Journal of Medical Internet Research最新文献

{"title":"Application of the Supportive Accountability Model in Digital Health Interventions: Scoping Review.","authors":"Gary Kwok, Shannon Pui Ying Cheung, Jennifer Duffecy, Katie A Devine","doi":"10.2196/72639","DOIUrl":"https://doi.org/10.2196/72639","url":null,"abstract":"<p><strong>Background: </strong>Digital health interventions (DHIs) harness technological innovation to address challenges in the accessibility and scalability of health care. However, the effectiveness of DHIs is challenged by low user engagement and adherence, as users tend to drop out over time. The supportive accountability model (SAM) is a theoretical framework designed to enhance adherence to DHIs by incorporating structured human support.</p><p><strong>Objective: </strong>Guided by SAM, this scoping review answers the following research questions: (1) What is the extent of research on human support factors and their influence on engagement with and adherence to DHIs? and (2) What is the extent of research applying SAM (ie, accountability, bond, and legitimacy) to improve engagement with and adherence to DHIs?</p><p><strong>Methods: </strong>Our search strategy followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). We conducted our literature search using 6 databases selected based on relevance to our research topic: MEDLINE, PsycINFO, Embase, CINAHL, Scopus, and ClinicalTrials.gov. Search terms included (\"human support\" OR \"supportive accountability\") AND (engagement OR adherence) AND intervention, applied to titles, abstracts, and keywords. Hand-searching was also used to identify additional relevant articles. Two authors (SPYC and GK) screened articles in multiple rounds using predefined inclusion and exclusion criteria. The final sample consisted of 36 empirical, peer-reviewed articles published in scholarly journals. All articles examined human-supported DHIs.</p><p><strong>Results: </strong>Implementation of human support among the interventions varied by the source, delivery method, and frequency and duration of support. Overall, there were inconsistencies in the application of SAM to intervention designs. Support was provided by 4 main groups: peers and peer specialists, health experts and practitioners, trained coaches, and members of the research study team. Modes of communication included phone or video calls, as well as text-based support, such as messaging or email. The frequency and duration of support varied across studies and were influenced by the communication method used, with more structured and frequent contact occurring in interventions that relied on synchronous support, such as phone or video calls. In addition, we found that some studies used human support as the primary mode of intervention delivery rather than as an adjunctive tool, focusing on improving engagement and adherence, as proposed by SAM. Aside from accountability, there was also a lack of explicit focus on other constructs within the model (eg, bond and legitimacy).</p><p><strong>Conclusions: </strong>This scoping review highlights the current use of human support to promote DHI adherence and reveals gaps in the application of SAM. Future research should address all core SAM component","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e72639"},"PeriodicalIF":6.0,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145176007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data Donation as a Method to Measure Physical Activity in Older Adults: Cross-Sectional Web Survey Assessing Consent Rates, Donation Success, and Bias.","authors":"Florian Keusch, Bella Struminskaya, Joris Mulder, Stein Jongerius","doi":"10.2196/69799","DOIUrl":"10.2196/69799","url":null,"abstract":"<p><strong>Background: </strong>Accurate measurement of physical activity (PA) is key to identifying determinants of health and developing appropriate interventions. Self-reports of PA (eg, in surveys or diary studies) often suffer from measurement error. Providing study participants with wearable devices that passively track PA reduces reactivity and recall error but participants' noncompliance and high device costs are problematic. Many older adults now have smartphones that track PA. Based on legal requirements, data controllers (eg, health apps) must provide users with access to their data, and individuals can request and donate these data for research. This user-centric approach provides researchers with access to individual-level data, and it gives users control over what data are shared.</p><p><strong>Objective: </strong>We conduct a first test of the data donation approach for PA data among older adults. We study (1) how willing and successful older adults are to donate their PA data from different smartphone apps, (2) what drives donation of PA data at the different stages of participation, and (3) what biases arise from selective data donation.