Latest Submissions Open for Peer Review

Titles/Abstracts of Articles Currently Open for Review:

• Effects of Immersive Virtual Reality Interventions on Symptom Management in Patients With Spinal Disorders: A Systematic Review

  Date Submitted: Oct 12, 2026

  Open Peer Review Period: Oct 12, 2026 - Dec 7, 2026

  • Peer Review Me

  Background: Spinal disorders are associated with persistent or perioperative pain, fear of movement, anxiety, functional limitation, and impaired health-related quality of life. Immersive virtual reality (IVR), delivered through head-mounted displays (HMDs), can combine attentional distraction, graded exposure, behavioral skills training, relaxation, and sensorimotor feedback. However, the evidence relevant to symptom management in spinal populations is fragmented across low back pain, neck pain, and spinal cord injury literatures, while many prior reviews combine immersive and nonimmersive technologies. Objective: Objective: To systematically review randomized evidence on HMD-based IVR interventions for symptom management in adults with spinal disorders, with emphasis on pain, anxiety or fear-related symptoms, quality of life, disability, functional outcomes, adherence, and IVR-related adverse events. Methods: Methods: We designed the review in accordance with PRISMA 2020 and structured the quantitative narrative synthesis according to the Synthesis Without Meta-analysis (SWiM) reporting guideline. The prespecified eligibility framework included adults with spinal disorders or spinal cord injury–related symptom burden; HMD-based immersive VR interventions; randomized or randomized crossover comparisons; and patient-centered symptom or functional outcomes. Searches were executed in PubMed/MEDLINE, Europe PMC, Web of Science Core Collection, OpenAlex (open multidisciplinary index), and CNKI (China National Knowledge Infrastructure; Chinese-language subject-field search) from database inception to 30 September 2026, complemented by backward and forward citation chasing of the included studies. Risk of bias was assessed with the Cochrane Risk of Bias 2 (RoB 2) tool. Results: Results: Thirteen published randomized studies were identified as reasonably compatible with the strict immersive-HMD eligibility concept, spanning chronic low back pain, chronic neck pain, lumbar disc herniation, and spinal cord injury–related neuropathic pain. Across chronic low back pain, skills-based or embodied IVR showed clinically promising improvements in pain interference, fear of movement, disability, or related outcomes in several trials, although a large 2026 three-arm trial found no superiority of active skills-based or distraction IVR over sham VR for the primary pain-interference outcome. For chronic neck pain, several HMD-based sensorimotor or kinematic-training trials improved selected range-of-motion, proprioceptive, disability, or quality-of-life outcomes, but effects on pain were inconsistent and newer trials found little or no incremental benefit over conventional exercise. In spinal cord injury, small randomized trials suggest short-term reductions in neuropathic pain and improvements in embodiment/depersonalization-related outcomes. A 2025 randomized trial in lumbar disc herniation reported improvements in pain, disability, and quality of life after VR-based exercise. Adverse effects were generally mild and self-limited when reported, with cybersickness, dizziness, nausea, or discomfort being the most relevant IVR-specific concerns. Conclusions: Conclusions: HMD-based IVR is a plausible adjunct for symptom management in selected spinal populations, particularly when the intervention explicitly targets pain coping, fear avoidance, relaxation, graded movement, or sensorimotor retraining. The evidence does not support a single uniform effect across all spinal disorders or all IVR modalities. The most defensible clinical interpretation is that benefit is mechanism- and context-dependent, with stronger signals for psychological and functional symptom modulation than for structural disease outcomes. Larger multisite RCTs, standardized IVR reporting, longer follow-up, and clearer separation of immersive from nonimmersive VR are needed.

• Using AI-Enabled User Research Software for Digital Health Innovation: A Content Analysis and Scoping Review

  Date Submitted: Oct 11, 2026

  Open Peer Review Period: Oct 12, 2026 - Dec 7, 2026

  • Peer Review Me

  Background: User experience research is increasingly important for designing, evaluating, and improving digital health technologies, yet many approaches remain manual, resource intensive, and difficult to integrate across the innovation lifecycle. AI-enabled user research software offers capabilities spanning study planning, recruitment, data collection, behavioral analytics, synthesis, and insight translation, but its role in academic digital health research remains unclear. Objective: The objective of our study was to identify and characterize user research software relevant to digital health innovation, examine how these software platforms have been used and reported in peer-reviewed digital health studies involving patient, caregiver, or public end users, and identify methodological gaps and future opportunities for software-supported user research. Methods: We conducted a two-phase study comprising a software identification and content analysis followed by a rapid scoping review informed by Joanna Briggs Institute guidance and PRISMA-ScR. In Phase 1, user research software was identified through a structured Google search and characterized using publicly available website information. In Phase 2, PubMed records published from 2021 onward were searched for use of the identified software platforms from Phase 1, supplemented by a ChatGPT-assisted search. Eligible studies used at least one of these platforms to conduct remote or online user research representing patient, caregiver, or public end users of a digital health technology. Findings were synthesized descriptively and narratively, including software use, theory use, alignment with Phase 1 software capabilities, and translation of findings into digital health innovation changes. Results: Phase 1 identified 36 user research software platforms with capabilities spanning 11 areas of the user research lifecycle, from study planning and participant recruitment through usability testing, behavioral analytics, and insight synthesis. The rapid scoping review included 14 studies. Academic use of the user research software was concentrated on usability testing (9/14), with less frequent use for recruitment (4/14), exploratory qualitative feedback (5/14), structured survey feedback (5/14), and user behavior analytics (4/14). How studies used the user research software to support study planning, innovation experimentation, and insight synthesis were not clearly documented. Academic theories, models, and frameworks were inconsistently integrated with software use, and studies varied in how explicitly they connected the user research findings to implemented or proposed digital health changes. Conclusions: AI-enabled user research software has the potential to support more of the digital health innovation lifecycle than is currently reflected in academic research. Future work should determine how these emerging AI software capabilities can support digital health user research that is rigorous, transparent, and methodologically grounded across design, implementation, and ongoing optimization.

• Comparative effectiveness of digitally delivered interventions for sarcopenia: systematic review with network and component network meta-analyses of randomised controlled trials

  Date Submitted: Oct 11, 2026

  Open Peer Review Period: Oct 12, 2026 - Dec 7, 2026

  • Peer Review Me

  Background: Sarcopenia is characterized by declines in muscle strength, muscle mass, and physical function. Exercise and nutrition are key to its management, and digital delivery may enhance the accessibility of these interventions. Objective: To compare and rank digitally delivered exercise and nutritional strategies and estimate the incremental effects of intervention components using component network meta-analysis (CNMA). Methods: We searched comprehensive electronic databases from inception to July 27, 2026. Randomized controlled trials evaluating digitally delivered exercise and/or nutritional interventions in patients with sarcopenia were included. Risk of bias was assessed using RoB 2. Bayesian network meta-analysis was used to compare and rank different intervention strategies. Intervention components were prespecified and coded for CNMA, with incremental mean differences (iMDs) estimating the added benefit or harm of each component. Methods: We searched comprehensive electronic databases from inception to July 27, 2026. Randomized controlled trials evaluating digitally delivered exercise and/or nutritional interventions in patients with sarcopenia were included. Risk of bias was assessed using RoB 2. Bayesian network meta-analysis was used to compare and rank different intervention strategies. Intervention components were prespecified and coded for CNMA, with incremental mean differences (iMDs) estimating the added benefit or harm of each component. Results: Sixteen studies involving 1,355 participants were included. In the Bayesian NMA, digital exercise, digital nutrition, and combined digital exercise plus nutrition significantly improved handgrip strength (HGS) versus usual care, with MDs of 1.35 kg (95% CrI, 0.33 to 2.54), 2.07 kg (95% CrI, 0.06 to 4.27), and 2.21 kg (95% CrI, 1.15 to 3.56), respectively. In the additive CNMA, automated reminders/prompts had a positive incremental effect on appendicular skeletal muscle mass index (ASMI) (iMD, 1.71 kg/m²; 95% CI, 0.19 to 3.23; P = .027), whereas digital nutrition had a negative incremental effect (iMD, −0.97 kg/m²; 95% CI, −1.74 to −0.19; P = .015). Self-monitoring had a positive incremental effect on gait speed (iMD, 0.38 m/s; 95% CI, 0.16 to 0.60; P < .001). Conclusions: Digitally delivered exercise and nutrition, particularly in combination, may improve handgrip strength in patients with sarcopenia. Automated reminders/prompts and self-monitoring showed positive incremental effects on ASMI and gait speed, respectively, whereas digital nutrition showed an unexpected negative effect on ASMI.

• Evidence Map of Machine Learning and Large Language Models for Emergency Department Triage Accuracy: A Scoping Review

  Date Submitted: Oct 12, 2026

  Open Peer Review Period: Oct 11, 2026 - Dec 6, 2026

  • Peer Review Me

  Background: Emergency department (ED) triage determines treatment priority and resource allocation. Artificial intelligence (AI), including machine learning (ML) and large language models (LLMs), has been increasingly applied to triage, but evidence remains heterogeneous. Objective: This scoping review mapped review-level and primary-study evidence on AI/ML/LLM for ED triage accuracy. Methods: Guided by the Joanna Briggs Institute and reported according to PRISMA-ScR and PRISMA-S, we searched multiple electronic databases for records published in recent years. Independent reviewers screened, selected, and extracted data. Quality was assessed using validated critical appraisal tools for primary studies and reviews. Findings were synthesized thematically. Results: This scoping review provides an evidence map of ML and LLMs for ED triage accuracy. Traditional ML models performed well on structured retrospective data, often improving accuracy and reducing over- and under-triage. LLM performance varied widely across models, versions, prompts, languages, and clinical settings, and no consistent superiority over experienced clinicians was demonstrated. Sensitivity for the highest-acuity category was limited, and over-triage was common, raising safety concerns. Pediatric evidence suggested that experienced nurses often outperformed AI, with additional risks of over-triage and missed high-acuity cases. Electronic decision support systems improved triage consistency but sometimes increased triage time. Most primary studies were retrospective, single-center, or simulation-based, with high risk of bias, and prospective multicenter validation, fairness audits, and patient-centered outcomes remain scarce. Conclusions: AI/ML/LLM triage is promising but immature. Current evidence does not support autonomous AI triage. AI should remain a human-supervised adjunct, beginning with lower-risk clinician-facing applications. Future research should prioritize prospective multicenter validation, patient-centered outcomes, standardized reporting, external validation, fairness audits, workflow integration, human–AI interaction, and continuous safety monitoring.

• Using practice analytics to understand variation and support reflective practice: A modified integrative review

  Date Submitted: Oct 7, 2026

  Open Peer Review Period: Oct 11, 2026 - Dec 6, 2026

  • Peer Review Me

  Background: The health system collects a plethora of electronic health data that is a potentially rich source of information on the performance of health professionals and teams. There is a range of drivers for using electronic health data in this way including desire from health professionals themselves, potential improvements to care quality if clinicians can discover ways to improve their performance through data insights, improving the return on investment of electronic health records and other widely adopted clinical information systems, and policy developments in several jurisdictions expanding practice-based CPD requirements and opportunities for data use. Practice Analytics in Healthcare (PAH) is a term used to describe enquiry into how these diverse Electronic Health Data (EHD) sets support the health workforce engage in performance reflection, professional learning, and quality improvement. Over the last five years a major national program of work in the field of Practice Analytics has been undertaken in Australia investigating the viability of using electronic health data, including data from Patient Administrative Systems (PAS) and Patient Reported Measures (PRMs), to support medical practitioners reflect on their practice and engage in workplace-based CPD activities. Objective: This review was designed to synthesise key findings across a major program of work in Practice Analytics, describe implications for practice, and identify opportunities for future research and development. Methods: A modified integrative review of all selected research outputs (n=45) from the research program was undertaken to synthesise key findings across all projects. Key outputs from this program were analysed to distil key research insights, implications for practice, and recommendations for the future of the field of Practice Analytics. Results: The review analysed a total of 48 outputs from the Practice Analytics program. This analysis identified a range of contributions to the literature particularly related to how Artificial Intelligence can be used to structure EHD to understand health professionals’ performance and scaffold its feedback. Research outputs also identified policy implementation gaps and organisational and ecosystemic barriers to data-informed CPD, and investigated the ethical and legal implications of Practice Analytics. Conclusions: Repurposing routinely collected EHD to support health professionals understand their performance and reflect on their practice is possible. There are many ways to transform data into such insights. Despite this, realising the potential of EHD to support reflective practice requires overcoming many challenges including accessing high-quality data; ethical and equity concerns; broader conceptual, organisational, cultural and ecosystemic barriers; and best-practice approaches to designing data-driven reflective tools for the workforce. Findings from the Practice Analytics program highlighted that operational, organisational, regulatory, cultural and ecosystemic factors influence implementation of practice analytics programs. If data access issues can be overcome data from these systems can be transformed into meaningful indicators of performance that can help engage health professionals in data-driven learning.

• Thinking Like an Industry Scientist: How Evidence Creates Value in Digital Health Companies

  Date Submitted: Oct 9, 2026

  Open Peer Review Period: Oct 11, 2026 - Dec 6, 2026

  • Peer Review Me

  Digital health companies increasingly depend on scientific evidence to raise capital, secure contracts, and stand out in a crowded market, yet little has been written about how science is practiced inside these companies. In 2023 and 2024, I described the role of the industry scientist and the barriers organizations face in integrating science, drawing on experience as head of science at a single company. This viewpoint revisits those claims from a different position: leading a science consultancy that has partnered with, advised, or held discovery conversations with more than 300 digital health and wellness companies. Three earlier claims endure. Science contributes to revenue when it answers questions buyers ask, resource-efficient methods such as retrospective analyses remain the right entry point, and science succeeds when business leaders own its use. Others require revision. Not every company needs a full-time scientific executive, hiring a scientist does not by itself produce a scientific strategy, and for artificial intelligence tools that make clinical claims, the runway before prospective trials become necessary has shortened. Working across many companies also revealed patterns no single role could show. Companies rarely ask for the science they need, evidence must align with business timelines without sacrificing rigor, and some of the most valuable scientific work involves shaping claims, measurement, and product decisions rather than conducting a study. I close with implications for scientists, company leaders, investors, payers and academic training programs, and a call for formal study of how science creates value in industry.

• Social Media as a Platform for Patient Voice: Implications for Patient-Centred Care and Healthcare Decision-Making – A Scoping Review

  Date Submitted: Oct 9, 2026

  Open Peer Review Period: Oct 11, 2026 - Dec 6, 2026

  • Peer Review Me

  Background: Patient experience is an important component of healthcare quality, but conventional approaches to collecting data on the patient experience may not fully capture unsolicited and real-world experiences. Social media provides an abundance of patient-generated content and a complementary source of information on healthcare experiences. However, the ways in which social media data have been used to capture and assess the patient experience in different healthcare settings has not been systematically mapped. Objective: This scoping review aims to systematically map available evidence on the social media platforms used by patients to share healthcare experiences, identify the types and forms of information shared, and describe how patient-generated information could be used to inform and improve healthcare service delivery. Methods: We conducted a scoping review following the Joanna Briggs Institute (JBI) methodology for scoping reviews and reported it according to the criteria outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). We searched Embase, MEDLINE, and Web of Science in February and April 2025 to identify studies examining patients’, family members’, or caregivers’ use of social media to share experiences and perceptions of healthcare services and providers. Two independent reviewers screened studies and extracted data using Covidence, with disagreements resolved through discussion and consultation with a third reviewer. Extracted data were descriptively analysed and thematically synthesised to map social media use, patient-shared information, and applications of data to improve healthcare quality. Results: Sixty studies were identified as meeting the inclusion criteria. Facebook and Twitter/X were the most commonly used platforms, followed by Yelp and physician-rating websites. Patient-generated content covered informational, experiential, interpersonal, clinical, and psychosocial dimensions of healthcare, with platforms used for peer support, information-seeking, provider evaluation, and quality-improvement activities. The evidence base was predominantly observational and qualitative, with limited mixed-methods and experimental research. Social media provided timely and naturalistic insights into patient experiences and patient-defined dimensions of care, including communication, responsiveness, waiting times, and quality. However, evidence linking social media data to provider choice or objective clinical outcomes was limited and inconsistent. Conclusions: Social media provides a rich and timely source of patient-generated information and is an important platform for patient engagement, empowerment, and peer support. It has potential to complement conventional patient-experience measures and inform healthcare quality improvement, but its effectiveness in improving health-system outcomes remains insufficiently established. Further research should combine computational methods that analyse large-scale social media data with qualitative approaches that capture the context and meaning of patient narratives, strengthen evidence from diverse settings, particularly low- and middle-income countries, and evaluate how social-media-informed interventions can be responsibly integrated into healthcare systems. Clinical Trial: Not applicable

• Discussions of Oral Versus Injectable Glucagon-Like Peptide-1 Receptor Agonists for Weight Loss on Reddit: Content Analysis

  Date Submitted: Oct 9, 2026

  Open Peer Review Period: Oct 11, 2026 - Dec 6, 2026

  • Peer Review Me

  Through content analysis of 73,740 posts on glucagon-like peptide-1 receptor agonists (GLP-1 RAs) from 44 Reddit communities (October 2025-September 2026), we found the share of oral GLP-1 RA discussion rising from 1.2% to 7.6%. Compared with injectable posts, oral posts more frequently discussed medication-use practice (47.4% vs 40.7%) and cash-pay and direct-to-consumer channels (13.7% vs 8.1%), with less attention to diet, psychosocial concerns, appearance, and comorbidities. These findings suggest oral GLP-1 RAs are framed as consumer goods for self-acquisition and self-management rather than as medical interventions embedded in the body like injectables, a framing that might lower treatment barriers while weakening recognition of obesity as a chronic disease requiring medical management and increasing risks of nonstandard use.

• Short-Form Video Use and Sleep in Adolescents: Systematic Review

  Date Submitted: Oct 10, 2026

  Open Peer Review Period: Oct 11, 2026 - Dec 6, 2026

  • Peer Review Me

  Background: Short-form video (SFV) has transformed how entertainment is consumed. This is especially true for the adolescent population, which is the highest proportion of users, with 74% of 16-17 year olds having TikTok. Studies have shown that SFV can be addictive, through features such as infinite scrolling and autoplay features. This has led to concerns regarding the impact of SFV on sleep in adolescents. Objective: The objective of this systematic review was to examine the impact that SFV has on adolescent sleep. Methods: We used the PRISMA guidelines to help form our study and conducted searches of Embase, MEDLINE, and PubMed, supplemented by a grey literature search conducted using Google Scholar. This study used an extraction table modelled after the Joanna Briggs Institute extraction table, which collected data on study characteristics, participant characteristics and key quantitative and qualitative findings. Moreover, the Joanna Briggs Institute critical appraisal checklist was used to assess the risk of bias. The data searched covered literature published from January 1st, 2000 to 31st December, 2025. Results: From the initial search, 753 references were identified across three databases and a grey literature search. After removal of 288 duplicates, 465 original references remained eligible for title and abstract screening, of which 47 progressed to full-text screening and 14 were selected for data extraction. At extraction, 2 studies were removed as they had erroneously passed to the full text stage, and a further 4 studies assessing physical activity were subsequently excluded following removal of this secondary outcome, leaving a total of 8 studies which assessed SFV use and sleep. All 8 included studies were cross-sectional, with six of the studies being conducted in China, one in Greece and one in Spain. Study samples ranged from 219 to 3,309 (total N = 9,060). Five studies reported poorer sleep with SFV use, one reported better sleep, and two found no statistically significant association. Only one of the five studies reporting harm had adjusted for any covariate. Unadjusted analyses reported harm more often than adjusted analyses, which tended to report no association or even a protective association between SFV use and sleep. Conclusions: This review found that the current evidence is insufficient to establish a robust, independent association between short-form video use and sleep outcomes. Findings across the eight included studies were mixed: five reported poorer sleep, one reported better sleep, and two found no association. However, studies reporting harm were largely unadjusted, while the most rigorously adjusted studies found no or protective associations, suggesting that the negative effects on sleep observed in the unadjusted analyses might be attributable to confounding. Clinical Trial: The protocol for this systematic review was preregistered in the PROSPERO database (Reference ID: CRD420261360477)

• Feasibility, Acceptability, and Patient-Reported Outcomes of a Digital Multidimensional Intervention for Post-COVID-19 Condition: A Pilot Randomized Controlled Trial

  Date Submitted: Oct 9, 2026

  Open Peer Review Period: Oct 9, 2026 - Dec 4, 2026

  • Peer Review Me

  Background: Post-COVID-19 condition (PCC) involves central nervous system symptoms such as cognitive impairment, fatigue, emotional distress, and disability that negatively affect daily functioning and quality of life. Although digital health interventions may improve access to rehabilitation for people with PCC, evidence regarding their feasibility, safety, and user acceptability remains limited. Objective: This pilot randomized controlled trial aimed to assess the feasibility, adherence, acceptability, and exploratory patient-reported outcomes (PROs) of SuperCAP, a fully remote, multidimensional intervention for adults with PCC and cognitive symptoms, developed through a co-design process involving people with PCC and multidisciplinary clinicians. Methods: Adults with PCC and self-reported cognitive complaints were randomly allocated (2:1 ratio) to a SuperCAP intervention group (IG) or a standard care control group (CG). The intervention comprised 60 digital sessions combining psychoeducational videos on symptom management and pacing, cognitive exercises with metacognitive feedback, and gamified cognitive stimulation, completed flexibly over 12 to 20 weeks. Primary outcomes included feasibility (recruitment and retention), adherence, and acceptability. Secondary exploratory outcomes included patient-reported cognitive functioning, emotional symptoms, disability, instrumental activities of daily living (IADL), and perceived subjective improvement. These outcomes were assessed using standardized questionnaires, while perceived subjective improvement was explored through structured follow-up interviews. Questionnaire-based outcomes were analyzed using non-parametric between-group and within-group comparisons, while categorical outcomes were analyzed using chi-square or Fisher's exact tests. Results: A total of 60 participants were randomized to the IG (n=39) or CG (n=21), corresponding to a recruitment rate of 49% (60/121). The retention rate was 91% (55/60). Among IG who completed the follow-up assessment, 71% (25/35) met the predefined adherence criterion of completing at least 80% of the sessions, with a median of 60 completed sessions (IQR: 39-60). Acceptability was high, with a median total satisfaction score of 7.4 out of 10 (IQR: 6.7-8.4). Most participants considered the intervention helpful (88%), and 71% reported incorporating new healthier daily habits. No adverse events or symptom worsening attributable to the intervention were reported. No significant between-group differences emerged in questionnaire-based PROs. However, subjective cognitive improvement was reported significantly more frequently in the IG than in the CG (IG: 37%; CG: 5%; P<.01). Exploratory within-group analyses suggested relative cognitive, emotional, and functional stability in the IG. In contrast, the CG showed significant worsening in anxiety (GAD-7, P=.04) and disability (WHODAS 2.0, P=.02). Conclusions: SuperCAP appears to be a feasible, acceptable, and well-tolerated fully remote intervention for adults with PCC and cognitive complaints. Although questionnaire-based PROs did not differ significantly between groups, findings from subjective reports and exploratory analyses support further evaluation of the intervention in a fully powered randomized controlled trial. Clinical Trial: ClinicalTrials.gov identifier: NCT06315894. Registered March 15, 2024 (First submitted); March 18, 2024 (First posted).

