Background: Clinical handover is the process during which responsibility and accountability for care are transferred between clinicians. AI has the potential to improve the reliability and completeness of clinical handover by helping clinicians detect predefined content areas that have been communicated, identify explicit information gaps, and prompt clarification before responsibility is transferred. Objective: This study evaluated the performance of several large language models and prompt optimization strategies for structured information extraction of synthetic #nursing handover transcripts. Methods: Two registered #nurses independently annotated a dataset of 203 synthetic handover transcripts to produce consensus labels for information extraction tasks. Tasks included (1) labeling spans of text into SBAR (Situation, Background, Assessment, Recommendation) categories, (2) content detection to determine if specific pieces of information were communicated, and (3) labeling spans of text that communicated information using uncertain terms that included a subtask for identifying unknown facts. Baseline and Genetic-Pareto (GEPA)–optimized prompts were compared for the GPT-5.2, GPT-5-nano, and MedGemma 27B large language models. Additionally, the LangExtract framework was evaluated for span-extraction tasks. Results: The GPT-5.2–optimized model achieved a micro-score of 0.85 (95% CI 0.83‐0.88) for content detection, an absolute improvement of +0.08 compared with the matched baseline. GPT-5-nano also performed better after optimization for content detection (micro-score 0.81, 95% CI 0.78‐0.84), suggesting that this structured task was not limited to the highest-capacity model. For SBAR span extraction, GPT-5.2 with prompt optimization achieved a micro-score of 0.76 (95% CI 0.72‐0.79), improving by +0.24 compared with baseline and exceeding LangExtract; GPT-5-nano also improved to a micro-score of 0.69 (95% CI 0.66‐0.72). Broad uncertainty-span extraction remained comparatively weak despite prompt optimization (micro-score 0.41, 95% CI 0.33‐0.48; absolute improvement +0.06). In contrast, explicit unknown-fact extraction was more accurate with GPT-5.2 (micro-score 0.84, 95% CI 0.63‐1.00), GPT-5-nano (micro-score, 0.84 95% CI 0.63‐1.00), and MedGemma 27B (micro-score 0.80, 95% CI 0.63‐1.00). Genetic-Pareto–optimized prompts outperformed the LangExtract approach across each span-extraction task. Conclusions: Prompt optimization improved matched-model point estimates, with the highest performance observed for predefined content detection and SBAR span extraction. Broad uncertainty extraction remained less accurate than the narrower unknown-fact task. These technical results do not establish clinical effectiveness, safety, or readiness for real-time use. Validation using authentic #nursing handover communication and prospective evaluation in clinical workflows are required before clinical application.

Interest in physical AI and robotics in health care is increasing, but the #nursing literature shows that the evidence base remains early, #nurse-centered applications are underdeveloped, and real-world experiential evidence is limited. #nurses are more likely to accept robots that reduce physically demanding and repetitive work while preserving the interpersonal and judgment-intensive core of #nursing practice. This conceptual paper proposes a #nursing-centered framework in which virtual reality functions not merely as a simulator but as a scaffolded training infrastructure for supportive physical AI systems, enabling #nurse augmentation, site-specific adaptation through digital twins, and staged simulation-to-real transfer. The framework was developed through a conceptually integrative and implementation-aware synthesis drawing on #nursing robotics, AI in #nursing, immersive simulation, digital twins, human-in-the-loop learning, and physical AI development literature. It is organized around 5 linked elements and supported by a 4-layer technical architecture that outlines functional requirements and implementation pathways. Four key propositions ground the framework: (1) #nursing robot training should focus on competency formation, not on decontextualized data accumulation; (2) training should proceed through progressive fidelity and staged autonomy; (3) digital twins should function as operational bridges for local ward adaptation; and (4) simulation-to-real transfer should be governed by explicit #nursing-relevant validation criteria and retained human accountability. The proposed #nurse-in-the-loop, site-specific virtual reality framework offers a #nursing-centered complement to general-purpose physical AI pipelines by making workflow fit, role boundaries, local adaptation, and governed transfer explicit design requirements.

Background: Technologies such as virtual reality (VR) and augmented reality (AR) have been increasingly incorporated into #nursing education to support the development of cognitive, psychomotor, and behavioral competencies. In midwifery, immersive environments offer opportunities to simulate high-risk or low-frequency clinical scenarios, strengthening students’ confidence and preparedness for professional practice. However, there is still significant variability in how VR and AR are pedagogically implemented, with limited understanding of their theoretical grounding, instructional design, and educational outcomes. Objective: This systematic review aimed to synthesize and critically evaluate the characteristics of educational programs using VR and AR to develop competencies among #nursing and midwifery students and professionals. Methods: This review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 and Synthesis Without Meta-Analysis (SWiM) guidelines and was registered in PROSPERO (International Prospective Register of Systematic Reviews). A comprehensive search was conducted in August 2024 across PubMed, Web of Science, Scopus, and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases. Eligible studies were experimental, quasi-experimental, or observational, and involved undergraduate or postgraduate students in #nursing, obstetric #nursing, or midwifery, as well as certified midwives or #nurse-midwives. Two independent reviewers conducted screening, data extraction, and quality appraisal using Joanna Briggs Institute (JBI) tools and the Medical Education Research Study Quality Instrument (MERSQI) scale. Data were synthesized narratively according to predefined thematic categories. Results: Four studies published between 2021 and 2024 met the inclusion criteria, encompassing 634 participants (360 midwifery students and 274 midwives or #nurse-midwives). Two randomized controlled trials and 2 quasi-experimental studies were included. Interventions used VR in 3 studies and AR in one, simulating scenarios such as normal and complicated childbirth, neonatal resuscitation, and emergency obstetric procedures. Most interventions focused on developing technical competencies, while one also addressed teamwork and communication. Only 2 studies explicitly reported theoretical or pedagogical frameworks, and 2 mentioned alignment with the INACSL Healthcare Simulation Standards of Best Practice. Structured briefing or debriefing was rarely described. All studies assessed learning outcomes (Kirkpatrick level 2), and 3 included participants’ satisfaction (level 1). Methodological quality, measured by MERSQI, ranged from 10.0 to 14.5, with a mean score of 12.9 (SD 2.0), indicating moderate to high quality. Conclusions: Evidence suggests that VR and AR can enhance learning in obstetric #nursing and midwifery education by providing safe, experiential, and interactive learning environments. However, the limited use of theoretical frameworks and structured instructional design highlights the need for more pedagogically grounded interventions. Future research should prioritize the integration of learning theories, validated assessment tools, and best-practice simulation standards to strengthen the educational impact and curricular integration of immersive technologies in #nursing and midwifery education. Trial Registration: PROSPERO CRD42025635292; https://www.crd.york.ac.uk/PROSPERO/view/CRD42025635292

Background: Tobacco use remains the leading modifiable risk factor for causing #Cancer worldwide, particularly among people with HIV. In Laos, 61%‐80% of male people with HIV and 3%‐10% of female people with HIV smoke cigarettes. They currently have no theoretically and empirically based smoking cessation support. Our team developed a scalable and affordable #mHealth (mobile health)–based automated treatment program to support Lao and Cambodian smokers to quit smoking. We also pioneered the Ask-Advise-Connect approach to identify patients who smoke and to connect them to treatment. Objective: This hybrid type-2 pragmatic effectiveness-implementation #Study aims to compare 2 smoking cessation implementation strategies in 8 antiretroviral therapy (ART) clinics in the 6 most populous regions across Laos, using a parallel cluster randomized trial design. Methods: We will compare an Ask-Advice-Connect approach paired with an #mHealth-based automated treatment program (AA-MAP) with an Ask-Advice-Connect approach paired with less resource-intensive printed self-help material (AA-SH). To guide assessment of implementation determinants and outcomes, we use the Practical, Robust Implementation and Sustainability Model framework. Aim 1 is to evaluate the reach and effectiveness of AA-MAP vs AA-SH. Reach is the proportion of people with HIV who smoke and are willing to make a quit attempt that enroll in treatment. Effectiveness is the proportion of enrolled participants (n=up to 1200) who achieve biochemically confirmed point prevalence abstinence 6 months after enrollment. We hypothesize that compared with AA-SH, AA-MAP will have a lower reach but will be more effective. We will also estimate the real-world impact (impact = reach × effectiveness) of each intervention. Aim 2 is to evaluate other implementation outcomes (eg, adoption, implementation fidelity, and sustainability) and identify implementation determinants of AA-MAP and AA-SH in the ART clinic setting using mixed methods. Aim 3 is to conduct a comprehensive assessment of the resource use and costs of implementing AA-MAP and AA-SH and calculate the absolute and relative cost-effectiveness of the 2 intervention strategies. Results: The #Study has been funded since August 2024. This #Study was approved by the ethical review boards of the Lao Ministry of Health–National Ethics Committee for Health #Research and the University of Oklahoma Health Campus. The Multiple Principal Investigators met with the selected ART clinics. As of September 2026, we have launched the implementation of AA-SH and AA-MAP at 4 ART clinics. We plan to expand the implementation at the other ART clinics by December 2026. Conclusions: This project will contribute important actionable inputs that will inform the influential Lao National Tobacco Control Committee and Ministry of Health to implement the strategies in diverse hospital settings in future large-scale hybrid type II/III trials. Ultimately, our course of #Research can transform health care delivery and contribute to reducing tobacco-related morbidities and mortalities in Laos. Trial Registration: ClinicalTrials.gov NCT07014605; https://clinicaltrials.gov/#Study/NCT07014605 International Registered Report Identifier (IRRID): PRR1-10.2196/100518

Background: Remote patient monitoring (RPM) systems based on the Internet of Medical Things (IoMT) technologies are increasingly integrated into chronic disease management and telemedicine pathways. Despite their widespread adoption, cybersecurity performance, system resilience, and user behavior in real-world clinical environments remain underexplored. Existing evidence is fragmented, often limited to laboratory simulations or vendor-driven assessments, leaving a critical gap in understanding how cybersecurity risks across the full life cycle of RPM systems deployed in health care settings. Objective: This #Study aims to systematically analyze the cybersecurity posture, system resilience, and user behavior across the full life cycle of IoMT-enabled RPM systems in a real-world hospital and home-care environment. The #Study aims to generate empirical evidence on how technical safeguards, operational workflows, and human factors influence cybersecurity risks during procurement, integration, deployment, routine use, and decommissioning of these platforms. Methods: This observational #Study will analyze 2 independent RPM systems used for chronic disease monitoring in a real-world hospital setting. The assessment framework includes (1) system-log analytics to evaluate authentication events, device connectivity, update and patch management, and anomalous behaviors; (2) a controlled phishing simulation targeting health care professionals to assess susceptibility and response patterns; (3) an evaluation of update management processes and vendor-hospital interactions; (4) measurement of cybersecurity awareness and practices among patients and health care professionals using validated instruments; and (5) mapping of vulnerabilities across all life cycle phases, from procurement to decommissioning. Although the #Study includes cybersecurity training, preassessments/postassessments, and controlled phishing and update-management scenarios, these activities are part of the observational framework and are not designed as experimental interventions that have an impact on the clinical aspects of patient care. Quantitative data will be analyzed using descriptive and inferential statistics, while qualitative insights from operational workflows will be integrated to contextualize system performance. Ethical approval has been obtained from the institutional ethics committee. Results: The CYMEDSEC (enhanced cybersecurity for networked medical devices through optimization of guidelines, standards, risk management, and security by design) project received funding from the European Union’s Horizon Europe program (grant 101094218) and started on November 1, 2024. Ethical approval was obtained on December 18, 2025, and institutional authorization on January 29, 2026. Patient enrollment is scheduled to begin on April 1, 2026. At the time of paper submission, no patients were recruited. Data analysis will begin after completion of patient involvement, with results expected before the project end date in October 2027. Conclusions: This #Study will provide real-world evidence on the cybersecurity performance of IoMT-enabled RPM systems, capturing the interplay among technical safeguards, operational processes, and human factors. Findings are expected to support the development of security-by-design approaches, inform procurement and regulatory frameworks, and guide the safe integration of connected medical devices into routine care within the framework of the CYMEDSEC #Research project. International Registered Report Identifier (IRRID): PRR1-10.2196/98934

