JMIR Publications
A leading open access publisher of digital health research and champion of open science. With a focus on author advocacy and research amplification, JMIR Publications partners with researchers to advance their careers and maximize the impact of their work.
- Artificial Intelligence for Clinical Competency Assessment: A Scoping Review of Methods and Applications (preprint) #openscience #PeerReviewMe #PlanP
Artificial Intelligence for Clinical Competency Assessment: A Scoping Review of Methods and Applications
Date Submitted: Feb 3, 2026. Open Peer Review Period: Feb 6, 2026 - Apr 3, 2026.dlvr.it - "Review of Mobile Applications for Women’s Physiology Tracking" (preprint) #openscience #PeerReviewMe #PlanP
"Review of Mobile Applications for Women’s Physiology Tracking"
Date Submitted: Feb 5, 2026. Open Peer Review Period: Feb 6, 2026 - Apr 3, 2026.dlvr.it - Reminder>> The Effect of Hydrotherapy on Work Related Stress Among Academics at a University of Technology in eThekwini, South Africa: A quantitative longitudinal #Protocol (preprint) #openscience #PeerReviewMe #PlanP
The Effect of Hydrotherapy on Work Related Stress Among Academics at a University of Technology in eThekwini, South Africa: A quantitative longitudinal #Protocol
Date Submitted: Jan 31, 2026. Open Peer Review Period: Feb 2, 2026 - Mar 30, 2026.dlvr.it - Reminder>> Nursing Students' Awareness, Perceptions, and Readiness for Artificial Intelligence Integration: An Extended UTAUT Analysis (preprint) #openscience #PeerReviewMe #PlanP
Nursing Students' Awareness, Perceptions, and Readiness for Artificial Intelligence Integration: An Extended UTAUT Analysis
Date Submitted: Jan 26, 2026. Open Peer Review Period: Feb 3, 2026 - Mar 31, 2026.dlvr.it - New JMIR Diabetes: Content Validation of an Electronic Health Record–Based #diabetes Self-Management Support Tool for Older Adults With Type 2 #diabetes: Qualitative Study
Content Validation of an Electronic Health Record–Based #diabetes Self-Management Support Tool for Older Adults With Type 2 #diabetes: Qualitative Study
Background: Older adults with #diabetes frequently access their electronic health record (EHR) notes but often report difficulty understanding medical jargon and nonspecific self-care instructions. To address this communication gap, we developed SEE-#diabetes (Support-Engage-Empower-#diabetes), a patient-centered, EHR-integrated #diabetes self-management support tool designed to embed tailored educational statements within the Assessment and Plan section of clinical notes. Objective: This study aimed to validate the clarity, relevance, and alignment of SEE-#diabetes content with the Association of #diabetes Care & Education Specialists 7 Self-Care Behaviors™ (ADCES7) framework from the perspectives of older adults and clinicians. Methods: An interdisciplinary team conducted expert reviews and qualitative interviews with 11 older adults with #diabetes and 8 clinicians practicing in primary care (family medicine) and specialty #diabetes care settings at a Midwestern academic health center. Patients evaluated the readability and relevance of the content, while clinicians assessed clarity, sufficiency, and potential clinical utility. Interview data were analyzed using inductive thematic analysis, and descriptive statistics summarized participant characteristics. Results: Patients (mean age 72 years; mean #diabetes duration 26 years) reported that the SEE-#diabetes statements were clear, relevant, and written in plain language that supported understanding of self-care recommendations. Clinicians (mean 13 years of #diabetes care experience) viewed the content as concise, clinically appropriate, and well aligned with patient self-management goals and the ADCES7 framework. Both groups identified the tool’s potential to enhance patient engagement and patient–clinician communication, while noting opportunities to improve the specificity of language, particularly within medication-related content. Conclusions: SEE-#diabetes demonstrated content validity as a practical, patient-centered digital health tool for supporting #diabetes self-management communication within EHR clinical notes. The findings support its use as a complementary approach to reinforce self-care communication in routine clinical practice and highlight areas for refinement to enhance personalization.dlvr.it - New in JMIR MedEdu: Digital Choice Architecture in medical education #mededu: Applying Behavioral Economics to Online Learning Environments
Digital Choice Architecture in medical education #mededu: Applying Behavioral Economics to Online Learning Environments
Healthcare has widely adopted behavioral economics to influence clinical practice, with documented success using defaults and social comparison feedback in electronic health records. Yet online medical education #mededu, now the dominant modality for continuing professional development, remains designed on assumptions of rational learning that behavioral science has disproven in clinical contexts. This viewpoint examines the paradox of applying sophisticated behavioral insights to clinical work while designing digital learning environments as if learners are immune to cognitive limitations. We propose digital choice architecture for medical education #mededu: intentional integration of behavioral design principles into learning management systems and online platforms. Drawing from clinical nudge units and implementation science, we demonstrate how defaults, social norms, and commitment devices can be systematically applied to digital continuing education. As medical education #mededu becomes increasingly technology-mediated, behavioral science provides theoretical foundation and practical tools for designing online learning environments that align with how clinicians actually make decisions.dlvr.it - Reminder>> Impact of the Virtual Character (Avatar) in a task-focused #Medical MR #Telecollaboration: post-hoc #Study of four randomized cross-over trials (preprint) #openscience #PeerReviewMe #PlanP
Impact of the Virtual Character (Avatar) in a task-focused #Medical MR #Telecollaboration: post-hoc #Study of four randomized cross-over trials
Date Submitted: Jan 28, 2026. Open Peer Review Period: Feb 3, 2026 - Mar 31, 2026.dlvr.it - Frequency, Clinical Outcomes, Anatomical Distribution, and Management Implications of Thromboembolic Complications Associated with Cardiac Myxomas: #Protocol for a Systematic Review (preprint) #openscience #PeerReviewMe #PlanP
Frequency, Clinical Outcomes, Anatomical Distribution, and Management Implications of Thromboembolic Complications Associated with Cardiac Myxomas: #Protocol for a Systematic Review
Date Submitted: Feb 5, 2026. Open Peer Review Period: Feb 6, 2026 - Apr 3, 2026.dlvr.it - New JMIR Diabetes: Predictors of Glycemic Response to Sulfonylurea Therapy in Type 2 #diabetes Over 12 Months: Comparative Analysis of Linear Regression and Machine Learning Models
Predictors of Glycemic Response to Sulfonylurea Therapy in Type 2 #diabetes Over 12 Months: Comparative Analysis of Linear Regression and Machine Learning Models