• Design Considerations for a Two-Sided Marketplace for Reusable Digital Therapeutics Software Components: Qualitative Interview Study

  Date Submitted: Oct 9, 2026

  Open Peer Review Period: Oct 9, 2026 - Dec 4, 2026

  • Peer Review Me

  Background: Digital therapeutics (DTx) development remains resource-intensive and fragmented, and reuse of validated software components could reduce duplicated engineering effort. However, exchange of reusable components in DTx is not well characterized, and existing platform-economics research has not examined the design of a two-sided marketplace specifically for DTx software components. Objective: This study aimed to identify stakeholder-perceived design considerations in a two-sided marketplace for reusable DTx software components and to use these to propose alternative marketplace concepts. Methods: We conducted 24 semi-structured interviews with digital health researchers and professionals. Data were analyzed using hybrid deductive-inductive codebook thematic analysis. The Magic Triangle provided the initial deductive structure for data collection and coding. One researcher coded all transcripts; a second independently applied the developing codebook to five transcripts to refine definitions and consistency. A total of 17 parent themes were synthesized into 12 design considerations – 7 tensions (bipolar trade-offs) and 5 design axes (dimensions with multiple viable positions) – which informed 3 author-generated marketplace concepts. These concepts were subsequently mapped onto Wortmann et al.’s (2024) Platform Navigator to translate them into structured specifications for prototyping. Results: Participants described 12 design considerations concerning target users and host, component scope and customization, quality labels, verification, geographic scope, pricing, interoperability, platform launch, risks and market niche. Validation/verification and pricing strategy emerged as the two most extensively discussed considerations in the dataset. The three resulting concepts emphasized a regional startup ecosystem, a low-barrier to entry research-oriented hybrid marketplace, or a global marketplace with stricter entry requirements. Conclusions: Stakeholders identified recurring design considerations for a DTx software-component marketplace, combining competing priorities in governance, openness, assurance, scalability and early platform growth. The concepts provide hypotheses for prototyping and evaluation; the study did not test technical feasibility, market demand, regulatory acceptability or effects on development cost or time.

• Facilitators and Barriers to Engagement in a Home-Based Augmented Reality Rehabilitation Program for Children with Cerebral Palsy: A Qualitative Study

  Date Submitted: Oct 8, 2026

  Open Peer Review Period: Oct 8, 2026 - Dec 3, 2026

  • Peer Review Me

  Background: Augmented reality (AR) has shown promise for enhancing engagement in pediatric rehabilitation. We partnered with an existing, commercially available AR mobile application, Augment Therapy™, to develop an adaptive game module for children with cerebral palsy (CP). A pilot clinic-based trial demonstrated initial feasibility and favorable reception of the platform among children with CP. Objective: This qualitative study aimed to identify and categorize the facilitators and barriers to participation in a 4-week home-based intervention using the Augment Therapy™ AR mobile app among children with CP and their parents. Methods: Children with CP (Gross Motor Function Classification System [GMFCS] levels I-II, ages 5-18 years, English- or Spanish-speaking) and their parents were recruited at a single pediatric tertiary care center between October 2024 and August 2025. Participants used the Augment Therapy™ app at home for 4 weeks, with a goal of at least 3 sessions per week consisting of five 2-minute assigned games followed by free play. Upon completion, parents completed a one-on-one semi-structured interview (20-60 minutes) to share their own and their child's experiences with the app. Interviews were audio-rOf 97 patients approached, 46 declined and 35 enrolled; 8 were lost to follow-up, 2 withdrew, and 25 completed the 4-week program. Nineteen parents completed a post-intervention interview. Four themes emerged: (1) the app supplemented physical therapy (PT) in an engaging way; (2) the app's user-friendly design motivated continued use; (3) technology issues, including login challenges and app crashing, were a barrier; and (4) concerns about additional total screen time were a barrier, particularly for younger children with CP.ecorded, transcribed via secure, HIPAA-compliant Microsoft Teams artificial intelligence transcription, and analyzed using rapid qualitative analysis to identify facilitators of and barriers to app engagement, motivation, usability, and integration into the home routine. App usage and demographic data were also collected. Results: Of 97 patients approached, 46 declined and 35 enrolled; 8 were lost to follow-up, 2 withdrew, and 25 completed the 4-week program. Nineteen parents completed a post-intervention interview. Four themes emerged: (1) the app supplemented physical therapy (PT) in an engaging way; (2) the app's user-friendly design motivated continued use; (3) technology issues, including login challenges and app crashing, were a barrier; and (4) concerns about additional total screen time were a barrier, particularly for younger children with CP. Conclusions: AR-based mobile applications such as Augment Therapy™ may be a feasible and acceptable adjunct to PT for increasing physical activity engagement in children with CP. Modifiable barriers—particularly technology reliability and parental screen-time concerns—should be addressed to maximize impact and sustain engagement in future implementations. Clinical Trial: Using Augmented Reality to Promote Physical Activity in Children With Cerebral Palsy, NCT06096272, https://clinicaltrials.gov/study/NCT06096272

• Rethinking methods for studying online information and health decision-making: a participant-centred approach

  Date Submitted: Oct 8, 2026

  Open Peer Review Period: Oct 8, 2026 - Dec 3, 2026

  • This manuscript needs more reviewers Peer Review Me

  Online information, accessed through social media, websites, apps and artificial intelligence, is frequently described as shaping health attitudes and decision-making. Many methodological approaches to understanding how online information influences decisions provide important insights into online content, information exposure, or user perspectives, but often struggle to capture the personalised and algorithmically curated digital environments people encounter, how they engage with information as it appears in their everyday lives, and how such encounters intersect with wider social, cultural and personal contexts to shape decision making. New methodological approaches are needed that can generate meaningful evidence about the varied realities of navigating online health information and the role it plays in decision-making. Drawing on reproductive justice and feminist principles that privilege participant expertise and lived experience and informed by literature and collaboration with lived and learnt experts, we co-designed a participant-centred longitudinal content-oriented study exploring the influence of online information on contraceptive decision-making. Inspired by Photovoice approaches, participants documented in real time the online information they encountered over a one-month period, capturing content presented through their own tailored digital environments. Interviews conducted before and after the collection period explored contraceptive experiences, attitudes, decision-making and reflections on the role of online information. By combining real-time documentation with in-depth interviews, this approach enabled interpretations of participants lived encounters with online information, including algorithmic changes, emerging trends, current events, and the types of content that resonated with them. Collaborators with lived experience were involved throughout the methodological development and interpretation and analysis of findings. This participant-selected content-oriented longitudinal design demonstrates the value of methodologies that capture and generate evidence on lived digital and offline realities as they unfold in real time. Understanding how online information shapes health decision-making requires moving beyond approaches that treat the internet as an isolated and static influence on behaviours. Online information is not a singular, stable object of inquiry but dynamic, personalised, algorithmically mediated and embedded within broader social, cultural, and political contexts. Methodologies that recognise this complexity are essential for generating evidence capable of informing empathetic and meaningful health care and support and care for people prioritising their needs and navigating complex health decisions.

• A Single-Group, Longitudinal Effectiveness Study of an Artificial Intelligence (AI)-Supported Tool for Teaching Spanish-speaking Mental Health Providers the Skill of Reflective Listening

  Date Submitted: Oct 7, 2026

  Open Peer Review Period: Oct 8, 2026 - Dec 3, 2026

  • Peer Review Me

  Background: Mental health services in Latin America face shortages of trained professionals, and access to expertise in evidence-based communication skills remains severely limited. In particular, motivational interviewing (MI) "reflections" are a foundational communication skill for empathic listening, but opportunities for individualized practice with feedback are limited. Digital resources supported by artificial intelligence (AI) may expand opportunities for skill development. Objective: To evaluate an AI-supported online training tool designed to improve the quality of MI reflections among psychologists, psychology students, and community-based providers in Honduras, and to characterize participants’ experiences with the training. Methods: We conducted a longitudinal single-group study among 56 Spanish-speaking adults in Honduras, including 22 psychologists, 11 psychology students, and 23 community leaders working in mental health service roles. Participants completed four rounds of online exercises in which they generated reflective responses to 10 client statements and received AI-generated scores ranging from 0 to 1, along with examples of high-quality responses. The scoring model used the Prompt-Aware Margin Ranking (PAIR) framework, a RoBERTa-based cross-encoder trained using ordinal categories identifying "skilled" responses, "acceptable" responses, and "MI-inconsistent" responses. Participants also received a live booster session with a psychologist between the second and third rounds. Changes in reflection scores were examined longitudinally, and differences in performance among the three participant groups were assessed. Semi-structured interviews were conducted with 12 purposively selected participants to identify perceived benefits, limitations, and recommendations for improving the intervention. Results: All participants completed all four rounds, contributing 4480 reflection responses. Mean scores increased across rounds (P<.001). Improvements in scores were observed among psychologists (P=.009), psychology students (P<.001), and community leaders (P<.001). The proportion of responses classified as MI-skilled increased from 26.3% in the first round to 48.2% in the fourth (P<.001), while MI-inconsistent responses decreased from 42.4% to 22.7% (P<.001). Improvements were greatest among community leaders, whose skilled reflections increased from 5.4% to 37.7%. The live booster was associated with significant score improvements over-and-above the linear trend (P<.001), with notable benefit among community leaders (P=.002). Interview participants generally described the tool as engaging, useful, and helpful for translating knowledge about reflective listening into practice. Participants valued both the automated feedback and live booster session, while recommending greater cultural and contextual adaptation of scenarios to Honduran experiences. Conclusions: An AI-supported training platform combined with brief human reinforcement was associated with substantial short-term improvements in the quality of MI reflections among psychologists, psychology students, and community-based providers in Honduras. The largest gains occurred among participants without formal training in clinical psychology, suggesting that the tool could improve the quality of interactions in environments relying on paraprofessionals. Future controlled studies should determine whether improvements are sustained and translate into changes in communication with clients and subsequent clinical outcomes. Clinical Trial: Not applicable

• FDA Review Timeliness, Regulatory Capacity, and Postmarket Reporting During the Rise of AI-Enabled Medical Devices: Cross-sectional Study

  Date Submitted: Oct 7, 2026

  Open Peer Review Period: Oct 8, 2026 - Dec 3, 2026

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  Background: AI-enabled medical devices are increasing rapidly, but whether FDA review capacity, regulatory resources, and postmarket surveillance indicators have scaled in parallel is unclear. Objective: To analyze temporal trends in time to decision for AI-enabled versus non-AI devices, (Center for Devices and Radiological Health) CDRH resources and workload, review timeliness, and medical device reports. Methods: Cross-sectional analysis of publicly available FDA data from 2015 to 2025. Traditional 510(k) clearances, CDRH program activity metrics, MDUFA performance measures, statutory response metrics, and MAUDE reports information was extracted from FDA publically available databases and FDA Reports to Congress. Main outcomes included review time trends; funding composition, workload, full-time equivalents, and workload per full-time equivalent; MDUFA goal performance; statutory on-time response rates; and MAUDE report counts and rates. Results: From 2017 to 2025, AI-enabled Traditional 510(k) clearances increased from 20 of 2614 (0.77%) to 288 of 2655 total (10.85%; annual growth rate, 39.30%). Annual median review time increased by 3.94 days/year for AI-enabled devices, and 1.93 days/year for non-AI devices. AI-enabled median review time was higher in 12 of 15 shared clinical specialties. CDRH user-fee share increased from 26.42% to 46.90%, and pre-submissions increased from 2154 to 3910. MDUFA 510(k) monthly performance shortfalls were greatest in 2021 and 2022 (11.7% and 9.0%). Median statutory on-time response rates were 30% for 513(f)(2), 29% for 513(g), and 77% for postmarket surveillance plans. MAUDE included 17,707,433 all-device reports; AI-enabled devices accounted for 2097 reports, including 1847 malfunctions, 240 injuries, and 10 deaths. Conclusions: Rising AI-enabled device volume, steeper annual review-time increases, increasing user-fee dependence, selected MDUFA shortfalls, low information-request response performance, and expanding postmarket reporting signal volume suggest growing operational pressure on FDA device review and surveillance functions.

• How eHealth Literacy Takes Shape: A Qualitative Study Among Chinese Men Who Have Sex With Men

  Date Submitted: Oct 7, 2026

  Open Peer Review Period: Oct 8, 2026 - Dec 3, 2026

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  Background: EHealth literacy is essential for men who have sex with men (MSM) to effectively access, evaluate, and use digital health information for health self-management. Understanding the factors associated with eHealth literacy within China’s specific cultural and digital information environment is important for developing effective digital health interventions for MSM. Qualitative research can provide in-depth insights into individuals’ experiences, while capturing the contextual and sensitive dimensions of MSM interview data. Large language models (LLMs) are increasingly being applied to qualitative data analysis. However, limited research has evaluated their ability to interpret qualitative data rich in complex cultural and identity-sensitive contexts. Objective: To compare researcher-led and LLM-assisted thematic identification and interpretation when analysing culturally embedded and sensitive qualitative data, and to explore factors associated with eHealth literacy among Chinese MSM through integrated human-LLM analysis. Methods: We conducted a multistage qualitative study involving semistructured interviews with 18 Chinese MSM recruited through purposive and maximum-variation sampling. Data analysis was guided by the context-specific LLM workflow. Researchers conducted thematic analysis of the Chinese interview transcripts and established a standardized workflow for LLM-assisted analysis. ChatGPT 5.6, DeepSeek R1, and Gemini 3.5 conducted thematic analyses using the same interview data and analytical tasks. We systematically compared researcher-led and LLM-assisted outputs in terms of theme identification, interpretive depth, and contextual understanding, and revisited the original transcripts to verify the evidentiary basis and contextual appropriateness of the interpretations. A final thematic framework of factors associated with eHealth literacy among Chinese MSM was developed through researcher verification, comparison, and integration. Results: The researcher-led, Gemini 3.5-, DeepSeek R1-, and ChatGPT 5.6-assisted analyses respectively identified 6-14, 4-11, 4-18, and 9-42 themes-subthemes. All four analytical approaches converged on three common themes: individual cognitive foundations and experience, the digital health information environment, and social support. LLMs were able to identify culturally embedded expressions and provide contextually appropriate interpretations. However, the LLM-generated outputs differed in their coding emphases and interpretive styles, with Gemini 3.5-generated outputs more frequently exhibiting overinterpretation. A final integrated coding framework comprising six themes and 14 subthemes was developed to characterize factors associated with eHealth literacy among Chinese MSM: individual cognitive foundations and experience; digital technology and health information practices; digital health information environment; perceptions of discrimination against MSM; social support; and medical insurance policy gaps. Conclusions: EHealth literacy among Chinese MSM is associated with individual, digital, sociocultural, and health service factors. LLM-assisted coding can provide additional perspectives in analysing culturally embedded and sensitive qualitative data, but researcher verification remains important to ensure that generated interpretations are consistent with the original evidence.

• Interventions to Support Eco-Directed Sustainable Prescribing of Medicines: Scoping Review

  Date Submitted: Oct 6, 2026

  Open Peer Review Period: Oct 7, 2026 - Dec 2, 2026

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  Background: Medicines contribute substantially to healthcare-related greenhouse gas emissions, yet it is unclear what interventions have been developed and/or implemented to support prescribers with the selection of medicines with a lower environmental impact. Objective: This scoping review mapped interventions used or recommended to support eco-directed sustainable prescribing of medicines. Methods: The Joanna Briggs Institute (JBI) methodology was used, and the review reported according to PRISMA-ScR. Eight large databases were searched, including MEDLINE, Embase, PsycINFO, Scopus, Web of Science, CINAHL, IEEE Xplore, and GreenFILE. Search terms relating to ‘environmental sustainability’, ‘prescribing’ and ‘medicines’ were used. Eligible evidence was charted and synthesised descriptively, with primary research studies critically appraised to contextualise findings. Results: Five primary research studies and three policy/guidance documents were included. All primary studies were conducted in high-income hospital settings and focused on respiratory care or anaesthesia. Three approaches were identified: standalone education, education with digital support, and education with hospital-wide interventions. All five studies reported reductions in the targeted high-carbon medicine or prescribing practice, with some also reporting lower CO₂e emissions and costs. Key components that helped EDSP included brief face-to-face education, optimisation of fresh-gas flow, reduced desflurane availability and regular feedback. Policy documents extended these approaches through recommendations on prescribing guidance, formularies, procurement and governance. Conclusions: Primary research studies were found to concentrate on a small number of therapeutic areas. Future research should explore wider clinical contexts and examine how environmental information can be co-designed with prescribers and embedded into electronic prescribing and decision-support systems without compromising clinical judgement, patient choice or workflow. Clinical Trial: N/a

• Digital Technology-Supported Unstructured Physical Activity Interventions for Adults: Scoping Review

  Date Submitted: Oct 6, 2026

  Open Peer Review Period: Oct 6, 2026 - Dec 1, 2026

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  Background: Unstructured physical activity integrates movement into everyday life rather than requiring dedicated exercise sessions and may provide a flexible alternative to structured exercise. Digital technologies may facilitate its integration into daily routines through self-monitoring, prompts, goal setting, feedback, and other behavior change support. However, evidence on digitally supported unstructured physical activity remains fragmented. Objective: This scoping review aimed to characterize digital technology–supported unstructured physical activity interventions for adults, examine their conceptualization, intervention and digital technology characteristics, and theoretical foundations, and synthesize evidence on effectiveness, feasibility, and acceptability. Methods: This scoping review followed the Arksey and O’Malley framework and PRISMA-ScR guidelines. PubMed, Web of Science Core Collection, Embase, Cochrane Library, CINAHL, and Scopus were searched from inception to September 1, 2026, supplemented by citation searching. Eligible studies involved adults and evaluated digitally supported physical activity integrated into daily routines without requiring a dedicated exercise session. Two reviewers independently screened studies and charted data. Descriptive statistics, descriptive content analysis, and narrative synthesis were used. Results: Thirty-eight records representing 33 distinct studies were included. Unstructured physical activity was categorized into lifestyle-integrated physical activity (n=17), walking (n=8), lifestyle-integrated functional exercise (n=5), and zero-time exercise (n=3). Most interventions combined digital technologies with human support (n=27). Digital technologies were used primarily to support self-regulation, knowledge and skills, professional and social support, and engagement and maintenance. Only 15 studies explicitly reported a theoretical framework, most commonly habit formation theory. Among 17 studies assessing effectiveness, favorable findings were most consistent for physical activity and sedentary behavior, whereas psychosocial, physical function and fitness, and clinical and physiological outcomes were more variable. Feasibility and acceptability were generally favorable, although declining adherence, technical difficulties, limited personalization, and poorly timed or intrusive prompts were reported. Conclusions: Digital technologies show promise for supporting the integration of unstructured physical activity into everyday life. Evidence is most encouraging for physical activity and sedentary behavior, with generally favorable feasibility and acceptability; however, evidence on broader health benefits and long-term maintenance remains limited and heterogeneous. Future research should clarify the conceptual and classification framework of unstructured physical activity, develop more personalized and context-sensitive digital interventions, identify optimal intervention components and activity doses, and evaluate sustained effectiveness using rigorous designs and longer follow-up.