Background: Noise-induced hearing loss is permanent yet preventable. Agricultural workers routinely experience hazardous noise exposure, and hearing protection use remains low. Existing interventions emphasize individual knowledge and intentions and have rarely been tested using objective outcome measures. This #Protocol describes the adaptation and pilot evaluation of an individual-level component of a multilevel noise-induced hearing loss prevention intervention for farmworkers. This pilot focuses on refining and testing a brief #Digitally delivered novella. Objective: The objectives of this #Study are to (1) culturally and linguistically adapt a #Digitally delivered, Spanish-language entertainment-education hearing protection training (#Digital novella with facilitated discussion and coached earplug fitting) for farmworkers in the Southwest United States, using community-based participatory #Research; and (2) pilot test the adapted intervention to assess acceptability, feasibility, and appropriateness and to estimate preliminary effect sizes for changes in objective hearing protector fit. Methods: This is a single-arm, pre-post, Hybrid Type 1 effectiveness-implementation pilot #Study. Phase 1 includes formative interviews with farmworkers and farm supervisors (n=30), informed by the Consolidated Framework for Implementation #Research, to guide adaptation of training content and delivery. Phase 2 involves a single training session delivered to farmworkers not involved in the formative work (n=15). The primary exploratory outcome is pre-post change in the Personal Attenuation Rating, an objective measure of hearing protector fit. Secondary outcomes include changes in hearing protection beliefs and implementation outcomes (acceptability, feasibility, and appropriateness). Results: Funding for this #Study was obtained in July 2025, and the #Study was registered on ClinicalTrials.gov in December 2025 (NCT07271290). Phase 1 recruitment began in July 2025, and data collection concluded in January 2026, with 30 participants enrolled. Phase 2 pilot recruitment began in July 2026, and 1 participant had been enrolled as of July 2026. Phase 2 data collection is expected to conclude in Spring 2027. Data analysis will follow completion of data collection, and #Study findings are expected to be published in 2028. Conclusions: This #Protocol describes a community-engaged adaptation and pilot evaluation of a culturally tailored, #Digitally delivered hearing protection microtraining for farmworkers. By pairing objective fit testing with pragmatic implementation measures and systematic adaptation tracking, the #Study will assess feasibility and estimate preliminary effect sizes to inform a subsequent multilevel trial that integrates supervisor and organizational components. Trial Registration: ClinicalTrials.gov NCT07271290; https://clinicaltrials.gov/#Study/NCT07271290 International Registered Report Identifier (IRRID): DERR1-10.2196/93134

Background: The need for gender-affirming care (GAC) for young people has increased rapidly worldwide. GAC includes social, psychological, and medical interventions aimed at alleviating gender-related distress. Given that many outcomes of GAC relate to how young people function and feel, a rigorously developed patient-reported outcome measure (PROM) is needed. To address this need, GENDER-Q Youth was developed with extensive input from youth with lived experience. Concept elicitation interviews were performed with 47 youth from Canada and the United States. A conceptual framework and draft PROM were developed and refined with feedback from 33 experts and 17 youth, and were pilot tested with 406 older youth. The field test version of GENDER-Q Youth includes 16 independently functioning scales (248 items) that measure health-related quality of life, gender practices, voice, and experience of care. Objective: This paper outlines the #Protocol for an international cross-sectional #Study to field test GENDER-Q Youth in a sample of #Transgender and gender diverse (TGD) adolescents and young adults aged 12 to 25 years receiving GAC. Methods: This #Study follows international guidelines for PROM development and validation (eg, US Food and Drug Administration and Consensus-Based Standards for the Selection of Health Measurement Instruments [COSMIN]). GENDER-Q Youth was translated into Danish, Dutch, and German following the Professional Society for Health Economics and Outcomes #Research (ISPOR) guidelines for the translation and cultural adaptation of PROMs. For the field test, TGD youth are being recruited using multiple strategies (eg, face-to-face, emails, and so on). A REDCap survey is used to collect sociodemographic and clinical data. Branching logic is used to ensure relevant GENDER-Q Youth scales are completed as some scales are gender-specific. For psychometric analysis, Rasch measurement theory (RMT) analysis will be used to examine fit of the observed data to the Rasch model. A series of tests and criteria will examine item fit and scale reliability and validity. Test-retest reliability will be examined with intraclass correlation coefficients. For construct validity, 147 predefined hypotheses of expected group differences and correlations between GENDER-Q Youth scales will be examined. Acceptance of at least 75% of hypotheses is considered sufficient evidence of construct validity per COSMIN criteria. Results: The #Research described in this #Protocol is funded by a Canadian Institutes of Health #Research Sex and Gender Science Chair (April 2020) and supported by a Canada #Research Chair in Patient-Reported Outcomes (April 2025). With the Danish, Dutch, and German translations now completed, the international field test is underway in Canada, Europe, Australia, and the United States. Data collection and analysis are expected to be completed by the end of 2026. Conclusions: This #Protocol describes the international GENDER-Q Youth field test #Study. Once developed, GENDER-Q Youth can be used to inform GAC, #Research, and quality improvement efforts. International Registered Report Identifier (IRRID): DERR1-10.2196/100420

Background: Dengue fever can lead to significant hepatic involvement and marked aminotransferase elevation and is associated with increased morbidity and prolonged hospitalization. N-acetylcysteine (NAC) has been proposed as a supportive therapy for dengue-induced liver injury. However, prospective comparative evidence from endemic regions such as Vietnam remains limited. Objective: This study aimed to evaluate the association between intravenous NAC administration and clinical outcomes (primarily length of hospital stay), as well as the secondary effects of NAC on liver transaminase levels (posttreatment aspartate aminotransferase [AST] and alanine aminotransferase [ALT] and their changes from baseline) and platelet recovery in patients with dengue fever with marked aminotransferase elevation (AST or ALT ≥400 U/L). Methods: A prospective, nonrandomized comparative study was conducted at the Department of Tropical Diseases, Viet Tiep Hospital, Hai Phong, Vietnam, from June to December 2024. A total of 128 adult patients with laboratory-confirmed dengue fever and marked aminotransferase elevation (AST and/or ALT ≥400 U/L) were enrolled. Treatment allocation was not randomized and was determined by the treating physicians based on aminotransferase elevation and overall clinical condition. Patients received either intravenous NAC (100 mg per kilogram per day) plus standard supportive care (NAC group; n=68) or standard care alone (non-NAC group; n=60). The primary outcome was length of hospital stay. Secondary outcomes included liver transaminase levels and platelet recovery at discharge. Results: Baseline AST, ALT, bilirubin, and international normalized ratio values were broadly comparable between groups, although several clinical and hematological characteristics differed at baseline. The NAC group had a significantly shorter hospital stay (mean 5.01, SD 1.28 vs 6.12, SD 1.25 days; P

Background: Progression-free survival (PFS) is a critical end point in #Oncology, yet real-world applications of individualized, explainable machine learning (ML) predictions remain limited. Objective: This #Study aimed to develop and validate explainable ML models to predict PFS using retrospective data from a national prostate #Cancer cohort in Brunei Darussalam. Methods: We analyzed a retrospective cohort of 212 #Patients (478 longitudinal observations) treated at the Brunei #Cancer Centre (January 2018 to December 2024). Clinical, laboratory, and treatment data were harmonized, with missing values imputed via extremely randomized trees. Longitudinal patterns were captured using a recurrent autoencoder to generate latent representations. We compared 4 modeling approaches: Cox proportional hazards, random survival forest (RSF), gradient boosting survival (GBS), and deep neural network survival models. Performance was evaluated using time-dependent area under the receiver operating characteristic curve (AUC), Harrell C-index, and integrated Brier score (IBS), with Shapley additive explanations (SHAP) used for interpretability. Results: RSF demonstrated improved discriminative performance and balanced calibration, achieving a C-index of 0.906 and AUCs of 0.941 and 0.917 at 4 and 5 years (IBS=0.0698). In contrast, the traditional CPH model performed poorly (C-index 0.531 and AUC 0.706 at 4 years and 0.833 at 5 years). Deep survival (AUCs of 0.941 at 4 years and 0.917 at 5 years, C-index 0.719, and IBS=0.0887) and GBS (AUCs of 0.765 at 4 years and 0.833 at 5 years, C-index 0.844, and IBS=0.0590) models showed moderate performance. SHAP analysis identified sodium, alanine aminotransferase, mean corpuscular hemoglobin, platelet count, and specific treatment categories as key drivers of increased progression risk. Conclusions: Tree-based ensemble approaches, particularly RSF integrated with SHAP, offer high accuracy for personalized risk stratification in prostate #Cancer. These findings highlight the potential of explainable ML to enhance clinical decision-making. However, external validation in a larger multi-institutional, multiomics dataset is required before routine clinical implementation.

Background: Metabolic and bariatric surgery (MBS) is a safe and effective treatment for #Adolescents with severe obesity, yet no standardized lifestyle interventions exist to support sustained behavior change after MBS. Objective: This pilot study assessed the feasibility and acceptability of TeenLyft, an online lifestyle support program for #Adolescents undergoing MBS, and explored preliminary clinical and safety outcomes to inform the design of a future randomized trial. Methods: #Adolescents aged 13 to 18 years were recruited from a tertiary care MBS center and enrolled in TeenLyft. The study was not powered or designed as a randomized controlled trial but rather as a pilot to guide future randomized controlled trial development. Feasibility domains included recruitment, retention, data completeness, intervention fidelity, and engagement, with predefined progression criteria of ≥80% enrollment, ≥70% retention, ≥70% data completeness, and ≥90% fidelity. Exploratory clinical and safety measures (weight, BMI, and cardiometabolic markers) were collected descriptively to confirm that participation was not associated with harm. Results: Twenty-nine #Adolescents (mean age 15.9 years, SD 1.1; n=22, 75.9% female; n=13, 44.8% Hispanic; mean BMI 47.8, SD 7.3 kg/m) were enrolled, representing 145% of the recruitment target (n=20), with 72.4% retention at 6 months. All feasibility progression criteria were met. Descriptive trends showed expected post-MBS reductions in weight and BMI and improvements in blood pressure and hemoglobin A, with no adverse cardiometabolic effects observed. Conclusions: TeenLyft demonstrated high feasibility, acceptability, and safety as an adjunct to #Adolescent MBS care. Findings support progression to a fully powered randomized trial to evaluate long-term effectiveness and sustainability. Trial Registration: ClinicalTrials.gov NCT05393570; https://clinicaltrials.gov/study/NCT05393570

Background: AI-based clinical decision support systems (CDSS) can improve diagnostics and treatment decisions, but they are rarely implemented in practice. Barriers include limited integration into clinical workflows, lack of transparency, and insufficient involvement of end users in system design. Participatory and user-centered approaches offer ways to address these challenges by aligning development processes with the needs and routines of clinical staff. However, systematic evidence on how such approaches are applied in the development of AI-based CDSS remains limited. Objective: Our study examined how participatory approaches are used in the development, piloting, and implementation of AI-based CDSS. We analyzed which user perspectives were included, which participatory methods were applied, how they contributed to technical design, and which ethical, legal, and social implications were addressed. Methods: This scoping review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guideline, with a protocol published in advance. A systematic search was conducted in MEDLINE, ACM #digital Library, CINAHL, and PsycInfo for studies published from 2012 onward, complemented by snowballing and manual searches. Primary studies in English or German were included if they involved clinical staff in the development, piloting, implementation, or evaluation of AI-based CDSS. Moreover, 3 independent reviewers conducted screening and data extraction, resolving disagreements by consensus. Data analysis followed JBI methodology and focused on the scope of participation, theoretical and methodological foundations, and reported impacts of participatory approaches. Results: Of 4318 identified records, 37 met the inclusion criteria. The studies showed broad variation in terminology and methods, most often describing user-centered or iterative processes and less frequently co-design. Physicians were involved in nearly all studies, nurses frequently, and other professional groups only occasionally. Participation mainly supported requirements analysis, adaptation of models to clinical workflows, and the design of explainable interfaces. In several projects, it also influenced data selection, annotation, and visualization. Common barriers included time constraints, limited continuity of participation, and uncertainty toward AI. Ethical, legal, and social aspects were addressed implicitly through themes such as autonomy, responsibility, and traceability, while fairness and bias were rarely discussed. Conclusions: Participatory processes in AI-based CDSS development should extend across all stages of system design and address not only usability but also data quality, bias, and broader ethical, legal, and social issues. Equal inclusion of nursing and therapeutic expertise is essential to reflect the diversity of clinical decision-making. Clear methodological standards are needed to ensure comparability and to strengthen participation as a genuine co-design process shaping data, models, and values in clinical AI.

