorder diagnoses. A wealth of clinical, behavioral, and technical data was collected and used to train the MoodMon machine learning system under the supervision of human experts- experienced psychiatrists. The AI module consists of an ensemble of selected supervised learning and clustering algorithms In the first stage, the AI was trained using objective data and clinical assessments conducted by psychiatrists, including 17-item versions of the HDRS and YMRS, as well as the CGI scale. The second stage involved further refinement of the AI using individual and population data and generating alerts when subtle changes in mental state were detected. Results: 19 of the 243 specific physical voice parameters tested were found to be most effective in detecting changes in mental status. The system demonstrated high performance, achieving the following sensitivity (true positive rate – TPR) and specificity (true negative rate – TNR) values for both diagnoses: TPR = 89.5%, TNR = 98.8%; BD: TPR = 89.6%, TNR = 98.9%; MDD: TPR = 89.1%, TNR = 98.5%. Voice alerts in the MoodMon system are a key tool supporting clinical decision-making. They increase the probability of a clinical visit and exert a significant influence on the likelihood of treatment modification. Conclusions: The system confirmed the presence of parameters that may serve as biomarkers of mental state changes in bipolar disorder (BD) and major depressive disorder (MDD). A key clinical implication is the increased probability of prompt treatment modification following an alert, thereby supporting the primary objective underlying the development of the MoodMon AI tool. Clinical Trial: Study: UR.D.WM.DNB.39.2021; Funder: National Centre for Research and Development, Poland. Project title: Development of a system supporting the monitoring of the course and early detection of relapses of affective disorders based on artificial intelligence algorithms. Agreement: POIR.01.01.01-00-0342/20
Background: An estimated 5–8 million U.S. children live with a parent who uses cannabis. A majority of cannabis users report smoking cannabis inside their home, which places children at risk for can...
Background: An estimated 5–8 million U.S. children live with a parent who uses cannabis. A majority of cannabis users report smoking cannabis inside their home, which places children at risk for cannabis secondhand smoke (cSHS) exposure. Emerging evidence links caregiver cannabis use to children’s respiratory and behavioral problems. Indoor air quality (IAQ) monitoring, an approach that provides real-time feedback on airborne pollutants, has been successful in reducing in-home tobacco SHS exposure, and may similarly be an effective harm reduction strategy for cSHS. Objective: This pilot study evaluated the feasibility, acceptability, and preliminary effectiveness of using low-cost, off-the-shelf IAQ monitors to increase caregivers’ awareness of children’s cSHS exposure risk and change smoking behavior. Secondary aims were to assess participant engagement, perceived usefulness, and household communication around in-home cannabis smoking. Methods: Fourteen adults who smoked cannabis indoors and lived with at least one child under age 16 completed a 3-week trial. Participants received an Awair Element IAQ monitor, printed health education materials, and SMS prompts for brief surveys. The IAQ monitor continuously measured PM2.5, VOCs, CO₂, temperature, and humidity. Brief daily surveys captured self-reported PM2.5 readings and recent cannabis use, while baseline and end-of-study assessments evaluated IAQ perceptions, cSHS risk awareness, and in-home smoking behavior. Survey results were summarized via descriptive statistics, and linear mixed-effects models were used to characterize objective IAQ trends. Six additional adult household members provided parallel end-of-study data. Results: Engagement was high, with 84% of participants indicating that they reviewed the monitor at least daily. The average number of days in the previous week that a caregiver reported a child home while cannabis was smoked declined from 4.5 at the beginning of the trial to 2.8 at the end (41.6% of participants had a reduction, 8.3% increased). Sixty-two percent of participants reported that they either reduced (31%) or thought about changing (31%) their smoking habits during the study. Sixty-nine percent either agreed or strongly agreed that IAQ monitoring helped drive conversations about changing indoor smoking rules, while 100% reported no IAQ-driven disagreements among household residents regarding in-home smoking rules. A linear mixed-effects model did not indicate a consistent trend in PM2.5 levels across participants over time (β = –0.28, p = 0.81), but there was substantial heterogeneity in trends, and those with the largest reductions in PM2.5 over the trial had the largest reduction in reported children’s cSHS exposure. Conclusions: In-home IAQ monitoring was feasible, acceptable, and perceived as useful among caregivers who smoked cannabis indoors. Real-time IAQ feedback supported risk awareness, promoted family dialogue, and coincided with reductions in in-home smoking around children. These findings suggest IAQ feedback may represent a scalable tool for reducing children’s cSHS exposure and merit further testing in larger, controlled trials. Clinical Trial: No
Physician burnout and administrative overload have reached crisis levels in many healthcare systems, threatening both clinician wellbeing and quality of patient care. Advances in robotics, ambient AI,...
Physician burnout and administrative overload have reached crisis levels in many healthcare systems, threatening both clinician wellbeing and quality of patient care. Advances in robotics, ambient AI, and automation now make it possible to reimagine inpatient hospital workflows — reducing cognitive load, streamlining tasks, and restoring time for bedside care. This commentary presents a first-person, realist narrative of a future hospital day, where AI-driven systems manage documentation, sterilization, supply delivery, diagnostics triage, and workflow coordination — letting physicians focus on complex decisions and compassionate care. If implemented responsibly, such an AI-assisted infrastructure could significantly reduce burnout, improve clinician retention, and reshape hospital culture.
Background: Stroke is a global health problem that often causes physical disability and mental health issues in the survivor. Whilst physical activity improves patient outcomes post-stroke, it can be...
Background: Stroke is a global health problem that often causes physical disability and mental health issues in the survivor. Whilst physical activity improves patient outcomes post-stroke, it can be challenging to maintain. Barriers to maintaining physical activity post-stroke include setting of physical activity (PA), motivation, and impairments from the stroke. There is often a desire to maintain physical activity after stroke, but effective interventions are currently limited. Objective: The aim of this study is to co-produce an intervention to support long term PA maintenance for adults with stroke in Northern Ireland. The objectives of this study are as follows:
1. Understand the perspectives of key stakeholders on the components, structure, and content of an intervention to support PA maintenance ensuring the intervention is relevant, acceptable, and feasible for all stakeholders.