</p><p><strong>Methods: </strong>To answer our research questions, we use cross-sectional observational data from a probability-based online panel of the Dutch general population. A total of 2086 members of the Longitudinal Internet Studies for the Social Sciences panel aged 50 years and older completed a web survey in 2024. All iPhone and Android smartphone owners were asked to download passively collected PA data from their devices (Apple Health, Google Location History, or Samsung Health) and donate them via the Port platform.</p><p><strong>Results: </strong>Out of the 2086 survey participants, 1889 (91%) reported owning an iPhone or Android phone compatible for data donation, 606 (29%) reported willingness to donate PA data, 354 (17%) started the data donation, and 256 (12%) successfully provided a data package. Gender, age, educational attainment, monthly personal net income, smartphone usage behavior, privacy- and trust-related attitudes, and type of health app from which the data were requested correlated with behavior at the different stages of study participation. Self-reported reasons for nonwillingness to donate related mainly to expected technical issues, privacy concerns, and perceived usefulness. Compared with the entire sample, data donors reported better health, fewer health-related limitations, fewer difficulties performing tasks, and more PA.</p><p><strong>Conclusions: </strong>Our study shows that data donation from smartphones as part of a probability-based web survey of older adults is a feasible alternative for the measurement of PA, especially for iPhone owners younger than 70 years. Limitations relate to nonparticipation which correlates strongly with characteristics of smartphone ownership and comfort with device use. Substantive bias in health and PA outcomes persists for","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e69799"},"PeriodicalIF":6.0,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145149430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adherence to Usability and Accessibility Principles in Digital Health Applications for Patients With Diabetes: Systematic Review.","authors":"Sarah Louise Watson, Hanan Khalid Mofty, Michael Donnelly, Tunde Peto, Ruth Esther Hogg","doi":"10.2196/71567","DOIUrl":"10.2196/71567","url":null,"abstract":"<p><strong>Background: </strong>Health apps have the potential to enable people with diabetes to access care more easily, monitor their condition, and reduce the number of times they need to attend health care appointments. However, the development pipeline for apps may differ widely before the apps are released for use, due to limited funding, difficulty in obtaining iterative feedback from patients/users, and varying levels of developer expertise. In response to concerns about the quality and consistency of apps being released, two guidelines were created: the Digital Technology Assessment Criteria (DTAC) and the National Institute for Health and Care Excellence (NICE) Evidence Standards Framework. These two frameworks aim to standardize the development and evaluation of digital health technologies (DHTs). They outline core requirements, such as accessibility, clinical safety, data protection, interoperability, usability, and safeguarding, which help ensure that digital health apps are accessible, safe, effective, and suitable for real-world use.</p><p><strong>Objective: </strong>This systematic review evaluated the performance of diabetes digital health apps, as presented in published studies, in terms of adherence to DTAC 2021 and NICE 2022 guidelines during development.</p><p><strong>Methods: </strong>We systematically searched Embase and MEDLINE and identified 43 studies that met the inclusion criteria. Each study was assessed against 13 binary scoring criteria derived from the two frameworks.</p><p><strong>Results: </strong>Our findings highlighted that 93% (n=40) of the studies met fewer than 40% of the recommended criteria. Specifically, 88.4% (n=38) studies did not report accurate and reliable measurements, 86% (n=37) omitted app accuracy validation, and 83.7% (n=36) failed to address inequalities considerations. Only 3 (7%) studies achieved scores between 7 and 9 out of a possible 13, and none fully adhered to the guideline criteria.</p><p><strong>Conclusions: </strong>These results suggest a significant gap between digital health guidelines and real-world app development practices. We recommend the adoption of DTAC and NICE guidelines more widely and consistently during design and development. Additionally, we suggest that journals request that authors submit an adherence checklist alongside their manuscript to improve standardization and transparency across digital health publications.</p><p><strong>Trial registration: </strong>PROSPERO CRD42022322040; https://www.crd.york.ac.uk/PROSPERO/view/CRD42022322040.