• Feasibility and Acceptability of Mixed Reality Cognitive and Physical Training and Multicomponent Exercise with Tai Chi in Older Adults With Cognitive Frailty: A Pilot Randomized Controlled Trial

  Date Submitted: Oct 1, 2026

  Open Peer Review Period: Oct 6, 2026 - Dec 1, 2026

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  Background: Mixed reality–based combined cognitive and physical training (MR-CPT) may offer an innovative approach for older adults with cognitive frailty; however, its feasibility and acceptability in this population remain unexplored. Objective: This study examined the feasibility and acceptability of an 8-week MR-CPT intervention among older adults with cognitive frailty symptoms. Methods: Participants were randomized to an 8-week MR-CPT intervention or dose-matched multicomponent exercise +Tai Chi (ME + Tai Chi), attending sixteen 45-minute group sessions. Feasibility was assessed using recruitment, retention, intervention adherence, intervention engagement, and adverse events. Acceptability was evaluated using formative assessments of satisfaction with and perceived fit of the intervention components and post-intervention ratings of feasibility, acceptability, and appropriateness. Outcomes were summarized using descriptive statistics. Results: Of 39 individuals screened, 30 were eligible for the study (recruitment rate: 76.9%), and 24 completed the study (retention rate: 80.0%). MR-CPT participants achieved 92.8% session adherence, and achieved high levels of intervention engagement, with the mean task accuracy ranging from 83.72% to 98.30% and reaction time from 1.50 to 2.01 seconds across 16 sessions. Formative evaluation of acceptability indicated high satisfaction with (4.67 ± 0.54; 93.4%) and perceived fit of MR-CPT (4.30 ± 0.59; 86.0%) on a 5-point rating scale. Post-intervention evaluations showed high overall acceptability (4.97 ± 0.07; 99.4%), with similarly high ratings for the three subscales: acceptability (4.96 ± 0.14; 99.2%), appropriateness (5.00 ± 0.00; 100%), and feasibility (4.94 ± 0.11; 98.8%). Conclusions: The 8-week MR-CPT intervention was feasible and highly acceptable among older adults with cognitive frailty symptoms. These findings support further evaluation of MR-CPT in larger trials to determine its effectiveness for improving cognitive and physical function and its potential for broader implementation. Clinical Trial: ClincialTrials.gov (NCT07652996)

• Effectiveness of social media recruitment to a large web-based population cohort study: An observational analysis

  Date Submitted: Oct 5, 2026

  Open Peer Review Period: Oct 6, 2026 - Dec 1, 2026

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  Background: Rising global social media use offers health researchers new ways to reach large, diverse populations. However, few studies have assessed the effectiveness of social media advertising for recruiting into longitudinal, population-based cohorts with broad eligibility criteria. Building on prior findings that social media recruitment is scalable and cost-effective in comparison to other methods, we evaluated the effectiveness of social media advertising, including paid agency delivered material, to inform future tailored recruitment strategies. Objective: The objective of this study was to compare the sociodemographic characteristics of participants > 16 years recruited via social media into an extension of the Generation Scotland longitudinal cohort (‘NextGenScot’) with the general Scottish population. Second, to use engagement and conversion metrics to assess the performance of platforms and advertisement styles in a responsive, real-world recruitment setting. Methods: Recruitment to NextGenScot ran from May 2022-June 2025 with broad eligibility (anyone aged > 12 years living in Scotland). Multiple recruitment channels were used, including paid agency-led campaigns on Meta (Facebook/Instagram), TikTok, Snapchat and YouTube. Recruitment routes were determined through self-report questionnaire. Platform and advertisement performance were assessed using click-through rate (CTR), cost per click (CPC), and estimated sign-ups. Results: Of N=13,619 NextGenScot participants >16 years, N=6,705 (49.2%) reported recruitment via social media. Compared with the Scottish population, social media recruits were more likely to be female (80.5% vs 51.9%) and older (41.7% vs 30.8% > 60 years), while socioeconomic and geographic representation broadly matched population estimates. Facebook had the highest CTR (0.87%) and estimated sign-ups. Snapchat generated the most impressions and lowest CPC (£0.04). A professionally produced TV-style advert adapted for social media generated the most sign-ups, and short-form video outperformed image-based ads. Conclusions: Social media, including a paid reactive approach, was an effective and adaptable recruitment tool, achieving broad geographic and sociodemographic coverage. Findings highlight the potential of responsive social media approaches to support recruitment into large-scale research studies. Clinical Trial: NA

• A Condition for Authorizing Cross-Institutional Clinical AI in Latin America: Who Resolves the Discrepancy?

  Date Submitted: Oct 5, 2026

  Open Peer Review Period: Oct 6, 2026 - Dec 1, 2026

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  Clinical artificial intelligence (AI) tools increasingly draw on records held by more than one institution. Latin American countries, from Uruguay's national platform to recent laws in Colombia, Brazil, Argentina, Chile, and Mexico, are building the channels for this exchange. The provisions we examined address record custody, exchange, validation, and correction, including duties toward recipients of corrected data, but we did not identify one that designates who resolves a clinically relevant discrepancy between two institutions' records. Such discrepancies are common at transitions of care and can harm patients, and the closest international arrangements assign only part of that responsibility. This Viewpoint argues that where a clinical AI tool draws on records from more than one institution, local authorization should require an explicit agreement on who resolves clinically relevant discrepancies between those records, and on how the tool behaves pending resolution, even where each institution is meeting its existing duties to exchange, correct, and rectify the data in its charge. We explain what assigning that responsibility means: naming in advance who resolves the conflict, how quickly, and how the tool behaves meanwhile. We place it within an author-proposed framework compared with existing guidance, weigh its costs and the strongest objections, and propose next steps. The framework is not validated, no patient data were analyzed, and we do not claim that requiring the agreement improves implementation, safety, equity, or sustainability, of health care ad health systems, which requires evaluation.

• Participatory Technology Development for Aging in Place in a Rural Community: A Qualitative Living Lab Study

  Date Submitted: Oct 3, 2026

  Open Peer Review Period: Oct 4, 2026 - Nov 29, 2026

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  Background: Demographic change, limited care resources, and workforce shortages pose particular challenges for supporting older adults in rural communities. Although Internet of Things (IoT)-based technologies may contribute to independent living and aging in place, their potential remains limited when solutions do not reflect older adults’ everyday needs, capabilities, and living contexts. Participatory approaches may help bridge this gap by involving older adults directly in exploring and developing technologies under real-world conditions. Objective: This study investigated what can be learned from the development and implementation of a participatory Living Lab for technology-supported aging in place in a rural community. It examined the interplay between older adults’ participation, Living Lab infrastructure, the perceived relevance of technological applications, and conditions for sustainability and transfer. Methods: LoRaLAB was established as a participatory Living Lab in rural Germany comprising an IoT-equipped smart living apartment and a creative workshop. The exploratory qualitative study combined participatory workshops and accompanying focus group interviews with older adults, complemented by expert interviews with stakeholders from comparable initiatives across Germany. Audio-recorded data were transcribed and analyzed using qualitative content analysis according to Mayring. Observations, think-aloud activities, research memos, and a continuous research diary complemented the analysis by providing contextual and reflexive information. Results: Overall, 63 older adults participated in LoRaLAB activities, of whom 50 were included in the analysis. In addition, 32 experts contributed perspectives from comparable initiatives. Older adults evaluated technologies primarily in relation to concrete everyday needs and existing coping strategies rather than technological sophistication. Perceived usefulness, usability, affordability, and access to reliable technical support were important for their evaluations. The participatory process itself required continuous adaptation, and participants influenced not only the evaluation of technologies but also participation formats and the development of the Living Lab. Expert interviews highlighted stable organizational structures, long-term financing, local networks, committed actors, and accessible support as important conditions for sustainable technology-supported aging initiatives. Conclusions: Technology-supported aging in place cannot be addressed through technological development alone. Participatory Living Labs can provide real-world environments in which older adults’ everyday needs, technological possibilities, participation, and implementation conditions are explored together. The experience of LoRaLAB suggests that their value lies not only in developing and evaluating technologies but also in creating adaptable local structures for collaborative learning and technology development. Sustaining these structures requires continued participation, local collaboration, technical support, and organizational continuity beyond time-limited research funding. Clinical Trial: The study was prospectively registered in the German Clinical Trials Register (DRKS; registration No. 00037829).

• Digital Health Literacy and Use of Digital Health Services Among Patients with Hypertension: Cross-Sectional Survey with Linked Claims Data in Northeast Germany

  Date Submitted: Oct 3, 2026

  Open Peer Review Period: Oct 4, 2026 - Nov 29, 2026

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  Background: Digital health services can support the management of chronic diseases by facilitating self-management and providing access to health information and communication with health care providers. In hypertensive care, these services could complement long-term monitoring and prevention strategies. However, the use of these services remains uneven, particularly among older adults, individuals with lower socioeconomic resources, rural population, and those with limited digital health literacy. This could limit the potential of digital health services to improve care and perpetuate the digital divide. Objective: This study aimed to assess the use and interest in digital health services among statutorily insured patients with hypertension in northeastern Germany. It further examined how service use was related to sociodemographic and structural characteristics, digital health literacy, and clinical factors derived from claims data. Methods: This cross-sectional study, survey data were collected between 2023 and 2024 from AOK Nordost members in north-eastern Germany and linked with claims data from 2022 to 2023. Participants were adults with hypertension, who were recruited via stratified sampling by age (under 50 vs 50 years and older), sex, and residential area (rural vs urban). Interest in and use of digital health service was assessed across eleven categories of service, including self-management tools, communication-based telehealth, health information services, and administrative services. Digital health literacy was measured using the eHealth Literacy Scale (eHEALS). A comorbidity burden index was derived from claims data based on ICD-10. For each of the eleven services, multivariate logistic regression models were fitted and adjusted for nine covariates: age, gender, residential type, educational attainment, monthly net income, digital health literacy, Internet connection quality, partnership status, and comorbidity burden. Results: Of the 21,024 eligible individuals who were contacted, 1,751 (8.3%) responded to the survey and were included in the analysis. The mean age was 63.3 years (SD 22.1). Reading health information online (63.8%), general health apps (56.6%), and the insurance portal “Meine AOK” (50.3%) were the most widely used services among younger participants, compared to older participants, for whom the respective percentages were 23.1%, 11.7%, and 15.0% (all P<.001). Interest substantially exceeded current utilization for most services in both age groups. Attitudes toward digital health services varied significantly by age: 81.7% (602/737) of younger participants considered them beneficial compared to 55.0% (440/800) of older participants (P<.001). Overall, adequate digital health literacy (eHEALS score ≥27) was the most robust predictor, with significant positive associations across nine of eleven services. Younger age, female gender, urban residence, higher income, higher levels of education, reliable internet connectivity, being in a partnership, and comorbidity burden showed selective associations depending on the service. Conclusions: Digital health literacy is the most consistent modifiable factor associated with digital health service use across all service categories. The gap between interest and actual use suggests that adoption is not limited by demand alone, but also by barriers related to digital literacy, access, and support. Digital health services for chronic disease management should therefore be accompanied by supportive onboarding, reliable digital access, and multiple access routes, such as hybrid approaches, particularly in older patients and in rural areas. Clinical Trial: Clinical trial registration: https://drks.de/search/de/trial/DRKS00029761, identifier DRKS00029761

• Moderated Online Social Therapy (MOST) as a follow-on support for university students after counselling: A qualitative multi-stakeholder study of acceptability and implementation

  Date Submitted: Oct 2, 2026

  Open Peer Review Period: Oct 4, 2026 - Nov 29, 2026

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  Background: University students experience high levels of mental health difficulties. However, due to limited capacity and high demand, university counselling services can only provide brief, time-limited support, and some students may therefore require further support after counselling ends. Moderated Online Social Therapy (MOST) combines self-directed therapeutic content with therapist support, peer support, and an online social network. MOST has shown promise across youth mental health settings, but its acceptability as a follow-on support after university counselling has not previously been examined, and comparatively little is known about its acceptability from the perspectives of those involved in delivery and referral. Objective: To explore how university students, peer support workers, MOST therapists, and university counselling staff perceived the acceptability of MOST as a follow-on intervention after university counselling including factors influencing implementation and integration. Methods: A qualitative study was conducted within a university setting where MOST was offered as a follow-on support from counselling. Semi-structured interviews were conducted with university students who had used MOST (n = 9), peer support workers (n = 6), MOST therapists (n = 2), and university counselling staff involved in referral (n = 5). Separate interview schedules were developed for each stakeholder group and informed by the Theoretical Framework of Acceptability. Interviews were analysed using Framework Analysis. Coding frameworks were developed separately for each stakeholder group before themes were developed within and across groups. Results: Four themes captured participants’ experiences of MOST, relating to its fit within university support, engagement with the platform, the importance of human connection, and the support required for effective delivery. MOST was viewed as an acceptable and valuable follow-on support after university counselling, filling a gap for students requiring ongoing support, although limited integration with the counselling service was highlighted as an issue. Human connection was central to MOST, with particular value placed on peer support. However, low activity within the social network and technological limitations reduced engagement. Participants also identified opportunities to further strengthen delivery through additional training, continuity, and communication within the MOST team. Conclusions: MOST appears to be an acceptable follow-on support for university students after counselling, with peer support identified as a clear strength of the intervention. Wider implementation may benefit from closer integration with university counselling services, improved platform functionality, and sufficient user activity to sustain the Community. Future research should examine MOST across additional university settings and among students with differing levels of engagement.

• Practices and habits of asynchronous, provider-to-provider, electronic consultation from a hospital perspective: a scoping review

  Date Submitted: Oct 1, 2026

  Open Peer Review Period: Oct 1, 2026 - Nov 26, 2026

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  Background: Electronic provider-to-provider consultations (eConsults) aim to improve access to specialist care by reducing waiting times and optimizing care pathways. While widely used in various healthcare settings, their implementation and utilization when the eConsult involves a hospital-based specialist, regardless of the requesting setting, remain underexplored. Objective: To review the practices and habits of asynchronous, provider-to-provider eConsults from a hospital perspective. Methods: A scoping review was conducted following the PRISMA-ScR guidelines. PubMed, Scopus, CINAHL, and Embase (Ovid platform) databases were systematically searched for studies published between 2010 and 2022 that focused on hospital-based eConsult practices and their impact on healthcare delivery. Results: Forty-five studies were included, with the majority originating from North America and Europe. The review identified three primary objectives of eConsults: i) reducing delays in patient care, ii) minimizing unnecessary referrals, and iii) improving access to expertise regardless of geographical constraints. eConsults were found to increase workflow efficiency, facilitate communication between providers, and improve decision-making. However, challenges related to workload distribution, regulatory frameworks, and financial incentives were also noted. Conclusions: eConsults offered significant benefits in improving specialist access and care coordination within hospitals. However, their integration into routine clinical workflows requires structured implementation strategies, appropriate financial models, and policy support. Future research should focus on long-term outcomes.

• Physicians in oncology providing consultation recordings to patients: A mixed-methods pilot study

  Date Submitted: Oct 1, 2026

  Open Peer Review Period: Oct 1, 2026 - Nov 26, 2026

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  Background: Providing patients with an audio recording of their medical encounter, known as consultation recording, is a well-established patient-centered intervention. Consultation recordings can improve recall and comprehension and have been shown to be particularly valuable for patients with cancer. However, concerns among healthcare professionals are a recognized barrier to their routine implementation. Furthermore, research on consultation recordings in German cancer care is lacking. Objective: This study aimed to pilot test consultation recordings in routine cancer care in Germany and assess feasibility of implementation and perceived effects from physicians’ perspectives. Methods: We used a sequential mixed methods approach to assess the use, usability, acceptability, appropriateness, feasibility, influencing factors, and perceived effects of consultation recordings. The pilot was conducted in an outpatient setting. Physicians working in oncology were eligible to participate. Four weeks after the pilot, participants completed a quantitative questionnaire followed by qualitative interviews. Quantitative data were analyzed using descriptive statistics, and qualitative data using a combination of Practical Thematic Analysis and qualitative content analysis. Results: Sixteen physicians participated in the pilot, providing 97 consultations recordings to their patients. Fifteen participants returned the quantitative survey (response rate 93.75%), and 13 participated in qualitative interviews (response rate 81.25%). Participants reported mixed experiences regarding benefits for patients and physicians, and most expressed a desire for routine implementation of consultation recordings. Nevertheless, participants perceived limited feasibility, and organizational barriers to implementation were identified. Conclusions: This study provides preliminary evidence on the feasibility and perceived effects of consultation recordings in German routine cancer care from physicians’ perspectives. Consultation recordings show potential for improving patient-centered care, but implementation may be hindered by physician concerns and organizational barriers. Future research should investigate these barriers and explore strategies to address them to facilitate implementation in the German healthcare context.

