JMIR Human Factors最新文献

{"title":"Telemedicine Adoption Among Health Care Professionals in Israeli Geriatric Medical Centers: Moderated Mediation Cross-Sectional Questionnaire Study.","authors":"Gizell Green, Alena Lochmannová, Riki Tesler, Tammy Porat-Packer","doi":"10.2196/95371","DOIUrl":"https://doi.org/10.2196/95371","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":"13 ","pages":"e95371"},"PeriodicalIF":3.9,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13637990/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149115907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Designing for Amplifying Voices-Supporting Bangladesh's Low-Income Ready-Made Garment Workers Through Technology Amid COVID-19 Pandemic: Interview Study Among Workers.","authors":"Rahat Jahangir Rony, Nova Ahmed","doi":"10.2196/90675","DOIUrl":"https://doi.org/10.2196/90675","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;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 techno","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":"13 ","pages":"e90675"},"PeriodicalIF":3.9,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13632911/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149115790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinician Trust and Human Factors in AI-Enabled Clinical Decision Support in Acute Care: Mixed Methods Study.","authors":"Meghana Darla, Danielle Miltz, Khushboo Chandnani, Saptarshi Purkayastha, John W Diehl, Sivasubramanium V Bhavani","doi":"10.2196/95472","DOIUrl":"https://doi.org/10.2196/95472","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Trust in AI varied significantly by patient acuity (Cochran Q=30.40, &lt;i&gt;P&lt;/i&gt;&lt;.001): stable patients (43/57, 75%, 95% CI 63%-85%), deteriorating patients (27/57, 47%, 95% CI 35%-60%), and critically ill patients in the ICU and undifferentiated patients in the ED (25/57, 44%, 95% CI 32%-57% for each scenario). Internal consistency was acceptable-to-good across three scales (Cronbach α: AI Perception=.891, Trust=.743, Implementation=.740; McDonald &lt;i&gt;ω&lt;/i&gt;: AI Perception=0.895, Trust=0.782, Implementation=0.746). Barriers included overreliance, insufficient training, and data quality concerns. For the exploratory AI-CDSS design, clinicians preferred opt-in alerts (10/11, 91%), evidence-linked recommendations (7/11, 64%), and avoiding overt mention of increased AI acceptance (8/11, 73%). Thematic analysis yielded 36 themes across six domains: trust and transparency, alert usability, workflow fit, data concerns, training needs, and perceived clinical impact. Clinicians favored AI-CDSSs that preserved autonomy, minimized disruption, and provided transparent rat","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":"13 ","pages":"e95472"},"PeriodicalIF":3.9,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13633065/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149115689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Authors' Reply: Empathy Cuts Both Ways in Clinical AI.","authors":"Sang Won Bae, Tongze Zhang, Tammy Chung, Anind Dey","doi":"10.2196/110739","DOIUrl":"https://doi.org/10.2196/110739","url":null,"abstract":"","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":"13 ","pages":"e110739"},"PeriodicalIF":3.9,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13633061/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149115265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-Assisted Clinical Documentation in Routine Danish General Practice: Quantitative Pre-Post Study.","authors":"Louise Nørgaard Olsen, Philipp Harbig, Anna Bay Laurberg, Jacob Laurberg, Morten Haaning Charles","doi":"10.2196/94431","DOIUrl":"https://doi.org/10.2196/94431","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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).</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":"13 ","pages":"e94431"},"PeriodicalIF":3.9,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13632910/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149116001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating Wearable Technology in Digital Therapy for People With Psychosis (SloMo): Mixed Methods Co-Design and Feasibility Study.","authors":"Kathryn M Taylor, Thomas Ward, Matteo Cella, Philippa Garety, Amy Grant, Katie Philps, Anna Wojdecka, Melanie Flory, Amy Hardy","doi":"10.2196/91188","DOIUrl":"https://doi.org/10.2196/91188","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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%).</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":"13 ","pages":"e91188"},"PeriodicalIF":3.9,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13633062/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149115865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Empathy Cuts Both Ways in Clinical AI.","authors":"Vera Sorin, Eyal Klang","doi":"10.2196/108778","DOIUrl":"https://doi.org/10.2196/108778","url":null,"abstract":"","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":"13 ","pages":"e108778"},"PeriodicalIF":3.9,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13633063/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149115741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utility of Augmented Reality Glasses With Waveguide Optics and Facial Recognition Technology for Patient Verification in a Simulated Outpatient Setting: Physician Questionnaire Survey Study.","authors":"Ryo Andy Ogasawara, Soichiro Yoshida, Kentaro Okamoto, Eiki Nagaoka, Hirotaka Sagawa, Kazutaka Sumita, Hajime Tanaka, Satoshi Shiraga, Tomohiro Amemiya, Yasuhisa Fujii","doi":"10.2196/95664","DOIUrl":"https://doi.org/10.2196/95664","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>The objective of this study was to assess the usability and acceptability of prototype AR glasses with facial recognition in a simulated outpatient setting.