Background: Young people exposed to adverse childhood experiences (ACEs) are at risk of poor mental and physical health across the life course, yet many struggle to access timely help. Serious #Games offer an engaging, scalable, and stigma-reducing support, but few are co-designed with young people who have lived experience of ACEs, and methodological accounts of how participatory design is operationalized alongside software delivery are scarce. Objective: We aimed to (1) develop and apply a novel methodological framework integrating experience-based co-design (EBCD) with agile #Game development to co-design a serious #Game with young people affected by ACEs, (2) describe the co-designed #Game prototype and the design decisions shaped by youth participation, and (3) examine the ethical and safeguarding considerations arising from co-designing digital mental health interventions with trauma-exposed young people. Methods: We conducted a participatory co-design study comprising 12 workshops across 8 iterative co-design sprints, involving 18 diverse young people (aged 12‐24 years) with lived experience of ACEs, 5 professional stakeholders, and a multidisciplinary #Game development team. Young people were recruited across England through partner youth organizations and regional Young People’s Advisory Groups. Co-design ran from June 2022 to April 2024 in hybrid and online formats. Baseline characteristics include demographics, and mental health and well-being measures. EBCD activities were mapped onto agile practices (sprint planning, sprint reviews, and retrospectives); all feedback was logged in a “you said, we did” form. Analyses were descriptive, and reporting followed the GRIPP2-LF (Guidance for Reporting Involvement of Patients and the Public, long-form) checklist. Results: The resulting prototype, ACE of Hearts, comprised a central “cozy den” hub and 4 mini-#Games addressing bereavement and caregiving, trauma and disability, gender dysphoria, and poverty. Youth feedback directly shaped 47 documented feedback and design decisions. Co-design participants (14 of 18 participants) were diverse in gender (8/14, 57% men or boys; 3/14, 21% women or girls; 1/14, 7% nonbinary; 2/14, 14% another identity; and 3/14, 21% transgender), ethnicity (5/14, 36% from minoritized ethnic backgrounds), sexuality (6/14, 43% bisexual or pansexual), and neurodivergence (7/14, 50% autistic), with elevated levels of #Depression (mean Patient Health Questionnaire 9-item scale 8.8, SD 4.5) and anxiety (mean Generalized Anxiety Disorder 7-item scale 8.4, SD 3.9). Mean posttraumatic stress symptom severity (Children's Revised Impact of Event Scale, 8-item; n=13) was 22.5 (SD 7.7). Design innovations included metaphoric storytelling, integration of narrative exposure therapy principles into #Gameplay, and an embedded ethics framework addressing representation, agency, and emotional safety. Conclusions: Integrating EBCD with agile development is a feasible and transparent method for coproducing trauma-informed serious #Games with young people who have lived experience of ACEs. The approach is transferable to other participatory digital-health projects involving vulnerable populations. Future work would evaluate acceptability, feasibility, and clinical outcomes.

Background: Accurate measurement of lifestyle factors is central to understanding how daily behaviors act as risk factors or protective buffers to noncommunicable diseases. While ##Wearable devices enable passive monitoring of physical activity or sleep, nutritional intake still depends on active participant input, such as manual dietary logs or image-based recordings. Adherence to such logging tasks often declines rapidly, impacting data completeness and clinical utility. Theory-based reminders drawing on loss-framing or logging consistency feedback (ie, tracking streaks) can improve adherence to lifestyle data collection, but the effects of these strategies on repeated dietary logging remain unclear. Objective: The #Study aims to examine the effects of 2 theory-driven reminder strategies—loss-framing and logging consistency—on adherence to repeated, image-based meal logging. Methods: We conducted a microrandomized trial embedded within a 4-week observational lifestyle phenotyping #Study in Switzerland (N=200, age ≥45 y, BMI ≥25 kg/m²). Participants photographed their meals at each mealtime (breakfast, lunch, and dinner) using a mobile #App over 28 days. A decision point was scheduled prior to participant-defined habitual mealtimes. Participants were randomly assigned with equal probability to (1) a reminder emphasizing loss of a daily financial reward for not logging (“loss-framing”), (2) a reminder providing feedback on recent logging consistency (“logging consistency”), or (3) a neutral reminder (“active control”). The proximal outcome is whether the participant logs a meal within 2 hours of receiving a reminder. Participants earned a daily financial reward contingent on meal-logging completion. To estimate intervention effects, we will use marginal excursion effect models for binary outcomes, adjusting time-varying covariates (eg, day in #Study) and baseline covariates (eg, age and gender). Ethics approval for this #Study was granted by the Cantonal Ethics Committee of Eastern Switzerland (BASEC ID 2025‐00972). Results: Enrollment began in November 2025, and the #Study was initiated on December 8, 2025. As of August 2026, 130 participants have been recruited, 120 have been enrolled, and 76 participants have completed the #Study, with an anticipated completion date of January 2027. Data analysis has not yet begun, and #Study results are expected to be published in Q2 2027. Conclusions: By clarifying the proximal effects of loss-framed and consistency-based reminders, findings will inform the design of future #Digital health studies to improve meal-logging adherence in daily life. This work contributes to the development of scalable, theory-driven reminder strategies for enhancing dietary data quality in observational and interventional #Research. Trial Registration: ClinicalTrials.gov NCT07555262; https://clinicaltrials.gov/#Study/NCT07555262 and NCT07373418; https://clinicaltrials.gov/ct2/show/NCT07373418 International Registered Report Identifier (IRRID): DERR1-10.2196/100239

Background: Tabletop exercises (TTXs) are commonly used to enhance prehospital readiness for mass casualty incidents (MCIs). However, evidence on their design, facilitation, and evaluation remains scattered. TTXs simulate organized interactions at 3 levels: among individual responders and response protocols, within interdisciplinary teams, and across organizations and systems. While existing reviews cover tabletop simulation generally, they do not specifically focus on the prehospital MCI interface or assess whether evaluation methods match the exercise objectives. Objective: This scoping review aimed to explore how TTXs are designed, facilitated, and evaluated in prehospital MCI preparedness. It classified outcomes using the Kirkpatrick Evaluation Model and analyzed how evaluation methods align with the purpose of each exercise. Methods: We performed a scoping review following Arksey and O’Malley’s framework, with enhancements from Levac et al, along with PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) and PRISMA-S (PRISMA Extension for Reporting Literature Searches) guidelines. Searches across PubMed, Embase, Scopus, PsycINFO, CINAHL, the Cochrane Library, ClinicalTrials.gov, Google Scholar, and references were limited to English peer-reviewed and gray literature published from 2015 to May 2026. Eligible studies focused on TTXs related to prehospital disaster or MCI preparedness and reported measurable educational, clinical, or system outcomes. The review protocol was registered beforehand on Protocols.io. Two reviewers (PP and AA) independently screened records, extracted data, and classified outcomes using Kirkpatrick levels, with an added level 2+ (Applied Learning) category for structured performance evaluation during exercises. Results: Thirteen studies from 9 countries were included. They were categorized into a preliminary 3-tier typology called the TTX Design Spectrum, representing increasing levels of interaction: algorithm-rehearsal exercises (n=2) focused on individual responders’ interaction with triage protocols, scenario-based decision-training exercises (n=7) aimed at multidisciplinary team decision-making under realistic conditions, and systems integration exercises (n=4) centered on interagency coordination and system-level preparedness. Facilitation varied by exercise purpose, from standardized assessment-focused approaches to expert-led and multidisciplinary facilitation. Evaluation primarily targeted Kirkpatrick level 1: Reaction (n=10), level 2: Learning (n=10), and level 2+ (Applied Learning) (n=8), with fewer studies examining level 3: Behavior (n=2) or level 4: Results (n=3). Operational frameworks were reported more consistently than formal educational design or assessment frameworks. Additionally, natural disaster scenarios and evidence from resource-limited settings were underrepresented. Conclusions: TTXs for prehospital MCI preparedness should be viewed as a collection of related exercise types rather than a single, uniform intervention. This review introduces an initial typology called the TTX Design Spectrum, along with the level 2+ (Applied Learning) classification, which operationalizes the distinction between in-training performance and behavioral transfer. These tools aim to help align exercise purpose, facilitation, and evaluation strategies. Future research should focus on validating this typology, enhancing follow-up assessments of behavioral transfer, and adapting TTX design for natural disaster and resource-limited settings.

Background: Online health communities (OHCs) provide vital peer support and health information to individuals managing chronic conditions. However, sustained user engagement remains challenging, with many users reducing activity over time despite the ongoing benefits these platforms offer. While some individuals may improve or become more knowledgeable, sustained engagement remains critical because ongoing participation fosters trust, peer support, and continuous access to evolving health information that may persist beyond initial recovery or learning. Hence, it is important to consider how community needs evolve as users’ health needs and information-seeking behaviors change to inform how we support users through different stages of their health and participation journeys. Objective: The aim of the study is to examine user engagement in a large OHC, identifying perceived stage-based behaviors, motivation, barriers, and design opportunities that could facilitate progression between stages and enhance long-term participation. Methods: We conducted semistructured interviews with 19 members of the American Heart Association Support Network Community. Participants were patients or survivors managing various cardiovascular diseases. Using narrative thematic analysis, we examined users’ perceived engagement motivation, behavior, challenges, and design opportunities across different stages of their community involvement. Results: This study highlighted 4 distinct engagement stages: discovery (crisis-driven initial engagement), exploration (navigation and orientation), commitment (active engagement and information management), and integration (sustained engagement and mentorship). Key barriers included information architecture complexity, concerns about misinformation, limited support for role transitions, and decreased participation as health management improved. Participants identified opportunities through which OHCs could increase long-term engagement, including adaptive recommendation systems, health information literacy programs, structured role transition support, and alternative engagement modalities, such as synchronous interactions and health tracking tools. Conclusions: User engagement in OHCs is dynamic and evolves with changes in health status, knowledge, and personal circumstances. Supporting sustained engagement requires stage-appropriate interventions, including personalized content delivery, health information literacy education, structured pathways for role transitions, and diversified engagement options. These findings provide actionable insights for designing OHCs that better support users throughout their health journey.