• Facilitators and Barriers to Remote Glucose Monitoring Implementation in the Safety-Net Context: A mixed methods study

  Date Submitted: Sep 30, 2026

  Open Peer Review Period: Sep 30, 2026 - Nov 25, 2026

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  Background: Remote glucose monitoring (RGM) has been shown to improve diabetes self-management by providing patients and clinicians with access to glucose data between scheduled visits, resulting in changes to management strategies. However, how implementation and engagement in safety-net settings is shaped by contextual, technological, and policy-related barriers are under explored. Objective: To identify facilitators and barriers to RGM implementation and engagement in an urban safety-net health system and to examine how these factors interact across multiple levels of care delivery. Methods: We conducted a convergent mixed methods study as part of the Improving Diabetes Equity and Advancing Care (IDEA) study at an endocrinology clinic within a safety-net health system. Data were obtained from four patient focus groups (n=15), staff interviews (n=8), weekly study team meeting notes, and patient acceptability survey data. Quantitative data were summarized descriptively. Qualitative data were analyzed using the Consolidated Framework for Implementation Research (CFIR), and findings were integrated to develop metainferences across data sources. The study was approved by the Mount Sinai Hospital Institutional Review Board. Results: Fifty-seven participants from the parent study were randomized to the RGM arm. Forty-eight (84%) successfully transmitted glucose data to the diabetes management platform, and 80% of surveyed participants reported that the platform met their approval. We identified 16 facilitators and 18 barriers to RGM implementation and utilization across the five CFIR domains. Facilitators included improved care coordination, access to longitudinal glucose data, shared decision-making, interdisciplinary teamwork, and vendor partnerships that reduced organizational burden. Barriers included low digital literacy, trust concerns, alert fatigue, workflow inefficiencies, lack of standardized protocols, staffing limitations, technical complexity, vendor support issues, and insurance-related restrictions that limited access to compatible devices. Many barriers interacted across patient, organizational, innovation, and outer-setting levels, reducing the feasibility and perceived value of RGM despite strong interest from both patients and staff. Conclusions: RGM has potential to improve diabetes care in safety-net settings, but successful implementation requires attention to multilevel barriers. Clear workflows, adequate staffing, tailored training, responsive vendor support, and policy changes that improve equitable access to devices are needed to support sustained RGM use. These findings offer actionable guidance for health systems planning to implement remote monitoring interventions in resource-constrained settings. Clinical Trial: This trial is registered with ClinicalTrials.gov, NCT06040463

• Neuro-Symbolic Knowledge Graphs Improve Motivational Interviewing Fidelity in Clinical Artificial Intelligence Tools: Development and Simulation Study

  Date Submitted: Sep 30, 2026

  Open Peer Review Period: Sep 30, 2026 - Nov 25, 2026

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  Background: Large language models (LLMs) are increasingly used in health counseling, but adherence to evidence-based approaches such as motivational interviewing (MI) is inconsistent, and their reasoning is difficult to inspect or audit. Supervised fine-tuning (SFT) trains a model to imitate expert responses but does not explicitly encode the sequential clinical decisions MI requires. Knowledge graphs (KGs), which represent clinical knowledge as explicit, inspectable rules and relationships, may offer a more transparent way to govern LLM counseling. Objective: This study aimed to develop a neuro-symbolic KG encoding MI clinical logic and to evaluate, in simulated sessions, whether KG guidance improves MI fidelity compared with a base LLM and SFT, alone and in combination. Methods: We built a 3-layer KG comprising an MI ontology (34 techniques, stages of change, and patient talk types), a deterministic rule engine (34 decision rules governing technique selection and phase progression), and 13 hard safety constraints, and integrated it into CHIA (Chatbot for HIV Prevention and Action), a preexposure prophylaxis (PrEP) counseling platform. Using a 2×2 factorial design crossing SFT (GPT-4.1 base vs fine-tuned on 2400 expert MI examples) with KG guidance (disabled vs enabled), we simulated 8-turn sessions with 20 LLM-generated patient personas (3 runs per persona per arm; 239/240 sessions completed). A blinded LLM judge (Claude Sonnet 4.5) scored Motivational Interviewing Treatment Integrity (MITI) 4.2 global scores (Empathy, Partnership, Cultivating Change Talk, and Softening Sustain Talk) and flagged premature planning. We used 2-way repeated-measures ANOVA and generalized estimating equations with false discovery rate correction, and computed linguistic metrics including lexical diversity. A blinded MI expert and a second LLM judge (Gemini 2.5 Pro) rescored a stratified subsample of 40 sessions. Results: The base LLM with KG guidance (Base+KG) achieved the highest mean Empathy (4.90, SD 0.30) and Partnership (4.88, SD 0.32). Compared with SFT alone, Base+KG showed higher Empathy (Cohen d=0.68, 95% CI 0.37-1.01) and Partnership (d=0.91, 95% CI 0.60-1.25). SFT had significant negative main effects on Empathy (F1,19=21.26; P<.001) and Partnership (F1,19=17.39; P<.001) and reduced lexical diversity (d=−1.47). The LLM judge flagged premature planning in 0/60 Base+KG sessions vs 1/60 Base, 4/59 SFT, and 6/60 SFT+KG sessions; however, the human expert identified more premature planning in every arm. Human-LLM agreement was moderate for Empathy (intraclass correlation coefficient [ICC] 0.55) and Partnership (ICC 0.42) and poor for Softening Sustain Talk, but all 3 raters produced consistent arm rankings. Conclusions: In this simulation study, KG guidance applied to a base LLM yielded higher MI fidelity than SFT, which degraded relational quality. Externalizing clinical reasoning into an inspectable KG is a promising, auditable approach to governing LLM counseling, pending confirmation with human participants and larger human-rated samples.

• The reply is ready. Is the site? False operational closure in AI-assisted clinical trial workflows

  Date Submitted: Sep 28, 2026

  Open Peer Review Period: Sep 30, 2026 - Nov 25, 2026

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  Generative artificial intelligence (AI) can faithfully reproduce a status that no longer reflects the operational state of a clinical trial. A reply confirming that a site is "ready" can be accurate about its source and wrong about the world. We define false operational closure as the adoption of a completion or readiness status when the evidence required to support it is absent or no longer current. Unlike model error or automation bias, it is defined at the level of the workflow and can be identified without inferring why anyone accepted the status. The key distinction is between verifying a document and verifying the state it represents. The failure mode is not new, but we hypothesize that generative AI may increase its frequency by making closure-ready messages instant and fluent while the evidence behind them still takes hours or days, and while that evidence remains scattered across systems the tool cannot see. We propose evidence-based closure for statuses that bear on critical-to-quality factors and outline observational and experimental tests of the construct.

• Digital Health Technologies for Screening, Assessing, and Monitoring Psychological Health in People Receiving Dialysis: A Scoping Review

  Date Submitted: Sep 29, 2026

  Open Peer Review Period: Sep 30, 2026 - Nov 25, 2026

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  Background: Psychological problems are common among people receiving dialysis, but conventional assessments are often episodic and may not capture changes across treatment cycles, time, and everyday contexts. Digital health technologies (DHTs) may support more flexible and repeated psychological screening, assessment, and monitoring, yet their applications in dialysis care have not been comprehensively mapped. Objective: This scoping review aimed to review and synthesize the applications of DHTs for psychological screening, assessment, and monitoring among adults receiving dialysis. Methods: This scoping review followed established scoping review methodology and was reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) and PRISMA-S (PRISMA Statement for Reporting Literature Searches in Systematic Reviews) guidelines. PubMed, Embase, Web of Science, CINAHL, the Cochrane Library, SinoMed, China National Knowledge Infrastructure, Wanfang Data, and the China Science and Technology Journal Database were searched from inception to August 14, 2026. Eligible studies involved adults receiving maintenance dialysis and evaluated DHTs used to screen, assess, identify, or monitor psychological outcomes. Study selection and data extraction were conducted using prespecified eligibility criteria based on the population-concept-context framework. Results: Database searches identified 3467 records, of which 2560 remained after deduplication. After screening and supplementary searching, 21 reports representing 17 unique studies were included. Of these, 13 studies were first reported in 2020 or later, and 11 focused exclusively on in-center hemodialysis. Six digital assessment approaches were identified, with electronic patient-reported outcome measure-based assessment being the most common, followed by ecological momentary assessment and experience sampling. Active patient self-report was the primary data source in 16 studies, whereas passive sensing and multimodal data integration were uncommon. The most frequently assessed outcomes were depression, anxiety, mood or affect, and mental health-related quality of life, and assessment schedules ranged from single assessments to repeated and longitudinal monitoring. Feasibility information was reported in 16 studies. Conclusions: DHTs are being applied in increasingly diverse ways to assess and monitor psychological health among people receiving dialysis. However, existing evidence relies predominantly on active patient self-report and focuses largely on in-center hemodialysis populations. Temporal assessment designs vary substantially, ranging from episodic assessment to intensive repeated monitoring. Future research should strengthen long-term and multiscale monitoring, improve the integration and validation of multimodal data, and establish clearer clinical pathways for reviewing and responding to clinically meaningful psychological findings. Clinical Trial: https://osf.io/zfmgw/overview

• Consent to sharing shopping data for health research: a mixed-methods study

  Date Submitted: Sep 29, 2026

  Open Peer Review Period: Sep 30, 2026 - Nov 25, 2026

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  Background: Shopping data offer detailed, objective records of diet and lifestyle habits, providing a valuable but under-used resource for health research. While willingness to share personal data has been widely studied, no previous research has directly compared stated willingness with subsequent actual data sharing within the same cohort. Objective: Understanding whether willingness predicts actual consent, and how this varies by sociodemographic characteristics, motivations, and barriers, is essential to improve recruitment strategies and the representativeness of collected data. Methods: We employed a mixed-methods approach using the Avon Longitudinal Study of Parents and Children (ALSPAC). Quantitative analyses combined sociodemographic variables with responses from a 2018 survey assessing willingness to consent to shopping data linkage and outcomes from a 2023 loyalty card consent campaign. These were complemented by semi-structured interviews in 2019 exploring attitudes towards sharing shopping data. Results: In 2018, 60.7% of participants indicated willingness to share shopping data (n = 2142, 9230 invited). In 2023, actual consent—requiring provision of loyalty card details for five UK retailers (Sainsbury’s, Tesco, Boots, Morrisons, and Coop)—was obtained from 511 of 6170 invited participants. Demographic characteristics were broadly comparable across samples. Stated willingness was associated with higher odds of actual consent (adjusted OR = 1.48, 95% CI: 1.16–1.90), indicating that willingness is a meaningful predictor of subsequent data sharing. Sociodemographic characteristics and motivations (e.g., societal benefit, scientific interest) showed modest associations with both willingness and consent and moderated the willingness–consent relationship. Interviews reinforced the importance of trust in ALSPAC and clear communication about data use, while highlighting privacy concerns as minor barriers. Conclusions: This study provides the first within-cohort evidence that willingness to share shopping data predicts subsequent data sharing, albeit imperfectly. Willingness surveys can therefore inform recruitment planning, but additional strategies are required to improve realised consent rates. Addressing practical barriers, clarifying data use, and highlighting societal and scientific benefits may help narrow this gap; Targeted engagement, including through informative materials and outreach events, may help to reach groups less likely to consent. These insights can inform data collection in longitudinal cohorts and wider population samples.

• Weight change following discontinuation of a medicated digital weight loss program: a retrospective cohort study from Australia and the United Kingdom

  Date Submitted: Sep 28, 2026

  Open Peer Review Period: Sep 28, 2026 - Nov 23, 2026

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  Background: Weight regain following discontinuation of anti-obesity medications is a recognised clinical concern, yet real-world data on post-discontinuation weight trajectories following structured digital weight loss programs remain scarce. Objective: To characterise weight trajectories at 6, 9, and 12 months following discontinuation of a medicated digital weight loss program, and to identify baseline and in-program behavioural predictors of post-exit weight change. Methods: This retrospective cohort study analysed data from 8,408 adults who completed the Juniper medicated digital weight loss program in Australia (N=1254) or the United Kingdom (N=7154) and subsequently discontinued. Participants received semaglutide or tirzepatide and voluntarily recorded post-exit weights via the Juniper application, to which all users retain access following discontinuation. Post-exit weight change from program exit was categorised into five bands (>5% additional loss to >5% regain). Linear regression models identified predictors of weight change at 6 months (primary; N=6181) and 12 months (primary; N=1889), with 9-month analyses as supplementary (N=3349). Two sequential model blocks tested baseline predictors alone, then baseline plus in-program behavioural predictors. Results: Among the 6736 participants with 6-month follow-up weight data, mean post-exit weight change was −0.4% (SD 8.2); 28.7% achieved >5% additional weight loss, 14.0% lost 2–5%, 19.4% maintained within 2% of exit weight, 13.7% regained 2–5%, and 24.2% regained >5%. This heterogeneous distribution persisted at 9 months (N=3788; mean −0.3%, SD 10.1; >5% additional loss: 32.6%; >5% regain: 29.2%) and 12 months (N=2,165; mean −0.1%, SD 11.6; >5% additional loss: 32.7%; >5% regain: 33.9%). In linear regression, higher baseline BMI predicted continued post-exit weight loss at both primary timepoints (6m: β=−0.33, SE=0.02; 12m: β=−0.54, SE=0.04; both P<0.001), while UK participants showed significantly greater post-exit weight gain than Australian participants (6m: β=1.97, SE=0.28; 12m: β=4.40, SE=0.82; both P<0.001). Greater in-program weight loss was associated with more post-exit weight regain relative to exit weight in a dose-response pattern (≥20% vs <5% in-program loss: 6m β=3.93, SE=0.48; 12m β=8.99, SE=1.27; both P<0.001). Higher health coaching and digital support engagement was independently associated with less post-exit regain (25–50% of program weeks vs 0%: 6m β=−0.68, SE=0.31, P=0.03; 12m β=−2.66, SE=0.82, P<0.001). In-program behavioural predictors improved model fit beyond baseline predictors alone at 6 months (R²: 0.090 to 0.116) and 12 months (R²: 0.136 to 0.185). Conclusions: Post-discontinuation weight trajectories following a medicated digital weight loss program appear to be highly variable, with approximately one-third of participants continuing to lose weight and nearly one-quarter experiencing substantial regain. Paradoxically, greater in-program weight loss predicted more post-exit regain relative to exit weight, consistent with the known rebound effect following Semaglutide and Tirzepatide withdrawal. Digital health coaching engagement was an independently modifiable predictor of better post-exit outcomes, supporting the development of targeted off-ramp support to reduce post-discontinuation regain.

• Real-World Interprofessional Instant Messaging in Inpatient Care: A Mixed-Methods Corpus Study in Psychosomatic Medicine

  Date Submitted: Sep 27, 2026

  Open Peer Review Period: Sep 27, 2026 - Nov 22, 2026

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  Background: Secure instant messaging is increasingly used for inpatient coordination, but evidence from authentic interprofessional message content remains limited, particularly in German-speaking clinical settings. Objective: This study aimed to characterize the structural, temporal, lexical, and clinical content patterns of routine messaging and to compare the communicative profiles of direct and group communication. Methods: We conducted an observational mixed-methods corpus study of anonymized routine messages cumulatively exported on January 11, 2023, from an on-premises Mattermost platform in clinical production since August 2021. The corpus comprised 7,462 messages from 293 direct-message conversations and 86 group channels. Analyses covered corpus structure, temporal distributions, lexical frequencies and n-grams, direct-versus-group keyness, targeted collocations, communication-unit dispersion, medical terminology, emoji shortcodes, and selected workflow markers. All 4,961 retained anchor-collocate rows were manually assigned to contextual domains; 282 prioritized rows underwent complete, temporally separated intra-rater review. Quantitative results and manual keyword-in-context review were integrated to support the interpretation of recurring communicative profiles. Results: The corpus comprised 4,915 direct messages and 2,547 group messages. Group messages contained more cleaned tokens per message than direct messages (35.45 vs. 17.95). Direct messages showed more dialogic and addressive language: du occurred 10.55 versus 1.43 times per 1,000 tokens, and ich 20.04 versus 7.92. Group messages showed more structured patient and workflow markers: hashtagged patient references occurred 36.21 versus 7.90 times per 1,000 tokens, and kein_Todo 7.21 versus 0.12. Friday group messages frequently contained handover (105/631, 16.6%), weekend-preparation (73/631, 11.6%), and no-active-to-do markers (225/631, 35.7%). Unit-level analysis showed that dialogic tokens occurred across most direct conversations, whereas several group workflow markers were concentrated in a small number of functionally specialized channels. Manual validation retained 1,203 clinical entity mentions in direct messages and 2,076 in group messages (13.64 and 22.99 per 1,000 cleaned tokens). Integrated review supported distinct but complementary profiles of bilateral clarification and coordination in direct messages and team-visible handover, task-status, and therapy-process documentation in selected group channels. Conclusions: Authentic clinical messaging showed a distinction between broadly distributed bilateral micro-coordination and workflow documentation concentrated in functionally specialized group channels. Secure messaging systems should support modality-specific channel conventions, structured patient referencing, explicit task status communication, and handover-oriented workflows. The transferability of our findings beyond psychosomatic inpatient care requires replication in other coordination-intensive settings.

• Acoustic Speech Features for Mental State Monitoring: Systematic Review

  Date Submitted: Sep 27, 2026

  Open Peer Review Period: Sep 27, 2026 - Nov 22, 2026

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  Background: Acoustic and prosodic speech features could allow continuous measurement of mental state in long term care, where monitoring relies on staff observation and periodic self assessment. Existing reviews show these features differ between people with and without a diagnosis, mostly for depression and in conversational speech. Within speaker stability, which tracking change against a person’s own baseline requires, and adherence to repeated recording have not been synthesized. Objective: To characterize acoustic features reported against 5 mental states (depression, anxiety, psychological stress, agitation or irritability, and apathy), the speaking tasks and designs behind that evidence, their within speaker repeatability, and adherence to repeated voice collection. Methods: Five databases and 3 preprint sources were searched on August 17, 2026, for English language publications from 2005 onward. Eligible studies related a named acoustic feature or feature family to 1 of the 5 states in adults, reported within speaker repeatability across separate occasions, or reported adherence to repeated voice collection. Each feature by state result was an extraction unit. Studies of short fixed content speech (sustained vowels, isolated words or phrases, or read passages) were extracted in full and formed the primary synthesis; conversational and picture description studies received reduced extraction. Risk of bias was appraised per contribution and certainty rated per feature family; synthesis was narrative, following the Synthesis Without Meta-analysis guideline. Results: The 125 included records yielded 1,873 results from 79 independent participant samples. Depression accounted for 1,685 results, and no record reported an acoustic feature against agitation. The primary synthesis comprised 341 results for individually named features from 15 studies of short fixed content speech; 573 of the 914 feature level results rested on conversational or pooled speech. Intensity was the only family whose direction held on both kinds of speech, and pitch was unsettled on both. Across 570 detection results on a common scale, median performance was 0.70. Three records from 3 independent samples assessed within speaker repeatability; the 2 reporting an intraclass correlation coefficient (ICC) gave only its average measure form, so the single measure ICC needed to judge change against a person’s baseline is unreported. Seven records reported adherence, which reached 79 percent across 40 weeks with clinical support and 71 percent in an unsupervised protocol. All 27 certainty ratings were very low. Conclusions: Acoustic evidence for mental state is concentrated in depression and conversational speech, and a direction found on one speaking task does not reliably transfer to another. The measurement property an individual monitoring system depends on, the reliability of a single recording, has not been reported by any included study, while repeated collection is achievable with human support. The literature supports a measurement design rather than a feature list. Clinical Trial: PROSPERO CRD420261473970

• Home-Based Gamified Binocular Digital Therapy Versus Part-Time Patching for Unilateral Amblyopia in Young Children: A Multicenter Randomized Noninferiority Trial

  Date Submitted: Sep 24, 2026

  Open Peer Review Period: Sep 25, 2026 - Nov 20, 2026

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  Background: Patching is a standard treatment for childhood amblyopia but is frequently limited by adherence challenges. Home-based binocular digital therapies offer an alternative treatment format and can enable automated monitoring of treatment use, although randomized evidence directly comparing active gamified interventions with patching in children younger than 7 years remains limited. Whether baseline amblyopic-eye visual acuity modifies comparative treatment response is also uncertain. Objective: To determine whether home-based gamified binocular therapy is noninferior to part-time patching for improving amblyopic-eye best-corrected visual acuity (BCVA) in children aged 4 to <7 years with unilateral amblyopia, and to explore treatment-effect heterogeneity according to baseline amblyopic-eye acuity. Methods: Prospective, multicenter, randomized, evaluator-masked, parallel-group noninferiority trial conducted at 3 tertiary centers in Shanghai, China, enrolling 144 children with unilateral amblyopia (amblyopic-eye BCVA 20/32 to 20/200).Participants were randomized 1:1 to 8 weeks of home-based gamified binocular therapy (1 hour/day, 5 days/week) or part-time patching (2 hours/day, 7 days/week). Treatment use in the binocular therapy group was recorded automatically by the system, whereas patching duration was recorded by caregivers in paper diaries.The primary outcome was mean change in amblyopic-eye BCVA from baseline to week 8, analyzed using a mixed model for repeated measures, with a noninferiority margin of 0.10 logMAR. Secondary outcomes included stereoacuity and recorded treatment adherence. Results: Among 136 participants who received assigned treatment (mean age, 4.76 years; 49.3% female), mean BCVA improvement at week 8 was 0.15 logMAR in the binocular therapy group and 0.13 logMAR in the patching group. The adjusted least-squares mean difference was 0.01 logMAR (95% CI, -0.02 to 0.04), establishing noninferiority (lower bound > -0.10). A significant treatment-by-baseline-BCVA interaction was observed (P = 0.013), and Johnson-Neyman analysis identified a data-derived threshold near 0.48 logMAR. Consistent with this finding, among 37 participants with baseline BCVA ≥0.50 logMAR, mean improvement was greater with binocular therapy than with patching (0.25 vs. 0.15 logMAR; P = 0.017), and more participants achieved a ≥2-line gain (89.5% vs. 50.0%; P = 0.013). Recorded adherence was 99.8% in the binocular therapy group based on device-recorded treatment minutes and 87.6% in the patching group based on caregiver diaries (P = 0.007). Stereoacuity outcomes did not consistently favor binocular therapy. Conclusions: Home-based gamified binocular therapy was noninferior to part-time patching for improving amblyopic-eye BCVA at 8 weeks and enabled automated recording of treatment use. Exploratory analyses suggested that relative treatment effects varied according to baseline acuity, but the data-derived threshold and subgroup findings require prospective validation before informing treatment selection. Clinical Trial: Chinese Clinical Trial Registry; ChiCTR2300079090

• Caring with Machines: Reimagining Relationships in Health and Social Care

  Date Submitted: Sep 24, 2026

  Open Peer Review Period: Sep 24, 2026 - Nov 19, 2026

  • Peer Review Me

  The rapid development and deployment of Artificial Intelligence has transformed how humans and technologies interact. The rapidly developing field presents both promise and perils which require investigation. In this viewpoint, we suggest AI-driven digital health technologies are emerging as agents with capacity to shape decisions, influence relationships, and hold power within care dynamics. Drawing on traditions from Science and Technology Studies, and using integrated health and social care as a case context we ask: How does AI integration blur the boundaries of agency and shift power between human and non-human actors? By applying Actor-Network Theory, Structural Theory, and Critical Theory to this question, we offer some conceptual clarity on where power can exist within caring relationships in health and social care systems, and how AI can, often invisibly, influence those power structures. Implications for researchers studying AI-enabled health care delivery and practitioners adopting technologies are offered, concluding with a call to action for the field to make these invisible forces visible through careful and critical assessment.