Background: Sulphonylureas are commonly prescribed for managing type 2 #diabetes, yet treatment responses vary significantly among individuals. Although advances in machine learning (ML) may enhance predictive capabilities compared to traditional statistical methods, their practical utility in real-world clinical environments remains uncertain. Objective: This study aimed to evaluate and compare the predictive performance of linear regression models with several ML approaches for predicting glycaemic response to sulphonylurea therapy using routine clinical data, and to assess model interpretability using SHapley Additive exPlanations (SHAP) analysis as a secondary analysis. Methods: A cohort of 7,557 individuals with type 2 #diabetes who initiated sulphonylurea therapy was analysed, with all patients followed for one year. Linear and logistic regression models were used as baseline comparisons. A range of ML models was trained to predict the continuous change in HbA1c levels and the achievement of HbA1cdlvr.it - Mixed Methods Studies Examining the Physical Activity Practices of African American Women: A Methodological Scoping Review #Protocol (preprint) #openscience #PeerReviewMe #PlanP
Mixed Methods Studies Examining the Physical Activity Practices of African American Women: A Methodological Scoping Review #Protocol
Date Submitted: Feb 6, 2026. Open Peer Review Period: Feb 6, 2026 - Apr 3, 2026.dlvr.it - Reminder>> Evaluating a Dual #Digital Cognitive Behavioral Therapy and #Health & Wellness Coaching Intervention for #Anxiety and #Depression: Pilot #Study (preprint) #openscience #PeerReviewMe #PlanP
Evaluating a Dual #Digital Cognitive Behavioral Therapy and #Health & Wellness Coaching Intervention for #Anxiety and #Depression: Pilot #Study
Date Submitted: Jan 29, 2026. Open Peer Review Period: Feb 3, 2026 - Mar 31, 2026.dlvr.it - New JMIR MedInform: Enhancing Anesthetic Depth Assessment via Unsupervised Machine Learning in Processed Electroencephalography Analysis: Novel Methodological Study
Enhancing Anesthetic Depth Assessment via Unsupervised Machine Learning in Processed Electroencephalography Analysis: Novel Methodological Study
Background: General anesthesia induces temporary loss of consciousness, and electroencephalography (EEG)-based monitoring is crucial for tracking this state. However, EEG-based indices that are used to assess the depth of anesthesia can be influenced by various factors, potentially leading to misleading outputs. Objective: This study aimed to explore the feasibility of using unsupervised machine learning on processed EEG data to enhance anesthetic depth assessment. Methods: Over 16,000 data points were collected from #patients who underwent elective lumbar spine surgery. The EEG data were processed using a bandpass filter and Fast Fourier Transform for power spectral density estimation. Unsupervised machine learning with Fuzzy C-means clustering was applied to categorize anesthesia depth into three clusters: slight, proper, and deep. Results: Fuzzy C-means clustering identified distinct anesthesia depth groups based on delta, alpha, theta, and beta band power ratios. Visual representations validated the clustering results, which were consistent across individual #patient data. The figures demonstrate the application of clustering to EEG data, revealing detailed anesthesia depth estimations. Conclusions: This study developed a machine learning-based methodology for anesthesia depth assessment, demonstrating feasibility and providing preliminary insights into classification, visualization, and #patient-specific management. By applying Fuzzy C-Means clustering to processed EEG data, this approach enhances anesthesia depth understanding and integrates with existing monitoring modalities.dlvr.it - JMIR HumanFactors: Opportunities for Improved Device Design Based on Central Line Placement Practices: Contextual Inquiry Study
Opportunities for Improved Device Design Based on Central Line Placement Practices: Contextual Inquiry Study
Background: Central venous catheters (CVCs) are indispensable to contemporary critical care, perioperative management, and emergency resuscitation, yet their insertion remains fraught with preventable harm and inefficiency. Objective: This study aimed to identify all areas of CVC placement that can be improved through device design using human-centered design and qualitative research methods. Methods: This qualitative study was a contextual inquiry of CVC placement, which included observation alongside brief face-to-face interviews with physicians. It was aimed at providing a depth of understanding using evidence to demonstrate causality. This study was conducted at 3 hospitals in the emergency department, the intensive care unit, and the operating rooms. Where possible and with additional consent, sessions were recorded in video or still photography, or at times both. This study included 19 observations and 24 interviews. Results: In this study, the approach to CVC insertion was consistent across hospitals and care environments, with moderate variability spanning a few sections, such as suture and dressing use or lack thereof in specific care environments. The described and observed difficulties leave room for improvement in device design. The results of this study indicated that there are 34 discrete steps to placing a CVC line, with most time spent during sterile preparation. As a result of the device or kit design, challenges were observed. These included missing essential materials from kits, difficulty distinguishing between nonsterile and sterile items, challenges with lidocaine ampules, patient claustrophobia from draping, and a lack of user preference for kit contents. Additional challenges included obscured ultrasound views, kinked guidewires, overall procedural untidiness, and considerable waste management issues. Conclusions: An intuitive kit that aligns with predictable human behavior and eliminates unnecessary multistep detours can reduce novice failure rates, cognitive load, and practice inconsistency, and it could also curb nonrecyclable waste from “backup” kits opened for a single missing item. By reframing CVC systems as sociotechnical solutions rather than static assortments of parts, the same design moves that minimize improvisation and coordination errors for physicians may also reduce dwell time and manipulation events for patients, thereby advancing the core triad of safety, procedural efficacy, and everyday #usability. By examining how clinicians place central lines, this study reveals modifiable design flaws that perpetuate risk despite decades of procedural standardization. Contextual inquiry provides the evidentiary bridge between clinical imperatives to reduce complications and the practical realities of device use. Embedding such investigations at the outset of design and iteratively throughout product life cycles offers a path toward safer, more efficient, and more humane central venous access for both patients and providers.dlvr.it - New JMIR MedInform: AI Scribes: Are We Measuring What Matters?
AI Scribes: Are We Measuring What Matters?