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":" ","pages":"e71567"},"PeriodicalIF":6.0,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144707785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Impact of Digital Health Interventions on Psychological Health, Self-Efficacy, and Quality of Life in Patients With End-Stage Kidney Disease: Systematic Review and Meta-Analysis.","authors":"Jiayi Zhu, Jianfei Xie, Yating Luo, Xiaoqian Dong, Zitong Lu, Huiyi Zhang, Jingying Wang, Min Liu, Andy Sk Cheng","doi":"10.2196/74414","DOIUrl":"10.2196/74414","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;End-stage kidney disease (ESKD) imposes a significant global health burden, with patients often experiencing poor quality of life (QoL) due to psychological distress and low self-efficacy. Digital health interventions (DHIs) offer potential to address these challenges. However, their effects in this population remain inconsistent, and a comprehensive synthesis of the evidence is lacking.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The present study aims to assess the impact of DHIs on the psychological health, self-efficacy, and QoL of patients with ESKD and to evaluate engagement, adherence, and satisfaction with these interventions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A comprehensive search was conducted across six electronic databases (PubMed, Web of Science, Cochrane Library, PsycINFO, Embase, and CINAHL) up to January 21, 2025. Randomized controlled trials (RCTs) examining the effects of DHIs on psychological health, self-efficacy, or QoL in patients with ESKD were included. Two reviewers independently screened studies, extracted data, and assessed the risk of bias using the Cochrane Risk of Bias Tool (RoB 2). A meta-analysis was performed using Review Manager 5.4, with subgroup analyses by treatment modality, intervention type, and duration. Evidence quality was assessed using the Grading of Recommendation, Assessment, Development, and Evaluation (GRADE) approach.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Twenty-three RCTs involving 2407 patients with ESKD from 12 countries were included. DHIs significantly improved depression (standardized mean differences [SMD] -0.41, 95% CI -0.63 to -0.19, P=.003) and overall QoL (SMD 0.55, 95% CI 0.07-1.03, P=.03). While DHIs did not significantly improve overall self-efficacy (SMD 0.56, 95% CI -0.06 to 1.18, P=.08), a benefit was observed in patients on hemodialysis (SMD 0.59, 95% CI 0.34-0.83, P&lt;.001). Engagement was favorable, with completion rates above 63%, adherence rates of 54%-79%, and generally positive patient feedback on DHIs. Application-based interventions improved self-efficacy (SMD 0.66, 95% CI 0.31-1.02, P&lt;.001) and overall QoL (SMD 0.50, 95% CI 0.04-0.96, P=.003); telemedicine improved depression (SMD -0.88, 95% CI -1.21 to -0.56, P&lt;.001) and self-efficacy (SMD 2.76, 95% CI 2.32-3.20, P&lt;.001); and video-based interventions improved depression (SMD -0.34, 95% CI -0.55 to -0.13, P=.002) and overall QoL (SMD 0.31, 95% CI 0.15-0.46, P&lt;.001). Due to high heterogeneity and risk of bias, evidence quality was rated as low for depression and overall QoL, moderate for general anxiety, and very low for stress and self-efficacy.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;DHIs can significantly improve the psychological health and QoL of patients with ESKD, particularly when tailored to patients' needs and delivered through interactive platforms such as apps and telemedicine. High engagement and positive patient feedback suggest good acceptability in clinical practice. However, low evidence quality","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e74414"},"PeriodicalIF":6.0,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12466795/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145175983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computer-Interpretable Quality Indicators for Intensive Care Medicine: Development and Validation Study.","authors":"Falk von Dincklage, Viktor Karl Bublitz, Oliver Kumpf, Carlo Jurth, Reimer Riessen, Maria Deja, Christiane Maria Schewe, Dirk Schädler, Christian Fuchs, Sebastian Gibb, Christian Scheer, Jens-Christian Schewe, Hartmuth Nowak, Felix Balzer, Michael Adamzik, Gernot Marx, Gregor Lichtner","doi":"10.2196/77077","DOIUrl":"https://doi.org/10.2196/77077","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Quality indicators (QIs) can help assess intensive care quality, identify potential for improvement, and ultimately enhance patient outcomes. Therefore, the German Interdisciplinary Association of Critical Care and Emergency Medicine (DIVI) has developed QIs for intensive care medicine. However, variability in how these are technically implemented across health care facilities currently limits their comparability.