Background: The undergraduate stage is critical for developing nursing students’ disaster nursing competencies; however, traditional lecture-based teaching often fails to promote active learning, and innovative instructional models remain underused in this field. The BOPPPS (Bridge-in, Objective, Preassessment, Participatory Learning, Postassessment, and Summary) model is a student-centered, closed-loop instructional framework that emphasizes active participatory learning and real-time feedback. Objective: This study aimed to evaluate the effects of a BOPPPS-based blended teaching model on disaster nursing competencies and academic outcomes among undergraduate nursing students. Methods: This study used a quasi-experimental design with 77 undergraduate nursing students allocated to 3 groups: an experimental group receiving BOPPPS-based blended teaching (n=27), a control group with traditional lecture-based teaching (n=25), and a comparison group without systematic instruction (n=25). Questionnaires were used to assess students’ self-perceived disaster nursing knowledge, professional operational skills, and overall comprehensive competencies. The final examination assessed students’ mastery of theoretical knowledge and applied reasoning. Results: All 3 groups showed significant within-group improvements in disaster competence (all

Background: The UGO lower limb exoskeleton #Rehabilitation robot is designed for patients with lower limb motor dysfunction, and it can be used to assess the peak torque of hip and knee flexors and extensors. Objective: The primary aim of this #Study was to establish the concurrent validity of peak isokinetic torque using the UGO exoskeleton and Humac Norm dynamometers in healthy young adults. Methods: Twenty healthy young adults were enrolled in this #Study. All participants underwent isokinetic muscle strength tests using both the UGO exoskeleton and the Humac Norm dynamometers. Peak torque (Nm) of the hip and knee flexors and extensors was measured. The Pearson correlation coefficient was used to determine concurrent validity. Bland-Altman analysis was used to assess the agreement between the 2 devices. Results: At the knee, peak torque of the extensors measured by the UGO exoskeleton correlated strongly with that measured by the Humac Norm dynamometer (right: r=0.662; P=.001; left: r=0.768; P

Background: Ambient voice technology (AVT) uses conversational AI to record and organize clinical consultations in real time. It is being adopted quickly across the National Health Service (NHS). However, evidence about its effects on productivity, costs, or staff experience across different health care settings is limited. Phase 1 of this #Research program developed a taxonomy, logic model, and outcome framework for evaluating AVT. Phase 2 will carry out a multisite, mixed methods evaluation of AVT in 4 NHS trusts. These include mental health outpatient services, acute hospital outpatient clinics, and accident and emergency departments. Objective: This #Study aims to explore the real-world impact of AVT on productivity, costs, and staff experience across NHS adult services. Methods: The #Study includes three parts: (1) a quantitative quasi-experimental analysis of routine NHS data to estimate the impact of AVT on documentation time, clinician activity, and service outcomes; (2) a comprehensive health economic evaluation comprising cost-consequence analysis, cost-benefit analysis, and budget impact modeling; and (3) interviews with up to 36 staff from 3 services (mental health outpatient, acute hospital outpatient, and accident and emergency) to explore their experience of using AVT, their views on its impact, and what helps and gets in the way of its use. Sites will be chosen to include different care settings, organizational environments, and AVT products. Quantitative and economic analyses will use NHS Electronic Health Record #ehrs and national datasets to understand changes over time and measure the impact of AVT. Interview data will be analyzed using thematic analysis and rapid assessment procedures to identify key themes. All #Study findings will be combined to give a clear view of AVT and its impact. Results: Data collection is expected to begin in August 2026 and conclude by January 2027. Publication of results is anticipated in February 2027. Conclusions: This evaluation will provide real-world evidence on the productivity, economic, and experiential impacts of AVT in the NHS. Outputs will include peer-reviewed papers, a slide-deck summary for the funder, and a publicly available health economic decision-support tool to help NHS organizations decide whether they should adopt AVT. International Registered Report Identifier (IRRID): PRR1-10.2196/105261

No measurement, no understanding; no understanding, no control: this foundational scientific principle was exposed as a public health dysfunction by the COVID-19 pandemic. Transmission chains spread invisibly, and the contact histories, mobility patterns, and biosignals necessary for control were never systematically collected. Although sensors and digital technologies existed, the fundamental reason measurement failed was the absence of privacy infrastructure that would have enabled people to provide data with confidence. This failure had structural reasons. The object of measurement in infectious disease control is not a physical phenomenon but human beings, and measurement therefore enters the core of privacy: contact histories, social relationships, and bodily states. Because greater precision also deepens privacy intrusion, contact-tracing apps faced 2 failures: privacy-centered designs lost epidemiological utility, while utility-centered designs were rejected through public distrust. Neither achieved sufficient measurement. This Viewpoint reframes the problem. Privacy protection is not a constraint that impedes infectious disease control but the enabling condition upon which effective measurement depends. Existing regulations and technical methods have not been designed from this premise and have therefore failed to break the cycle of structural distrust. As an institutional approach to filling this gap, we present VRAIO (verifiable record of AI output), which integrates democratic rule-setting, metadata declaration, third-party verification, tamper-proof ledgers, and violation-deterrence incentives. Once privacy infrastructure is established, this foundational principle can operate freely in infectious disease control for the first time. It will enable high-resolution epidemiology and precision intervention, opening a new path for public health that reconciles infection control with individual autonomy and social freedom without relying on blanket social shutdowns.

Background: Intrinsic capacity (IC) has become a central concept in healthy aging because it emphasizes functional ability across the aging trajectory rather than disease alone. However, current IC assessment primarily relies on episodic clinical evaluations, which are insufficient for continuous monitoring and early identification of functional decline. Recent advances in AI and digital health technologies have created new opportunities for objective, continuous, and real-world assessment of IC. However, existing evidence remains fragmented across AI-enabled devices, digital biomarkers (DBs), AI techniques, and IC domains. Objective: This scoping review aimed to systematically synthesize the current evidence on AI-based measurement tools for IC and to characterize the landscape of AI-enabled IC assessment using a 3D analytical framework integrating AI-enabled digital devices and systems, DBs, and AI techniques. Methods: A comprehensive search of PubMed, Embase, CINAHL, PsycINFO, the Cochrane Library, SinoMed, and China National Knowledge Infrastructure (CNKI) was conducted from database inception to July 2025 and updated on May 31, 2026, in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guideline. Studies investigating AI-based measurement tools applicable to one or more IC domains were included. Results: A total of 161 studies met the inclusion criteria. Research on AI-based measurement tools for IC has expanded rapidly since 2016, with studies conducted in 28 countries, predominantly the United States and China. Most studies focused on a single IC domain, with cognition accounting for the largest proportion. Eleven categories of AI-enabled digital devices and systems were identified, among which multimodal data acquisition devices, computer vision (CV) systems, and AI-driven health platforms were the most frequently reported. Twenty-one types of DBs were extracted and classified into 3 major categories, with gait parameters, digital task performance, physical activity features, speech and language features, and facial features representing the most commonly used biomarkers. Machine learning and deep learning were the predominant AI techniques, while CV and natural language processing played central roles in multimodal data interpretation. The distribution and maturity of evidence varied substantially across domains, with cognition and locomotor capacity representing the most developed areas, whereas vitality, hearing, and multidomain IC assessment remained comparatively underrepresented. Conclusions: This scoping review provides a 3D synthesis of AI-enabled digital devices and systems, DBs, and AI techniques across the 6 World Health Organization (WHO)–defined domains of IC. Unlike previous technology-, disease-, or domain-specific reviews, it compares evidence across the broader IC framework, identifying more developed areas, key evidence gaps, and priorities for standardization, external validation, and multidomain assessment. AI-based measurement tools may complement conventional assessment in community, primary care, and home settings, although their clinical translation will require robust validation, integration into care pathways, and implementation approaches that address the needs of older adults.

Background: Telemedicine adoption among health care professionals is hindered by resistance to change (RTC), comprising routine-seeking, emotional reactivity, short-term focus, and cognitive rigidity, which interacts with perceived ease of use (PEU) and anxiety within the Technology Acceptance Model framework. Objective: This study aimed to test a moderated mediation model in which PEU mediates the association between RTC, assessed both overall and by dimension, and intention to use telemedicine, while telemedicine-related anxiety moderates the association between PEU and intention to use. Participants were recruited from 4 governmental geriatric medical centers in Israel. Methods: A cross-sectional study was conducted among 377 health care professionals from 4 Israeli governmental geriatric medical centers. Telemedicine use in the participating centers was limited, optional, and consisted mainly of remote patient monitoring. Data collection was conducted between March and June 2024. Data were collected using validated self-administered questionnaires measuring telemedicine-related anxiety, RTC with 4 dimensions (routine seeking, emotional reactivity, short-term focus, and cognitive rigidity), PEU, and intention to use telemedicine. Statistical analyses were performed using SPSS (version 28) and PROCESS Model 14 for moderated mediation analysis. Results: The moderated mediation analysis revealed that RTC was negatively associated with the intention to use telemedicine through the lower PEU, with this indirect pathway being attenuated but not eliminated by high telemedicine-related anxiety. The overall indirect effect remained negative and statistically significant across all anxiety levels. Conclusions: Health care professionals’ RTC reduced their intention to use telemedicine by lowering PEU, with telemedicine-related anxiety moderating but never eliminating this relationship. These findings suggest that telemedicine implementation in geriatric settings should consider both RTC and PEU. The positive indirect association observed for cognitive rigidity was unexpected and should be replicated before specific practical recommendations are made.

Background: Although pharmacotherapy is the primary treatment for patients with acute-phase panic disorder, its incomplete efficacy causes them to experience frequent panic attacks and severe anticipatory anxiety for a considerable period. A prescription-based mobile digital therapeutic (DTx) that integrates self-guided cognitive behavioral therapy (CBT), real-time symptom management, and lifestyle tracking can be used as an adjunct to pharmacotherapy for these patients, helping them achieve rapid symptom recovery. Objective: This study aimed to evaluate the efficacy of this adjunctive DTx in alleviating symptoms in patients with acute-phase panic disorder. Methods: In total, 66 acute-phase patients experiencing frequent panic attacks were recruited from 6 institutions and randomly divided into a group receiving pharmacotherapy combined with DTx or pharmacotherapy alone, participating in an 8-week multicenter single-blind trial. The DTx app included 3 major services: training service consisting of structured CBT modules, companion service of just-in-time modules for coping with panic attacks, and care service providing daily self-management tracking tools. The self-report scales used as efficacy indicators were administered via paper questionnaires during a total of 4 visits at baseline, week 2, week 4, and week 8. The primary end point, the change in the Panic Disorder Severity Scale-Self Report (PDSS-SR) score, and the secondary end points, such as changes in overall anxiety, depression, panic-related catastrophic cognitions, and fear of bodily sensations, were compared between the 2 groups. Adherence to the DTx was objectively assessed using device use metrics. Results: As 5 enrolled patients were excluded due to insufficient safety analysis or efficacy evaluation, the final analysis included 32 in the DTx group and 29 in the control group. The DTx group showed a significantly greater reduction in PDSS-SR scores at week 8 compared to the control group (mean 40.29%, SD 26.68% vs mean 17.61%, SD 30.37%; =.007). This therapeutic benefit appeared rapidly by week 2 (mean 26.66%, SD 22.03% vs mean 11.05%, SD 21.73%; =.02). The responder (≥40% PDSS-SR reduction) rate was also significantly higher in the DTx group (17/32, 53.12% vs 5/29, 17.24%; =.007). While changes in depression and somatic fears were transient or similar between groups, the DTx group showed significantly greater sustained improvements in overall anxiety and catastrophic cognitions. The overall mean adherence rate was 78.12%, with no significant difference between responders and nonresponders. Conclusions: The adjunctive use of our prescription-based DTx resulted in early and sustained symptom reductions across panic severity, overall anxiety, and catastrophic cognitions, suggesting that this multifunctional and self-guided DTx including just-in-time management and lifestyle tracking can serve as a practical complement to routine psychiatric care for acute-phase patients experiencing frequent panic attacks. This app is expected to present a new framework for the treatment of acute-phase panic disorder by enabling the incorporation of various symptomatic aspects in patients’ daily life into clinicians’ evaluations and guidance in the clinic. Trial Registration: Clinical Research Information Service KCT0010500; https://cris.nih.go.kr/cris/search/detailSearch.do?seq=34277

Background: Promotional activities, such as social marketing approaches, are important for raising #Cancer awareness and increasing breast #Cancer screening participation. Such interventions are designed to facilitate change and maintain positive health behaviors at both the community and society level. With the growing use of these promotional activities in public health, it is pertinent to map available studies to inform future evaluations and the development of effective interventions. Objective: This #Study aimed to review existing studies on how social marketing interventions have been used to promote breast #Cancer screening and early diagnosis. The specific objectives were to assess the characteristics of social marketing interventions aimed at promoting breast #Cancer screening, identify the methodological approaches used in evaluating the interventions, identify the outcomes used to measure the impact of social marketing interventions for breast #Cancer screening, and assess the impacts of social marketing interventions when promoting breast #Cancer screening. Methods: A comprehensive literature search was performed in 6 electronic databases to identify qualitative and quantitative evaluations of social marketing interventions targeting breast #Cancer screening and early diagnosis. Titles and abstracts were reviewed independently by two reviewers, followed by data extraction and verification. The methodological quality was evaluated using the Cochrane Handbook for Systematic Reviews of Interventions. Outcomes were categorized using an adapted version of key performance indicators and metrics related to social media use in health promotion. Results: Overall, 15 research articles were eligible and analyzed. The 4 types of social marketing interventions identified were health professional and educational strategies, social media or mass media campaigns, community outreach campaigns, and video-based marketing. The predominant intervention source was public health government bodies, followed by regional health services, #Cancer charities, and educational institutions. Most interventions were designed to influence breast #Cancer screening behaviors by raising awareness through advertisements, educational interventions, and fundraising activities. Most interventions reached a large audience (>500 people), but participants had mixed awareness and perceptions toward social marketing strategies, and the outcomes were shaped by demographic factors such as cultural backgrounds, age, education, marital status, and occupation. Knowledge gaps, cost concerns, and the lack of physician recommendations also constituted key barriers to screening uptake. Behavior change outcomes were sparingly assessed in most interventions, thereby contributing to limited understanding of mechanisms affecting screening uptake. Conclusions: Available evidence suggests that social marketing interventions may enhance breast #Cancer screening and early diagnosis. However, robust evaluations of these interventions and assessment of behavior change outcomes could be performed in future research by using well-established evaluation frameworks and reporting guidelines. Developers and funders of these campaigns should also consider using an evaluation plan before conducting the actual campaign.