AI scribes, software that can convert speech into concise clinical documents, have achieved remarkable clinical adoption at a pace rarely seen for #digital technologies in #healthcare. The reasons for this are understandable: the technology works well enough, it addresses a genuine pain point for clinicians, and it has largely sidestepped regulatory requirements. In many ways, clinical adoption of AI scribes has also occurred well ahead of robust evidence of their safety and efficacy. The papers in this theme issue demonstrate real progress in the technology and evidence of its benefit: documentation times are reported to decrease when using scribes, clinicians report feeling less burdened, and the notes produced are often of reasonable quality. Yet as we survey the emerging evidence base, there remains one outstanding and urgent unanswered question: Are AI scribes safe? We need to know the clinical outcomes achievable when scribes are used compared to other forms of note taking.dlvr.it - JMIR Public Health: Problematic Alcohol Use Among Adolescents in Germany: Representative Cross-Sectional Study
Problematic Alcohol Use Among Adolescents in Germany: Representative Cross-Sectional Study
Background: Alcohol is a widely used psychoactive substance, and its use constitutes a major #PublicHealth challenge due to its immediate and long-term adverse effects on various health-related outcomes. Adolescence has been identified as a particularly vulnerable phase regarding alcohol use. Although consumption rates in this age group have declined in Germany over the past decades, a plateau has been reached, and there is a continued need for interventions to further reduce consumption rates. Objective: This study aimed to assess problematic alcohol use among adolescents in Germany and explore associations with sociodemographic and psychosocial characteristics, particularly with health literacy, to inform future interventions tailored to the specific needs of this target group. Methods: In a cross-sectional quota-based survey, 2006 adolescents (aged 12-17 years) completed an online survey (n=1406) or face-to-face interview (n=600) assessing the frequency of weekly alcohol use, the presence of problematic alcohol use (German version of the Car-, Relax-, Alone-, Forget-, Friends-, Trouble- questionnaire [CRAFFT-d]), sociodemographic information, and health literacy (European Health Literacy Survey instrument [HLS-EU-Q16]). Based on their CRAFFT-d and HLS-EU-Q16 scores, participants were identified as exhibiting problematic alcohol use (vs no problematic alcohol use) and inadequate or problematic health literacy levels (vs adequate health literacy levels), respectively. Chi-square tests were computed to analyze differences between different groups (as defined by the sociodemographic factors, weekly alcohol consumption frequency, and health literacy) in terms of problematic alcohol use (binary CRAFFT-d outcome). Results: Approximately 20% (390/2006) of the participants reported consuming alcohol on at least 1 day per week, and 12.7% (255/2006) of the sample met the CRAFFT-d screening criterion for problematic alcohol use. Problematic alcohol use was significantly associated with gender (χ21=20.96, V=0.10; Pdlvr.it - New in JMIR Aging: Developing Consumer Consensus on Remote Assessment and Management of Physical Function in Older Adults (RAMP): International Modified Delphi Process #RemoteHealth #DigitalHealth #ElderlyCare #PhysicalFunction #HealthResearch
Developing Consumer Consensus on Remote Assessment and Management of Physical Function in Older Adults (RAMP): International Modified Delphi Process
Background: Remote health care delivery, including the use of digital health interventions, is emerging as a tool for assessing and managing physical function, but its design and implementation often overlook the needs and preferences of older adult end users. Objective: The primary aim of this modified Delphi process was to develop consumer consensus on preferences for remote assessment and management of physical function in older adults. Methods: Research and consumer experts of the Remote Assessment and Management of Physical Function in Older Adults (RAMP) Working Group co-developed the Round 1 Delphi survey, which was advertised to consumers (adults aged ≥60 years) via international clinical and research networks and social media between August and November 2023. The online survey presented 23 Delphi statements for which respondents reported their level of agreement using an 11-point Likert scale (0-10; scores ≥7 indicated agreement). Statements were classified as having “strong agreement” and achieving consensus if ≥80% of participants indicated agreement. Statements classified as having “moderate” (70%-80% of participants indicated agreement) or “low” (dlvr.it - Consensus-Based Recommendations for Optimizing Diversified TCM Data Collection during Clinical Work (preprint) #openscience #PeerReviewMe #PlanP
Consensus-Based Recommendations for Optimizing Diversified TCM Data Collection during Clinical Work
Date Submitted: Feb 5, 2026. Open Peer Review Period: Feb 6, 2026 - Apr 3, 2026.dlvr.it - Occam’s Razor in AI-assisted complex diagnosis: a comparative effectiveness #Study of single large language models versus multi-agent systems in resource-constrained primary care settings (preprint) #openscience #PeerReviewMe #PlanP
Occam’s Razor in AI-assisted complex diagnosis: a comparative effectiveness #Study of single large language models versus multi-agent systems in resource-constrained primary care settings
Date Submitted: Feb 5, 2026. Open Peer Review Period: Feb 6, 2026 - Apr 3, 2026.dlvr.it - How pandemics have reshaped respiratory virus data landscape in Europe? A scoping review (preprint) #openscience #PeerReviewMe #PlanP
How pandemics have reshaped respiratory virus data landscape in Europe? A scoping review
Date Submitted: Feb 5, 2026. Open Peer Review Period: Feb 6, 2026 - Apr 3, 2026.dlvr.it - Associations Between Hospital Structural Characteristics and Adoption of Public Health Data Integration and Automation: National Cross-Sectional proofsStudy
Associations Between Hospital Structural Characteristics and Adoption of Public Health Data Integration and Automation: National Cross-Sectional proofsStudy
Background: Public health data integration and automation systems are crucial for effective healthcare delivery and public health surveillance. However, the factors associated with hospitals' adoption and successful implementation remain inadequately explored. Objective: To examine how hospital characteristics influence the adoption of public health data integration and automation. Methods: We analyzed 2,277 hospitals from the 2023 American Hospital Association Annual Survey and its Health Information Technology supplement, focusing on six public health reporting categories. Multivariable logistic regression models were used to examine the relationship between hospital characteristics and two primary outcomes: active electronic data submission and use of automated transmission processes. Results: System-affiliated and not-for-profit hospitals demonstrated significantly higher rates of electronic data submission and automated reporting across most categories (ORs ranging from 1.70-2.27, pdlvr.it - New in JMIR mhealth: Features of #Mobile #Health Apps for Tobacco Cessation That Appeal to Black Adults Who Use Tobacco Products: Focus Group Study
Features of #Mobile #Health Apps for Tobacco Cessation That Appeal to Black Adults Who Use Tobacco Products: Focus Group Study