2. Co-production and refine a prototype intervention using an iterative process, actively involving stakeholders in the development and customisation of the intervention to meet their specific needs and preferences. Methods: A mixed methods study will be conducted consisting of three stages informed by the DECIPHer co-production framework. Stage 1 will include a scoping review on PA maintenance in survivors of stroke and stakeholder consultation via focus groups to gain understanding from their perspective of PA. Survivors of stroke and their carers, care coordinators and physiotherapists will be recruited from ongoing Post Rehabilitation Enablement Programme (PREP) classes. Additional healthcare professionals with experience in physical activity and stroke will also be recruited via relevant organisations. Individuals who complete stage 1 focus groups will be invited to take part in stage 2 co-design workshops to develop a physical activity maintenance programme for participants post-PREP. Stage 3 will involve expert review of the co-production programme by members of the Project Advisory Board via a questionnaire survey. Results: Qualitative data will undergo reflexive thematic analysis from data collected in stage 1 and 2. Data from the scoping review will help shape the questions for the focus groups and data from the focus groups will help inform questions for the three workshops. All stages will involve the stakeholders to gain feedback and suggestions for the next wave. Conclusions: This study provides necessary information in regards to PA amongst survivors of stroke once they stop community rehabilitation. To our knowledge, there is no further support for the survivors to help maintain their PA levels once they finish the 6-12 week community programme. Engaging with survivors of stroke and their carers, PREP staff, and other exercise professionals will help shape the beginning stages of this study. Upcoming results from the pilot study will provide vital information on how to help PA maintenance in this population. Clinical Trial: The study is registered with clinicalTrials.gov - due to restriction in government funding we are awaiting the trial registration number.
Background: Background: Type 2 diabetes (T2D) is one of the most prevalent non-communicable diseases, requiring ongoing lifestyle change and continuous glucose management to support medication use, di...
Background: Background: Type 2 diabetes (T2D) is one of the most prevalent non-communicable diseases, requiring ongoing lifestyle change and continuous glucose management to support medication use, diet, and physical activity. Traditional self-monitoring of blood glucose can be burdensome, particularly with frequent finger pricks. As continuous glucose monitoring (CGM) becomes more affordable and widely available, it offers clear benefits, including improved glucose awareness, behavioural adjustments, and reduced anxiety. However, challenges persist, such as cost, pain from sensor insertion, skin reactions to adhesives, and privacy concerns. In the UK, patient perceptions of CGM among people with T2D, both users and non-users, remain under-explored, limiting understanding of factors that influence adoption and sustained use, and the support needed to promote adherence. To the authors’ knowledge, this is the first UK-based study to explore the perspectives of both CGM users and non-users with T2D using a large, nationally representative sample. The identified benefits and challenges emerging from this study provide valuable insights to inform research, clinical practice, and policy aimed at supporting the equitable adoption and sustained use of CGM in the UK. Objective: Objectives: This qualitative study aims to explore how adults with type two diabetes (T2D) perceived the benefits and challenges of using continuous glucose monitoring (CGM), including both current users and non-users. Methods: Methods: This study employed a cross-sectional, online survey using YouGov’s nationally representative panel to explore experiences of continuous glucose monitoring (CGM) among adults with type 2 diabetes (T2D) in the UK. A total of 531 participants were recruited in December 2024. Thematic analysis of responses to two open-ended questions identified key perceived benefits and challenges associated with CGM use. Results: Results: A total of 531 adults with type 2 diabetes (T2D) completed the YouGov online survey. Over half were male (55.9%) and aged 65+ years (53%). Two-thirds (65%) had lived with T2D for more than five years, and 9.5% had ever used a CGM.
Nearly half of participants (49%) provided free-text responses on CGM benefits and 33% on challenges. Thematic analysis identified five key benefit themes: (i) practicality and user-friendliness, (ii) better understanding of lifestyle impacts on glucose levels, (iii) improved self-management, (iv) enhanced safety, and (v)improved data sharing with healthcare providers. The main challenges identified included (i) limited access, (ii) usability and technological issues, (iii) overreliance on passive monitoring, (iv) emotional burden, and (v) data-related matters. Conclusions: Conclusions: Continuous glucose monitoring (CGM) was perceived by adults with T2D as a practical and empowering tool that enhances understanding, safety and collaboration with healthcare providers. However, access barriers, usability challenges and emotional and data-related burdens remain significant obstacles to the equitable adoption of these technologies. Addressing these challenges through improved affordability, digital literacy support, and tailored clinical guidance may help promote sustained and inclusive CGM use in routine diabetes care.
Background: Online virtual worlds are platforms that allow users, represented as avatars, to meet and interact with other users in real time within 3D virtual environments. These platforms have potent...
Background: Online virtual worlds are platforms that allow users, represented as avatars, to meet and interact with other users in real time within 3D virtual environments. These platforms have potential utility as vehicles to deliver/receive clinical services, especially as a preference to video-conferencing-based telehealth. However, commercial virtual worlds (e.g.,“Second Life”) are often deemed unsuitable due to privacy and safety concerns. Objective: The aim of this study was therefore to co-develop and test a bespoke virtual world platform to deliver routine youth mental health services. Methods: We undertook a participatory-design process to develop the platform (Orygen Virtual Worlds) involving 10 young people with lived experience of mental health difficulties, researchers, software designers and mental health clinicians. We then tested two types of clinic-led interventions delivered through the virtual world (a structured therapy group and an individual therapy) in a public youth mental health service setting in Australia. Participants were patients receiving treatment in the service. The main outcomes were acceptability and feasibility; we also measured symptom change, usability, presence and therapeutic alliance. We conducted qualitative interviews post-intervention with the participants and analysed these interviews using thematic analysis. Results: 15 young people were recruited to the structured group (27% consented from referred) and 8 were recruited to the individual therapy (36% consented from referred). Drop out was higher in the individual therapy than the structured group therapy (38% versus 80%). Acceptability ratings were high for both therapy approaches and there were no significant safety events attributed to using the platform. There were no significant pre-post differences in the symptom outcome measures in either the structured group intervention or individual therapy. The platform was perceived as being comfortable and safe, enjoyable, fun and interactive, and was not confusing to navigate or difficult to use. The qualitative themes included the platform being fun and engaging, making treatment more accessible, providing a safe and inclusive place, fostering connections, positively impacting wellbeing and providing a catalyst for real life functional change. Young people perceived decreased barriers, increased comfort with help-seeking and reduced social stress facilitated by the avatar, communication options (emoji, text, voice) and accessibility from home. Conclusions: Our findings indicate that online virtual world platforms, such as the one we have designed, hold considerable promise for providing interventions for young people in clinical services. Virtual worlds can provide fun and engaging experiences of therapeutic interventions for young people with mental health difficulties which are safe and inclusive, especially for harder to reach groups.