Background: Cervical degenerative diseases are a global public health issue, and their incidence is rising worldwide. Although an increasing number of studies on traditional machine learning (TML) and deep learning (DL) have been conducted in the detection and segmentation of cervical degenerative diseases and have reported promising task-specific results, the performance of these models has not yet been systematically analyzed. Objective: This systematic review and meta-analysis aimed to summarize and evaluate existing evidence on TML and DL approaches for diagnosing cervical degenerative diseases, thereby comprehensively guiding future research and clinical applications. Methods: This systematic review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A comprehensive literature search was conducted on PubMed, Embase, the Cochrane Library, Web of Science, Scopus, and the Institute of Electrical and Electronics Engineers (IEEE Xplore) from January 2000 to June 2026, supplemented by backward and forward citation searching in Scopus. Studies evaluating TML and DL algorithms for diagnosing cervical degenerative diseases using medical imaging were included. Methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool and the Quality Assessment of Diagnostic Accuracy Studies AI (QUADAS-AI) tool. For the primary diagnostic accuracy meta-analysis, data were synthesized using a bivariate mixed-effects logistic regression model. Sensitivity and specificity were summarized separately using random-effects meta-analysis with the Knapp-Hartung adjustment, and 95% prediction intervals (PIs) were reported. Certainty of evidence was assessed using the GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach. Results: This systematic review included 30 studies, of which 21 involved a total of 25,301 patients included in the meta-analysis. The pooled sensitivity and specificity were 0.92 (95% CI 0.89‐0.96; 95% PI 0.80‐1.00) and 0.88 (95% CI 0.84‐0.91; 95% PI 0.72‐1.00), respectively. The positive likelihood ratio (LR) was 8.36 (95% CI 6.14‐11.36), and the negative LR was 0.07 (95% CI 0.04‐0.11). The area under the summary receiver operating characteristic (SROC) curve was 0.96 (95% CI 0.94‐0.97). Leave-one-out analyses did not materially alter the pooled estimates. High risk of bias was identified in 4 studies using QUADAS-2 and in 17 using QUADAS-AI. The overall certainty of evidence was rated as low according to the GRADE approach. Conclusions: TML and DL models demonstrated satisfactory diagnostic performance for cervical degenerative diseases, although external validation was limited. Unlike previously published reviews in this field, this study provides pooled estimates of the diagnostic performance of TML and DL for cervical degenerative diseases and indicates that, given between-study heterogeneity and low certainty of evidence, AI should currently be used as clinical decision support rather than an independent replacement for physicians.

The rapid diffusion of generative AI is transforming health care delivery, education, administration, and research. In response, health care organizations have invested heavily in AI literacy initiatives and workforce development programs. Existing frameworks primarily focus on whether health care professionals understand AI and whether they can engage with AI effectively. However, health care practice increasingly reveals that individuals with similar levels of AI literacy and AI engagement often contribute very differently to AI-enabled work and organizational adoption. Some professionals primarily use AI to improve their own work, whereas others facilitate AI adoption, coordinate stakeholders, and integrate AI into routine practice. This observation suggests that current perspectives may overlook an important dimension of workforce AI enablement. In this Viewpoint, we argue that AI literacy and AI engagement alone provide an incomplete explanation of how health care organizations realize the benefits of AI. Drawing upon literature from AI literacy, fluency theory, human-AI interaction, innovation diffusion, implementation science, and health care workforce development, we propose the health care workforce AI enablement matrix (HWAEM). HWAEM conceptualizes workforce AI enablement through 2 complementary capabilities: AI fluency and AI harnessing. AI fluency refers to the capability to engage with AI effectively, appropriately, and responsibly across professional contexts, whereas AI harnessing refers to the capability to identify opportunities for AI-enabled improvement, mobilize stakeholders, facilitate adoption, and integrate AI into collective work practices. The interaction of these capabilities generates 4 workforce profiles: AI novices, AI practitioners, AI facilitators, and AI leaders. Through HWAEM, this Viewpoint argues that health care workforce AI enablement is better understood through the complementary capabilities of AI fluency and AI harnessing than through AI literacy and AI engagement alone. We illustrate how these profiles manifest in health care practice and discuss implications for workforce development and AI implementation. HWAEM offers a new perspective for understanding health care workforce preparedness in the generative AI era and provides a foundation for future empirical research.

Background: Frontline workers experience substantial occupational demands that may affect sleep, physical activity, sedentary behavior, recovery, and mental health. Although 24-hour movement behavior (24hrMB) frameworks conceptualize these behaviors as interdependent components of a finite day, it remains unclear how extensively frontline #Research has adopted integrated approaches, or how evidence is distributed across behaviors, mental health domains, occupations, and measurement methods. Objective: This study aimed to map evidence examining movement behaviors and mental health among adult frontline workers; characterize its distribution and overlap across movement and mental health domains, occupational groups, methods, and contexts; and identify gaps for future #Research and intervention development. Methods: Following Joanna Briggs Institute methodology and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidance, 6 databases (PsycINFO, CINAHL, MEDLINE, SPORTDiscus, Scopus, and PubMed) were searched for peer-reviewed studies published from 2000 to 2026. The search was updated on July 30, 2026. Eligible studies included adult civilian frontline workers and examined at least 1 movement behavior in relation to at least 1 mental health domain. One reviewer (AS) screened and charted all studies, while 2 additional reviewers (GH-B and EC) independently screened and charted random subsets to assess reliability. Movement behaviors and mental health domains were coded as non–mutually exclusive. Findings were synthesized using descriptive statistics, narrative synthesis, and evidence mapping. Results: Across the original and updated searches, 613 studies were included. #Research was concentrated among health care populations (n=468, 76.3%), with studies predominantly using cross-sectional (n=451, 73.6%), association-focused (n=526, 85.8%), and self-report movement measurement approaches (n=533, 86.9%). Sleep was assessed in 538 (87.8%) studies, physical activity in 177 (28.9%) studies, and sedentary behavior in 24 (3.9%) studies. Most studies assessed 1 movement behavior (n=500, 81.6%), and only 13 (2.1%) assessed all 3 behaviors. Stress was the most frequently assessed mental health domain (n=285, 46.5%), followed by depression (n=243, 39.6%), anxiety (n=224, 36.5%), burnout (n=154, 25.1%), and well-being (n=129, 21.0%). Evidence concentrated on sleep in relation to stress (n=248, 40.5%), depression (n=226, 36.9%), and anxiety (n=208, 33.9%), while comparatively little examined mental health alongside sedentary behavior. Only 9 (1.5%) studies used objective-only movement measurement. Conclusions: Despite a large and rapidly expanding literature, evidence remains dominated by sleep-focused, health care–based, cross-sectional, association-focused, and self-report #Research. Sedentary behavior, integrated 24hrMB approaches, non–health care frontline occupations, and longitudinal and objective methodologies remain underrepresented. By mapping movement and mental health domains as overlapping rather than mutually exclusive categories, this review identifies both major concentrations and substantive gaps in evidence. Future #Research should broaden occupational representation, use longitudinal and repeated-measures designs, and examine integrated 24hrMBs using complementary objective and subjective measures. In practice, current evidence can inform priorities for occupational monitoring and intervention development but is insufficient to support specific behavioral prescriptions.

Background: Competency-based medical education #mededu increasingly uses digital workplace assessment platforms to collect longitudinal evidence of trainees’ readiness for progressive responsibility. Entrustable professional activities (EPAs) translate competencies into observable clinical work, but individual workplace observations are context-dependent and may not support dependable progression decisions. Empirical guidance remains limited on how many observations, faculty raters, EPA titles, and workplace settings are needed for a dependable EPA portfolio. Objective: This study aimed to estimate EPA portfolio dependability for competency-based progression review using nationwide data from a digital workplace assessment platform in otorhinolaryngology-head and neck surgery residency training. Methods: We conducted a retrospective, nationwide cross-sectional generalizability and decision study using routinely collected EPA-based workplace assessment data from Taiwan’s E-MyWay platform. The study included completed EPA observations from accredited otorhinolaryngology-head and neck surgery residency programs between August 2022 and May 2026. Faculty-assigned entrustment-supervision ratings were scored on a 5-level national EPA scale. The resident was the object of measurement, whereas faculty rater, EPA title, and workplace setting were measurement facets. The generalizability study partitioned rating variance into resident, facet, interaction, and residual components, which were subsequently used in decision-study projections of the generalizability coefficient, Phi coefficient, and absolute standard error of measurement under alternative portfolio configurations. Results: The analytic sample included 45,526 EPA observations involving 466 residents and 448 faculty raters across 35 training programs, 11 EPA titles, and 5 workplace settings. Residents had a median of 87 (IQR 47-124) observations and were assessed by a median of 9 (IQR 6-12) faculty raters across 11 (IQR 10-11) EPA titles and 5 (IQR 4-5) workplace settings. Single EPA observations were insufficient for resident-level summative interpretation. In the primary generalizability study, residual/encounter-level variance was the largest component, accounting for 40.1% of total variance; resident variance accounted for 26.9%, faculty rater variance accounted for 10.8%, and resident × faculty rater interaction for 10.4%. A study-defined well-distributed 10-observation portfolio achieved a generalizability coefficient of 0.77 but an absolute dependability coefficient (Phi) of 0.65. A 20-observation portfolio across 5 faculty raters, 5 EPA titles, and 3 workplace settings yielded a Phi of 0.76, and a 30-observation portfolio across 6 faculty raters, 6 EPA titles, and 4 workplace settings yielded a Phi of 0.80. Conclusions: Nationwide digital workplace assessment data showed that EPA portfolio dependability reflected both observation counts and sampling breadth across faculty raters, EPA titles, and workplace settings. These study-defined portfolio configurations may help clinical competency committees, residency programs, and specialty societies monitor portfolio completeness before progression deliberation. They should complement, rather than replace, narrative feedback, case complexity, performance trajectory, and committee judgment.

Background: Informal caregivers play a fundamental role in the diagnosis, treatment, and care received by their family members. Caring for someone with dementia is particularly burdensome and can have negative effects on caregivers’ health. Despite caregivers being widely recognized as vulnerable, the psychosocial needs of caregivers who are managing #Cancer themselves remain unknown. Objective: This #Study aims to investigate the experiences of UK family caregivers who have received a #Cancer diagnosis while caring for someone with dementia or memory problems. Methods: Twenty-five UK informal family caregivers who had received a #Cancer diagnosis were recruited through convenience sampling to take part in in-depth semistructured interviews. The interviews explored participants’ experiences of managing #Cancer alongside dementia caregiving, including the impact on treatment and caregiving, emotional challenges, support needs, and experiences of formal and informal support. Ethical approval was obtained from the Leeds Beckett University Research Ethics Committee, and all participants provided informed consent. Reflexive thematic analysis was conducted. Results: Three key themes were identified: (1) caring responsibilities can complicate and take priority over #Cancer care, (2) caregiving can intensify #Cancer-related distress and anxieties, and (3) managing #Cancer while caring increases the need for support and coping resources. For dementia caregivers, having #Cancer can surface/heighten feelings of predeath grief and worries about future care planning for their care recipient. Dementia caregivers viewed multifaceted social support as vital for enabling access to and coping with #Cancer treatment. Conclusions: Dementia caregivers who are also living with #Cancer continue to shoulder the burden of illness work on behalf of their care recipients while simultaneously managing their own #Cancer diagnosis and treatment. Caregiving responsibilities often take priority over #Cancer care, intensify the practical and emotional challenges of #Cancer, and increase the need for and reliance on support resources. These findings highlight the need for person-centered adjustments to #Cancer care, dementia-friendly health care environments, and tailored psycho-#Oncology support that address the practical and emotional needs of dementia caregivers.