Background: #Mobile #Health (#mHealth) interventions show promise in supporting tobacco cessation. However, Black adults who use tobacco products are not well represented in #mHealth studies for tobacco cessation and their preferred features of #mHealth apps are not well known. Identifying types of #mHealth #App features for tobacco cessation preferred by Black adults is critical to developing a culturally adapted #App, with increased uptake by the target population. Objective: The objective of this study was to identify features of #mHealth apps for smoking cessation that appeal to Black adults who use tobacco products. Methods: A comprehensive list of features of #mHealth apps for tobacco cessation was developed based on previous research and a review of existing #mHealth literature. Through a content analysis, this list was divided into subgroups and used to develop a focus group guide. Eligible focus group participants included people who reported current use of a tobacco product, identified as being African American or Black, and were 21 years old or older. Participants discussed their opinions about different #App features, including what features they felt would increase the use of an #App by Black adults. We conducted a thematic content analysis of resulting transcripts. Results: Forty adults aged 21 – 69 years old (mean age of 43 years) participated in the eight focus groups. Four central themes emerged: 1) Participants wanted representation and inclusivity through personalization and featuring people with similar lived experiences; 2) Participants desired the #App to feature a diversity of experiences rather than solely focusing on racial identity or excessive targeting of the Black community; 3) Participants desired accountability through trusted connections and #App tracking capability; and 4) Encouragement and motivation were more salient incentives than monetary rewards. Conclusions: Black people who use tobacco products prefer a tobacco cessation #App with features that are inclusive, relatable, supportive and motivating. These findings can serve as the groundwork for the development of a #mHealth #App that will appeal to Black adults, potentially increasing #App use, successful cessation and increased #Health equity.dlvr.it - JMIR Res Protocols: Virtual Reality to Improve Pain Management and Mental Health in Stroke Survivors With Chronic Pain: #Study #Protocol for a Feasibility #RCT #ClinicalTrial on Virtual Reality-Acceptance and Commitment Therapy
Virtual Reality to Improve Pain Management and Mental Health in Stroke Survivors With Chronic Pain: #Study #Protocol for a Feasibility #RCT #ClinicalTrial on Virtual Reality-Acceptance and Commitment Therapy
Background: Studies suggest that 40%-65% of stroke survivors develop chronic post-stroke pain (CPSP), which severely affects their quality of life and mental health. Empirical evidence suggests that existing treatments often fall short, underscoring the need for innovative, integrative interventions. Virtual Reality (VR) seems to provide valuable tools in stroke rehabilitation. Also, contextual-behavioural psychological approaches, such as Acceptance and Commitment Therapy (ACT), offer promising pain management and mental health resources, which seem to be feasible in VR formats. However, their combined application in CPSP remains unexplored. Objective: This #Study #Protocol describes the VR-ACT #Study, which will test the feasibility and preliminary efficacy of an 8-week VR-ACT program for CPSP. Methods: A pilot #RCT #ClinicalTrial (N = 30) will compare a VR-based ACT intervention with a sham-VR control. The #Study will follow a mixed-methods approach. Quantitative outcomes include pain intensity, psychological symptoms, and quality of life (via self-report measures), and brain network connectivity of the Triple Network (via fMRI). Feasibility will be evaluated through adherence, engagement, and acceptability. Qualitative feedback will be collected post-intervention. Results: This #Study was funded by the Portuguese Foundation for Science and Technology (FCT) in February 2025. Data collection is expected to start in December 2025 and end in June 2026. Results are expected to be published in the fall/winter of 2026/2027. Conclusions: This trial is expected to corroborate the hypothesis that a VR-delivered ACT program is a feasible, acceptable, and potentially effective tool to support pain self-management and mental health in CPSP patients, laying the groundwork for larger, multicenter trials. Clinical Trial: ClinicalTrials.gov NCT06990646; https://clinicaltrials.gov/#Study/NCT06990646dlvr.it - Reminder>> Cultural Adaptation of a mobile #Health application for Aboriginal and/or Torres Strait Islander mothers and families: A Qualitative #Study (preprint) #openscience #PeerReviewMe #PlanP
Cultural Adaptation of a mobile #Health application for Aboriginal and/or Torres Strait Islander mothers and families: A Qualitative #Study
Date Submitted: Feb 2, 2026. Open Peer Review Period: Feb 3, 2026 - Mar 31, 2026.dlvr.it - Reminder>> Extracting Quality of Life Information from Forum Posts Using Open-Source Large Language Models: Feasibility #Study (preprint) #openscience #PeerReviewMe #PlanP
Extracting Quality of Life Information from Forum Posts Using Open-Source Large Language Models: Feasibility #Study
Date Submitted: Feb 2, 2026. Open Peer Review Period: Feb 3, 2026 - Mar 31, 2026.dlvr.it - Socratic Prompting Breakthrough: How Question-Based Prompts Boost Claude and ... (mentions @jmirpub)
Socratic Prompting Breakthrough: How Question-Based Prompts Boost Claude and ...
... Journal of Medical Internet Research. From a technical standpoint, Socratic prompting leverages chain-of-thought reasoning, a concept popularized ...dlvr.it - Global Health Watch: Foreign Affairs Bill Passes, Aid Cuts Projected to Cause Millions of ... - AVAC (mentions @jmirpub)
Global Health Watch: Foreign Affairs Bill Passes, Aid Cuts Projected to Cause Millions of ... - AVAC
... Journal of Medical Internet Research; A Year of Disruption: 5 Resources to Understand Foreign Aid Cuts—Partners in Health; NIH rolls back red tape ...dlvr.it - Interactions of Technology and Obsessive-Compulsive Disorder Symptomatology in Adults (mentions @jmirpub)
Interactions of Technology and Obsessive-Compulsive Disorder Symptomatology in Adults
Journal of Medical Internet Research · Journal of Medical Internet Research 10894 articles · JMIR Research Protocols 5406 articles · JMIR Formative ...dlvr.it - JMIR Formative Res: Evaluating a Wearable-Based Pain Monitoring System in Palliative #Cancer Care: #usability and #feasibility Study #CancerCare #PalliativeCare #PainManagement #WearableTech #HealthInnovation
Evaluating a Wearable-Based Pain Monitoring System in Palliative #Cancer Care: #usability and #feasibility Study
Background: Effective pain management is a cornerstone of #Cancer palliative care, yet it remains challenging in low- and middle-income countries (LMICs) due to limited resources, regulatory constraints, and a lack of objective tools. While wearable technologies offer promise for augmenting pain-related patient-reported outcomes (PROs) with physiological data, their #usability in LMIC palliative settings is underexplored. Objective: This article presents an evaluation on technology #usability and implementation #feasibility of the NEST system, a low-cost, smartwatch-based pain monitoring solution for palliative #Cancer care co-designed with health care staff from a #Cancer hospital in Ecuador. Methods: An observational #usability study was conducted with seven #Cancer patients receiving palliative care treatment, combining hospital- and home-based monitoring phases. We used a qualitative and quantitative approach to assess the #usability of the NEST system and to identify sociotechnical factors affecting #feasibility using the Nonadoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework. Results: Quantitative results showed strong preference for the smartwatch over the mobile phone for submitting PROs (83%), with wear-time adherence of the smartwatch ranging from 36% to 92% of the time. Qualitative feedback from patients and healthcare staff indicated good #usability and perceived clinical value, though technical and organizational challenges such as charging habits, training needs, and dashboard integration into daily workflow of healthcare staff were noted. As for #feasibility, most of the complexity was found on the dynamics of the health condition while the technology shows clear promising signs of having value to patients and healthcare staff. Conclusions: Our findings suggest that the commonly reported #usability hurdles of a smartwatch-based sociotechnical health solution are surmountable given fluid communication between stakeholders during all stages of design and deployment. The primary threats to #feasibility in our context seem to lie in the highly complex and dynamic environment of palliative #Cancer care, regulatory ambiguity regarding use of medical devices, and the workload burden on healthcare staff.dlvr.it - New in JMIR Cardio: Perceived Potential and Challenges of Supporting Coronary Artery Disease Treatment Decisions With AI: Qualitative Study