Background: A significant proportion of patients with major depressive disorder do not achieve remission after two antidepressant trials and are considered to suffer from Treatment-Resistant Depressio...
Background: A significant proportion of patients with major depressive disorder do not achieve remission after two antidepressant trials and are considered to suffer from Treatment-Resistant Depression (TRD). Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for TRD. However, relapse rates among remitters within the first year post-treatment are significant, and there are no validated markers of relapse. Wearable devices have shown positive results for longitudinal monitoring of health metrics and may be a promising tool for an early detection of relapse following rTMS treatment. Objective: To evaluate the feasibility of a wearable device (Oura ring) to monitor individuals receiving rTMS treatment for depression and its utility to detect early signs of depressive relapse in a 6-month follow-up period. Methods: This single-arm pilot study will recruit 20 outpatients with a major depressive episode receiving rTMS at St. Joseph’s Healthcare Hamilton, Ontario, Canada. Participants will be required to wear an Oura ring throughout the treatment course and during the six-month follow up. Clinical assessments, including Montgomery-Åsberg Depression Rating Scale (MADRS), Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), Insomnia Severity Index (ISI), and World Health Organization-Five Well-Being Index (WHO-5), will be collected at baseline, treatment end, and 3- and 6-month follow-ups, alongside bi-weekly PHQ-9 and GAD-7 scores. The primary outcomes are feasibility metrics (i.e., recruitment, adherence, retention, missing data, usability). Secondary outcomes will include the utility of wearable-based data for predicting relapse. Results: The study was funded in December 2025, and data collection will start following Ethics Approval. Conclusions: This study will provide initial evidence on the feasibility and utility of a wearable-based digital phenotyping in individuals receiving rTMS for TRD. Our findings will inform the design of future large-scale studies aimed at wearable-supported relapse prevention and precision monitoring in depression care.
Background: Artificial intelligence (AI) demonstrates considerable potential in nursing education. However, its specific effects on knowledge acquisition, practical skills, satisfaction, competence, a...
Background: Artificial intelligence (AI) demonstrates considerable potential in nursing education. However, its specific effects on knowledge acquisition, practical skills, satisfaction, competence, and confidence remain inadequately characterized. Objective: This study aims to assess the effects of AI on nursing students’ education. Methods: We will follow the preferred reporting items for systematic review and meta-analysis protocol guidelines. Systematic literature searches will be conducted across six electronic databases, namely, PubMed, Web of Science, EMBASE, CINAHL, EBSCO, and the Cochrane Library. The inclusion criteria follow the PICOS framework, incorporating nursing students from academic institutions and clinical internship settings. This review examines studies comparing AI-based educational interventions with traditional teaching methodologies. The outcomes encompass (1) knowledge level, (2) practical ability, (3) satisfaction, (4) competence, and (5) confidence. Eligible study designs include randomized controlled trials (RCTs) and quasi-experimental studies. The search timeline is from the inception of each database to February 2026, with no language restriction. Two independent reviewers will screen studies and extract data. Any disputes will be resolved through discussion. Unresolved disputes will be decided by consulting the third author. For the risk of bias assessment, the Cochrane risk-of-bias (ROB) tool for RCTs and the risk of bias in non-randomized studies of intervention (ROBINS-I) tool will be used. Moreover, RevMan 5.3 is used for meta-analysis. Results: / Conclusions: / Clinical Trial: PROSPERO registration number: CRD420251170836.
Background: Mechanically ventilated critically ill patients face significant risks from prolonged immobilization, including ICU-acquired weakness and prolonged recovery. Early mobilization is increasi...
Background: Mechanically ventilated critically ill patients face significant risks from prolonged immobilization, including ICU-acquired weakness and prolonged recovery. Early mobilization is increasingly advocated to mitigate these risks. While existing studies suggest early mobilization may reduce ventilator days and ICU length of stay, its impact on mortality remains unclear due to conflicting results and methodological limitations, particularly insufficient statistical power and the lack of conclusive evidence. This uncertainty necessitates a more robust synthesis incorporating Trial Sequential Analysis (TSA) to evaluate the reliability and conclusiveness of current evidence regarding early mobilization's efficacy. Objective: evaluate the reliability and conclusiveness of current evidence regarding early mobilization's efficacy. Methods: Randomized controlled trials (RCTs) that compare early mobilization and usual care in mechanically ventilated critically ill patients will be included. Literature searches will be conducted in PubMed, Web of Science, Embase, and Cochrane Library. Two reviewers will independently perform the processes of literature retrieval, screening, data extraction, and assessment of risk of bias. Risk of bias in included studies will be evaluated using Revised Cochrane risk-of-bias tool (ROB 2) for RCTs. Review Manager (RevMan) will be used for data pooling. Subgroup analysis, trial sequential analysis (TSA), and sensitivity analysis will be conducted. Results: Not applicable Conclusions: Not applicable
Background: Demographic shifts are increasing the global demand for long-term care services, coinciding with a worldwide shortage of healthcare personnel. Service robots, designed to perform tasks in...