Background: Emergency departments (EDs) face persistent challenges related to overcrowding, boarding, ambulatory care access barriers, and workforce strain, contributing to compromised patient care and high rates of physician burnout. Virtual care has emerged as a potential strategy to alleviate pressure on emergency care systems. In 2020, the Veterans Health Administration (VA) launched the national Tele-Emergency Care (TEC) program, in which patients who call a call center can be connected to an emergency medicine clinician by phone or video. Although virtual care may help address ED capacity and clinician burnout, the perspectives of emergency medicine–trained clinicians remain limited. Objective: The aim of this study is to examine the experiences of emergency medicine clinicians participating in VA’s TEC program. Methods: As part of a national mixed methods evaluation of TEC, we conducted semistructured interviews with clinicians delivering emergency care through TEC between February 2025 and June 2025. Participants (n=15) were recruited via multistage purposeful sampling from 4 of 18 regional TEC programs that varied in geography, volume, duration, and operational models. Interviews explored experiences of providing care in a virtual environment, including perceived benefits and challenges. We performed a descriptive qualitative analysis. Results: We interviewed 14 physicians and 1 nurse practitioner with formal emergency medicine training. Interviewees described four primary themes: (1) clinical decision-making in a virtual environment; (2) development of the provider-patient relationship; (3) clinician job satisfaction and professional well-being; and (4) challenges. Participants reported that TEC provided perceived opportunities to avoid ED referrals, more focused patient interactions, and improved job satisfaction related to flexible virtual shifts. Reported challenges included filling primary care gaps and performing care coordination tasks. Conclusions: TEC represents an emerging model of emergency care delivery that clinicians perceive may expand access, prevent avoidable ED visits, and support clinician well-being while also introducing distinct clinical and operational challenges. Our findings can inform the implementation of similar emergency telehealth services in other health systems.

AI is increasingly incorporated into psychiatric triage, risk prediction, passive monitoring, clinical documentation, and patient-facing conversational systems. These applications may improve access, continuity, efficiency, and pattern recognition, but they also redistribute epistemic authority and complicate responsibility when harm occurs. European regulation is developed in relation to market access, data governance, risk management, and product safety, yet remains fragmented regarding civil liability, organizational negligence, and the psychiatric standard of care. This Viewpoint examines how liability and standard of care should be understood when AI becomes part of psychiatric reasoning in Europe. It advances one central thesis: psychiatric AI requires justified integration supported by layered accountability within, but not determined by, European regulation. It presents a targeted doctrinal and normative synthesis of binding European Union instruments, regulatory guidance, selected national governance materials, and psychiatric, bioethical, legal, and digital mental health literature. It distinguishes binding law from guidance and policy, and separates ex ante regulation from ex post liability, and from professional standards of care. Four illustrative domains are analyzed: conversational or therapeutic chatbots, suicide prediction, digital phenotyping and passive monitoring, and large language model documentation. Psychiatric AI raises distinctive concerns because psychiatric judgment depends heavily on testimony, contextual meaning, therapeutic trust, risk interpretation, privacy, and liberty-sensitive decisions. Existing European instruments, including the AI Act, Medical Device Regulation, General Data Protection Regulation, revised Product Liability Directive, and European Health Data Space Regulation, establish governance duties, but do not provide a harmonized fault-based liability framework for AI-assisted health care. Regulatory compliance may inform later legal assessment, but it does not determine whether psychiatric care was reasonable. The proposed standard of justified integration requires knowledge of intended use and model limits, assessment of local and patient-level applicability, active clinical interpretation, disclosure when AI use is material to consent or trust, documentation in high-stakes decisions, and organizational audit. Accountability should be distributed across developers, deployers, and clinicians according to control and preventability. Mixed-fault scenarios are therefore likely to be common. The augmented-clinician model and layered accountability are offered as normative proposals rather than settled European legal standards. Clinicians should remain responsible for contextual, patient-centered judgment; developers for design, validation, documentation, and foreseeable misuse; and deployers for procurement, training, workflow integration, local validation, monitoring, and escalation. Future empirical research should evaluate effects on clinician reliance, documentation burden, patient outcomes, coercive interventions, therapeutic trust, and feasibility across differently resourced services.

Background: Chronic liver diseases (CLDs) are frequent in Europe, including in France, and are mainly driven by alcohol, metabolic dysfunction (#Diabetes and obesity), and viral hepatitis. Although risk factors are well established and easy to identify, CLDs are frequently diagnosed at an advanced stage, translating into poor prognosis. Descriptive data on CLD burden in France remain scarce, and health trajectories of these patients are uncharted. However, such data are crucial to guide clinical practice, to determine the target population for personalized public health policies, and to develop innovative strategies to reduce inequities in access to health care and ultimately improve survival. Objective: The aim of the French RESONANCE cohort is to provide a detailed description of CLD burden in France and #Study health trajectories. Methods: The RESONANCE cohort was derived from the French National Health Data System (Système National des Données de Santé [SNDS]). Patients with at least one CLD-specific ICD-10 (International Statistical Classification of Diseases, Tenth Revision) code (including primary liver #Cancer [PLC] and rare diseases), or related procedure, biology, or drug and/or a cause of death related to one of these ICD-10 codes, were targeted from the 2% representative SNDS sample (ESND [Échantillon du Système National des Données], Simplified Sample of the French National Health Data System). Demographic characteristics, risk factors, comorbidities, etiologies, social environment and health care accessibility, as well as severity of the liver disease at first identification and medical management, were assessed. This paper describes the procedures used to build the cohort, outlines main variables, and defines the #Research objectives. Results: Overall, 26,663 incident and prevalent CLD cases identified between 2015 and 2021 were included in the RESONANCE cohort. Men accounted for 58.1% (15,490) of the cohort, and the mean age was 59.9 (SD 17.4) years. The cohort covers a broad spectrum of etiologies, including alcohol-related liver disease (7580/26,663, 28.4%), metabolic-associated liver disease (6030/26,663, 24.8%), viral hepatitis (3450/26,663, 12.9%), and rare liver diseases (839/26,663, 3.1%). A total of 43% (11,461/26,663) of the patients lived in a socially deprived environment (according to the 4th and 5th quintiles of the French Deprivation Index). Conclusions: This paper will enable a detailed, real-world analysis of epidemiology, care trajectories, and outcomes of CLDs across France. With a particular focus on gender and socioeconomic disparities, it offers a unique opportunity to identify vulnerable populations and inform proportionate universalism strategies in prevention and care.

Background: Alcohol use disorder (AUD) involves an impaired ability to stop or control alcohol use despite adverse consequences and represents a major public health problem. While AUD is most prevalent among college students, integrated evidence-based treatments for this population are lacking. Objective: The objectives of our study were to test the #feasibility and acceptability of a newly developed behavioral intervention, problem-solving therapy and Apple Watch (PST-APPLE), and to preliminarily investigate the effectiveness of PST-APPLE among college students with symptoms of AUD. Methods: Participants were recruited through online advertising and on campus at universities in the Chicago area between May and September 2024. They were randomized in a 1:1 ratio using block randomization to the intervention group (PST-APPLE: n=12 individuals) or the control group (education-only: n=14 individuals), with the data analyst blinded to treatment assignment. Participants in the intervention group completed 12 weeks of the PST-APPLE intervention, delivered remotely via Zoom videoconferencing and interactions with the Apple Watch. Participants in the control group were asked to watch a 20-minute abstinence-motivation video and then participate in a 30-minute group discussion via Zoom. All participants completed follow-up assessments at 3 months. Outcomes included alcohol-related problems (Alcohol Use Disorders Identification Test [AUDIT] and Rutgers Alcohol Problem Index [RAPI]) and drinking motivation (Drinking Motives Questionnaire Revised [DMQR]). Results: We enrolled 26 participants aged 18‐25 years. The mean age was 22.5 (SD 2.20) years. We found reduced scores between preintervention and postintervention measures in the intervention group using 2-tailed paired tests and baseline-adjusted analysis of covariance, including reduced alcohol misuse and problems (AUDIT and RAPI scores) and reduced DMQR social and coping motivation scores. Baseline-adjusted mean differences comparing the intervention with the control group showed reductions in alcohol use (AUDIT; mean difference −4.70, 95% CI −8.69 to −0.70) and in DMQR social motivation (mean difference −0.85, 95% CI −1.51 to −0.18) and enhancement motivation scores (mean difference −0.75, 95% CI −1.47 to −0.04). Conclusions: Our study findings indicate that the PST-APPLE intervention is feasible, acceptable, and hypothesis generating among college students with AUD. While findings are promising, this pilot study was not powered for definitive efficacy testing. A fully powered randomized controlled trial is needed to confirm these effects. Future studies could adapt PST-APPLE to other wrist-worn electronic devices/watches that capture physiological data (eg, Fitbit, Samsung Galaxy Watch) to increase reach and scalability, particularly among younger adults and digitally literate populations. Trial Registration: ClinicalTrials.gov NCT06333288; https://clinicaltrials.gov/study/NCT06333288

Background: Access to eye care is a persistent challenge due to the high global burden of visual impairment and barriers to receiving eye care, such as transportation and cost. Conventional ophthalmic equipment is im#mobile and unsuitable for bedside or community use, and this limits timely diagnosis and care delivery. Portable ophthalmic devices, such as handheld slit-lamps and fundus cameras, offer the potential to extend diagnostic capabilities to nonclinical settings and improve accessibility of eye care. Objective: This study examined eye care professionals’ perceptions and experiences with portable ophthalmic devices, focusing on their roles in both clinical and nonclinical settings. Methods: Thirty-one practicing eye care professionals (16 ophthalmologists, 15 optometrists; 16 female, 15 male) with 2 to 45 years of clinical experience (mean 20.5, SD 13.1 y) participated. Semistructured interviews were conducted in person or via videoconferencing. The interview included open-ended qualitative questions on current and past use, benefits, and desired features of portable devices, along with quantitative ratings of device attributes on a 1 to 10 scale. Transcripts were coded in NVivo using thematic analysis, and descriptive statistics were calculated for the quantitative data. Results: Participants described portable devices as essential in settings where conventional equipment is impractical, including hospital rooms, emergency departments, nursing homes, and outreach. They emphasized utility for #patients with mobility challenges and cognitive impairments. Key benefits included earlier disease detection and support for screening and referrals. Challenges involved image quality and device stabilization. Quantitative ratings of portable device attributes showed the highest scores for speed (median 8, IQR 6-8) and ease of use (median 8, IQR 6-8). Conclusions: Portable ophthalmic devices expand access to eye care beyond standard clinics and accommodate #patients underserved by traditional equipment. Addressing technical and usability challenges and enhancing training opportunities will strengthen their integration into routine and outreach care.

This letter raises a safety question about emotion-adaptive clinical AI. Empathic delivery may help users understand and accept correct advice, but it may also make incorrect advice harder to reject. We propose comparing empathic and neutral AI responses when advice is correct and when it is wrong, while measuring decision accuracy, appropriate acceptance and rejection, and user understanding. Trust should follow correctness.

This Author Reply clarifies that our study evaluated perceived trust and emotion-adaptive explanation delivery rather than decision accuracy or appropriate reliance, and outlines future research using objective behavioral measures to examine warranted trust and reliance on clinical artificial intelligence.