Perceived Potential and Challenges of Supporting Coronary Artery Disease Treatment Decisions With AI: Qualitative Study
Background: Coronary revascularization decision-making for patients with coronary artery disease (CAD) can be complex and challenging. Artificial intelligence (AI) has the potential to improve this decision-making by bringing data-driven insights to the point of care. Objective: To elicit, collect, and analyze various stakeholders’ perceived potential and challenges related to developing, implementing, and adopting AI-based CAD treatment decision support systems. Methods: A facilitated small-group discussion method, known as a World Café, was conducted with general #cardiologists, interventional #cardiologists, cardiac surgeons, patients, caregivers, health system administrators, and industry representatives. One-on-one interviews were conducted for participants who could not attend the World Café. Perceived potential and challenges of AI-based CAD treatment decision support systems were solicited by asking participants three broad questions: 1) what is most challenging about revascularization decision-making? 2) how could an AI tool be integrated into the existing clinical workflow? 3) what are critical components that need to be considered when developing the AI tool? Thematic analysis was performed to identify themes from the data. Results: Nine participants completed the World Café and three participants completed the one-on-one interviews. Five main themes emerged: 1) evidence-based care, 2) workload and resources, 3) data requirements (subthemes: patient-centered approach; evidence-based AI; data integration), 4) tool characteristics (subthemes: end-user built; generation and presentation of decision support information; user-friendliness and accessibility; system logic, reasoning, and data privacy), and 5) incorporation into clinical workflow (subthemes: AI as an opportunity to improve care; knowledge translation). Conclusions: While healthcare providers aim to provide evidence-based care, CAD treatment decision-making can often be subjective due to the limited applicability of clinical practice guidelines and randomized controlled trial evidence to individual patients. AI-based clinical decision support systems may be an effective solution if the development and implementation focus on the issues identified by end-users in this study (patient preference, data privacy, integration with clinical information systems, transparency, and usability).dlvr.it - JMIR HumanFactors: Development of the ERATbi App, a Clinical Decision Support System for Early Recovery After Traumatic Brain Injury in the ICU: #usability Study
Development of the ERATbi App, a Clinical Decision Support System for Early Recovery After Traumatic Brain Injury in the ICU: #usability Study
Background: Early rehabilitation in neurocritical care is frequently underutilized due to fragmented workflows, interdisciplinary coordination challenges, and a lack of structured digital decision support. Traditional clinical decision support systems (CDSS) often address single domains and do not accommodate the dynamic and multi-professional nature of ICU environments. Objective: This study aimed to design and evaluate the #usability of the ERATbi App, a modular, tablet-based CDSS developed to support early rehabilitation planning for patients with moderate-to-severe traumatic brain injury (TBI) in intensive care settings. Methods: The ERATbi App integrates four functional modules—delirium risk management, precision nutrition, stepwise early mobilization, and respiratory care for rib fractures—into a unified interface. A simulation-based #usability study was conducted with 18 ICU clinicians. Metrics included System #usability Scale (SUS) scores, task completion rates, error rates, and task durations. Additional feedback was gathered via a 5-point Likert satisfaction scale and open-ended responses. Results: The app demonstrated high #usability (mean SUS = 83.6 ± 7.4), 100% task completion, and a low error rate (4.2%). Average module completion time was 6.5 minutes, and participants reported strong satisfaction (mean = 4.7 ± 0.5). Users highlighted the value of the app’s visual logic, real-time alerts, adaptive thresholds, and modular workflow integration for enhancing team coordination and decision consistency. Conclusions: The ERATbi App exhibited strong #usability, high user satisfaction, and clinical relevance in simulated ICU workflows. Its logic-driven, workflow-embedded design may support scalable, interdisciplinary implementation of early rehabilitation in neurocritical care environments. Clinical Trial: Not applicable (this study does not meet the WHO definition of a clinical trial)dlvr.it - Measuring Substance Use with Ecological Momentary Assessment: A Systematic Review of Methods and Key Recommendations for a Methodological and Reporting Framework (preprint) #openscience #PeerReviewMe #PlanP
Measuring Substance Use with Ecological Momentary Assessment: A Systematic Review of Methods and Key Recommendations for a Methodological and Reporting Framework
Date Submitted: Feb 3, 2026. Open Peer Review Period: Feb 6, 2026 - Apr 3, 2026.dlvr.it - Reminder>> From Pilot Trap to Infrastructure: A Governance Framework for Clinical AI Institutionalization in #Health Systems (preprint) #openscience #PeerReviewMe #PlanP
From Pilot Trap to Infrastructure: A Governance Framework for Clinical AI Institutionalization in #Health Systems
Date Submitted: Feb 2, 2026. Open Peer Review Period: Feb 3, 2026 - Mar 31, 2026.dlvr.it - JMIR Public Health: Knowledge, Attitudes, Practices, and #Vaccination Willingness Toward Mpox (Monkeypox) Among Chinese Medical Students: Cross-Sectional Study
Knowledge, Attitudes, Practices, and #Vaccination Willingness Toward Mpox (Monkeypox) Among Chinese Medical Students: Cross-Sectional Study
Background: Monkeypox (mpox) remains a global #PublicHealth threat. However, data on mpox-related knowledge, attitudes, and practices (KAP) and #Vaccination willingness among Chinese medical students, who are key future healthcare practitioners, remain lacking. Objective: This study aimed to investigate systematically the KAP and mpox #Vaccination willingness of Chinese medical students and identify the factors influencing their #Vaccination decisions. Methods: An anonymous self-designed questionnaire was used to assess basic information, KAP toward mpox, #Vaccination-related behaviors, and willingness. Data were analyzed using chi-square test, t-test, analysis of variance, Kruskal−Wallis H test, and multinomial logistic regression. Results: Among the 4,098 participants, 84.63% (3,468/4,098) accepted mpox #Vaccination. The median scores of KAP toward mpox were 43 (interquartile range [IQR]: 33, 50), 33 (IQR: 32, 36), and 20 (IQR: 19, 24), respectively, with a median score of 73 (IQR: 68, 79) for #Vaccination-related practices. Multinomial logistic regression showed that factors associated with #Vaccination hesitancy (vs. acceptance) included male gender (OR = 1.416, 95% CI = 1.158–1.732), being an only child (OR = 1.340, 95% CI = 1.098–1.635), no history of #COVID19 #coronavirus in family or friends (OR = 1.520, 95% CI = 1.161–1.991), no #Influenza #Vaccination (OR = 1.429, 95% CI = 1.146–1.783), and low mpox knowledge (OR = 0.948, 95% CI = 0.941–0.955). Factors associated with #Vaccination rejection (vs. acceptance) included male gender (OR = 1.641, 95% CI=1.003–2.686), high academic grade (OR = 1.442, 95% CI = 1.154–1.802), family or friends working on #COVID19 #coronavirus frontlines (OR = 2.243, 95% CI = 1.337–3.764), no internship experience (OR = 2.049, 95% CI = 1.076–3.901), presence of organic diseases (OR = 3.733, 95% CI = 1.778–7.838), and low mpox knowledge (OR = 0.954, 95% CI = 0.938–0.971). Good self-reported health status was a protective factor against refusal (OR = 0.748, 95% CI = 0.580–0.965). Conclusions: This study demonstrates high mpox #Vaccination acceptance among Chinese medical students. The identified influencing factors provide critical targets for developing targeted educational interventions and #Vaccination strategies, which are essential for enhancing preparedness against future mpox outbreaks and leveraging medical students’ role in #PublicHealth promotion.dlvr.it - A Pocket Laboratory for Functional Neuroimaging Research Using Mobile Visual Oddball, Multimodal Electroencephalography, and Functional Near-Infrared Spectroscopy Imaging: Instrument Validation Study #Neuroimaging #CognitiveScience #MobileApp #WearableTech #Electroencephalography