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The aim of the study is to develop unambiguous computer-interpretable representations of the DIVI QIs for intensive care medicine using Fast Healthcare Interoperability Resources (FHIR) and to establish a replicable process for translating narrative QIs into standardized digital formats.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We first decomposed the narrative DIVI intensive care medicine QIs into two sets of semantic concepts that characterize (1) the targeted patient population and (2) the care aspect specified by each indicator. We mapped the concepts to international vocabularies, defining a supplementary code system for concepts not appropriately represented in existing vocabularies. The decomposed and semantically mapped QIs were then implemented in FHIR using an implementation guide we previously developed to represent clinical practice guideline recommendations. As the translation process holds risks of inducing logical and semantic deviations, the final FHIR representations were back-translated into a narrative form and reviewed with clinical experts, including the authors of the original QIs. The decomposition and semantic mapping were iteratively adjusted based on the experts' feedback until the results accurately reflected the original intent of the QIs.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The 10 DIVI QIs were decomposed into 31 separately measurable indicators, including 9 structural indicators, 17 process indicators, and 5 outcome indicators. All process and outcome indicators were successfully specified as computer-interpretable representations in FHIR. In total, 58 unique medical concepts were used, of which 52 (90%) could be mapped to concepts from international vocabularies. The remaining 6 concepts-mostly intensive care unit-specific scores or roles-were defined in a supplementary code system. Nested Boolean logic and temporal conditions were fully supported using standard FHIR mechanisms. After iterative adjustments, the final representations were approved as accurate representations of the DIVI QIs by the clinical expert panel.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Our work demonstrates that the structured process developed here enables the unambiguous, computer-interpretable representation of QIs for intensive care. These representations can be used in automated quality management systems to standardize quality assessments across health care facilities. Our newly defined structured process can serve as a blueprint for similar efforts in other specialties. The here","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e77077"},"PeriodicalIF":6.0,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145175993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Cost-Effectiveness of AI-Assisted Colonoscopy as a Primary or Secondary Screening Test in a Population-Based Colorectal Cancer Screening Program: Markov Modeling-Based Cost Effectiveness Analysis.","authors":"Martin Cs Wong, Junjie Huang, Thomas Yt Lam, Louis Hs Lau, Philip Wy Chiu","doi":"10.2196/67762","DOIUrl":"https://doi.org/10.2196/67762","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Colorectal cancer (CRC) is the third most common cancer worldwide and poses a heavy burden on health care systems. Early screening for CRC through colonoscopy can effectively reduce both the incidence and mortality associated with CRC. However, the sensitivity of conventional colonoscopy is limited by the level of experience of physicians. Recently, artificial intelligence (AI)-assisted colonoscopy has been shown to have higher sensitivity in detecting CRC and mitigating the limitations concerning physician experience, but few studies have evaluated the cost-effectiveness of AI-assisted colonoscopy in CRC screening.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to evaluate the cost-effectiveness of various CRC screening strategies, including no screening, fecal immunochemical test (FIT) positive result followed by a conventional colonoscopy, FIT positive result followed by AI-assisted colonoscopy, direct colonoscopy, and direct AI-assisted colonoscopy.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This study modeled a hypothetical population based on current clinical practice in Asia, where CRC screening typically begins at the age of 50 years. The cost-effectiveness of various population-based CRC screening strategies, including AI-assisted colonoscopy, was evaluated by comparing incremental cost-effectiveness ratios (ICERs) and outcome measures such as cancer-related life years lost, number of CRC cases prevented, life years saved, and total cost per life year saved. Data from the international literature and the government gazette were accessed to calculate relevant cost and performance estimates. The data were entered into a decision analysis algorithm based on a Markov model.