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":"13 ","pages":"e95664"},"PeriodicalIF":3.9,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13632967/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149115858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Conversational AI in Hereditary Cancer Care: Sociotechnical Study of Responsible Design Requirements.","authors":"Dominic Lammert, Jacqueline Lammert, Justin Hofenbitzer, Tristan Radtke, Yingping Sun, Martin Haimerl, Stefanie Betz, Doran Holger Scholl, Jürgen Pfeffer","doi":"10.2196/97869","DOIUrl":"https://doi.org/10.2196/97869","url":null,"abstract":"<p><strong>Background: </strong>Individuals with <i>BRCA1/2</i> germline variants face complex, preference-sensitive medical decisions under psychological distress. Structural constraints in genetic counseling create support gaps during waiting periods, prompting patients to rely on fragmented or misleading online information. Conversational AI may offer low-threshold, continuous informational support, yet methods for its responsible integration into clinical care remain unclear.</p><p><strong>Objective: </strong>This study identifies and systematizes stakeholder-informed sociotechnical design requirements and governance conditions for a clinically bounded conversational AI system intended to support comprehension, appraisal, and appropriate use of complex hereditary cancer information in <i>BRCA1/2</i>-related care.</p><p><strong>Methods: </strong>We conducted an exploratory, qualitative, predevelopment requirements study using 2 structured participatory workshops with interdisciplinary stakeholders (N=18). Guided exercises included stakeholder analysis, the value proposition canvas, and the sustainability awareness framework. Structured workshop outputs, field notes, and artifacts were analyzed using inductive thematic analysis.</p><p><strong>Results: </strong>Stakeholder workshops revealed that responsible design was shaped less by consensus around standalone responsible AI principles than by three recurrent cross-stakeholder tensions: (1) accessibility and continuity of support versus clinical role boundaries, (2) simplification and emotional reassurance versus medical precision and epistemic transparency, and (3) technical scalability versus governance, data protection, and institutional accountability. These tensions translated into design requirements for constrained system authority, mandatory human oversight, context-sensitive communication, and integration into clinical and regulatory governance structures.</p><p><strong>Conclusions: </strong>Conversational AI in this context should be explicitly constrained and embedded within clinical governance structures to be perceived as responsible. Responsible implementation requires transparent design, human oversight, integration into existing care pathways, and early alignment with German/European Union (EU) regulatory requirements concerning data protection, medical device classification, genetic data, and AI transparency.</p>","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":"13 ","pages":"e97869"},"PeriodicalIF":3.9,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13626551/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149115728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mental Health Monitoring in Esports Athletes Using an mHealth App: Requirements Analysis and Usability Study.","authors":"Leona Stolberg, Daniel Hieber, Peter Kuhn, Michael Hebel, Johannes Schobel","doi":"10.2196/90140","DOIUrl":"https://doi.org/10.2196/90140","url":null,"abstract":"<p><strong>Background: </strong>Esports athletes face substantial psychological stressors comparable to those experienced by traditional athletes, yet mental health tools tailored to their specific needs remain scarce.</p><p><strong>Objective: </strong>This study addresses this critical gap by introducing MindAthlete, a prototype for a user-centered mobile health (mHealth) app intended to enhance mental health monitoring and support among esports athletes.</p><p><strong>Methods: </strong>A 3-stage development process was used over 5 months. First, structured qualitative interviews were conducted online via Google Meet with 7 experienced (sports) psychologists recruited through snowball sampling within professional esports networks. The interviews explored current mental health monitoring practices and key feature requirements for MindAthlete. Data were analyzed using Mayring's approach to qualitative content analysis. Subsequently, a 32-screen high-fidelity prototype was developed in Figma based on the identified requirements. In the final stage, usability testing was conducted with 25 semiprofessional and professional esports athletes recruited via convenience and snowball sampling. Participants interacted with the Figma prototype and completed an online questionnaire administered via LimeSurvey, which included demographic items, the validated 10-item System Usability Scale (SUS) rated on a 5-point Likert scale, and open-ended questions on navigation, aesthetics, and overall functionality. Inferential analyses included Mann-Whitney <i>U</i> tests, Kruskal-Wallis tests, and Spearman correlations. Open-ended responses were analyzed using Mayring's content analysis.</p><p><strong>Results: </strong>Expert interviews identified 8 core features for mental health monitoring in esports, clustered around 3 themes: continuous self-monitoring, structured assessment, and intervention and insight. Athlete usability testing yielded a median SUS score of 77.5 (IQR 67.5-87.5), classifying usability as \"good\" and exceeding a benchmark median of 68.3 derived from comparable mHealth apps. No significant differences were found by sex, age, years of professional experience, or prior mHealth usage. Qualitative feedback highlighted strengths including intuitive navigation and appealing aesthetics, alongside areas for improvement such as shorter animations and clearer initial onboarding. However, findings must be interpreted in light of the sample's demographic homogeneity, which limits generalizability to the broader esports population.</p><p><strong>Conclusions: </strong>This research provides a stakeholder-driven, empirically validated blueprint for esports-specific mental health monitoring via mHealth. Future work will focus on technical implementation, integration of wearable sensor data, and broader evaluations with larger, more diverse samples to confirm generalizability and enhance usability.</p>","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":"13 ","pages":"e90140"},"PeriodicalIF":3.9,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13626294/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149115876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}