A Pocket Laboratory for Functional Neuroimaging Research Using Mobile Visual Oddball, Multimodal Electroencephalography, and Functional Near-Infrared Spectroscopy Imaging: Instrument Validation Study
We present the Wearable Cognitive Assessment and Augmentation Toolkit (WearCAAT), a cross-platform mobile application to conduct functional neuroimaging research with modern mobile devices. The need to observe human cognition in more natural environments, i.e., outside of sterile Laboratory Settings, is critical to understanding human cognition and behavior. Smartphones offer a unique perspective, their ubiquity and computational power make them excellent candidates for “Pocket Labs,” Laboratories that fit in a pocket and can travel with their subjects. However, mobile app development is inherently difficult; the mobile-device ecosystem is massive, and growing, and requires deep technical knowledge and considerable time investments, which bar non-technical researchers from participating. WearCAAT offers a robust yet flexible platform for neuroimaging which implements mobile versions of well-known cognitive tasks, with a seamless integration of existing setups and third-party neuroimaging sensors, via Lab-Streaming Layer a well-vetted software suite for sensor synchronization and data collection. We designed WearCAAT to bypass the deep technical knowledge requirements and time-investment of mobile app development and offer a Bring Your Own Device (BYOD) platform for non-technical users whose domain expertise in cognitive neuroscience is most valuable. To our knowledge, WearCAAT is the first attempt at a general-purpose neuro-imaging laboratory designed for mobile devices and represents a major step toward a “Pocketable Lab” for cognitive neuroscience research.dlvr.it - JMIR Formative Res: Challenges for a Maternal-Care Health Recommender System in Indonesia: Formative Preimplementation Qualitative Study #MaternalHealth #HealthTech #AntenatalCare #HealthcareInnovation #PregnancySupport
Challenges for a Maternal-Care Health Recommender System in Indonesia: Formative Preimplementation Qualitative Study
Background: Maternal evaluation during routine antenatal care visits may reduce maternal morbidity and mortality by identifying and addressing issues early on. A health recommender system could help health professionals and pregnant women monitor daily health parameters, provide tailored recommendations, and support timely antenatal care. Objective: This study aims to qualitatively analyze challenges in the preimplementation of health recommender system for maternal care in Indonesia as perceived by multiple stakeholders, including health care providers, patients, health system managers, government officers, and technology vendors. Methods: The methodology used a qualitative approach, where qualitative data were obtained from interviews of 37 respondents from multiple stakeholders, consisting of 15 health workers and 15 patients from private and government health care facilities, 4 officers from government health offices, 2 directors of health application vendors, and 1 manager from a private health clinic. These semistructured interview results were analyzed using thematic analysis. Results: This qualitative study identifies key challenges in implementing a health recommender system for maternal care in Indonesia across the people, process, infrastructure, and policy dimensions. Intercoder reliability for the coding process demonstrated almost perfect agreement (Cohen κ=0.90), supporting the consistency of the coding process. Six major challenges were revealed, mostly regarding skill, accuracy, completeness, timeliness, cost, and standardization. These 6 major challenges were mentioned 96 times, accounting for 64.43% of all codes extracted from the interviews. These findings emphasize the value of user involvement in system design to meet health care professionals’ and patients’ needs, technical advancements to foster trust and support effective decision-making, as well as enhanced data accuracy, reliable and timely service delivery, cost management, and clear regulatory standards. Conclusions: This formative, preimplementation qualitative study highlights the importance of involving users in system design and future implementation to meet the needs of health care professionals and patients. Reducing input errors and improving system reliability are critical to building trust and supporting effective point-of-care decision-making and, in later phases, facility-level monitoring as part of public health surveillance. Adherence to regulatory standards and the establishment of standardized guidelines will be key to enabling broader implementation. Further #usability, #feasibility, and pilot studies are required before any evaluation of effectiveness.dlvr.it - Reminder>> Automated Pattern Recognition in #Cardiological Conditions: A Case #Study on Shockable Rhythm Detection (preprint) #openscience #PeerReviewMe #PlanP
Automated Pattern Recognition in #Cardiological Conditions: A Case #Study on Shockable Rhythm Detection
Date Submitted: Jan 29, 2026. Open Peer Review Period: Feb 3, 2026 - Mar 31, 2026.dlvr.it - JMIR Formative Res: Human Papillomavirus Vaccine Perceptions Among Noncollege Young Adults and TikTok Influencers: Qualitative Study #HPV #VaccinationAwareness #PublicHealth #CancerPrevention #YoungAdults
Human Papillomavirus Vaccine Perceptions Among Noncollege Young Adults and TikTok Influencers: Qualitative Study
Background: Human papillomavirus (HPV) vaccination is a proven and effective tool for preventing several types of #Cancers, yet vaccination rates among young adults remain suboptimal, particularly among those not enrolled in 4-year colleges. This population can be more difficult to reach due to fewer established institutional touchpoints, limited engagement with campus-based health services, and greater variability in access to preventive care. At the same time, social media has become a dominant source of information for young adults, with TikTok (ByteDance) emerging as one of the most widely used platforms. Approximately 41% of TikTok’s users are between the ages of 16 and 24 years, making it a potentially important channel for public health communication. However, little is known about how noncollege young adults perceive HPV-related content on TikTok, or how influencers themselves view their role in communicating about vaccination. Objective: This study explored the perspectives of young adults and TikTok influencers regarding the dissemination and reception of HPV vaccine information on TikTok. The goal was to assess the potential of leveraging influencers as trusted messengers for this hard-to-reach population. Methods: Researchers conducted 5 focus groups with noncollege young adults, stratified by gender and vaccination status. Each group included 5-8 participants, resulting in a total of 34 individuals. Participants who reported being extremely hesitant about the HPV vaccine were excluded to focus on those more receptive to information. In parallel, researchers recruited 9 TikTok influencers who reached audiences aged 18-25 years and conducted in-depth individual interviews. Influencers represented a diverse mix of identities, follower counts, and content genres, providing varied perspectives on engagement with health-related topics. Results: Across the focus groups, young adults described regularly encountering or actively seeking health-related information online, with TikTok emerging as a primary or supplementary source for some. However, very few participants reported seeing content specifically related to HPV vaccination. Despite this gap, most expressed openness to such content if it was delivered in a relatable, authentic manner and included concise, relevant facts. Influencers echoed the importance of authenticity, emphasizing that their credibility is grounded in genuine connections with their audiences. Many described frequent, meaningful exchanges with followers about sensitive issues, suggesting comfort in addressing health topics. Influencers noted that they would be willing to share HPV-related content under certain conditions, including alignment with existing content, personal relevance, or participation in a structured campaign or partnership. Conclusions: Findings suggest that TikTok may be a promising platform to engage noncollege young adults in HPV vaccination messaging. The strong parasocial relationships influencers maintain with their audiences could position them as effective messengers on sensitive health topics. Strategic collaborations with influencers, coupled with carefully crafted, authentic content, may help bridge communication gaps and support increased awareness of HPV vaccination in this underserved population.dlvr.it - New in JMIR Cancer: Reinforcement Learning–Based #digital Therapeutic Intervention for Postprostatectomy Incontinence: Development and Pilot Feasibility #Study