• Sycophancy and Pressure Resistance in Medical Large Language Models: Systematic Review, Taxonomy, and Minimum Evaluation Framework

  Date Submitted: Sep 23, 2026

  Open Peer Review Period: Sep 23, 2026 - Nov 18, 2026

  • Peer Review Me

  Background: Medical large language models can perform well on static questions yet alter clinically relevant outputs when users introduce false premises, misleading evidence, authority claims, or repeated disagreement. Objective: We systematically reviewed how sycophancy and pressure resistance have been evaluated in patient- and clinician-facing medical settings. Methods: The registered review record reported searches of MEDLINE/PubMed, Embase, IEEE Xplore, arXiv, and CENTRAL through 31 May 2026 and yielded a frozen corpus of 31 benchmark and simulation studies. Studies were organized post hoc as direct sycophancy or pressure-resistance evidence (n=16), direct-challenge evidence (n=1), or adjacent interactive-robustness evidence (n=14). Fourteen were archival publications, 1 was accepted, 2 were nonarchival workshop reports, and 14 were preprints as of 4 September 2026. We extracted user role, counterfactual, comparator, analytic unit, denominator, model snapshot, native endpoint, uncertainty, and publication status. Results: Direct evaluations documented user-concordant output shifts under pressure in controlled settings. However, construct definitions, comparators, conditioning rules, analytic units, turn structures, model versions, and endpoints were incompatible; no common estimand existed, and pooling was not methodologically defensible. No study estimated routine-care incidence or patient outcomes. Conclusions: We derived a taxonomy that distinguishes progressive correction from regressive capitulation and propose a minimum evaluation framework covering construct definition, counterfactual, user role, comparator, model identity, conversation protocol, outcome, denominator, analysis, and openness. The evidence supports pressure resistance as a distinct target for benchmark design and lifecycle regression testing but does not establish a universal failure rate or protection conferred by release year, medical specialization, or a reasoning label.

• Large Language Models Across the Acute Stroke Care Pathway: A Scoping Review of Applications, Evidence Maturity, and Implementation Readiness

  Date Submitted: Sep 22, 2026

  Open Peer Review Period: Sep 23, 2026 - Nov 18, 2026

  • Peer Review Me

  Background: Large language models (LLMs) are being tested at nearly every step of acute stroke care, yet evidence is fragmented across specialties and study types, and the maturity of that evidence along the time-critical clinical pathway has not been mapped. Objective: We aimed to map the applications of generative LLMs across the acute stroke pathway—from prehospital recognition to discharge communication—and to grade the maturity of the evidence and its readiness for implementation. Methods: We conducted a scoping review following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) framework, with a protocol registered on the Open Science Framework (DOI: 10.17605/OSF.IO/XDWHS). PubMed, Europe PMC (including preprints), and Google Scholar were searched on August 25, 2026 (records from November 1, 2022 onward). Eligible studies evaluated a generative LLM on a task situated within the hyperacute-to-discharge stroke pathway. Three reviewers independently screened records and charted data using a piloted form, with disagreements resolved by consensus. Each study was mapped to one of five pathway stages and graded on a three-tier evidence-maturity scale (Tier 1: simulation or benchmark; Tier 2: retrospective real-world data; Tier 3: prospective or real-world deployment). Results: Of 601 records, 71 studies (published 2023-2026) were included. Applications concentrated in documentation and communication (n=19), imaging-related text tasks (n=18), emergency department triage and diagnosis (n=15), reperfusion decision support (n=13), and prehospital recognition (n=5). Evidence maturity was low: 29 studies were simulation or benchmark evaluations, 39 used retrospective real-world data, and only 3 reported prospective or deployment-stage evidence. Performance was highly polarized—structured data extraction from radiology reports reached 93.5%-94.8% accuracy, whereas zero-shot interpretation of CT images showed poor sensitivity (recall 0.14) and consumer chatbots under-triaged 52% of emergency scenarios. Research output was geographically concentrated (United States, China, Germany, and Türkiye contributed 47/71 studies), GPT-family models dominated (used in 80% of studies), and no included study evaluated regulatory approval or medico-legal liability. Conclusions: Generative LLMs show credible near-term value for documentation- and extraction-type tasks in acute stroke care, but remain unproven for triage, image interpretation, and reperfusion decision-making, where prospective, regulated evaluation is essentially absent. Research should now shift from benchmark performance to deployment studies, reporting standards, and regulatory science. Clinical Trial: Protocol is registered on the Open Science Framework (DOI: 10.17605/OSF.IO/XDWHS)

• Fidelity of Virtual Self-Management Programs in Pediatric Autoimmune Diseases: A Scoping Review

  Date Submitted: Sep 23, 2026

  Open Peer Review Period: Sep 23, 2026 - Nov 18, 2026

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  Background: Virtual self-management programs for pediatric autoimmune diseases combine digital content, participant activity, and human support. Understanding whether their intended components are delivered, received, and enacted requires fidelity evidence that is distinguishable from platform use and clinical outcomes. Objective: To map how intervention and implementation-strategy fidelity are defined, assessed, and reported in virtual self-management programs for pediatric autoimmune diseases, and distinguish fidelity evidence from related process and outcome measures. Methods: Guided by the Joanna Briggs Institute and Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR), we searched six databases (2002–2026) for peer-reviewed articles involving pediatric autoimmune populations and relevant fidelity measures, assessment procedures, implementation strategies, or quality-assurance processes. Synthesis was informed by the Dunst framework, the Consolidated Framework for Implementation Research (CFIR), and the Expert Recommendations for Implementing Change (ERIC) taxonomy. Direct fidelity assessments were distinguished from participation indicators, acceptability and usability measures, and health outcomes. Results: Of 284 identified records, 39 articles were included: 25 addressing type 1 diabetes, 7 inflammatory bowel disease, 5 juvenile idiopathic arthritis, 1 systemic lupus erythematosus, and 1 mixed rheumatic-disease population. Direct fidelity evidence included interventionist checklists, recorded-session review, automated treatment-integrity controls, competency assessment, and structured delivery-standardization procedures. Other articles contributed intended-dose, attendance, module or app use, engagement, acceptability, and usability indicators. Two trial design/baseline articles contributed fidelity procedures without final effectiveness findings. Fidelity to implementation strategies was not consistently distinguished from fidelity to intervention delivery, and the heterogeneous evidence did not establish that higher fidelity caused better outcomes. Conclusions: This review adds a cross-modality account of what reported fidelity procedures and indicators establish in virtual pediatric autoimmune self-management. Separating intended standards, assessment procedures, observed delivery, receipt and enactment, implementation support, and patient outcomes can support interpretation, replication, and service planning. The synthesis does not establish that higher fidelity causes better outcomes. Clinical Trial: N/A

• Exhaled Breath Detection for the Diagnosis of Oral Squamous Cell Carcinoma: A Systematic Review

  Date Submitted: Sep 22, 2026

  Open Peer Review Period: Sep 23, 2026 - Nov 18, 2026

  • Peer Review Me

  Early diagnosis of oral squamous cell carcinoma (OSCC) is critical for improving outcomes, yet biopsy is invasive and imaging has limited sensitivity. Exhaled breath analysis of volatile organic compounds (VOCs) offers a non invasive alternative for early screening. This systematic review searched PubMed, Embase, and other databases, identifying 16 studies. We examined four aspects: sampling, detection technologies, data analysis, and biomarkers. Mixed VOC patterns showed better diagnostic performance than single biomarkers. Electronic noses demonstrated high sensitivity and specificity. Proton Transfer Reaction-Time-of-Flight Mass Spectrometry (PTR-TOF-MS) offers real-time online monitoring, requires no sample pretreatment, achieves pptv-level sensitivity, and provides high mass resolution. GC IMS and the combination of TD GC MS with GC IMS performed well across different sample types. Machine learning algorithms (Random Forest, SVM, ANN) performed robustly on small high dimensional datasets, with cross validation and permutation tests effectively mitigating overfitting. However, sampling standardization, mechanistic validation, and algorithm interpretability remain major barriers to clinical translation. Future work should establish international SOPs, elucidate VOC biological pathways, and develop interpretable AI models to enable clinical translation for OSCC screening.

• Tailored outreach by predictive modeling: Testing phone call effectiveness between levels of patient risk

  Date Submitted: Sep 22, 2026

  Open Peer Review Period: Sep 23, 2026 - Nov 18, 2026

  • Peer Review Me

  Background: Telephonic outreach can be an effective but time and resource consuming method to encourage patient outcomes, and often resources are insufficient to reach all patients. Predictive modeling can help determine which patients respond best to outreach, in order to prioritize limited resources to be more effective at achieving patient health outcomes. Objective: Test effectiveness of telephonic outreach at different levels of patient likelihood of achieving outcomes, calculated by predictive models based on historical data. Methods: Within each of two rapid randomized tests in 2021 and 2022, a predictive model was created using historical data to calculate the likelihood of patients achieving a target health outcome by year end. For Study 1, the outcome was completion of an annual wellness visit. For Study 2, the outcome was reaching 80% percent of days covered (PDC) for a medication prescription. Participants included patients enrolled in a shared savings or risk contract for which the payer made monthly individual-level data available on target measures. Study 1: patients with no active MyChart account due for annual wellness visit between July 1, 2020 and January 31, 2021. Study 2: patients at with at least one unfilled prescription for a cholesterol, diabetes or hypertension medication between July 18 and December 19, 2022. Patients were randomized 1:1 to receive either no outreach (control) or telephonic outreach to encourage patients to complete health outcomes (intervention): Study 1: calls to complete annual wellness visit. Study 2: calls to fill medication prescriptions. Results: Study 1: Significantly more patients (n=26,861) completed their annual wellness visit in intervention than control overall (4.5% intervention vs. 0.3% control; RR=13.1; 95% CI: 9.7-17.7) and within each quartile of predicted risk. Study 2: Patient medications (n=6,784) had no significant difference in hitting PDC target between study arms overall (69.8% intervention vs. 68.6% control; RR=1.02, 95% CI: 0.98-1.06), but within the fourth quartile of predicted risk (patients most likely to hit target), intervention subjects were significantly more likely than control to hit their target (81.3% intervention vs. 76.3% control, RR=1.07; 95% CI: 1.01, 1.13). Conclusions: Precision medicine can help evaluate the effectiveness of patient outreach for different situations, allowing resources to be targeted to where they have most impact.

• Understanding Multiple System Atrophy from Patient and Caregiver Perspectives: A Social Media Data Analysis

  Date Submitted: Sep 21, 2026

  Open Peer Review Period: Sep 21, 2026 - Nov 16, 2026

  • This manuscript needs more reviewers Peer Review Me

  Background: Multiple system atrophy (MSA) is a progressive neurodegenerative disorder that substantially impairs patients’ health-related quality of life through widespread motor, autonomic, and non-motor dysfunction. Currently, there is limited literature documenting the experiences of patients with MSA and their caregivers. Objective: This study conducted a qualitative investigation of social media data to explore the experiences and perspectives on diagnosis, unmet needs, caregiver burden, and treatment perceptions of patients and caregivers navigating the MSA journey. Methods: A non-interventional retrospective analysis of social media data of patients with MSA and their caregivers was conducted. Relevant social media posts published between January 2016 to September 2023 were collected from the United States, the United Kingdom, France, Germany, Italy, Japan, and Spain using specific keywords. Brandwatch® scanned publicly available sources (Twitter, Healthunlocked.Com, Reddit, Ameblo.Jp, Instagram etc.) to retrieve messages containing the search keywords. All data were analyzed using descriptive statistics. Results: A total of 7,241 social media posts from 4,102 users across 342 sources were analyzed. Conversations were predominantly female led (n = 3,192, 77.8%) with most users (1,658, 40.4%) belonging to ≥ 60 years age group. Most messages included discussions about family dynamics and support strategies (n = 2,201, 30.4%) followed by messages describing their experience with MSA, including symptom presentation, disease progression, and diagnostic journeys (n = 1,282, 17.7%). The most mentioned challenges and unmet needs were for information/experiences/community support (n = 1,232, 27.8%), disease progression/worsening/complications (n = 707, 16.0%), and challenges in diagnosis and understanding the disease (n = 645, 14.6%). Treatment perception analysis revealed that 42.5% (n = 364) of the mentions were neutral, 26.4% (n = 226) reflected negative perceptions, 20.3% (n = 174) conveyed positive perceptions, and 10.8% (n = 93) showed mixed sentiments. Approximately half of the messages (n = 3,968, 54.8%) mentioned at least one element of the healthcare pathway. The diagnostic journey for MSA was typically initiated when the patients noticed early symptoms and consulted their primary care physician for initial evaluation. Diagnostic delays ranged from 4 to 10 years, with some patients reporting symptom onset as early as 20 years prior to diagnosis. Following diagnosis, patients were commonly prescribed medications such as Levodopa, carbidopa/levodopa (Sinemet), or levodopa/benserazide (Madopar). Non-pharmacological interventions such as physical exercise, rehabilitation, and dietary modifications also played a key role in symptom management. Conclusions: This study offers critical insights into the lived experience of patients with MSA and their caregivers, revealing significant diagnostic delays and persistent gaps in treatment and support. The findings underscore an urgent need for improved early detection strategies, comprehensive multidisciplinary care, and robust community support systems to improve disease management.

• An AI-Driven Conversational Agent to Promote Physical activity in Adults with Obesity: Development and Mixed Methods Evaluation

  Date Submitted: Sep 19, 2026

  Open Peer Review Period: Sep 20, 2026 - Nov 15, 2026

  • Peer Review Me

  Background: Physical activity plays a crucial role in managing obesity, while achieving sustained behavioral change is challenging when support is confined to clinic-based interactions. AI-driven conversational agents have potential to provide scalable and continuous support; however, there is a paucity of evidence regarding theory-informed conversational agents specifically designed to promote physical activity in adults with obesity. Objective: This study aimed to translate the situation-specific theory of Physical Activity in Adults with Obesity (PAAO) into an AI-driven conversational agent designed to promote physical activity, and to evaluate its feasibility, acceptability, and preliminary effects. Methods: A four-stage mixed-methods development and evaluation process was employed, wherein evidence was mapped onto PAAO constructs (stage 1), refined through expert consultation (stage 2), and translated into a WeChat-delivered conversational agent (stage 3). The resulting intervention was then evaluated and refined (stage 4); 16 adults with obesity were randomized in a 1:1 ratio to either a 6-week intervention supported by the conversational agent or to usual care. The feasibility and acceptability of the AI agent were evaluated through the use of implementation indicators, satisfaction ratings, the System Usability Scale (SUS), and semistructured interviews. Results: The development process translated the constructs of PAAO theory into the functions of a conversational agent, incorporating elements such as episodic future thinking prompts, habit formation reminders, adaptive plan adjustments, and performance monitoring with personalized feedback. In the evaluation phase, recruitment rate was 76.2% (16/21), retention within the intervention group was 100%, and the task response rate was 77.4%; no intervention-related adverse events were reported. Mean satisfaction score was 4.41 (SD = 0.47) out of 5, and the mean SUS score was 71.25 (SD = 10.44). Qualitative analysis identified three primary themes: perceived behavioral change and motivational factors, appropriateness of conversational agent’s design and human–agent collaboration, and recommendations for content and functional improvement. Conclusions: This study proposed and initially validated an obesity-specific theory-to-agent development pathway. The agent, delivered via human-AI collaboration, holds the potential to offer a scalable tool for continuous support in physical activity and obesity management, and may serve as a replicable model for the development of conversational agents for other chronic conditions.

• Design of a Multifaceted Mobile Health Application for Cardiovascular Rehabilitation: User-Centred Design Approach

  Date Submitted: Sep 18, 2026

  Open Peer Review Period: Sep 20, 2026 - Nov 15, 2026

  • Peer Review Me

  Background: Cardiovascular rehabilitation (CR) programs are a critical yet underutilized resource for patients who have experienced a cardiovascular event. Mobile health (mHealth) applications can support patients in completing these programs. However, existing CR mHealth applications often focus on exercise training and provide limited support for other core components of CR, such as CR education. Objective: Our objective was to design, develop, and evaluate a multifaceted, patient-facing mHealth application for CR. Methods: This study used a user-centred design approach, involving patients in the design, development, and evaluation of a CR mHealth application. Our study was split into three phases: (i) we hosted three co-design sessions with a patient partner and a caregiver to identify design requirements and create sketches of a low-fidelity application interface; (ii) we hosted three feedback sessions with two patient partners, two caregivers, and a clinician to refine the design requirements and design a medium-fidelity interactive prototype; (iii) we developed a high-fidelity web-based mobile emulator which we evaluated and improved through 15 usability testing sessions with CR patients. Results: We gathered input from patient partners and caregivers throughout the co-design and feedback sessions and identified seven design requirements for a multifaceted CR mHealth application. We designed and developed low-, medium-, and high-fidelity prototypes based on these design requirements. Overall, patients and caregivers underscored their need for a multifaceted application that holistically supports their journey throughout CR. Conclusions: Our study highlights CR patients' interest in an mHealth tool that integrates support for multiple aspects of CR progression. Furthermore, the prototypes produced throughout this study provide a foundation for designing and developing future mHealth systems for supporting patients in CR. Future work should investigate integrating a multifaceted mHealth application into clinical settings.

• Implementing Large Language Models in Global Health Organizations: A Qualitative Study of Drivers, Challenges, Safety Strategies, and Evaluation Gaps

  Date Submitted: Sep 18, 2026

  Open Peer Review Period: Sep 20, 2026 - Nov 15, 2026

  • Peer Review Me

  Background: Artificial Intelligence (AI)-enabled systems are increasingly being positioned to address structural gaps in healthcare access, including in low- and middle-income countries (LMICs) facing workforce shortages and limited specialist availability. In particular, Large Language Models (LLMs), a type of AI model that generates and interprets text (e.g., GPT, Claude, and Gemini), are rapidly integrated into healthcare systems. Most research has focused on implementation in well-resourced settings, leaving LLM integration in low-and middle-income countries poorly understood. This study examines real-world LLM adoption in global health practice, focusing on the motivations, risks, and the infrastructural and organizational factors that shape implementation in low-and middle-income countries. Objective: This study aimed to examine how LLMs are implemented in real-world global health settings and to explore the motivations, perceived opportunities and risks, and organizational and infrastructural factors that shape their integration into health workflows and decision-making. Methods: We conducted semi-structured interviews with 17 professionals involved in the design, engineering, implementation, study, and oversight of LLM-based tools in global health. We recruited participants using purposive and snowball sampling to capture variation across role type and geographies, including South Asia, the Eastern Mediterranean, Africa, and multi-country global programs. We analyzed data using reflexive thematic analysis. Results: Five themes emerged. First, participants described LLMs as useful for reducing provider workload and expanding access to health information, but adoption was also shaped by funding pressures. Second, implementation was constrained by inadequate infrastructure, unreliable connectivity, reduced accuracy in low-resource languages, and data limitations. Third, local system integration required extensive sociotechnical effort, including co-design and linguistic customization. Fourth, organizations relied on multiple safety architectures and hybrid care pathways to safely deploy LLM tools in healthcare contexts. Fifth, organizations lacked clear outcome measures linking LLM use to health outcomes. Conclusions: LLM implementation in global health is shaped by healthcare gaps, funding availability, and substantial socio-technical work, including workflow integration, human oversight, and investment in local language data. Gaps in accountability, safety evaluation, and outcome measurement mean limited evidence for safe and equitable scale-up, highlighting the need for stronger governance, safety, and outcome-focused evaluation frameworks. Clinical Trial: NA

• Verbal disclosure of emotional events in daily life: a micro-randomized ecological momentary assessment study of perceived control, affect, and disclosure content

  Date Submitted: Sep 17, 2026

  Open Peer Review Period: Sep 18, 2026 - Nov 13, 2026

  • Peer Review Me

  Background: Previous work has demonstrated that thought disclosure can have psychological benefits, but research has focused predominantly on structured written disclosure (i.e. expressive writing). In contrast, very little is known about the effects of verbal disclosure, especially immediate effects following naturally occurring emotional events. Perceived control (PC) has been proposed as one mechanism that could underlie improved affect following disclosure. Objective: The study had three key aims: 1) to examine whether verbal disclosure of emotional events immediately increases PC and improves affect in daily life, 2) to test whether affective language in verbal disclosures corresponds to momentary PC and affect, and 3) to investigate which disclosure content is associated with subsequent changes in PC and affect. Methods: A transdiagnostic clinical sample of 52 adults completed 14 days of ecological momentary assessment. Participants reported naturally occurring positive and negative emotional events and were micro-randomized at the event level to verbally disclose the event (66.7%) or a no-disclosure control condition (33.3%). PC over the emotional state and affective valence were assessed before and approximately 5 minutes after the manipulation. Across the study, participants reported 1,557 emotional events and provided 1,039 audio recordings. We tested the effects of disclosure using multilevel models. We additionally quantified the affective content of speech using dictionary-based lexical analyses and used a large language model to code seven higher-order disclosure features in negative-event recordings: situation description, specific emotion labeling, reappraisal, solution orientation, acceptance, self-validation, and self-criticism. Results: Verbal disclosure did not significantly increase PC from before to after speaking (b = 1.90, p = .06), and change in PC did not differ between disclosure and control conditions (b = 0.82, p = .47). However, increases in PC were strongly associated with more positive changes in affect (b = 0.22, p <.001). Lexical analyses showed that participants used more positive and more affect-related language on occasions when they reported more positive affect and higher PC than usual. LLM-based analyses showed that disclosures predominantly contained situation description and emotion labeling, whereas several more explicitly regulatory processes occurred infrequently. None of the seven LLM-derived content features significantly predicted immediate change in affect or PC. Conclusions: To our knowledge, this was the first study to experimentally test verbal disclosure of naturally occurring emotional events in daily life. Brief recounting primarily elicited situation description and emotion labeling, with little evidence of more explicit regulatory processes. Although disclosure did not immediately improve PC, the study demonstrates the feasibility of experimentally studying verbal disclosure and its content in everyday life. Future work should test more targeted prompts in larger samples.