Background: AI is rapidly transforming clinical #nursing, promising administrative relief and decision support. However, the frontline reality presents a double-edged sword effect, where technological empowerment is frequently offset by novel occupational burdens and technostress. Objective: This study aims to theoretically deconstruct the bidirectional impacts of AI application among clinical #nurses and identify buffering conditions, using the Job Demands-Resources (JD-R) theoretical framework. Methods: A descriptive qualitative study was conducted across multiple general hospitals in mainland China. Using maximum variation and purposive sampling, semistructured in-depth interviews were conducted with registered #nurses who actively use clinical AI systems. Data were analyzed using directed qualitative content analysis guided by predefined JD-R constructs. Results: The analysis revealed 3 overarching domains comprising 9 main themes and 24 subthemes. On the gain path (job resources), AI empowered #nurses through a workflow efficiency leap, clinical cognitive empowerment, and professional capital appreciation. Conversely, along the drain path (job demands), hidden costs were exposed, conceptualized as cognitive impediment, relational attrition, and digital involution driven by performance inflation and competitive perfectionism. The interplay between these pathways was perceived to be buffered by contextual mechanisms, specifically #nurses’ proactive coping strategies, professional boundary demarcation, and the provision of a cohesive organizational support architecture. Conclusions: The impact of AI integration in #nursing is not technologically deterministic. While AI provides valuable cognitive and operational support, it concurrently generates novel digital demands. To prevent AI from devolving into an occupational hazard, health care administrators must establish multidimensional life cycle AI governance, cultivate comprehensive AI literacy, and safeguard the irreplaceable humanistic core of clinical care.

Background: Digital health interventions for psychosis, like SloMo, leverage smartphone technology to help transfer learning from therapy to real-life situations. Usage relies on motivation, recall, and awareness. Wearable devices that track physiological signs of stress can boost engagement by encouraging the use of coping strategies when most needed. Objective: This study aims to co-design an integrated user interface for wearable technology and the SloMo mobile app, and explore its #usability, acceptability, engagement, and preliminary clinical outcomes for individuals with psychosis through a feasibility study. Methods: A co-design team developed the wearable augmented SloMo app using the Double Diamond framework. The team included 10 experts by experience based across three UK National Health Service (NHS) mental health trusts, who had completed SloMo as part of a randomized controlled trial, and were purposively recruited to ensure a range of demographic backgrounds. Users tested the app for 8 consecutive weeks during a 5-month data collection period in 2018, providing feedback that guided iterative improvements. Self-reported engagement, #usability, and acceptability were collected through quantitative ratings and interviews, evaluated through qualitative content analysis. Assessments of paranoia and well-being were completed pre- and post use, summarized descriptively, with change scores calculated and evaluated for reliable change. Results: The Spire Stone clothing-adhered biosensor, which monitors respiratory rate and stress physiology, was chosen to augment SloMo. A minimum viable product of an integrated interface was developed. Feedback identified that users needed the technology to be easy to use, secure, and tailored to them through user control. Eight (8/10, 80%) users reported that the wearable prompted increased use of the SloMo app’s features and other coping strategies. Self-reported engagement varied according to EBE user experience of the technology: higher use of the technology (n=3) was associated with higher usefulness (25% reduction in paranoia, SD 6%) and acceptability (84% satisfaction, SD 13%). Conversely, individuals that engaged with the device less (n=7) did not find it as useful (11% increase in paranoia, SD 17%) and were only modestly satisfied (63%, SD 20%). Conclusions: Wearable technology is acceptable and can be integrated with a digitally supported therapy for psychosis to promote engagement and potentially support management of difficulties. However, further development work is needed to ensure acceptability and usefulness across a broader range of user needs. Trial Registration: ISRCTN ISRCTN32448671; https://www.isrctn.com/ISRCTN32448671

Background: AI has the potential to enhance clinical decision-making in high-acuity settings such as intensive care units (ICUs) and emergency departments (EDs). However, despite promising performance, many AI-driven clinical decision support systems (AI-CDSSs) face poor adoption due to issues of trust, workflow disruption, and alert fatigue. Understanding the human factors that shape clinician acceptance is critical to guide safe and effective implementation of AI-CDSS in acute care. Theoretical frameworks, including the Systems Engineering Initiative for Patient Safety (SEIPS) 2.0 model and the technology acceptance model (TAM), suggest that successful adoption requires addressing sociotechnical interactions among clinician trust, system design, organizational readiness, and task complexity, yet few empirical studies have applied these frameworks to AI-CDSSs in acute care settings. Objective: This study aimed to evaluate emergency medicine and critical care clinicians’ perceptions of AI-CDSSs and to identify key factors influencing adoption, including trust, design preferences, and workflow integration. Methods: A SEIPS 2.0–informed mixed methods study evaluated ICU and ED clinicians from Emory Healthcare on perceptions of AI in clinical practice. An expert-reviewed survey (N=57) assessed clinician perceptions, trust, and implementation preferences. Semistructured interviews (n=11) included A/B testing of AI-CDSSs and clinical sepsis scenarios to explore decision-making in context. Transcripts were thematically analyzed using the Braun and Clarke framework in ATLAS.ti (version 26, ATLAS.ti Scientific Software Development). Quantitative data were analyzed descriptively. This study assessed clinician perceptions using mock alerts and hypothetical scenarios rather than real-world AI-CDSS deployment. Results: Trust in AI varied significantly by patient acuity (Cochran Q=30.40,

In a diagnostic study of 104 western blot and subcutaneous xenograft tumor images, a high-fidelity generative model produced forgeries that could alter the conclusions of a study; 24 PhD-level expert reviewers could not reliably distinguish the forgeries from authentic figures (mean accuracy 50.5%, SD 6.9%), while the best-performing commercial AI detector achieved only moderate discrimination (area under the curve 0.790, 95% CI 0.695-0.885), revealing critical vulnerabilities in current research-integrity safeguards.

Background: Mild cognitive impairment (MCI) represents a transitional phase between normal aging and dementia. While no pharmacological treatments have proven effective, nonpharmacological interventions, such as cognitive stimulation and psychosocial support, have shown promise. Integrating #Digital tools and socially assistive robotics may improve engagement, personalization, and access to care, contributing to the preservation of cognitive function and overall well-being in older adults. Objective: The DHEAL-COM COGNITIVE pilot #Study aims to evaluate the feasibility, acceptability, and preliminary efficacy of a sociotechnological intervention that combines #Digital cognitive stimulation, social robot interaction, and group training in #Digital and health literacy to counteract cognitive decline. Methods: This is a single-blind, quasi-experimental feasibility trial involving 60 older adults with MCI, recruited from the Neurology and Alzheimer Units of IRCCS INRCA (INRCA – IRCCS Istituto Nazionale di Ricovero e Cura per Anziani). Participants were allocated into 2 groups: the experimental group received home-based cognitive training via the Brainer #App, a month-long interaction with the NAO social robot, and weekly group sessions on eHealth literacy and cognitive stimulation; the control group received a well-being booklet with optional activities. Assessments were conducted at baseline, postintervention after 12 weeks, and follow-up after 3 months, using validated instruments, such as the Montreal Cognitive Assessment, the EQ-5D-5L, the Italian version of the eHealth Literacy Scale, the System Usability Scale, the unified theory of acceptance and use of technology, and the Psychological Well-Being Scale. Descriptive statistics will be used to summarize feasibility, usability, and acceptability outcomes, while preliminary differences over time will be explored using appropriate inferential analyses, acknowledging that the #Study is not powered to detect effectiveness. Results: Patient recruitment began in March 2025 and continued through September 2025. As of April 2026, 36 patients have been recruited, with 18 participants per group. The trial started in May 2025 and ran throughout the year. Results will focus on changes in cognitive performance, psychological well-being, quality of life, eHealth literacy, and user acceptability and usability of the technologies. Findings will be analyzed quantitatively and qualitatively and are expected to be published by the end of 2026. Conclusions: The DHEAL-COM COGNITIVE #Study proposes an innovative, integrated intervention for older adults with MCI, using #Digital platforms and social robotics to support cognitive health and autonomy. This pilot #Study represents a promising step toward a more integrated and technology-supported model of care for older adults with MCI. Trial Registration: ClinicalTrials.gov NCT06984367; https://clinicaltrials.gov/#Study/NCT06984367 International Registered Report Identifier (IRRID): DERR1-10.2196/80233

Background: Assessing medication literacy in children is essential for evaluating school-based health education, yet traditional measurement tools often fail to engage children, compromising data quality. #Gamified assessments offer an alternative, but their psychometric properties, comparability to conventional formats, and responsiveness to intervention effects require systematic evaluation. Objective: This study developed and evaluated a #Gamified assessment tool for measuring medication literacy across 4 domains (knowledge, attitude, perceived behavioral control, and behavioral intention) in elementary school children through 3 sequential phases: psychometric validation, methodological comparison with a paper-based questionnaire, and application to interventions. Methods: Three independent cohorts were recruited from a single elementary school in Nanjing, China, using cluster random sampling of intact classes (grades 4-6). We adapted the CHERRIES (Checklist for Reporting Results of Internet E-Surveys) framework. Phase 1 (40/81, 49.4% female) includes content validity via 2 rounds of expert review, construct validity using Rasch analysis, and 1-month test-retest reliability. Phase 2 (42/85, 49.4% female) includes a randomized crossover design (10-minute washout), equivalence assessed with an intraclass correlation coefficient (ICC), Bland-Altman analysis, decision consistency, and superiority using the chi-square test (data quality) and 2-tailed independent t tests (enjoyment and preference on 5-point Likert scales). Phase 3 (41/85, 48.2% female) includes a single-group pre-post design, with responsiveness evaluated using generalized linear mixed models (GLMMs) with random intercepts. Analyses used R software with an α of .05. Results: Phase 1 had perfect content validity (scale-level content validity index/average [S-CVI/Ave]=1.00), and the Rasch analysis supported unidimensionality (eigenvalues 2.14-2.25, ratios 0.34-0.40) and item fit (mean infit/outfit mean-square [MNSQ] 0.83-1.23). The test-retest ICCs were 0.76 to 0.92 (95% CI 0.64-0.95). Phase 2 equivalence was confirmed (ICC 0.94-0.99, 95% CI 0.90-0.99; κ=0.87-0.97). Validity rates were 100% (42/42, 95% CI 91.6%-100%) for #Gamified vs 90.7% (39/43, 95% CI 77.4%-96.9%) for paper (P=.13). The #Gamified tool yielded higher enjoyment (mean 4.26, SD 0.94 vs mean 2.46, SD 0.85; Cohen d=2.00, 95% CI 1.46-2.55; P

Background: Augmented reality (AR) glasses with waveguide optics and facial recognition may enhance patient identification and clinical workflows. However, their #usability among physicians has not yet been evaluated. Objective: The objective of this study was to assess the #usability and acceptability of prototype AR glasses with facial recognition in a simulated outpatient setting. Methods: In May 2025, 14 urologists from a single institution tested prototype AR glasses developed by Cellid Inc. The device incorporates waveguide-based optical displays, a miniature camera, and deep learning–based facial recognition software. Physicians identified preregistered mock patients, with demographic and clinical data displayed on the right lens. After the trial, participants completed an anonymous questionnaire on #usability, comfort, visibility, and self-reported symptoms using 3- or 5-point Likert scales. Results: Eleven participants (79%; 95% CI 52%‐92%) found the device helpful in performing clinical procedures, and 12 (86%; 95% CI 60%‐96%) believed it improved patient safety. Most participants adapted quickly to wearing the device, and 79% (95% CI 52%‐92%) expressed a willingness to use it in future practice. Four of 13 respondents (31%; 95% CI 13%‐58%) selected “somewhat tired” for eye fatigue, and only 1 participant experienced minor and transient physical symptoms including headache and nausea. Three of 13 respondents (23%; 95% CI 8%‐50%) rated the glasses as slightly heavy, and among the 5 participants who wore prescription glasses, 4 (80%; 95% CI 38%‐96%) reported difficulty wearing the prototype over their glasses. Surrounding-environment visibility and perceived facial recognition responsiveness were generally favorable; however, 8 of 13 respondents (62%; 95% CI 36%‐82%) rated the displayed patient information as slightly difficult to see. Conclusions: In this pilot study, AR glasses with facial recognition were well accepted by physicians. This technology has demonstrated perceived clinical utility and has the potential to assist medical professionals, though further refinement and larger clinical evaluations are needed.