Background: Demographic shifts are increasing the global demand for long-term care services, coinciding with a worldwide shortage of healthcare personnel. Service robots, designed to perform tasks in both professional and personal use, are perceived as a potential solution to alleviate healthcare personnel’s workload and enhance the quality of care. However, the existing literature is fragmented and heterogeneous, with a limited emphasis on the role of service robots in supporting residents rather than healthcare personnel. Furthermore, there is a lack of consistent definitions of service robotic technologies and a scarcity of studies on implementation models and frameworks. Objective: This scoping review aims to map and synthesize evidence regarding the implementation of service robots as work support for healthcare personnel in long-term care settings. Methods: A comprehensive three-step search will be conducted in Embase, MEDLINE, APA PsycInfo, CENTRAL, Scopus, and CINAHL, along with grey literature databases and institutional repositories. Eligible sources encompass empirical studies and grey literature involving service robots, healthcare personnel, residents aged 65 years or older, and stakeholders such as informal caregivers within institutional long-term care. Exclusions apply to studies on home care, medical or industrial robots, and non-robotic technologies. Data will be extracted and analysed using the JBI methodology, with findings presented in tables, diagrams, and narrative summaries to identify gaps and inform future research and implementation strategies. Results: This protocol was developed in October 2025 and subsequently registered in November 2025. A comprehensive search strategy was formulated and completely conducted on October 24, 2025. The screening of titles and abstracts was completed in November 2025. The processes of data extraction, analysis, evidence synthesis, and results presentation will be conducted sequentially, with the scoping review expected to be finalized by April 2026. Conclusions: The findings of this proposed scoping review are anticipated to delineate the scope, characteristics, and implementation models of service robots employed to support healthcare personnel in executing routine tasks for older adults in long-term care settings. Furthermore, the review is expected to identify research gaps within the existing literature, elucidate key concepts, and establish a foundation for future empirical investigations into implementation strategies for service robots in healthcare. Clinical Trial: This protocol was registered in Open Science Framework on November 7, 2025. Register: https://osf.io/t5p2x
“First, do no harm” is a fundamental principle in healthcare, and clinical researchers carefully monitor adverse drug reactions to ensure patient safety. However, educational researchers and clini...
“First, do no harm” is a fundamental principle in healthcare, and clinical researchers carefully monitor adverse drug reactions to ensure patient safety. However, educational researchers and clinical educators rarely apply the same level of scrutiny to potential adverse effects arising from their own interventions. This reflects a persistent misconception that educational interventions are inherently harmless, an assumption that warrants critical examination.
In this review, we highlight the underrecognized concept of adverse effects in medical education by introducing twelve representative educational adverse effects and offering corresponding tips for mitigating them. These include the Dunning-Kruger effect, in which increased confidence does not align with actual competence; the undermining effect, whereby external rewards reduce intrinsic motivation; spoon feeding that stunts independent learning; cognitive overload resulting from excessive information delivery; patient dehumanization when education prioritizes technical proficiency over empathy; and the expertise reversal effect, in which instructional strategies beneficial for novices become counterproductive as expertise grows. Additional adverse effects include compromised psychological safety despite formal safeguards, authority and confirmation biases that reinforce outdated practices, developer bias in intervention evaluation, the Hawthorne effect influencing observed behavior, and concerns that overreliance on generative artificial intelligence may hinder the development of critical thinking and metacognitive skills.
To better understand the nature of these adverse effects, we categorize them into three overarching domains. Cognitive and psychological adverse effects occur within the learner. Structural and cultural adverse effects result from features of the educational environment. Methodological and evaluative adverse effects arise from how educational interventions are designed and assessed. While these domains overlap, they provide a practical framework for identifying how well intended educational strategies may lead to harm.
Some may argue that these phenomena represent unintended consequences rather than adverse effects. However, the term unintended consequence presumes that sufficient effort was made to anticipate and manage possible effects, an assumption that may not always hold true in medical education. We argue that educators and educational researchers should explicitly recognize adverse effects, critically evaluate educational interventions, and adopt mitigation strategies with a level of rigor comparable to that applied in clinical research, in order to better protect learners and improve the quality of medical education.
Background: Digital transformation is fundamentally reshaping medical education. In rehabilitation medicine, where skill acquisition and personalized intervention are paramount, educational technologi...
Background: Digital transformation is fundamentally reshaping medical education. In rehabilitation medicine, where skill acquisition and personalized intervention are paramount, educational technologies must evolve beyond static simulations to model dynamic patient processes. Digital twin (DT) technology, which creates real-time, data-driven virtual replicas, offers a transformative leap from conventional virtual reality (VR) and augmented reality (AR) simulations. Objective: This study aimed to analyze the paradigm shift from static virtual simulation to dynamic DT ecosystems in rehabilitation education, identify the multidimensional challenges faced by faculty, and propose a comprehensive development framework to support this transition. Methods: We employed a qualitative, theory-building methodology comprising three phases: (1) a systematic synthesis of literature (2015-2024) on DT technology, virtual simulation, and rehabilitation pedagogy; (2) the inductive development of a “Triple-Drive Transformation Model” to explain the shift’s underlying logic; and (3) the construction of a conceptual “Four-Dimensional faculty Development Framework” based on synthesized challenges and insights from preliminary educator engagements. Results: We position digital twins as catalysts for intelligent, data-rich educational ecosystems. faculty encounter significant barriers across four dimensions: cognitive (perceiving DTs as tools rather than ecosystems), competency (gaps in digital intelligence literacy), cultural (tensions between experiential and data-driven pedagogy), and institutional (lack of training, incentives, and supportive policies). In response, we propose an integrated framework addressing these dimensions, coupled with an OSRI (Observation-Simulation-Reflection-Innovation) cyclical practice model for competency building. Conclusions: The successful integration of DTs in rehabilitation education requires a systemic approach to faculty development that transcends technical upskilling. Our framework is designed to guide faculty in evolving from traditional instructors to designers and facilitators within human-machine collaborative learning environments. Future empirical research is needed to validate and adapt this framework across diverse institutional contexts.