• Self-selected versus therapist-selected internet CBT with regular or on-demand feedback for psychological distress in cardiovascular disease: a factorial non-inferiority randomised trial

  Date Submitted: Sep 17, 2026

  Open Peer Review Period: Sep 17, 2026 - Nov 12, 2026

  • This manuscript needs more reviewers Peer Review Me

  Background: Internet-delivered cognitive behavioural therapy (iCBT) reduces psychological distress in patients with cardiovascular disease (CVD), but whether formats with less clinical support achieve outcomes comparable to structured therapist-directed approaches remains unclear. Objective: To evaluate the non-inferiority of self-selected versus therapist-selected content and feedback on demand versus regular scheduled therapist feedback in individualised iCBT for patients with CVD and psychological distress. Methods: This 2×2 factorial non-inferiority randomised controlled trial compared therapist-selected programs with regular scheduled feedback (TS-RF) or feedback on demand (TS-OD), and self-selected programs with regular scheduled feedback (SS-RF) or feedback on demand (SS-OD). Participants with CVD and symptoms of depression, anxiety, or stress were recruited nationwide in Sweden. Primary analyses compared therapist-selected (TS-RF and TS-OD) with self-selected programs (SS-RF and SS-OD), and regular scheduled feedback (TS-RF and SS-RF) with feedback on demand (TS-OD and SS-OD). Exploratory analyses compared each alternative format (TS-OD, SS-RF, and SS-OD) with TS-RF. Primary outcomes were depressive symptoms (Patient Health Questionnaire-9 [PHQ-9]), anxiety (Generalized Anxiety Disorder-7 [GAD-7]), and perceived stress (Perceived Stress Scale-10 [PSS-10]); cardiac anxiety (Cardiac Anxiety Questionnaire [CAQ]) was secondary. Outcomes were assessed at baseline, post-treatment (9 weeks), and 6-month follow-up. Non-inferiority used a predefined margin of d=0.30. Results: Among 237 randomised participants, all groups showed large improvements (Cohen's d >0.90) in depressive symptoms, anxiety, and stress, largely maintained at 6 months. Differences between therapist-selected and self-selected programs and between regular scheduled and on-demand feedback were generally small, but confidence intervals frequently exceeded the predefined non-inferiority margin, precluding firm conclusions. High adherence was more common in therapist-selected than self-selected programs (71.2% vs 45.4%) and with regular scheduled than on-demand feedback (64.4% vs 52.1%). On-demand feedback reduced therapist time (approximately 2 vs 6 minutes per participant per week). Exploratory comparisons of the original trial arms likewise failed to demonstrate non-inferiority of TS-OD, SS-RF, or SS-OD relative to TS-RF. Conclusions: iCBT formats with reduced clinician input may yield similar outcomes for some patients with CVD, although non-inferiority was not consistently demonstrated. Therapist-selected programs and regular scheduled feedback were associated with higher adherence. Thus, greater self-direction may increase flexibility and reduce therapist time, while regular scheduled support may remain important for engagement. Future research should identify patients who can successfully use lower-support formats. Clinical Trial: Clinical Trials.gov (registration number NCT04726722)

• Universal guidelines for the prevention, identification and management of imposter participants in online research

  Date Submitted: Sep 17, 2026

  Open Peer Review Period: Sep 17, 2026 - Nov 12, 2026

  • Peer Review Me

  The rise of online research methods for recruitment and data collection following the COVID-19 pandemic has coincided with a growing threat to research integrity: imposter participants who falsify experiences to participate in paid studies. This viewpoint article provides a summary of the contemporary evidence on combatting imposter participants and proposes universal, evidence-based guidelines. Case reports and scoping reviews examining this threat are numerous, and some recommend guidelines for various study designs. Few studies have examined prevalence, financial implications and personal impacts. Imposter participants pose a persistent, evolving threat to online research. Responsibility cannot rest with individual researchers alone. Guidelines along with investment in institutional supports are urgently needed to safeguard data integrity and maintain public trust in research. Our recommended guidelines target each part of the research eco-system: 1) publishers, editors and checklist authors; 2) national funding and ethics bodies; 3) universities and other research institutions; and 4) researchers and study teams.

• Digital Communication With Public Health Agencies: A Factorial Survey Study of Citizen Preferences

  Date Submitted: Sep 16, 2026

  Open Peer Review Period: Sep 16, 2026 - Nov 11, 2026

  • Peer Review Me

  Background: Public health agencies (PHAs) in Germany are increasingly exploring digital communication channels to improve citizen engagement and responsiveness. However, little is known about citizens' preferences for such digital services, especially concerning who initiates contact - whether citizens reach out to agencies or vice versa - and the conditions under which different digital channels are accepted or rejected. Privacy concerns and perceived benefits of digital services may jointly shape citizens' willingness to engage, consistent with a privacy calculus perspective. Objective: This study investigates citizens' preferences for digital information and advisory channels offered by PHAs in Germany. Specifically, we experimentally examine how service attributes (contact reason, channel, and perceived benefit) shape usage intentions for citizen-to-agency versus agency-to-citizen communication. Additionally, we explore the moderating roles of urban/rural residence, privacy attitudes, and sociodemographic characteristics. Methods: We conducted two factorial survey experiments in Saxony (Germany). The vignette experiments were embedded in a population-representative mixed-mode survey (N=2,133) of residents in a rural district (District of Bautzen (BZ), n=1,034) and an urban area (City of Dresden, n=1,099). Each experiment comprised a 3×3×3 design (27 vignettes), with usage intention rated on a 7-point scale. Multilevel regression models assessed the relative effects of vignette dimensions and respondent characteristics on usage intention, separately by region and experiment. Results: Across both experiments and regions, the digital channel was the strongest predictor of usage intention. In experiment 1 (citizen contacts the PHA), online appointment booking was strongly preferred over electronic medical history forms, while video consultation was consistently rejected. In experiment 2 (PHA contacts the citizen), a dedicated PHA app was strongly preferred over video consultations, while social media channels were strongly rejected. The reason of contact and stated benefits played comparatively minor roles. Greater informational privacy concerns significantly reduced usage intention among urban respondents in Dresden, not in the rural municipalities. Older respondents reported slightly lower usage intentions in both regions. Gender and residential context within the rural district were not associated with usage intention, indicating no systematic exclusion of specific population groups. Subjective social status (SSS) showed small regionally varying positive effects. Conclusions: Citizens reported higher usage intentions for digital PHA services such as online appointment bookings and dedicated apps, than for video consultations and social media. This pattern suggests a preference for familiar, self-directed services, requiring less personal disclosure. Limited sociodemographic differences in stated preferences suggest that equitable adoption may depend primarily on service design rather than user characteristics, while privacy concerns represent a relevant but regionally variable barrier. PHAs should prioritize low-threshold, autonomous digital services and avoid relying on social media or unsolicited general health information in saturated information environments.

• Disclosing Environmental Costs: A Transparency Framework for Healthcare AI

  Date Submitted: Sep 15, 2026

  Open Peer Review Period: Sep 15, 2026 - Nov 10, 2026

  • Peer Review Me

  Healthcare organizations increasingly procure artificial intelligence (AI) systems whose environmental impacts extend beyond the software itself to the energy, water, chemical, and community burdens of the supporting infrastructure, yet they lack a shared starting point for evaluating this information during procurement. Existing sustainability and procurement frameworks address parts of this problem, but we did not identify one that combines healthcare-specific, pre-contract use with disclosure expectations spanning energy, water, chemical, and community-siting dimensions. Drawing on a scan of existing frameworks, we propose a draft, discussion-stage question set covering 7 dimensions: disclosure quality, governance and accountability, energy and carbon, renewable energy, water, community burden, and lifecycle and chemical accountability. For each dimension, a request-for-information instrument records a vendor's disclosure status, verification, applicability, the responsible supply-chain actor, and documented effort to obtain missing information, rather than assigning a composite score; it characterizes disclosure, not environmental performance. Because smaller or downstream vendors often depend on upstream infrastructure providers, we distinguish a vendor's capacity to disclose from its documented effort to obtain unavailable information, an equity consideration central to the framework's design. Health systems and group purchasing organizations can use this instrument now as a discussion and negotiation tool, while standards bodies and policymakers develop complementary requirements for AI vendors and data-center operators. The instrument is unvalidated: further work is needed to establish inter-rater consistency, test applicability across vendor types, and clarify how disclosure relates to independently measured environmental outcomes.

• Accuracy of Sudden Unexpected Infant Death Prevention Content on TikTok: A Content Analysis and AAP Guideline Comparison

  Date Submitted: Sep 14, 2026

  Open Peer Review Period: Sep 14, 2026 - Nov 9, 2026

  • Peer Review Me

  Background: Sudden unexpected infant death (SUID) remains a leading cause of infant mortality. The American Academy of Pediatrics (AAP) provides clear guidelines to reduce SUID risk, but to what extent these guidelines are followed in online advice remains uncertain. Objective: This study investigates how SUID prevention is portrayed on TikTok and whether the content aligns with AAP recommendations. Methods: A content analysis was conducted on 293 TikTok videos uploaded before July 2024 and selected via purposive sampling based on relevant hashtags related to SUID prevention. Videos were analyzed inductively to identify SUID recommendations, and deductively to measure frequency of occurrence of specific recommendations. Each recommendation was compared to the AAP guidelines for accuracy. Differences in adherence were assessed based on content creator type (parent/caregiver, board certified health professional, wellness consultant or undeclared), using descriptive statistics and a one-way ANOVA. Results: Approximately half (43.1%) of all TikTok SUID prevention recommendations aligned with AAP guidelines, with supine sleeping and avoiding loose bedding most frequently mentioned. A minority were partially aligned (6.2%) or directly contradicted (12.3%) AAP guidelines. Finally, 38.5% of recommendations were not mentioned in AAP guidelines. Some AAP recommendations, such as safe sleep for preterm infants or swaddling safety, were underrepresented. Adherence varied significantly by creator type, with parents being least accurate. Conclusions: While many videos support AAP-aligned practices, inconsistent and incomplete information persists. The absence of several key AAP recommendations indicates a gap in comprehensive health messaging. These findings emphasize the need for more consistent, evidence-based health communication on social media.

• Beyond the Therapy Hour: Smart Interpersonal Psychotherapy for Continuous Mental Health Care

  Date Submitted: Sep 12, 2026

  Open Peer Review Period: Sep 13, 2026 - Nov 8, 2026

  • Peer Review Me

  Depression is among the leading causes of disability worldwide, yet access to effective psychotherapy remains limited across healthcare systems. Interpersonal Psychotherapy (IPT) is a well-established, evidence-based treatment for depression and other mental disorders that emphasizes the bidirectional relationship between mood and interpersonal functioning. However, traditional delivery models require trained clinicians and structured therapy sessions, limiting scalability and accessibility. Digital mental health technologies offer promising opportunities to expand treatment reach, but many existing tools lack grounding in established evidence-based intervention frameworks and demonstrate limited long-term engagement. In this conceptual paper, we propose Smart Interpersonal Psychotherapy (S-IPT), a digitally augmented adaptation of IPT designed to extend traditional in-person treatment beyond the therapy session through mobile sensing, digital phenotyping, and adaptive behavioral interventions. S-IPT preserves the core theoretical structure of IPT—including the four interpersonal problem areas—while integrating advances in mobile health technologies capable of monitoring behavioral indicators of social engagement. By translating interpersonal mechanisms into digitally supported interventions, and by mapping those interventions onto each of the four IPT problem areas, S-IPT aims to reinforce therapeutic insights in real-world contexts and support relational functioning between clinical encounters. We examine how digital behavioral signals, conversational interfaces, and just-in-time adaptive interventions can support the core mechanisms of IPT while maintaining fidelity to the treatment model. Ethical considerations, including maintenance of the therapeutic alliance, and suicide risk management, are also discussed. Finally, we outline a clinical research agenda necessary to evaluate the effectiveness and implementation of digitally augmented IPT within real-world mental health systems.  

• Machine learning for alcohol use disorder diagnosis: A systematic review and meta-analysis of prediction models

  Date Submitted: Sep 10, 2026

  Open Peer Review Period: Sep 12, 2026 - Nov 7, 2026

  • Peer Review Me

  Background: The application of machine learning (ML) approaches in the diagnosis of alcohol use disorder (AUD) has expanded rapidly. However, the methodological rigor of existing models and their applicability in real-world clinical settings remain insufficiently characterized. Objective: To systematically evaluate the predictive performance, methodological quality, and clinical translation potential of ML-based models developed for AUD diagnosis and identification. Methods: PubMed, EMBASE, Web of Science, CNKI, Wanfang, VIP, and SinoMed databases were systematically searched from inception to July 2026. Original studies reporting the development or validation of predictive models for AUD diagnosis or identification were eligible for inclusion. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was applied to evaluate risk of bias and applicability concerns. Random-effects models were used to pool the area under the curve (AUC), sensitivity, and specificity estimates. Results: A total of 33 studies involving 57 predictive models were included. The pooled AUC was 0.90 (95% CI, 0.87–0.92), with pooled sensitivity and specificity of 0.97 (95% CI, 0.93–0.99) and 0.98 (95% CI, 0.95–0.99), respectively. The pooled area under the summary receiver operating characteristic (SROC) curve was 0.99. Subgroup analyses demonstrated that deep learning models achieved a higher pooled AUC (0.944) than traditional machine learning approaches (0.893) and conventional statistical methods (0.869) (P < 0.05). Electroencephalography-derived features were the most frequently investigated predictors, followed by age and sex. All included studies were judged to have a high risk of bias, with the major methodological concerns being low events per variable (EPV < 20 in 13 studies), lack of external validation (19 studies), and inadequate handling of missing data. Conclusions: ML models demonstrate promising discriminative performance for AUD diagnosis; however, the current evidence base is substantially constrained by widespread methodological limitations. Future research should prioritize robust external validation, adherence to established methodological standards, and improved clinical usability to facilitate the translation of ML-based diagnostic tools into practice.

• Telehealth Preference and Health Care Utilization among Adult Telehealth Users with Chronic Conditions: Secondary Analysis of California Health Interview Survey Data

  Date Submitted: Sep 11, 2026

  Open Peer Review Period: Sep 12, 2026 - Nov 7, 2026

  • This manuscript needs more reviewers Peer Review Me

  Background: Telehealth is underutilized to access primary care by mid-life and older adults with chronic conditions. Emergency department over-utilization or delayed care may result from barriers to accessing primary care. We sought to address gaps in evidence around factors associated with telehealth preference over in-person care in adults with chronic conditions, and relationships between telehealth preference and delayed or forgone care or emergency department use. Objective: The primary goal of this study was to offer insights into potential disparities in telehealth preference in mid-life and older adults with chronic conditions and inform policy and clinical strategies to reduce delays in care and unnecessary emergency department utilization through telehealth expansion. Methods: We performed a secondary analysis of cross-sectional data from the 2023 California Health Interview Survey. Adult respondents with chronic health conditions who utilized telehealth in the past year were included. We used multivariable logistic regression to test associations between demographic and health characteristics, preference for telehealth, delayed or forgone care, and emergency department utilization. Results: In the sample of 5711 telehealth users, 58% preferred telehealth over in-person for future care. Those aged 60–69 years, females, and those with higher education and income had greater odds of preferring telehealth. Respondents who identified as Asian and those who were not exclusively English speakers had lower odds of preferring telehealth. Preference for telehealth was associated with lower odds of delayed or forgone care but had no significant associations with emergency department use. Conclusions: This study reveals disparities in telehealth preference among Californians with chronic conditions, shaped by sociodemographic and health-related factors. Future longitudinal studies are needed to confirm factors motivating telehealth preference, to develop telehealth programs tailored to the unique needs of aging individuals with chronic conditions and improve their timely utilization of quality health care services. Clinical Trial: Not applicable

• Berberine for Type 2 Diabetes: A Bayesian Network Meta-Analysis of Dose-Specific Comparisons With First-Line Antidiabetic Agents

  Date Submitted: Sep 10, 2026

  Open Peer Review Period: Sep 11, 2026 - Nov 6, 2026

  • This manuscript needs more reviewers Peer Review Me

  Background: Background: Berberine is widely used off-label for type 2 diabetes mellitus (T2DM) without regulatory approval or guideline endorsement, and dose-specific comparisons with first-line antidiabetic drugs are lacking. Objective: Objective: This study aimed to compare berberine doses with placebo and standard antidiabetic agents across glycemic, metabolic, and safety outcomes. Methods: Methods: We searched PubMed, Embase, Web of Science, Cochrane CENTRAL, and ClinicalTrials.gov from inception to January 1, 2026, for randomized controlled trials (RCTs) comparing oral berberine monotherapy with placebo or any active antidiabetic drug in adults with T2DM, and performed a random-effects Bayesian network meta-analysis with SUCRA ranking and CINeMA certainty assessment. Results: Results: We included 16 RCTs with 1460 participants. Berberine 1500 mg/day produced the greatest HbA1c reduction versus placebo (mean difference −1.13%, 95% CrI −1.98 to −0.52; SUCRA 97.9%) and significantly lowered HbA1c versus metformin 1500 mg/day (−1.07%, −1.66 to −0.49) and rosiglitazone 4 mg/day (−1.12%, −2.15 to −0.08). Berberine 2000 mg/day gave the largest fasting glucose reduction versus placebo (−1.93 mmol/L, −3.59 to −0.29). Effects on lipids and body mass index were small, generally nonsignificant, and below minimal clinically important differences. Gastrointestinal events increased at higher doses, but sparse data precluded precise estimates. Conclusions: Conclusion: Berberine 1500 mg/day showed superior glycemic control versus placebo, metformin, and rosiglitazone, whereas metabolic benefits were modest and higher doses raised tolerability concerns. Confirmatory trials are needed before guideline recommendations can be made. Clinical Trial: The protocol for this systematic review and network meta-analysis was registered prospectively with PROSPERO (CRD420251088450). We conducted the review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for meta-analyses (Appendix 1)

• Effectiveness and User Experience of a Hybrid LLM-Based Conversational Agent (Chatbot) for Depression and Anxiety in Korean Young Adults: Randomized Controlled Trial and Thematic Analysis