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Compared to no screening strategy, the ICERs of FIT+colonoscopy (FIT followed by conventional colonoscopy if the FIT result is positive), FIT+AI-assisted colonoscopy (FIT followed by AI-assisted colonoscopy if the FIT result is positive), colonoscopy alone, and AI-assisted colonoscopy were US $138,539, US $122,539, US $203,929, and US $180,444, respectively. When compared with FIT+colonoscopy, the FIT+AI-assisted colonoscopy strategy resulted in fewer cancer-related life years lost (5355 y vs 5327 y), a higher number and proportion of CRC cases prevented (120 vs 132 and 3.7% vs 4.1%), more life years saved (280 y vs 308 y), and lower total cost per life year saved (US $944,008 vs US $854,367). FIT+AI-assisted colonoscopy, which had the lowest ICER (US $122,539) dominated all other strategies, particularly compared to FIT+colonoscopy, with an ICER of -US $36,462. Among primary screening methods, AI-assisted colonoscopy dominated conventional colonoscopy (ICER -US $39,040).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;For an Asian population, FIT followed by AI-assisted colonoscopy represented the most cost-effective CRC screening strategy. It had the lowest ICER and the lowest additional cost among all 4 evaluated strategies.","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e67762"},"PeriodicalIF":6.0,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145149397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preferences of Patients With Tuberculosis for AI-Assisted Remote Health Management: Discrete Choice Experiment.","authors":"Luo Xu, Qian Fu, Xiaojun Wang","doi":"10.2196/77491","DOIUrl":"10.2196/77491","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Tuberculosis remains a major global public health challenge, especially in low-resource settings where long-term treatment adherence and regular follow-up are critical. The integration of artificial intelligence (AI) into remote health management has the potential to improve care delivery and patient outcomes. However, evidence on the preferences of patients with tuberculosis regarding AI-assisted services remains limited.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to examine the preferences of patients with tuberculosis for AI-assisted remote health management services in China, identifying key service characteristics that influence their choices.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A discrete choice experiment was conducted among 203 patients with tuberculosis in Hubei province, China. Attributes and levels were identified through a systematic literature review, qualitative interviews, and expert panel consultations. The final design included 6 attributes: interaction method, service provider, service frequency, service content, out-of-pocket cost, and service integration. Each participant completed 8 choice tasks comparing hypothetical service options constructed based on these attributes. Preferences were analyzed using a mixed logit model to account for preference heterogeneity. Additional subgroup analyses were performed to explore variations in preferences across sociodemographic characteristics.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;All 6 attributes significantly influenced patients' preferences (all P values &lt;.05). Participants strongly favored services involving physician oversight (P&lt;.001), video-based interactions (P&lt;.001), and comprehensive content (P&lt;.001), while higher costs were associated with lower acceptance (P&lt;.001). Subgroup analyses indicated that higher-income patients demonstrated both a greater willingness to pay and a stronger preference for physician involvement. Female participants expressed a lower preference for AI-assisted physician-led services compared to AI-only configurations. Patients with higher educational attainment also reported lower preferences for physician-involved services. Age-related differences were not statistically significant. Across all subgroups, cost remained a critical determinant of service acceptance.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Patients with tuberculosis expressed a clear preference for high-quality, human-integrated remote health management services, emphasizing the importance of physician involvement and personalized, interactive care. These findings suggest that fully AI-driven models may face resistance and that hybrid models combining AI efficiency with professional oversight are more acceptable. Policymakers and service designers should prioritize affordability, provide targeted financial support for populations considered vulnerable, and invest in digital literacy initiatives to enhance equitable access. This study provides critical evidence to ","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e77491"},"PeriodicalIF":6.0,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145149391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Designing