Background: Mobile exposure notification applications (ENAs) can play a significant role in future pandemics. Identifying lessons learned and areas for improvement from such applications —particular...
Background: Mobile exposure notification applications (ENAs) can play a significant role in future pandemics. Identifying lessons learned and areas for improvement from such applications —particularly within specific social and cultural contexts—is therefore crucial. Objective: We evaluated the Finnish ENA “Koronavilkku”, according to chosen key performance indicators to assess the ENA’s use and performance during the COVID-19 pandemic in Finland, and to identify strengths and improvement areas. Methods: Using available data on Finland’s ENA we defined metrics for four key performance indicators: ENA usage extent, enablers and barriers to usage, the ENA’s influence on user behavior, and overall acceptability. We performed an evaluation study combining system data and data from a survey of 4 061 respondents conducted in April 2022. A sentiment analysis was performed on open-ended responses. Results: Koronavilkku ENA peak coverage reached approximately 53% of smartphone users shortly after launch. Usage declined over time, particularly among individuals diagnosed with COVID-19. User engagement remained high - among ENA users that received a token, the proportion that entered their token into the ENA was over 50% during the whole study period. Males, individuals over 34, and those living alone were less likely to use the ENA, while those with higher education, and households with children were more likely to be ENA users. Almost 70% of SARS-CoV-2-positive users reported not receiving a token from health authorities. Users adhered at least part-time to pandemic guidance more often than non-users with prevalence ratios ranging from 1.24 (95%CI: 1.19 – 1.29) to 1.65 (95%CI: 1.45 – 1.91). Exposure notifications were particularly effective in reinforcing adherence among partially compliant users but had limited impact on non-compliant individuals. Overall acceptability was relatively high, with 68% of respondents using it at least once. However, 49% of those users eventually discontinued use. Our sentiment analysis of open-ended feedback suggests that users expressed more positive attitudes toward the ENA compared to non-users. However, a significant portion of the feedback reflected neutral or negative sentiments. Conclusions: Based on observed ENA usage trends we recommend maintaining long-term public interest and engagement in ENAs during prolonged emergencies. Tailored communication should be developed encouraging uptake among demographic groups with lower adoption rates. Initial studies during app rollout could inform these strategies. A major barrier to the ENA’s effectiveness was token issuance, therefore earlier and more systematic automation of token distribution in Finland would likely have enhanced effectiveness. Future ENAs should define evaluation criteria during development, with periodic assessments measuring effectiveness and informing improvements. A deeper analysis of open-ended feedback provided for the Koronavilkku ENA using advanced language models could provide additional insights into user perceptions and concerns. By addressing these areas, future digital contact tracing tools can be more effective, widely accepted, and better integrated into pandemic response efforts.
Public interest in e-cigarettes in India has evolved in complex ways, particularly around the 2019 national prohibition on electronic nicotine delivery systems (ENDS). To understand how attention to t...
Public interest in e-cigarettes in India has evolved in complex ways, particularly around the 2019 national prohibition on electronic nicotine delivery systems (ENDS). To understand how attention to these products has shifted over the last decade, we analysed Google Trends data on searches for the term “e-cigarette” from 2014 to 2024 and examined both temporal changes and emerging spatial patterns across states. Using interrupted time-series modelling with ARIMA, the analysis showed that the ban was followed by an immediate and statistically significant reduction in search volumes, with a continued downward slope over the subsequent years. Yet this national decline masked important regional variations. While the pre-ban period was characterised by higher search intensity in technologically connected southern metropolitan states, the post-ban maps revealed a clear shift, with northeastern states such as Nagaland and Mizoram emerging as new hotspots of interest. Spatial clustering also became more pronounced in the post-ban period, with Moran’s I indicating modest but significant autocorrelation, suggesting that the rise in northeastern search activity was not random. Although online search data cannot directly explain the mechanisms driving this shift, the pattern aligns with global observations that regions with weaker enforcement, porous borders, and distinct cultural practices often witness greater challenges from illicit or unregulated e-cigarette markets. The findings therefore point to the need for region-specific responses: strengthening surveillance and enforcement in northeastern states, designing targeted communication strategies for urban southern regions where initial interest was highest, and continuously monitoring public interest as a proxy for evolving illicit demand. Together, these insights contribute to a more nuanced understanding of India’s post-ban landscape and highlight that national policy effects can diverge substantially across geographic and sociocultural contexts.
Background: Learners in under-resourced South African schools face barriers to physical activity (PA). These include limited infrastructure, inadequate teacher training, and unsafe environments which...
Background: Learners in under-resourced South African schools face barriers to physical activity (PA). These include limited infrastructure, inadequate teacher training, and unsafe environments which negatively affect their academic performance, health, and overall well-being. Objective: The review mapped the existing PA promotion strategies to inform context-specific school guidelines. Methods: Following the Joanna Briggs Institute framework, comprehensive searches were conducted in PubMed, Scopus, Web of Science, and EBSCOhost for studies published between 2015 and 2025. Studies reporting PA strategies in South African under-resourced schools were mapped and synthesized thematically. Results: Eleven studies met the inclusion criteria, comprising five with PA interventions and six that were contextual. Strategies included curriculum adaptations, in-class activity breaks, after-school programs, and teacher/caregiver engagement. Outcomes showed improvements in body mass index (BMI), cardiorespiratory fitness, and PA participation, though impacts on psychosocial well-being varied. Barriers included resource constraints and teacher capacity; facilitators included training, peer support, and community engagement. Conclusions: Multi-component, contextually tailored strategies are feasible and beneficial in disadvantaged schools. Evidence gaps, particularly in rural contexts and long-term evaluation, should guide future policy and research.
Background: Despite strong evidence supporting the benefits of regular physical activity (PA) for managing coronary heart disease (CHD), PA advice remains infrequent in general practice. Barriers incl...