  Date Submitted: Sep 10, 2026

  Open Peer Review Period: Sep 11, 2026 - Nov 6, 2026

  • This manuscript needs more reviewers Peer Review Me

  Background: Depression and anxiety are highly prevalent among young adults, yet access to evidence-based psychological care remains limited by cost, stigma, and workforce shortages. Large language model (LLM)–based conversational agents have been proposed as scalable, low-barrier formats for delivering cognitive behavioral therapy (CBT), but rigorous randomized comparisons against established self-help interventions are scarce, particularly in non-English contexts. Objective: This study evaluated the effectiveness and user experience of a hybrid LLM-based CBT conversational agent (chatbot) for depressive and anxiety symptoms in Korean young adults, compared with CBT bibliotherapy as an active control. Methods: We conducted a 2-week, 2-arm, parallel-group randomized controlled trial. Korean young adults aged 19 to 29 years who self-reported depressive or anxiety symptoms were randomly assigned to a web-based hybrid LLM-based conversational agent (Mango) or to a CBT-based self-help book (bibliotherapy). The intervention was fully automated, with a single in-person onboarding session and no therapist involvement. The Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) were self-administered online at baseline (T0), week 1 (T1), and week 2 (T2). All outcomes were analyzed using linear mixed models with an intention-to-treat approach; effect sizes were calculated as repeated-measures Cohen d (dRM) and Hedges g (gPPC2). A corresponding completer analysis served as a sensitivity analysis. Open-ended responses were analyzed thematically. The target sample (n=80) was based on feasibility rather than an a priori power calculation. Results: A total of 90 participants were randomized (experimental, n=49; control, n=41); 74 completed all assessments and adhered for at least 7 days. Baseline characteristics, including gender, age, occupation, and initial PHQ-9 or GAD-7 distribution, did not differ between groups. In the intention-to-treat analysis, both groups showed significant reductions from baseline to T2. In the experimental group, mean changes were −2.39 for PHQ-9 (95% CI −3.54 to −1.24; P<.001; dRM=−0.52) and −2.16 for GAD-7 (95% CI −3.07 to −1.26; P<.001; dRM=−0.59). Corresponding mean changes in the control group were −3.63 for PHQ-9 (P<.001; dRM=−0.81) and −3.15 for GAD-7 (P<.001; dRM=−0.83). Between-group differences in change were not statistically significant at either follow-up; at T2, the estimated differences were 1.24 for PHQ-9 (95% CI −0.44 to 2.92; P=.15; gPPC2=0.23) and 0.98 for GAD-7 (95% CI −0.34 to 2.30; P=.15; gPPC2=0.20). Thematic analysis indicated that, despite the absence of significant between-group differences, the 2 formats engaged users through different mechanisms: Mango users described contextualized emotional exploration and a perceived therapeutic alliance alongside the burden of sustained disclosure, whereas bibliotherapy users described declarative CBT learning without personalization. Conclusions: A hybrid LLM-based CBT conversational agent did not outperform CBT bibliotherapy over 2 weeks; significant within-group improvement was observed in both conditions in the completer analysis. Because the trial was underpowered and not designed for equivalence testing, these findings do not establish that the CBT conversational agent Mango is as effective as bibliotherapy. The study provides a transparent benchmark of a hybrid LLM architecture against an active comparator and qualitative evidence of distinct engagement mechanisms, informing adequately powered evaluation of future LLM-based interventions. Clinical Trial: Clinical Research Information Service (CRIS) KCT0012309; https://cris.nih.go.kr/cris/search/detailSearch.do?seq=33449

• Wearable-Supported, Large Language Model–Enhanced Physical Activity Coaching (HabitBot) in Adults With Prehypertension: Randomized Controlled Trial

  Date Submitted: Sep 10, 2026

  Open Peer Review Period: Sep 11, 2026 - Nov 6, 2026

  • Peer Review Me

  Background: Wearable devices support self-monitoring but rarely deliver the adaptive, individualized coaching that translates monitoring into sustained behavior change. Large language models (LLMs) could supply that coaching layer at low marginal cost, yet randomized evidence testing LLM-enhanced coaching against an active digital comparator, with a free-living behavioral outcome, is scarce. Objective: This study aimed to determine whether adding an LLM-enhanced conversational coach (HabitBot) to a blood pressure smartwatch increases free-living physical activity more than the same smartwatch combined with a standard companion app and a nurse-written exercise prescription in physically inactive adults with prehypertension, and to examine secondary physical activity, anthropometric, engagement, acceptability and theory-aligned behavioral outcomes. Methods: We conducted a 12-week, individual-level, 1:1, assessor-blind, parallel-group exploratory randomized controlled trial in Beijing, China. A total of 46 physically inactive adults aged 18-65 years with high-normal blood pressure were randomized to HabitBot or to an active comparator. HabitBot combined GPT-4o conversational coaching delivered through WeChat, progressive action and coping planning, context-linked cues, a blood pressure smartwatch and an optional peer-support group. The comparator received the same smartwatch, a standard wearable companion app and 1 nurse-written individualized exercise prescription. The primary outcome was mean daily smartphone-recorded steps at 12 weeks, analyzed by analysis of covariance adjusted for baseline. Secondary outcomes were questionnaire-assessed physical activity; body composition, blood pressure, theory-aligned behavioral determinants, engagement, acceptability and harms were exploratory. Results: All 46 randomized participants completed the 12-week assessment and were analyzed as assigned. HabitBot increased mean daily steps by 1397.6 steps/day relative to the comparator (95% CI 399.4-2395.8; P=.007). The estimate was unchanged across heteroscedasticity-robust, covariate-adjusted, change-score, randomization-inference, bootstrap and leave-one-out sensitivity analyses. Vigorous, moderate and total physical activity favored HabitBot; blood pressure did not differ. Weekly reach was sustained at 21-23 of 23 participants, although interaction volume declined. Behavioral determinants favored HabitBot after false-discovery-rate correction, but these self-reported outcomes were materially constrained by directional baseline imbalance and unblinded self-report. No serious adverse events occurred. Conclusions: LLM-enhanced coaching added to a wearable increased short-term physical activity beyond wearable access and standard app support. The trial was small, the intervention multicomponent, and the behavioral mechanism unresolved; adequately powered, component-separating trials are needed before implementation. Clinical Trial: Chinese Clinical Trial Registry ChiCTR2400085073; https://www.chictr.org.cn/showprojEN.html?proj=215626

• eHealth Literacy and All-cause Mortality among Community Dwelling Older Adults: A Prospective Cohort Study

  Date Submitted: Sep 10, 2026

  Open Peer Review Period: Sep 10, 2026 - Nov 5, 2026

  • Peer Review Me

  Background: As health information and services increasingly move to digital platforms, eHealth literacy may play an important role in health management among older adults. However, evidence on the prospective association between eHealth literacy and hard health outcomes, such as all-cause mortality, remains limited. Objective: This study aimed to examine the association between baseline eHealth literacy and all-cause mortality among community dwelling older adults in China. Methods: This prospective cohort study included 2,144 adults aged 60 years or older in Jinan, China. Baseline data were collected through face to face interviews from March to May 2021. eHealth literacy was measured using the 8-item eHealth Literacy Scale. Vital status was ascertained from March to May 2026 by linking participants to the vital registration system using their unique national identification numbers. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between eHealth literacy and all cause mortality. Sequential models adjusted for sociodemographic characteristics, health behaviors, body mass index, and chronic diseases. Kaplan Meier curves were plotted after dichotomizing eHealth literacy at the sample median. Subgroup and sensitivity analyses were also conducted. Results: Among the 2,144 participants, the mean age was 72.0 years (SD 7.0), and 1,069 (49.9%) were women. During a mean follow up of 56.8 months, 189 deaths were recorded. Participants who died had lower baseline eHealth literacy than those who survived (mean 12.28, SD 6.73 vs mean 18.07, SD 9.69; P<.001). In the fully adjusted Cox model, higher eHealth literacy was associated with a lower risk of all-cause mortality (HR=0.969, 95% CI 0.948 -0.992). Kaplan Meier analysis showed that participants with high eHealth literacy had a higher 60-month survival rate than those with low eHealth literacy (95.6% vs 84.7%). Subgroup analyses suggested that the association was more evident among participants aged 60 to 69 years (HR=0.915, 95% CI 0.856 - 0.979) and 70 to 79 years (HR 0.966, 95% CI 0.935 - 0.998), but not among those aged 80 years or older. The association differed by age group (P for interaction=.024). Sensitivity analyses yielded consistent results. Conclusions: Higher eHealth literacy was associated with lower all cause mortality among community dwelling older adults in China. These findings extend current evidence on the health relevance of eHealth literacy to long term survival and support its consideration as a modifiable factor in healthy aging. Further intervention studies are warranted to determine whether improving eHealth literacy can lead to better long term health outcomes. Clinical Trial: NA

• Theoretical approaches in studying access to digital health services in rural contexts in high-income countries: a scoping review

  Date Submitted: Sep 9, 2026

  Open Peer Review Period: Sep 10, 2026 - Nov 5, 2026

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  Background: Digital services are increasingly used in healthcare; however, access to these services may be hindered by several factors. People residing in rural areas may face additional challenges. Rural digital healthcare access is a complex and interdisciplinary topic; to what extent this complexity is captured and theorised in extant research remains unclear. Objective: We aimed to review the theoretical approaches used in previous studies on digital healthcare access in rural areas, specifically to identify the frameworks used and the application of intersectionality. Methods: We searched MEDLINE, Embase, CINAHL Plus, and Scopus for literature related to digital health access in rural areas in high-income countries. Search results were screened, extracted, and analysed with basic content analysis. Results: We included 101 studies in the analysis. Conceptual frameworks came from research fields of technology acceptance, healthcare access, and implementation research. The most used frameworks were the Technology Acceptance Model, the Unified Theory of Acceptance and Use of Technology, Andersen’s behavioural model, Levesque’s patient-centred framework, and the Consolidated Framework for Implementation Research. Forty-two studies focused on populations with some intersecting inequities, but none mentioned intersectionality specifically. Conclusions: All frameworks have their own limitations, especially when crossing disciplines in the specific rural context. We urge researchers to acknowledge these limitations and consider interdisciplinary frameworks. There is a need to theoretically approach the issue of inequity in this topic, and we recommend using equity-based frameworks for a comprehensive and intersectional understanding.

• Using inter-contact intervals to identify acute care needs in primary care patients: a secondary data analysis using electronic medical records

  Date Submitted: Sep 6, 2026

  Open Peer Review Period: Sep 8, 2026 - Nov 3, 2026

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  Background: The inter-contact interval (ICI), defined as the number of days between consecutive practice contacts, measures individual patient contact behavior but requires further validation as a useful tool for primary care research. Objective: This study applies the ICI to different electronic datasets from primary care practices to characterize individual patients’ contact patterns over time. The ICI and electronic “big data” are then used to develop and refine measures that may signal increased contact frequency and help identify acute care needs. Methods: This retrospective longitudinal observational study used electronic medical records (EMRs) from German primary care practices for a secondary data analysis, comprising (1) all patients from 155 primary care practices over 14 years, (2) all patients from one practice over 24 years, and (3) 100 patients receiving oral anticoagulation from seven practices over 7 years. ICI distributions were summarized using means, standard deviations, medians, 25th and 75th percentiles, interquartile ranges, and boxplots for a random sample. Ordinal time series analysis was used to identify increased practice contact frequency and ICI minima that best reflected a potential new need for acute care and patterns of contact behavior. Results: Among 333,610 patients with ≥2 practice contacts, 7,044,653 ICIs were identified across the three datasets. Patients had practice contacts, on average, every 38, 53, and 15 days, respectively. Nearly half of the patients had at least one contact at a 1-day interval every 3 months. ICI frequency distributions consistently showed peaks at 7 days and their multiples and at 90–93 days. Substantial variation in ICI distributions was observed within individual patients, between patients, between practices, and across datasets. Time series analyses and further refinements suggested defining a short ICI minimum as a contact with an ICI shorter than those of its direct neighbors and <8 days, potentially indicating acute care needs. Low ICI values, indicating frequent contacts, were not randomly distributed; instead, they frequently formed clusters (“islands”) of acute care within longer periods of chronic care. Patients could be divided into three groups: patients with a nearly constant consultation rhythm and no urgent practice visit; patients with 1 or more periods of intensified contact, suggesting acute care needs; and patients with frequent short-interval practice contacts. Conclusions: While a high number of contacts is a simple indicator of potentially intensified acute care needs, refined measures, such as ICI minima provide a more stringent indication of care intensity and the severity of acute care needs. Ordinal time series analysis and graphical ICI analysis can identify “islands” of frequent attendance and distinguish acute care episodes from the surrounding chronic care trajectory, demonstrating the usefulness of the ICI as a tool for longitudinal analyses of real-world primary care data. Clinical Trial: n.a.

• Applications of Hybrid Approaches to Care, Blending In-person and Virtual Care Modalities: A Scoping Review

  Date Submitted: Sep 6, 2026

  Open Peer Review Period: Sep 8, 2026 - Nov 3, 2026

  • This manuscript needs more reviewers Peer Review Me

  Background: The Inverse Care Law stipulates that healthcare resources are unevenly distributed and tend to vary inversely with actual clinical needs. Hybrid approaches, which deliberately combine in-person and virtual care, may help counteract this phenomenon in various settings, including rural healthcare, by leveraging clinician capacity across geographic regions. However, the literature on the potential of hybrid approaches to primary care has not been comprehensively mapped. Objective: This scoping review aimed to map the international evidence on applications of the hybrid approach to primary care and examine its impacts across the domains of the Quadruple Aim framework for quality improvement. Methods: We conducted a scoping review following the PRISMA extension for Scoping Reviews and the five-stage framework by Levac et al. MEDLINE, Scopus, Embase, and CINAHL were searched for studies published from 2020 onward, supplemented by a grey literature search. We included literature describing hybrid approaches to outpatient care involving primary care clinicians. Outcomes were mapped across the Quadruple Aim domains of patient experience, clinician and non-clinical staff experience, clinical impact, and economic impact. Results: Of 3,504 unique records screened, 24 peer-reviewed studies and 8 grey literature sources were included. Peer-reviewed studies were predominantly conducted in North America (16/24) and in rural or remote settings (15/24). Hybrid approaches varied considerably in design and implementation but commonly used virtual-first models and interdisciplinary teams combining off-site physicians with locally based clinicians. Clinical impact was the most frequently assessed domain (13/24) and showed the most consistently favourable findings (11/13), followed by patient experience (6/8 studies reporting favourable findings). Clinician experience showed mixed findings, while economic impact was infrequently evaluated. Conclusions: Hybrid approaches show promise as an innovative way to address gaps in healthcare access and mitigate workforce maldistribution in hard-to-serve regions, with reported benefits spanning multiple domains of the Quadruple Aim. However, evidence regarding economic impact remains limited, highlighting an important area for future research. Clinical Trial: NA

• Personalized Telephone Coaching for Depression Prevention in Farmers: A Health Economic Evaluation Alongside a Randomized Controlled Trial

  Date Submitted: Sep 3, 2026

  Open Peer Review Period: Sep 4, 2026 - Oct 30, 2026

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  Background: Major depressive disorder (MDD) is a leading contributor to the global burden of disease, associated with substantial impairment in daily functioning and reduced quality of life for the individual and considerable societal costs. Farmers constitute a particularly vulnerable population, facing an elevated depression risk due to occupational, financial, and social stressors, and simultaneously encountering considerable barriers to mental health care. Personalized telephone coaching offers a promising solution for indicative prevention in this group. However, despite the evidence on its clinical effectiveness, the cost-effectiveness and cost-utility of telephone coaching for preventing MDD remain insufficiently examined. Objective: This economic evaluation aimed to evaluate the cost-effectiveness and cost-utility of a personalized telephone coaching (IG) provided by psychologists compared to treatment-as-usual plus informational material (TAU+) in farmers from a societal and social insurance perspective within a time horizon of 18 months. Methods: An economic evaluation was performed alongside a pragmatic randomized controlled trial (RCT). Health service use, patient and family expenses, and productivity costs were assessed with the adapted German version of the self-report questionnaire TiC-P (Trimbos Institute and Institute of Medical Technology Questionnaire for Costs Associated with Psychiatric Illness). Outcomes were measured in terms of depression symptom-free status (Quick Inventory of Depressive Symptomology – Self-Report (QIDS-SR16)<6) and quality-adjusted life years (QALYs; Assessment of Quality of Life (AQoL-8D)). All assessments were conducted web-based. A cost-effectiveness (CEA) and cost-utility analysis (CUA) were performed from the societal and social insurance perspective. Analyses followed the intention-to-treat principle with multiple imputation. Uncertainty around incremental cost-effectiveness ratio (ICER) estimates was characterized using bootstrapped seemingly unrelated regression equations (2,500 simulations). Sensitivity analyses assessed the robustness of the findings. Results: In total, 314 participants were included in the RCT. From a societal perspective, the IG produced a greater number of participants achieving depression symptom-free status (ΔE=0.11, 95%CI –0.01 to 0.23) compared to TAU+ at higher costs (ΔC=€663, 95%CI 2820 to 4052; €1=US $1.12 as of 2019). The probability of the IG being cost-effective was 37% at a willingness-to-pay (WTP) threshold of €0 per depression symptom-free status and QALY gained, rising to 63% when applying a societal WTP of €20,000 per QALY gained (38% from a social insurance perspective). Analyses conducted from the social insurance perspective, as well as sensitivity analyses, indicated the dominance of TAU+ over the IG, with intervention costs as the primary cost driver. Conclusions: Findings suggest that MDD prevention via personalized telephone coaching yield larger effects, albeit accompanied by higher costs from both a societal and social insurance perspective within the observed time horizon. These results inform evidence-based decision-making on cost-effective MDD prevention in occupational groups, and highlight the need for comparative trials with alternative approaches. Clinical Trial: German Clinical Trial Registration: DRKS00015655. Registered on October 2, 2018.

• Human-Robot Collaboration in Long-Term Care: A Scoping Review

  Date Submitted: Sep 4, 2026

  Open Peer Review Period: Sep 3, 2026 - Oct 29, 2026

  • Peer Review Me

  Background: As care robots (CRs) are increasingly integrated into long-term care (LTC), human-robot collaboration (HRC) is becoming relevant to care delivery. However, how care staff and informal caregivers collaborate with CRs, how caregiving work changes, and the associated outcomes and challenges remain unclear. Objective: This review aimed to synthesize evidence on HRC in LTC, focusing on task allocation, changes in caregiving work, and associated outcomes and challenges. Methods: This scoping review followed Joanna Briggs Institute (JBI) methodology and PRISMA-ScR guidelines. English and Chinese literature published from database inception (2005) to May 2026 was searched in CINAHL, Embase, Scopus, PubMed, Web of Science, China National Knowledge Infrastructure, Wanfang Data, and Chinese Biomedical Literature Database, supplemented by grey literature. Two reviewers independently screened studies, and data were synthesized narratively. Results: Twenty-five studies were included, of which 21 were qualitative. Most were conducted in institutional LTC settings, with Japan contributing the largest number of studies (8/25, 32%), followed by Australia (4/25, 16%) and Canada (4/25, 16%). Care robots supported routine care (e.g., reminders, logistics, and safety monitoring), psychosocial care (companionship, activity facilitation, and telepresence), and physical assistance. Two distinct human–robot collaboration models were identified: robot-intensive and human-intensive collaboration. Across both models, care robots assumed structured, repetitive, and physically demanding tasks, while care staff remained responsible for supervision, coordination, and individualized care, indicating a redistribution rather than replacement of care work. HRC was associated with reduced workload for care staff, improved well-being among care recipients, and strengthened relationships between care staff, care recipients, and family members. Key implementation challenges included technical limitations, training needs, limited adaptability across care contexts, unclear role boundaries, ethical and safety concerns, and resource constraints. Conclusions: Rather than replacing human care, care robots may reshape how care is delivered by redistributing tasks between robots and care staff. Further research should focus on designing and evaluating sustainable HRC models that maximize efficiency while preserving person-centered care. Clinical Trial: This scoping review has been registered in the Open Science Framework. The link is https://osf.io/4xdcr/overview.