Clinical Decision Support Systems (CDSS)-A User-Centered Lens of the Design Characteristics, Challenges, and Implications: Systematic Review.","authors":"Andrew A Bayor, Jane Li, Ian A Yang, Marlien Varnfield","doi":"10.2196/84380","DOIUrl":"10.2196/84380","url":null,"abstract":"","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e84380"},"PeriodicalIF":6.0,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12463333/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145149423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use of and Medical Decision-Making in Portal Messages Among Patients With Type 2 Diabetes: Mixed Methods Study.","authors":"Terrence Liu, Eric A Waselewski, Ailish Dougherty, James D Lee, Nina E Hill, Brianna A Marzolf, Tammy Chang","doi":"10.2196/79413","DOIUrl":"10.2196/79413","url":null,"abstract":"<p><strong>Unlabelled: </strong>We used a mixed-methods approach to characterize and understand the content of secure messages exchanges between patients with type 2 diabetes and their healthcare teams.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e79413"},"PeriodicalIF":6.0,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12463334/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145149367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding the Role of Clinical Decision Support Systems Among Hospital Nurses Using the FITT (Fit Between Individuals, Tasks, and Technology) Framework: Qualitative Study.","authors":"Matthijs Berkhout, Koen Smit, Danielle Sent, Rob Kusters, Johan Versendaal, Thijs van Houwelingen","doi":"10.2196/76025","DOIUrl":"https://doi.org/10.2196/76025","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Clinical decision support systems (CDSSs) have gained prominence in health care, aiding professionals in decision-making and improving patient outcomes. While physicians often use CDSSs for diagnosis and treatment optimization, nurses rely on these systems for tasks such as patient monitoring, prioritization, and care planning. In nursing practice, CDSSs can assist with timely detection of clinical deterioration, support infection control, and streamline care documentation. Despite their potential, the adoption and use of CDSSs by nurses face diverse challenges. Barriers such as alarm fatigue, limited usability, lack of integration with workflows, and insufficient training continue to undermine effective implementation. In contrast to the relatively extensive body of research on CDSS use by physicians, studies focusing on nurses remain limited, leaving a gap in understanding the unique facilitators and barriers they encounter.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to explore the facilitators and barriers influencing the adoption and use of CDSSs by nurses in hospitals, using an extended Fit Between Individuals, Tasks, and Technology (FITT) framework.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A qualitative study was conducted using semistructured interviews with 22 nurses from across the Netherlands, representing 3 hospital types: general (n=9), top-clinical (n=12), and academic (n=1). The sample included a diverse mix of practicing nurses, nurses-in-training, and clinical nurse information officers, with clinical experience ranging from 1.5 to 38 years. Interview transcripts were analyzed thematically, beginning with an inductive coding approach to identify key factors. These were then categorized deductively using the extended FITT framework. In total, 988 code instances were examined. To ensure analytical rigor, the coding process was separately conducted by 2 researchers and reviewed by an expert panel.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 26 distinct factors were identified, categorized into 4 FITT dimensions: technology-individual, technology-task, task-individual, and organizational context. Of these, 11 factors were facilitators (eg, cognition, clarification, and prevention), 7 were barriers (eg, alarm fatigue, poor design, and limited digital proficiency), and 8 were both facilitators and barriers depending on the context (eg, acceptance, workload, and training). In addition, key value tensions emerged, such as the balance between standardization and professional autonomy, and the trade-off between enhanced decision support and increased administrative burden.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The findings underscore the complexity of CDSS adoption in nursing practice, highlighting the interaction of facilitators and barriers across FITT dimensions. Practical recommendations include participatory design processes, targeted training programs, advanced alert management systems, and strong organizat","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e76025"},"PeriodicalIF":6.0,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145137742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}