Background: Despite strong evidence supporting the benefits of regular physical activity (PA) for managing coronary heart disease (CHD), PA advice remains infrequent in general practice. Barriers include general practitioners’ (GPs’) limited familiarity with current PA guidelines and their application to CHD patients, insufficient skills for delivering brief advice, and time constraints. In Germany, no structured, theory-informed training exists to support GPs in delivering brief PA advice in routine CHD care. Objective: This study aimed to systematically develop a theory-based behaviour change intervention to strengthen GPs’ competencies in delivering brief PA advice to patients with CHD in routine primary care. Methods: Guided by the Behaviour Change Wheel, we conducted a multi-method formative research study to develop the training intervention. Preceding studies, including a cross-sectional survey, interviews, and focus groups with GPs and patients, were synthesised to identify needs, barriers, and preferences related to PA advice. These findings were mapped onto the COM-B model components (capability, opportunity, motivation – behaviour) to identify determinants of GP behaviour, and further specified using the Theoretical Domains Framework (TDF). Intervention components were described using the Behaviour Change Technique (BCT) Taxonomy, and GPs were involved throughout the development process to ensure practical relevance, feasibility, and acceptability. Results: The primary result of this study is a fully developed intervention: a bespoke, structured, 3.5-hour, small-group training for GPs focusing on the very brief 3As method (ask, advise, assist) for PA advice. The training comprises theoretical modules on PA and CHD, guided self-reflection and group discussions to explore personal practice and barriers, moderated role-plays with professional actors, and experiential learning using PA stimuli. Practical materials (e.g., handouts, digital resources) support skill acquisition, and short on-demand good practice videos are provided. The training is delivered by an experienced GP-researcher tandem, with full content documented in a comprehensive trainer manual to enable standardised replication. Conclusions: This formative research resulted in the first structured, theory-based GP training in Germany designed to strengthen brief PA advice in routine care of patients with CHD. By integrating the COM-B model and TDF framework, the intervention explicitly targets behavioural determinants of GP advising behaviour, while the use of the BCT Taxonomy enhances transparency and reproducibility. The intervention is currently being evaluated for its effects on GP behaviour in an ongoing trial, and a pilot of the evaluation study has recently been completed. Clinical Trial: Not applicable
Background: Childhood obesity remains a major public health challenge. Mobile health (mHealth) interventions offer a scalable approach to support behavior change, but their effectiveness depends heav...
Background: Childhood obesity remains a major public health challenge. Mobile health (mHealth) interventions offer a scalable approach to support behavior change, but their effectiveness depends heavily on participant engagement. While both caregiver and child adherence are crucial, few studies have objectively measured and jointly analyzed their impact within digital obesity interventions. Objective: This study aimed to evaluate the independent and synergistic effects of caregiver engagement with a health app and child behavioral adherence on obesity-related outcomes in a school-based mHealth intervention. Methods: We analyzed data from 684 child-caregiver dyads in the intervention arm of the Diet, ExerCIse and CarDiovascular hEalth (DECIDE)-Children cluster randomized trial, implemented across three socioeconomically diverse regions in China. Caregiver adherence was objectively measured via app usage frequency and total duration. Child adherence was assessed through weekly behavior monitoring scores covering diet and physical activity. Linear mixed models estimated associations between adherence levels and changes in anthropometric and physical fitness outcomes over 9 months. Results: Higher caregivers’ app usage frequency and duration were both significantly associated with reductions in children’s obesity-related indicators, such as body mass index (BMI)(β = -0.19, P = 0.005), BMI Z-score (β = -0.08, P = 0.006), and body fat percentage(BFP) (β = -0.70, P = 0.005) for frequency. Greater child behavioral adherence was linked to lower BFP (β = -0.63, P = 0.012) and better physical fitness, including shorter shuttle run time (β = -3.63, P < 0.001) and longer standing long jump distance (β = 2.88, P = 0.005). A significant interaction between app usage frequency and duration was found for rope skipping performance (P = 0.048), suggesting possible synergistic benefits of more frequent and prolonged app engagement. Joint adherence analyses indicated that children in the “high caregiver–high child adherence” group achieved the greatest improvements, including a BMI reduction of -0.22 (P = 0.017), a BFP reduction of -1.18% (P = 0.001), and 7.45 additional rope skipping repetitions (P = 0.003) compared with the “low caregiver–low child adherence” group. Conclusions: In this mHealth App-Assisted intervention, high adherence from both caregivers and children was associated with significantly greater improvements in weight status and physical fitness. These findings underscore the importance of co-engagement in digital health programs and highlight the potential of app-supported, family-engaged strategies for childhood obesity prevention. Clinical Trial: ClinicalTrials.gov Identifier NCT03665857
Background: Background: Falls are a critical global public health issue for community-dwelling older adults, with delayed emergency response being a leading contributor to pre-hospital mortality[1]. T...
Background: Background: Falls are a critical global public health issue for community-dwelling older adults, with delayed emergency response being a leading contributor to pre-hospital mortality[1]. The World Health Organization (WHO) emphasizes that as populations age, the disease burden of fall-related injuries in older adults continues to grow—yet existing intervention tools often lack adaptability to the unique needs of this group [1]. A retrospective analysis of electronic medical records from Shanghai’s Fengxian District Medical Emergency Center (June 1, 2020–May 15, 2025) revealed striking local trends: 72.37% of pre-hospital deaths among adults aged ≥60 years stemmed from delayed rescue following accidents or acute illness, with falls accounting for 89.2% (2111/2366) of these cases. Notably, adults aged 70–90 years formed the core high-risk cohort, representing 65.89% of fall-related deaths without resuscitation potential; key high-risk ages included 75 years (99 cases), 70 years (93 cases), 90 years (92 cases), and 80 years (90 cases). Industry interviews with smart elderly care technology providers, combined with findings from Moore K et al.’s [3] qualitative systematic review, confirm critical gaps in current products: overly complex operation, limited battery life, and inadequate integration with local Emergency Medical Services (EMS). These limitations underscore an urgent need for a tailored fall alert solution. Objective: Objectives: Primary objective: To assess the feasibility and operational suitability of a smartwatch-based emergency response system for community-dwelling older adults aged 70–90 years, with specific focus on system reliability and its potential to reduce fall-related rescue delays.