• Understanding Factors affecting User-Initiated Engagement in a Digital Mental Health Chat Platform: A Mixed Methods Analysis

  Date Submitted: Sep 2, 2026

  Open Peer Review Period: Sep 2, 2026 - Oct 28, 2026

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  Background: Chat-based online platforms have been shown to be effective intervention tools for mental health support. However, user attrition may limit their capacity to provide meaningful support and hinder the validation of their clinical benefits. Factors contributing to the continued use or non-use of chat-based platforms must be investigated to address barriers to engagement. Objective: This study investigates user engagement with a chat-based mental health platform. Specifically, it aims to identify the factors that contribute to the initiation and subsequent initiation of sessions with mental health professionals (MHPs) through the chat platform. The findings may inform the development, refinement and implementation of chat-based mental health interventions with improved user retention. Methods: To investigate user engagement, the study employed a prospective longitudinal design. A total of 119 students were enrolled in sequential batches, with each batch observed for a three-month period. During this period, data on chat-session activity and interactions with MHPs, who are licensed clinical psychologists, were automatically recorded and collected through the SAGIP mobile app. Each MHP handled no more than 10 students at any given time. Data pertaining to participants’ well-being, mental health, demographic information and experiences with the chat intervention were collected through surveys and interviews. Of the 119 participants, 32 were interviewed, and four of them initiated a subsequent session. A binomial mixed-effects generalized linear model was used to identify factors associated with the initiation and subsequent initiation of sessions with MHPs through the online mental health chat platform. Descriptive analyses were conducted to further examine these factors and their relationships with user engagement. In addition, thematic analysis was applied to the qualitative text data to explain other factors that may influence user engagement. Results: Based on the binomial mixed-effects generalized linear model, the number of user messages sent within the current session emerged as a key significant factor associated with user initiation in that same session. Meanwhile, the number of fast responses (i.e., responses provided within five minutes) users received during their current session was a significant factor associated with subsequent initiation. The thematic analysis further revealed three critical factors influencing user engagement: the availability of mental health professionals, therapeutic alliance, and usability of the platform. Conclusions: This study combined a binomial mixed-effects generalized linear model with thematic analysis that provided a comprehensive understanding of user-initiated engagement with a mental health chat-based platform. The findings suggest that users are more likely to initiate sessions when they have concerns, experiences, or needs they wish to share, and are more likely to continue using the platform when they receive prompt and useful responses. In addition, platform usability emerged as an important factor influencing engagement. Clinical Trial: WMSU REOC Code: 2025-EF-0379

• AnxEMU-VR: Randomized Controlled Trial Evaluating the Impact of Virtual Reality Exposure Therapy on Epilepsy-Specific Interictal Anxiety in People with Epilepsy

  Date Submitted: Aug 31, 2026

  Open Peer Review Period: Sep 1, 2026 - Oct 27, 2026

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  Background: Anxiety is the most common psychiatric comorbidity among people with epilepsy, yet epilepsy-specific (ES) interictal anxiety remains underrecognized and undertreated, highlighting the need for accessible non-pharmacological approaches. Virtual reality exposure therapy (VR-ET) can deliver controlled exposure to seizure-related fears that are difficult to reproduce in vivo, but its effects and delivery requirements need evaluation in the context of seizure unpredictability and heterogeneous seizure experiences. The epilepsy monitoring unit (EMU) allows repeated engagement under medi Objective: This mixed-methods randomized controlled trial evaluated the preliminary effectiveness of VR-ET for ES-anxiety compared with neutral VR, together with feasibility, acceptability, usability, safety, tolerability, and scenario fidelity among people with epilepsy admitted to an EMU. Methods: Fourteen people with self-reported epilepsy- or seizure-related anxiety were randomized to VR-ET (n=7) or neutral VR (n=7). Participants completed self-administered 5-minute 360-degree video sessions twice daily for up to 10 days or until discharge. VR-ET participants progressed through personalized hierarchies of graded seizure-related exposure scenes selected at baseline using a diagnostic interview; control participants viewed nature-based scenes in a standardized order. Quantitative outcomes included anxiety, fear and situational avoidance, quality of life, simulator sickness, usability, and presence, and were summarized descriptively. Post-intervention and 1-month interviews were analyzed using framework analysis with inductive coding within predefined domains. Findings were integrated through joint interpretation and cross-case comparison. Results: Quantitative outcomes were heterogeneous and showed no clear between-group pattern. Adherence was high, with all VR-ET participants and 5 of 7 (71.4%) neutral VR participants meeting the predefined threshold. Five of 7 (71.4%) VR-ET participants described the intervention as helpful, and 4 of 7 (57.1%) reported experiences consistent with exposure-related learning, including increased insight, self-efficacy, preparedness, cognitive reappraisal, and habituation. Fear and anxiety changes were variable, and incomplete 1-month follow-up limited interpretation of longer-term outcomes. Scenarios were generally realistic and representative of epilepsy-related fears, but emotional engagement depended on personal relevance. Seizure simulations were identified as most impactful, and participants recommended greater intensity, variety, interactivity, and treatment duration. VR-ET participants more often expressed interest in continued use outside the EMU, whereas neutral VR participants primarily described short-term calming and distraction during admission with some reporting proactive use during periods of heightened anxiety. Across groups, usability was favorable, simulator sickness was minimal, and no serious adverse events were attributable to VR. Engagement was affected by EMU-related fatigue, seizure activity, environmental interruptions, and technical problems. Conclusions: The VR interventions were feasible, acceptable, and safe in this sample. VR-ET showed no clear quantitative advantage over neutral VR, but qualitative findings were consistent with exposure-related learning. Neutral VR may provide low-intensity anxiety support during EMU admission. Future research should evaluate more personalized, varied, and interactive VR-ET and examine integration with therapist-supported care. Clinical Trial: ClinicalTrials.gov NCT06028945

• Collaboration With Assistive Technology Suppliers Across Selection, Implementation, and Development in Elderly Care: Interview Study Among Innovation and Care Managers

  Date Submitted: Aug 29, 2026

  Open Peer Review Period: Aug 30, 2026 - Oct 25, 2026

  • Peer Review Me

  Background: Assistive technology is increasingly recognized for its potential to support older adults amid increasing pressure on the care workforce, yet successful implementation in practice remains difficult. Care organizations play an important role in the selection, implementation and overall coordination surrounding the use of assistive technology in care settings, and technology suppliers can support them through the development of fitting technology and during implementation. Collaboration between them may facilitate uptake, but existing research provides only a fragmented understanding of how care organizations collaborate with suppliers. Objective: This study aimed to explore how innovation and care managers perceive and evaluate assistive technology suppliers, how these perceptions are formed, and how suppliers and care organizations collaborate in the development and implementation of assistive technology. Methods: The study employed a qualitative design using semi‑structured individual interviews with managers from organizations providing care for older adults, working in the same geographic region in the Netherlands. A semi-structured interview guide was used to guide the interviews. Interviews were audio-recorded, transcribed and analyzed using reflexive thematic analysis. Results: A total of 14 participants across 11 care organizations participated in the study. Five interconnected themes were derived pertaining to the collaboration between care organizations and assistive technology suppliers, including (1) collaboration with suppliers is shaped by individuals, (2) articulating product-related criteria for supplier evaluation, (3) seeking supplier dependability before and beyond collaboration, (4) balancing organizational oversight, autonomy, and supplier responsivity, and (5) technology development through ongoing supplier-care organization interaction. Conclusions: Findings show that care and innovation managers evaluate assistive technology suppliers not only through explicit product- and supplier-related criteria, but also through more implicit cues derived from interpersonal interactions and supplier reputation within the sector. Together, these considerations contribute to a broader assessment of suppliers as trustworthy and legitimate collaboration partners. Care organizations must navigate trade-offs within these collaborations, which may benefit from explicit consideration. More structured approaches may also help, albeit proportionate to the technology and organizational context. Sustainable supplier relationships may likewise require realistic commitments and concessions from both parties. Finally, existing supplier relationships can provide a foundation for ongoing technology development. Future research could explore forms of co-design that enable both care organizations and suppliers to contribute in ways that are feasible, meaningful, and mutually beneficial.

• Multi-Perspective Chain-of-Thought Reasoning for AI-Assisted Shared Decision Support in Cannabis-Related Care: A Simulation-Based Expert Evaluation

  Date Submitted: Aug 29, 2026

  Open Peer Review Period: Aug 30, 2026 - Oct 25, 2026

  • Peer Review Me

  Background: Large language models (LLMs) may enhance AI-assisted shared decision support by translating behavioral and contextual information into complementary patient- and clinician-facing messages. However, it remains unclear whether the perceived quality of such communication differs across prompting strategies in cannabis-related care. Objective: This study compared direct generation, standard Chain-of-Thought (CoT), and Multi-Perspective CoT (Multi-CoT) across multiple LLM backbones with respect to expert selections for patient helpfulness, clinical expertise, empathy, and overall preference in simulated cannabis-related shared decision support scenarios. Methods: We developed an AI-assisted shared decision support prototype that integrates behavioral context, community-experience retrieval, emotion-aware prompting, explainable artificial intelligence (XAI) summaries, and role-specific response generation. Four blinded evaluators with diverse health care–related backgrounds each assessed 50 simulated cases, including 20 shared and 30 evaluator-specific cases, yielding 200 case-level assessments. Each evaluator was assigned a distinct pair of LLMs. For each case and evaluation criterion, evaluators selected 1 of 6 response pairs, each comprising a patient-facing message and a clinician-facing summary generated by the 2 assigned LLMs under the 3 prompting strategies. Results were summarized descriptively using counts and percentages. Results: Across 200 case-level assessments, Multi-CoT received the most selections for overall preference (122/200, 61.0%) and patient helpfulness (100/200, 50.0%). Multi-CoT and standard CoT received the same number of selections for clinical expertise (83/200, 41.5% each), whereas standard CoT received the most selections for empathy (95/200, 47.5%). Selection patterns varied substantially across evaluator–model pairings; Multi-CoT’s overall-preference selection rate ranged from 12% to 92% across the 4 evaluations. Conclusions: In this simulation-based expert evaluation, Multi-CoT was favored in aggregate for patient helpfulness and overall preference but not for empathy, and its selection patterns varied markedly across evaluator–model pairings. These findings support Multi-CoT as a promising, context-dependent prompting approach for the response-generation layer of AI-assisted shared decision support. They do not establish clinical effectiveness, improved shared decision-making, or readiness for clinical use. Further work should include multi-stakeholder evaluation, real patient–clinician interactions, and formal assessments of factual accuracy and safety.

• Decision Aids Supporting Participation in People With Mild Cognitive Impairment or Mild-to-Moderate Dementia: A Scoping Review

  Date Submitted: Aug 28, 2026

  Open Peer Review Period: Aug 30, 2026 - Oct 25, 2026

  • Peer Review Me

  Background: People with mild cognitive impairment or mild-to-moderate dementia may retain the ability to participate in preference-sensitive decisions, although cognitive decline can make complex healthcare decision-making increasingly challenging. Decision aids may support meaningful participation, but their characteristics, development, evaluation, and implementation in this population have not been systematically synthesized. Objective: This scoping review aimed to provide an overview of decision aids for people with mild cognitive impairment or mild-to-moderate dementia, including their characteristics, applications, evaluation, and existing challenges. Methods: Our scoping review was conducted following the Joanna Briggs Institute recommendations for scoping reviews and Arksey and O’Malley’s framework. The process followed PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines and PRISMA-S (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Literature Search Extension) checklist. A comprehensive literature search was conducted across 6 databases: PubMed, Web of Science, Cochrane Library, IEEE Xplore, CINAHL, and EMBASE. Two reviewers independently screened titles, abstracts, and full texts via Rayyan, with disagreements resolved by a third reviewer. Results: Eleven studies were included, of which 10 were published from 2019 onward. Decision-support tools addressed a broad range of decisions across the care trajectory, including driving, diagnosis and prognosis, treatment and medication management, risk reduction, and future care planning. Visual formats predominated (7/11), and more than half of the tools were designed for fully self-guided use (6/11). Most tools remained at an early stage of development (10/11), with evaluations focusing primarily on usability, feasibility, and acceptability; only three studies assessed efficacy or effectiveness. Key challenges included limited integration into clinical workflows and EHR systems, digital accessibility, organizational readiness, and limited evidence across diverse sociocultural and resource settings. Conclusions: Decision-support tools for people with cognitive impairment are expanding across a broader range of decision contexts, reflecting growing attention to person-centred and supported participation across the care trajectory. However, most tools remain at an early stage of evaluation, with limited evidence on effectiveness, decision quality, long-term outcomes, and implementation in routine clinical practice. Future research should prioritize rigorous real-world evaluation, standardized decision-quality measures, and integration into clinical workflows across diverse populations and care settings.

• Early versus late internet-based cognitive behavioral therapy after MINOCA or takotsubo syndrome: one-year follow-up of a randomized trial

  Date Submitted: Aug 25, 2026

  Open Peer Review Period: Aug 26, 2026 - Oct 21, 2026

  • Peer Review Me

  Background: Cardiovascular (CV) events such as a myocardial infarction with non-obstructive coronary arteries (MINOCA) or takotsubo syndrome (TS) are associated with heightened levels of stress and anxiety. There are currently no established treatment guidelines for these conditions, but emerging research indicates that internet-based cognitive behavior therapy (ICBT) is effective in the short-term for reducing these symptoms. However, the long-term stability of effects is unknown, as well as the importance of timing of the intervention. Objective: This paper aims to investigate the stability of effects achieved by ICBT after MINOCA or TS for common mental health symptoms, as well as to compare the difference in effect between early and late initiation of the intervention. Methods: This was a parallel, multicenter, randomized controlled trial of ICBT on symptoms of stress and anxiety. The ICBT intervention was a guided, 7–9-week written program, where completion of at least 5/9 steps was considered a minimum dose. Participants were included if they reported elevated stress (Perceived stress scale, 14 items [PSS-14] > 24) or anxiety (Hospital anxiety and depression scale, anxiety subscale [HADS-A] > 7) and were randomized (1:1) to either early intervention group (EIG; immediately after randomization) or late intervention group (LIG; 10-12 weeks after randomization). Mental health outcomes were assessed using PSS-14, HADS, cardiac anxiety questionnaire [CAQ], and impact of event scale [IES]. Outcome data was collected at three follow-up time points: 10-12 (1), 20-22 (2), and 50-52 (3) weeks after randomization. This paper reports results from follow-up 2 and 3. Data was analyzed by the intention-to-treat principle, using linear mixed models, assuming data missing at random. Results: Eighty-eight participants were randomized (EIG: 45; LIG: 43). The response rate at follow-up 2 was 81% and at follow-up 3 was 73%. Eighty-seven percent completed the minimum dose in the EIG, and 51% in the LIG. Intervention effects achieved in the EIG at follow-up 1 remained stable over time. The average trend was lower levels of stress and anxiety in the EIG compared with the LIG, but no differences were statistically significant. Regarding symptoms of post-traumatic stress, the EIG reported significantly lower levels than the LIG, at follow-up 2 (-2.32, 95% CI: -4.42, -0.21, p=0.03). Conclusions: Short-term effects achieved by ICBT after MINOCA or TS for symptoms of stress and anxiety were stable over time and there was little evidence suggesting a difference in effect between early or late initiation of treatment. However, patients in the EIG completed the treatment to a higher degree and reported superior treatment effects on symptoms of post-traumatic stress. These novel findings are important for informing care after a MINOCA or TS, but more research is needed before firm conclusions can be drawn. Clinical Trial: clinicaltrials.gov NCT04178434

• Adolescents’ Use of AI Chatbots for Social, Emotional and Mental Health Support: A Cross-Sectional, School-Based Survey

  Date Submitted: Aug 24, 2026

  Open Peer Review Period: Aug 24, 2026 - Oct 19, 2026

  • This manuscript needs more reviewers Peer Review Me

  Background: An ongoing youth mental health crisis has been linked to social technologies, with gendered and developmental differences in youth mental health outcomes related to social technology exposure. Understanding their uses, attitudes about, and experiences of AI can guide policies, clinical practice, and parental and school-based approaches. Objective: Our objective was to characterize adolescents’ uses, attitudes, and experiences regarding GenAI chatbots and examine developmental stage and gender differences. Methods: A STROBE-compliant, cross-sectional evaluation using a youth-informed survey in late 2025 was conducted with school-based adolescents (ages 11-18) across eight sociodemographically diverse campuses. Exploratory survey measures assessed adolescents’ uses, attitudes, and experiences regarding GenAI chatbots. Results: 2,458 adolescents completed the survey (53% of eligible students). Participants included 38% students in grades 6 through 8 and 62% grades 9 through 12; 49% male, 49% female, 0.6% nonbinary/gender fluid; 30% Hispanic/Latine, 21% Black/African American, 18% Asian, 16% White, 9.9% multiracial, and .8% other race. Approximately three-fourths of adolescent participants reported any use of GenAI chatbots, most commonly for socioemotional support (49.5%) and help with school difficulties (62%). The mean age of first use was 12.75 years. Usage was slightly higher among females. Around one-fifth (21%) used AI to share mental health concerns, and around 13% used it to improve their mental health, with both behaviors more commonly reported by females. Early adolescents in grades six to eight (typically ages 11-13) used AI for socioemotional support more often than middle adolescents (typically ages 14-18). Regarding general attitudes, early adolescents held more positive views of AI as a wellness tool than middle adolescents. Additionally, females reported lower general AI optimism and greater AI pessimism than males. Most adolescents (65%) believed AI helped them or a friend, though 9% reported harmful experiences related to AI chatbot use. Conclusions: More adolescents may be using AI chatbots for social, emotional, and mental health support than previously estimated, with first use occurring around age 13. Early adolescence may be an optimal window for AI literacy and mental health literacy interventions, parental conversations about AI use (including risks of sycophancy and dependency), and age-appropriate access restrictions at home and school. Clinical Trial: Not applicable

• Associations Among Social Media Use Motivations, Body Appreciation, and Selfie Engagement in Chinese Women Aged 18 to 35 Years Using Rednote (Xiaohongshu): Cross-Sectional Questionnaire Study

  Date Submitted: Aug 18, 2026

  Open Peer Review Period: Aug 19, 2026 - Oct 14, 2026

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  Background: Selfies combine several appearance-related activities into a single routine: taking photographs, choosing among them, editing selected images, and deciding whether to post. Research has linked some of these activities, particularly comparison and editing, to poorer body image, while posting and supportive feedback can have different associations. Less is known about how the reasons people use a platform relate to this routine, or where body appreciation fits within those relationships on Chinese social media. Objective: This study examined the direct and indirect associations between four UGT-derived motivations for social media use (information seeking, social interaction, entertainment, and status seeking), body appreciation, and selfie engagement among young Chinese women who use Rednote (Xiaohongshu). Methods: A cross-sectional, self-administered web-based survey was distributed via Rednote and WeChat and hosted on Wenjuanxing in June 2026, following a May 2026 pilot (n=72). Eligible respondents were Chinese women aged 18 to 35 years who were active Rednote users. Of 567 submissions, 21 failed eligibility screening, and 94 were classified as straight-lined in the study's cleaning record, leaving 452 analyzable responses. Motivations were measured with a 12-item, 4-dimension UGT scale; body appreciation with the Body Appreciation Scale-2; and selfie engagement with the 15-item Selfie Engagement Scale, modelled as a reflective-formative higher-order construct comprising preoccupation, selection, editing, and posting. Partial least squares structural equation modelling used 5000 bootstrap resamples. Results: The measurement model met the prespecified criteria (Cronbach αlpha, 0.707-0.917; average variance extracted 0.574-0.697; all heterotrait-monotrait ratios <0.90). All four motivations were positively associated with body appreciation, jointly explaining 65.4% of its variance: social interaction (β=.310, P<.001), information seeking (β=.300, P<.001), entertainment (β=.233, P<.001), and status seeking (β=.124, P=.01). Three were directly associated with selfie engagement: information seeking (β=.222, P<.001), status seeking (β=.221, P<.001), and social interaction (β=.139, P=.02). Entertainment was not (β=.068, P=.07). Body appreciation had the largest standardized coefficient in the selfie engagement equation (β=.291, P<.001; R²=0.623). All four statistical indirect effects through body appreciation were significant (β=.036-.090; all bias-corrected 95% CIs excluded zero). Entertainment showed an indirect-only association; the other three were complementary Conclusions: In the fitted model, body appreciation accounted for a modest part of the association between each motivation and selfie engagement. Entertainment showed an indirect association only. The positive association between status seeking and body appreciation resembles an earlier finding from Korea, but culture was not measured and should not be treated as the explanation. The results support closer study of positive and negative body image processes together, using longitudinal data and measures that distinguish selfie selection, editing, and posting Clinical Trial: Not Applicable

• Digital Engagement among Culturally and Linguistically Diverse Population: A qualitative study

  Date Submitted: Aug 18, 2026

  Open Peer Review Period: Aug 19, 2026 - Oct 14, 2026

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  Background: Digital engagement among adults can impact healthcare outcomes and patient engagement; however there are barriers to utilizing digital tools. Objective: This study explored those barriers and facilitators to digital engagement among English and Spanish speaking adults in Houston, Texas. Methods: We conducted 4 focus groups, 2 in English and 2 in Spanish. Results: In this paper authors present the themes that emerged among them including, (1) Digital Literacy and Technology, (2) Trust, Privacy, and Security Concerns, (3) Support System and Workarounds, (4) Usability and Accessibility, (5) Communication Preferences, and (6) Digital Health Engagement. Results suggest participants desire simplicity, quick access, and easy to understand digital tools. Participants expressed familiarity with utilizing digital tools and accepted how advanced technology was a part of the world today. Participants shared frustrations and fears when providing health data to their providers. They also provided insights into educational information that might help facilitate utilization of digital tools. Conclusions: Findings from our study could inform the importance of customizing and personalizing digital tools for culturally and linguistically diverse populations served within hospital systems to optimize their adaptation and effectiveness.