Reinforcement Learning–Based #digital Therapeutic Intervention for Postprostatectomy Incontinence: Development and Pilot Feasibility #Study
Background: Postprostatectomy incontinence (PPI) is a common complication after robot-assisted radical prostatectomy and significantly impairs #Patients’ quality of life. Although behavioral interventions such as pelvic floor muscle training and bladder diaries are evidence-based, their effectiveness is often limited by poor adherence and lack of personalization. Objective: This #Study aimed to develop and evaluate a reinforcement learning (RL)–driven clinical behavioral intervention-supporting system (CBISs) for adaptive, personalized rehabilitation in #Patients with PPI. Methods: The #Study comprised 2 sequential stages. First, the CBISs was developed through (1) construction of a #Medical record database from a prospective cohort of PPI #Patients using standardized 3-day bladder diaries, (2) design of functional modules and user interfaces based on clinical rehabilitation needs, and (3) development of an RL model using XGBoost (extreme gradient boosting) and Bayesian optimization to generate individualized training plans. Second, a separate cohort of 16 #Patients participated in a single-arm, pre-post pilot #Study to evaluate feasibility and preliminary outcome trends over a 3-month intervention period, with assessments based on bladder diary parameters and system usage metrics. Results: The CBISs successfully implemented an adaptive, closed-loop behavioral rehabilitation framework that dynamically tailored training recommendations according to individual voiding patterns, fluid intake behaviors, and adherence signals. Feasibility outcomes were favorable, with high system engagement observed throughout the intervention (mean usage frequency 5.2, SD 1.1 times per day). In exploratory pre-post analyses (n=16), consistent directional improvements were observed across multiple outcomes. Mean daytime urinary frequency decreased from 5.74 (SD 1.21) episodes per day to 4.69 (SD 1.08) episodes per day, while median nighttime urinary frequency declined from 1.8 (IQR 1.6-2.2) episodes per night to 1.0 (IQR 1.0-1.6) episodes per night. Median incontinence episodes were reduced from 7.0 (IQR 6.0-11.0) episodes per day to 4.0 (IQR 2.0-6.0) episodes per day. Objective urine leakage measured by the 1-hour pad test decreased from a median of 8.5 (IQR 4.0-19.0) g to 3.5 (IQR 2.0-9.0) g. #Patient-reported symptom burden, assessed using the International Consultation on Incontinence Questionnaire–Short Form (ICIQ-UI SF), showed a median reduction from 14.0 (IQR 12.0-20.0) points to 9.0 (IQR 6.0-16.0) points. Although several within-participant changes were statistically detectable, effect magnitudes varied across individuals. Given the single-arm design, small sample size, and lack of a control group, findings are presented as exploratory and hypothesis-generating rather than confirmatory of clinical efficacy. Conclusions: The CBISs represents the first RL-powered #digital therapeutic system for PPI, enabling adaptive, evidence-based behavioral optimization. By addressing limitations of static rehabilitation protocols and declining adherence, it offers a scalable approach for personalized PPI management. Future multicenter trials are needed to confirm its clinical effectiveness.dlvr.it - JMIR Mental Health: Advancing Psychiatric Safety With the Predictive Risk Identification for #MentalHealth Events Tool: Retrospective Cohort Study #MentalHealth #PatientSafety #PredictiveRisk #MachineLearning #Psychiatry
Advancing Psychiatric Safety With the Predictive Risk Identification for #MentalHealth Events Tool: Retrospective Cohort Study
Background: Patient safety incidents are a leading cause of harm in psychiatric settings, yet early warning systems (EWS) tailored to #MentalHealth remain underdeveloped. Traditional risk tools such as the Dynamic #Appraisal of Situational Aggression–Inpatient Version (DASA-IV) offer limited predictive accuracy and are reactive rather than proactive. Objective: We introduce the Predictive Risk Identification for #MentalHealth Events (PRIME) tool, a deep learning–based EWS trained on longitudinal psychiatric electronic medical record (EMR) data to anticipate adverse events in 24-hour windows. Methods: A retrospective cohort study using routinely collected EMR data to train and validate machine learning (ML) models for short-term risk prediction was conducted. This study took place at Waypoint Centre for #MentalHealth Care, a large inpatient psychiatric hospital in Ontario, Canada, serving both high-security forensic and nonforensic patient populations. A total of 4651 patients and 403,098 encounters from January 2020 to August 2024 were included. For model evaluation, the 2024 test set included 900 patients and 48,313 encounters. PRIME was trained using recurrent neural networks with attention mechanisms on multivariate time-series data. The model used an autoregressive design to forecast risk based on 7 days of prior patient data and was benchmarked against the DASA-IV clinical tool and other ML baselines. The primary outcome was the occurrence of an adverse #MentalHealth event recorded in the EMR within the following 24 hours. Model performance was assessed using area under the receiver operating characteristic curve (AUC) and recall, alongside subgroup analyses and interpretability assessments using integrated gradients. Results: The long short-term memory with attention mechanism achieved the highest predictive performance (AUC=0.83), outperforming existing tools such as DASA-IV by 0.20 AUC (0.81 vs 0.61) and demonstrating the potential of ML-based models to support proactive risk management in #MentalHealth settings. Conclusions: The PRIME tool is one of the first developed and evaluated deep learning–based EWS for psychiatric inpatient care. By outperforming existing clinical tools and providing interpretable, rolling predictions, PRIME offers a pathway toward safer, more proactive #MentalHealth interventions. Future work should assess its equity implications and integration into routine psychiatric workflows.dlvr.it - JMIR Formative Res: Clinical Decision Support Tool for Early Pancreatic #Cancer Detection in Primary Care: Simulation Study #PancreaticCancer #CancerAwareness #EarlyDetection #ClinicalDecisionSupport #DigitalHealth
Clinical Decision Support Tool for Early Pancreatic #Cancer Detection in Primary Care: Simulation Study