Secondary objectives: (1) Validate key feasibility metrics, including a device wear compliance rate ≥75% (defined as daily wear time ≥12 hours), a false alarm rate ≤10% (confirmed via EMS and caregiver verification), and a System Usability Scale (SUS) score ≥70 [11]; (2) Evaluate the system’s ability to shorten emergency response time to ≤15 minutes (from alert trigger to EMS on-site arrival) and document at least one clinically confirmed timely rescue during the study period. Methods: Methods/Design: This feasibility study is centered on a purpose-built fall alert system, comprising a simplified smartwatch, a caregiver/community management application, and a cloud-based EMS integration platform. System design prioritizes localization: bilingual audio prompts (Shanghai dialect and Mandarin), magnetic charging for ease of use, and three large physical buttons to minimize operational barriers. We plan to recruit 300 households (each with one community-dwelling older adult aged 70–90 years) from Fengxian District. Sample size was determined based on Kokorelias KM et al.’s [4] scoping review, which recommends 200–500 participants for wearable technology feasibility studies, with a 10% attrition rate factored in to ensure statistical robustness (95% confidence level, 5% margin of error). Data will be collected over 12 months using three complementary sources: device-generated metrics, EMS rescue documentation, and structured user feedback questionnaires. Statistical analysis will be performed in SPSS 26.0 [14], with qualitative data analyzed via thematic coding. Results: Expected Results: Study initiation is contingent on securing government support and ethical approval. The study will proceed with a 6-month recruitment phase followed by 12 months of data collection, preceded by a system pre-test to refine usability. We anticipate meeting all preset feasibility benchmarks (wear compliance, false alarm rate, usability score, recruitment rate) and effectiveness targets (average response time, timely rescues). Age-stratified analyses (70–79 years vs. 80–90 years) will further clarify the system’s adaptability across the high-risk spectrum. Conclusions: Conclusion: This feasibility study will validate the system’s performance, usability, and real-world applicability, addressing critical shortcomings in existing fall alert technologies. If proven feasible, the system will provide a foundation for large-scale deployment, reduce fall-related pre-hospital mortality by mitigating rescue delays, and offer a scalable model for public health interventions in aging communities. Clinical Trial: Not applicable
Background: Background: Evidence-based Clinical Practice Guidelines (CPGs) are fundamental to translating research into practice but are often hindered by their complex, text-heavy format, limiting ac...
Background: Background: Evidence-based Clinical Practice Guidelines (CPGs) are fundamental to translating research into practice but are often hindered by their complex, text-heavy format, limiting accessibility for both clinicians and patients. Narrative Medicine offers a pathway to humanize evidence through storytelling, and short-video platforms present an unprecedented opportunity for mass dissemination. However, a systematic, theory-informed framework for converting text-based CPGs into narrative-based short videos is currently lacking. Objective: Objective: This protocol aims to develop and pilot-test a standardized "CPG-to-Video" framework that integrates Narrative Medicine principles to create engaging, accurate, and patient-centered short videos from CPGs. Methods: Methods: We will conduct the project using the following steps: (1) Needs assessment (systematic literature review to identify the necessity of developing a comprehensive framework for CPG-to-video translation), (2) Establishing international working groups (coordination team, evidence support group, and consensus group), (3) Conducting literature reviews and qualitative research to formulate an initial draft framework, (4) A consensus process including an expert survey and a consensus meeting, (5) Formulating and releasing the final framework, and (6) Testing the framework (collecting feedback through educating health professionals and applying the framework in practice to evaluate and improve it). Results: Expected Outcomes: The primary outcome is a replicable "CPG-to-Video" framework. Conclusions: Conclusion: This protocol provides a methodological foundation for leveraging Narrative Medicine and short-video platforms to bridge the evidence-practice gap, potentially enhancing guideline understanding, adherence, and patient engagement.
Background: Miners are essential rural workers who may work in close proximity with inadequate ventilation conditions, and who are unable to telecommute or work flexible hours, rendering them vulnerab...
Background: Miners are essential rural workers who may work in close proximity with inadequate ventilation conditions, and who are unable to telecommute or work flexible hours, rendering them vulnerable to SARS-CoV-2 transmission. Limited data indicate that particulate air pollution exposure is associated with and increased risk of SARS-CoV-2 infection and mortality. Chronic exposure to particulate matter causes overexpression of the alveolar angiotensin-converting enzyme 2 (ACE2) receptor, which facilitates the entry of SARS-CoV-2 into lung epithelial cells. Objective: We tested the hypothesis that coal miners with higher levels of self-reported particulate exposure (separately from exposure to occupational dust, smoke from residential wood burning and shared transportation) have a greater risk of SARS-CoV-2 infection than miners with lower exposure. Methods: We conducted a longitudinal analysis of data obtained from a study conducted from February 2021 to March 2022. For this study 169 surface coal miners in New Mexico and 61 in Wyoming were enrolled. Seropositivity was determined by measuring IgG antibodies to the nucleocapsid protein of SARS-CoV-2, performed by qualitative chemiluminescent immunoassay on plasma samples at baseline, 3, 6, and 12 months after study enrollment. The predictor variables were the self-reported levels of exposure to occupational dust, smoke from wood burning and participation in shared transportation, separately. The primary study outcome was the cumulative seroprevalence at 12 months. Logistic regression analyses were used. Results: The study included 44% Hispanic and 13% American Indian, mostly male (88%), miners. Mining dust exposure was associated with a lower cumulative seropositivity of SARS-CoV-2 infection (adjusted OR 0.84, p=0.05). Similar results were noted for baseline and incident seropositivity. However, self-reported exposure to smoke from residential wood burning “very often” was associated with greater baseline infection in the adjusted model (adjusted OR 3.35, 95% Cl: 1.33-8.47, p=.04), whereas a higher frequency of participation in shared transportation was not associated with increased SARS-CoV-2 infection. Conclusions: We demonstrate that occupational dust exposure is associated with lower, not higher, odds of cumulative seropositivity in miners. This unexpected finding may be explained by the “healthy worker effect” as well as possibly greater workplace use of masks in dustier jobs for respiratory protection. The study's strengths include a robust enrollment of rural and racial/ethnic minority workers. Our results highlight the need for additional research on how environmental exposures interact with infectious disease dynamics, which may aid in the development of targeted interventions for essential rural workforces. Clinical Trial: NA
Background: Work-related musculoskeletal disorders (WMRDs) are very prevalent among urologists. Understanding factors associated with increased work pain can help mitigate this discomfort and decrease...