Sexually transmissible enteric infections are increasingly recognised among #Gay, #Bisexual and other men who have sex with men, challenging the traditional separation between sexually transmitted and enteric pathogens. Building on recent work demonstrating the feasibility of longitudinal sampling for these infections, we highlight the need to translate emerging #Epidemiological knowledge into gastrointestinal diagnostic practice. Patients with sexually transmissible enteric infections may present with proctocolitis mimicking inflammatory bowel disease, risking diagnostic delay, inappropriate immunosuppression and missed opportunities for transmission control. We propose a risk-adapted diagnostic approach incorporating sexual history taking, initial rectal STI testing, selective multiplex gastrointestinal molecular testing and judicious use of colonoscopy. Such pathways may support precision medicine, antimicrobial stewardship and #PublicHealth #Surveillance.

Background: Precise localization of the mandibular condyle is essential for extraoral therapeutic procedures such as low-level laser therapy (LLLT) in patients with skeletal class II malocclusion undergoing myofunctional appliance therapy. Conventional localization methods rely on palpation and visual estimation, which are operator dependent and may compromise reproducibility and treatment outcomes. To address this limitation, a spectacle-mounted extraoral condyle locating device (EOCLD) has been developed to facilitate accurate and consistent condylar targeting. Objective: This #Study aims to develop and validate the EOCLD for precise condylar localization and evaluate its impact on the clinical efficacy of LLLT in patients with skeletal class II malocclusion undergoing myofunctional appliance therapy. Methods: This 2-phase #Study will be conducted in patients aged 10 to 13 years in a growth phase with skeletal class II malocclusion. In phase 1, the EOCLD will be evaluated for accuracy and reproducibility using standardized lateral cephalometric imaging. In phase 2, a prospective #RCT #ClinicalTrial will be conducted in which participants undergoing myofunctional appliance therapy will be randomly allocated to either EOCLD-guided LLLT or conventional unguided LLLT. Skeletal and dentoalveolar changes will be assessed using standardized cephalometric analyses, while condylar growth and mandibular changes will be evaluated using pretreatment and posttreatment cone beam computed tomography (CBCT) imaging. Treatment duration, patient-reported comfort, and compliance will also be assessed. Results: The #Study is self-funded and received approval from the institutional ethics committee on November 28, 2025. Phase 1 data collection took place from May 21 to August 30, 2026, with EOCLD validation performed in 50 participants. As of September 2026, the data are undergoing statistical analysis. Phase 2 is scheduled to commence on December 1, 2026. Conclusions: This #Study will establish the clinical validity of the EOCLD and determine whether guided LLLT enhances treatment efficiency and skeletal outcomes in skeletal class II therapy. Trial Registration: Clinical Trials Registry–India CTRI/2026/02/102982; https://tinyurl.com/4t3bwrnz

Background: Obsessive-compulsive disorder (OCD) is a common psychiatric disorder, with two-thirds of affected individuals reporting severe impairment. Despite its substantial burden and moderate heritability, the etiology of OCD remains poorly understood, and treatments are often suboptimal. Although recent genome-wide association studies (GWAS) have identified some risk loci, much of the genetic architecture of OCD remains undiscovered, underscoring the need for scalable #Approaches to identify large, well-defined patient cohorts. Objective: This study aimed to develop and validate a scalable electronic #Health record (EHR)–based phenotyping algorithm for identifying OCD cases to support large-scale genetic and translational research. Methods: We leveraged EHR-linked biobank data from 2 large hospital systems, namely Vanderbilt University Medical Center (VUMC) and Mass General Brigham (MGB), to develop a high-throughput phenotyping algorithm integrating diagnostic codes, medication records, and natural language processing (NLP) of clinical notes. Algorithm performance was evaluated through expert chart review, and genetic analyses were performed in individuals of European genetic ancestry using the polygenic scores (PGS) of OCD, major #depressive disorder (MDD), and height derived from the most recent GWAS. Results: Expert chart reviews demonstrated our algorithm combining both () codes and NLP achieved the highest positive predictive values (PPV) for OCD case identification (0.84 at VUMC; 0.91 at MGB) compared to using either codes or NLP alone, albeit with reduced case yield. At both sites, algorithm-defined OCD cases of European genetic ancestry showed significantly higher OCD PGS than controls. In sensitivity analyses adjusting for MDD status, OCD PGS associations were more robust than MDD PGS associations, while height PGS showed no association, supporting the genetic plausibility and relative specificity of the phenotype. Conclusions: This study presents a scalable and cost-efficient EHR-based #Approach for identifying OCD cases across #Health systems. The algorithm achieves high PPV, and among individuals of European genetic ancestry, algorithm-defined cases show significant OCD PGS enrichment, supporting its utility for large-scale genetic studies and advancing understanding of the disorder’s complex etiology.

Background: #health data management during disasters enables responders to assess the needs of survivors, efficiently allocate resources, and monitor survivors’ #health conditions. However, government agencies need to interpret #health data in real time during disasters because the volume and complexity of such data can hinder timely decision-making. Objective: In this study, we aimed to develop a visual interface for Fast #healthcare Interoperability Resources (FHIR)–based data from various sources, using the daily report form of the World #health Organization (WHO) Emergency #medical Teams Minimum Data Set (EMT MDS) as a template and ensuring compliance with the SATUSEHAT platform. Methods: The proposed solution involves creating a dashboard application within a web-based system to visualize #health data using FHIR, leveraging the WHO EMT MDS disaster profile. The interface includes Overview, Location, and Demography pages that display information in tables, graphs, and maps. This application was developed using JavaScript and React. The dataset consisted of 13,300 synthetic #patient records generated using Synthea, subsequently modified with GPT-5, and stored on the #health Level 7 (HL7) Application Programming Interface (HAPI) FHIR server. Results: We developed a dashboard application based on the WHO EMT MDS Daily Reporting Form comprising 3 main pages: Overview, Demography, and Location. The Demography page contains several sections, including the total number of #patients, #health conditions, outcomes, relationships, and protection. The Location page lists all #health care facilities, along with their corresponding locations, on a map. Each Location page displays detailed information regarding the organization and demographics of the respective location. Conclusions: The dashboard application demonstrates that #health data collected in accordance with FHIR standards can be integrated and presented in a form suitable for disaster response. This dashboard has the potential to support disaster management agencies by enabling real-time, data-driven decision-making during disaster response.

Background: Administrative workload in general practice limits time for direct patient care. AI-assisted documentation has been proposed as a way to reduce the documentation burden, but evidence from routine primary care settings remains limited. Objective: This study aimed to evaluate general practitioners’ (GPs) acceptance of AI-assisted documentation and its association with documentation time and clinical note quality in routine Danish general practice. Methods: We conducted a quantitative pragmatic pre-post quality improvement evaluation in Danish general practice. A total of 20 GPs documented 239 consultations before and 236 consultations after implementation of an AI-assisted documentation system. Documentation quality, structure, clinical clarity, and documentation time categories were self-assessed using standardized audit forms completed immediately after each consultation. Technology acceptance and #usability were assessed using the technology acceptance model (TAM) and the System #usability Scale (SUS). Results: Self-assessed documentation structure increased from 3.99 to 4.45, while self-reported documentation time categories decreased from 2.85 to 2.29. Technology acceptance and #usability were high (TAM domain means 3.76-4.19; SUS mean 77.5). GP-level paired analyses showed moderate improvements in structure and clarity and a reduction in documentation time. Combined blinded external assessments showed higher postimplementation scores for quality, structure, and clinical clarity, although reviewer-specific ratings diverged, and interrater reliability was low. The association between documentation time and perceived quality was negligible. TAM and SUS indicated high clinician acceptance. Conclusions: AI-assisted documentation was associated with lower self-reported documentation time categories while maintaining or modestly improving perceived clinical note quality. These findings support the feasibility of AI-assisted documentation in primary care, while highlighting the need for controlled studies with objective time measurement and longer follow-up.

Background: The ready-made garments (RMG) industry is a crucial component of Bangladesh’s economy, using over 4 million workers from low-income backgrounds who often neglect their health care needs. Historically, this sector has faced criticism for labor exploitation, unsafe working conditions, and rights violations, as highlighted by tragic accidents resulting in loss of life. Compliant factories may uphold higher labor standards, but many noncompliant factories expose workers to poor conditions, increasing health risks. The COVID-19 pandemic intensified vulnerabilities, leading to widespread factory closures and job losses, increased health risks, and left millions of workers without wages. Although the government attempted to provide some relief, it fell short in offering job security, social protection, health services, and emergency assistance. Limited access to technology due to digital literacy gaps further hinders these workers, who primarily use basic mobile phones for communication rather than accessing health or emergency services. Thus, there is a pressing need to develop a sustainable system that capitalizes on their existing technological familiarity. Objective: Our goal was to gain a deep understanding of RMG workers’ experiences, focusing on their work environments, technological interactions related to health care management, and the impacts of COVID-19 on their circumstances. This understanding aims to inform the design of a technology-based framework that is both sustainable and contextual. Methods: In phase 1, we conducted in-person interviews with 55 RMG workers, comprising 32 female and 23 male participants from urban and suburban areas of Dhaka and Gazipur, before the pandemic. Participants were aged between 18 and 40 years. In phase 2, we reconnect with 12 phase 1 participants during the pandemic and also consulted 3 stakeholders from RMG factories via phone. Each interview, conducted in Bengali, was recorded with consent, totaling 846 minutes of discussion that were translated and transcribed. Thematic analysis was used to analyze the results. Results: Insights gathered revealed variations in working conditions, personal experiences, perceptions of health care, lifestyle choices, and technology use tied to factory compliance. Workers at compliant factories had better health care support and used technology more effectively than those in noncompliant settings. The pandemic drastically altered the landscape for all workers, heightening health and safety concerns and shortages of emergency assistance. The RMG sector faces significant challenges, highlighting the urgent need for targeted emergency relief and health services. Conclusions: This research examined the challenges and technology use among RMG workers during the pandemic, with a focus on health care perspectives. Based on our findings, we proposed a technology-based framework called VOICE (Virtual Outlet for Integrated Community Engagement) that connects workers with service providers through a simplified interface. This aims to assist marginalized communities during emergencies and enhance their overall well-being.

Background: Training mental health clinicians to conduct standardized clinical assessments is challenging due to a lack of scalable, realistic practice opportunities. Traditional methods often fail to prepare trainees for the variability and complexity of real-world patient interactions, potentially impacting data quality in clinical trials. This paper introduces a novel approach to address this training gap using large language model (LLM)–based interview simulations. Objective: This study aimed to develop and validate a voice-enabled virtual patient simulation system as a proof of concept. We described the development of the system and evaluated whether it could generate virtual patients that (1) accurately adhered to predefined clinical profiles, (2) maintained a coherent and consistent narrative, and (3) produced dialogue that is perceived as realistic. Methods: We implemented a system that used an LLM to simulate patients with specified symptom profiles, demographic backgrounds, and distinct communication styles. The system’s performance was analyzed through a mixed methods evaluation, which included a formal assessment by 5 experienced clinical raters who conducted simulated structured Montgomery-Åsberg Depression Rating Scale (MADRS) interviews with 4 virtual patient personae, scored them on the scale, and provided qualitative feedback on the system’s clinical plausibility, narrative cohesion, and dialogue realism. Results: Across 20 interviews, the virtual patients demonstrated reasonable adherence to their configured clinical profiles, with human rater scores falling near their predefined score configurations. The mean item difference between MADRS rater scores and configured scores was 0.52 (SD 0.75); interrater reliability for the total score was 0.90 (95% CI 0.68‐0.99). Expert raters consistently gave average ratings of “agree” to “strongly agree” when asked to evaluate the qualitative realism and cohesion of the virtual patients. Conclusions: LLM-powered virtual patient simulations offered a promising, scalable tool for training clinicians in standardized clinical assessment. This pilot study provides initial evidence for the system’s ability to produce clinically relevant practice scenarios with reasonable fidelity.