Background: Early detection in primary care could improve pancreatic #Cancer survival, but diagnosis is often delayed due to the low prevalence of the disease, the nonspecific nature of early symptoms, and the broad range of conditions and volume of consultations managed by general practitioners (GPs). In Australia, improving pancreatic #Cancer outcomes, including via earlier diagnosis, is a priority being progressed under the National Pancreatic #Cancer Roadmap developed by #Cancer Australia. Computerized clinical decision support systems (CDSSs) have shown promise in aiding timely #Cancer diagnosis; however, barriers to adopting CDSS such as mistrust of the recommendations or not being embedded in the clinical workflow remain. Simulation techniques, which offer flexible and cost-effective ways to evaluate digital health interventions, can be used to test CDSS before real-world implementation. Objective: This study aims to assess the acceptability and #feasibility of identifying patients with symptoms associated with pancreatic #Cancer through a CDSS within a simulated environment. Methods: We developed a CDSS that interacted with an electronic health record used in general practice to identify patients with symptoms, which may indicate pancreatic #Cancer (unintended weight loss or new-onset diabetes), in a simulation laboratory for digital interventions. We tested it by inviting GPs (n=11) to use the CDSS, with patient actors simulating specific clinical scenarios. We then interviewed GPs about the interaction to assess the acceptability and #feasibility of the CDSS in their clinical practice. We used thematic analysis and 2 relevant frameworks to analyze the data. Results: GPs found the CDSS easy to use, unobstructive, and effective as a prompt to consider investigations for people with risk factors for pancreatic #Cancer. However, they expressed concerns about possible overtesting, financial costs, and the potential for anxiety in patients with a very low probability of having #Cancer. Conclusions: While GPs found the tool useful and compatible with their workflow, concerns about overtesting, lack of evidence, and cost-effectiveness were identified as barriers. GPs favored a stepwise approach to investigations rather than immediate imaging. Despite the overall acceptability of the tool, additional evidence to underpin clinical recommendations is necessary before implementing a CDSS with these specific recommendations for pancreatic #Cancer in primary care.dlvr.it - The Feasibility of Smartwatch Micro–Ecological Momentary Assessment for Tracking Eating Patterns of Malaysian Children and Adolescents in the South-East Asian Community Observatory Child Health Update 2020: Cross-Sectional Study
The Feasibility of Smartwatch Micro–Ecological Momentary Assessment for Tracking Eating Patterns of Malaysian Children and Adolescents in the South-East Asian Community Observatory Child Health Update 2020: Cross-Sectional Study
Background: Mobile phone ecological momentary assessment (EMA) methods are a well-established measure of eating and drinking behaviors, but compliance can be poor. Micro-EMA (μEMA), which collects information with a single tap response to brief questions on smartwatches, offers a novel application that may improve response rates. To our knowledge, there is no data evaluating μEMA to measure eating habits in children or in low-to-middle-income countries. Objective: In this study, we investigated the feasibility of micro-EMA to measure eating patterns in Malaysian children and adolescents. Methods: We invited 100 children and adolescents aged 7-18 years in Segamat, Malaysia, to participate in 2021-2022. Smartwatches were distributed to 83 children and adolescents who agreed to participate. Participants were asked to wear the smartwatch for 8 days and respond to 12 prompts per day, hourly, from 9AM to 8PM, asking for information on their meals, snacks, and drinks consumed. A questionnaire captured their experiences using the smartwatch and μEMA interface. Response rate (proportion of prompts responded to) assessed participants’ adherence. We explored associations between response rate with time of day, across days, age, and sex using multilevel binomial logistic regression modeling. Results: Eighty-two participants provided usable smartwatch data. The median number (IQR) of meals, drinks, and snacks per day was 2 (2-4), 3 (1-5), and 1 (0-2), respectively, on the first day of the study. The median response rate across the study was 68% (IQR 50-83). The response rate decreased across study days from 74% (68-78) on Day 1 to 40% (30-50) on Day 7 (odds ratio [OR] per study day 0.73, 95% CI 0.64-0.83). Response rate was lowest at the start of the day and highest between the hours of 12 PM and 2 PM. Female participants responded to more prompts than male participants (OR 1.72, 95% CI 1.03-2.86). There was no evidence of differential response by age (OR 0.73, 95% CI 0.41-1.28). Most participants (65%) rated their experience using the smartwatch positively, with 33% saying they were happy to participate in future studies using the smartwatch. For children that did not wear the smartwatch for the full study duration (n=22), discomfort was the most common complaint (41%). Conclusions: In this study of the feasibility of μEMA on smartwatches to measure eating in Malaysian children, we found the method was acceptable. However, response rates declined across study days, resulting in substantial missingness. Future studies (eg, through focus groups) should explore approaches to improving response to event prompts, trial alternative devices to increase children’s comfort, and evaluate revised protocols for reporting of intake events.dlvr.it - Reminder>> Noise-Robust Atrial Fibrillation Detection from Garment-Type #Wearable Holter Electro#Cardiogram Monitoring Using R–R Interval-Based Deep Learning: Algorithm Development and Validation #Study (preprint) #openscience #PeerReviewMe #PlanP
Noise-Robust Atrial Fibrillation Detection from Garment-Type #Wearable Holter Electro#Cardiogram Monitoring Using R–R Interval-Based Deep Learning: Algorithm Development and Validation #Study
Date Submitted: Jan 22, 2026. Open Peer Review Period: Feb 3, 2026 - Mar 31, 2026.dlvr.it - Reminder>> Machine Learning for Predicting #Patient Revisits and Future Diagnoses Using Electronic #Health Claims Data: A Retrospective Cohort #Study from Ghana (preprint) #openscience #PeerReviewMe #PlanP
Machine Learning for Predicting #Patient Revisits and Future Diagnoses Using Electronic #Health Claims Data: A Retrospective Cohort #Study from Ghana
Date Submitted: Jan 23, 2026. Open Peer Review Period: Feb 3, 2026 - Mar 31, 2026.dlvr.it - Reminder>> Web-based, open-source LGBTQ+ Affirming Care Education for Primary Healthcare Providers: A Descriptive Analysis (preprint) #openscience #PeerReviewMe #PlanP
Web-based, open-source LGBTQ+ Affirming Care Education for Primary Healthcare Providers: A Descriptive Analysis
Date Submitted: Feb 1, 2026. Open Peer Review Period: Feb 2, 2026 - Mar 30, 2026.dlvr.it - Reminder>> Effectiveness of enhanced computerised #Physician order entry and clinical decision support system in optimizing #Medication safety in special populations: a systematic review and meta-analysis (preprint) #openscience #PeerReviewMe #PlanP
Effectiveness of enhanced computerised #Physician order entry and clinical decision support system in optimizing #Medication safety in special populations: a systematic review and meta-analysis
Date Submitted: Feb 1, 2026. Open Peer Review Period: Feb 2, 2026 - Mar 30, 2026.dlvr.it - Reminder>> #Patient-Portal Message Framing Shifts Preferences for Managing Degenerative Meniscus Tears: A Randomized #Survey of Lay Adults (preprint) #openscience #PeerReviewMe #PlanP
#Patient-Portal Message Framing Shifts Preferences for Managing Degenerative Meniscus Tears: A Randomized #Survey of Lay Adults
Date Submitted: Jan 31, 2026. Open Peer Review Period: Feb 2, 2026 - Mar 30, 2026.dlvr.it - Reminder>> A web-based self-management intervention for return-to-work among persons with common mental disorder on sick leave: A Case #Study of mWorks (preprint) #openscience #PeerReviewMe #PlanP
A web-based self-management intervention for return-to-work among persons with common mental disorder on sick leave: A Case #Study of mWorks
Date Submitted: Feb 1, 2026. Open Peer Review Period: Feb 2, 2026 - Mar 30, 2026.dlvr.it - Reminder>> Strategic Planning for a #DigitalHealth Innovation Hub at a Saudi Academic #Medical Centre: A Qualitative Case #Study (preprint) #openscience #PeerReviewMe #PlanP
Strategic Planning for a #DigitalHealth Innovation Hub at a Saudi Academic #Medical Centre: A Qualitative Case #Study
Date Submitted: Jan 30, 2026. Open Peer Review Period: Feb 2, 2026 - Mar 30, 2026.dlvr.it - Reminder>> Enhancing palliative home care with #Telehealth and artificial intelligence: Kids’ stuff or complex intervention? A mixed methods #Study (preprint) #openscience #PeerReviewMe #PlanP
Enhancing palliative home care with #Telehealth and artificial intelligence: Kids’ stuff or complex intervention? A mixed methods #Study
Date Submitted: Jan 30, 2026. Open Peer Review Period: Feb 2, 2026 - Mar 30, 2026.dlvr.it - Reminder>> Application of Ecological Momentary Assessment in Maternal #Health Management: A Scope review (preprint) #openscience #PeerReviewMe #PlanP
Application of Ecological Momentary Assessment in Maternal #Health Management: A Scope review
Date Submitted: Feb 1, 2026. Open Peer Review Period: Feb 2, 2026 - Mar 30, 2026.dlvr.it