Background: Work-related musculoskeletal disorders (WMRDs) are very prevalent among urologists. Understanding factors associated with increased work pain can help mitigate this discomfort and decrease burnout. Objective: To quantify the number of Urologists who reported WRMDs > 25% of the time. Methods: The Florida Urological Society Task Force (FUSTF) developed a survey based on the Nordic Musculoskeletal Questionnaire with additional input from Cornell ergonomic studies. MCSRC conducted and distributed the survey to 504 members of the Florida Urological Society in 2023. Results: The total response rate was 18.6%. The primary outcome (number of urologists who reported pain > 25% of the time) was 45.3%. 32.4% reported pain associated >25% of the time with endoscopic surgery, 40.0% for major open cases, 20.6% for minor open cases, and 22.7% for robotic cases. 68.8% of respondents attributed their work-related pain to uncomfortable operating positions. 29.9% of respondents chose to ignore their pain. Conclusions: The total response rate was 18.6%. The primary outcome (number of urologists who reported pain > 25% of the time) was 45.3%. 32.4% reported pain associated >25% of the time with endoscopic surgery, 40.0% for major open cases, 20.6% for minor open cases, and 22.7% for robotic cases. 68.8% of respondents attributed their work-related pain to uncomfortable operating positions. 29.9% of respondents chose to ignore their pain. Clinical Trial: n/a
Background: Electronic health record (EHR) based phenotyping algorithms are typically trained and/or validated using a small set of gold-standard labels manually annotated via medical chart review by...
Background: Electronic health record (EHR) based phenotyping algorithms are typically trained and/or validated using a small set of gold-standard labels manually annotated via medical chart review by domain experts. To reduce the time and labor cost, silver-standard labels such as self-reported outcomes that are highly predictive of the true disease statuses have been used as a proxy of gold standard labels, which are unfortunately subject to misclassification due to insufficient documentation or human error. Objective: Ignoring such labeling errors may lead to biases in both the estimated classification model and its accuracy for predicting the true underlying phenotype status. The objective of this study is to develop a calibration algorithm for both phenotyping classification and downstream association analysis using noisy silver-standard labels, where the true gold-standard disease status is not observable. Methods: In this paper, we propose an imperfectly supervised calibrated algorithm for phenotyping and regression (SCAPER) that simultaneously produces calibrated phenotyping classifications and association regression modeling by utilizing a small number of noisy silver-standard labels and a large set of unlabeled observations on predictive features and biomarkers (possibly genetic data). The proposed approach yields an improved predicted probability of phenotype for each patient, a threshold for classifying participants with phenotype yes/no, and bias corrected regression coefficient for predictors in the downstream association study. The algorithm was validated by both phenotyping and genetic association studies for rheumatoid arthritis from a large tertiary care center, and that for type II diabetes from two large tertiary care centers. Results: When validating against the gold-standard labels for phenotyping performance, the proposed algorithm achieved higher AUC compared to the International Classification of Diseases (ICD) codes and existing unsupervised phenotyping algorithms. The downstream association study suggests that the proposed approach detected previously validated associations with higher power when compared to the standard association studies based on ICD codes. Conclusions: The proposed unsupervised calibration increased the accuracy of phenotype definition and corrected the biases in the downstream association studies such as genetic association studies.
Diagnostic errors kill 371,000 Americans and permanently disable 424,000 more each year, yet no technology exists to detect these errors before they cause harm. Cognitive factors—premature closure,...
Diagnostic errors kill 371,000 Americans and permanently disable 424,000 more each year, yet no technology exists to detect these errors before they cause harm. Cognitive factors—premature closure, anchoring, confirmation bias—drive 74-96% of diagnostic failures, but current clinical decision support systems intervene only after clinicians have already committed to erroneous diagnostic paths. This Viewpoint introduces negative telemetry, a paradigm shift proposing that diagnostic accuracy is more powerfully predicted by what clinicians fail to examine than by what they examine. We synthesize convergent evidence from behavioral biometrics (keystroke dynamics achieving 97.9% sensitivity and 94.7% specificity for cognitive impairment detection; r = -0.497, P<.001), electronic health record workflow analysis, and just-in-time adaptive intervention research (effect sizes of Hedges' g = 0.79-1.65). These behavioral signatures are sufficiently distinctive to enable individual-level cognitive modeling, yet sensitive enough to detect clinically meaningful state changes. The technical infrastructure for negative telemetry already exists within electronic health record systems; what has been missing is a conceptual framework for interpreting which omissions matter. We provide that framework and propose a research agenda for validation. The 371,000 Americans who die annually from diagnostic errors deserve more than retrospective analysis—they deserve real-time prevention.
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: 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).
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: 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. In this study, we sought to assess a youth-focused HIV clinic’s capacity for providing trauma-sensitive, inclusive practices. 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, behaviors, conditions and culture, practices, and hospital-wide initiatives, 2) Stigma and bias as barriers, 3) General barriers and efforts needed to address, which included the CFIR domains of Informational and Engagement Barriers, Resource Availability and Relative Priority, Clinic and Organizational Setting Opinion, and Institutional Policies/ Leadership Make-Up, and 4) 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: 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: 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: 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: 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: 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: 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: 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: 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
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 began on 29 December 2022. The data collection has been completed, and data analysis is scheduled to be 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.IEC approved on 07.06.2022 with approval number 6-5/CARI/BNG/IEC2020-21/399.
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.
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.
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: 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.
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: 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.
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.
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: 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.
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: 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
