Latest Submissions Open for Peer Review

Titles/Abstracts of Articles Currently Open for Review:

• Developing and implementing a web-based psychotherapy program to address anxiety and depression among patients receiving oncological and palliative care: An open-label, mixed-methods randomized controlled pilot trial

  Date Submitted: Nov 5, 2026

  Open Peer Review Period: Nov 5, 2026 - Dec 31, 2026

  • Peer Review Me

  Background: Cancer is a prevalent disease that commonly leads to comorbid mental health conditions that are often undertreated due to barriers to traditional psychotherapy. Online cognitive behavioural therapy (e-CBT) could be an effective way to reduce these barriers since it demonstrates efficacy in reducing depression and anxiety symptoms, but its effectiveness is understudied in oncological and palliative populations. Objective: This study aimed to develop a web-based psychotherapy clinic for individuals receiving oncological and palliative care to examine whether this program is an effective psychotherapy to address this population's needs. Methods: Sixty participants diagnosed with depressive disorder and/or anxiety disorder in the context of oncological and/or palliative care conditions were recruited in Kingston, Canada. Participants were randomly assigned to either 8 weeks of a Mindfulness-Enhanced e-CBT (MECBT) program plus treatment as usual (TAU; n=32) or TAU alone (n=28). Participants in the MECBT condition completed weekly pre-designed modules, homework, and were sent personalized feedback. Additionally, a survey was sent out to all MECBT participants to conduct a thematic analysis of their experiences with the program. Results: Both conditions demonstrated reductions in anxiety and depression symptoms over time, and a statistically significant difference in depression scores occurred when comparing treatments. Six main themes were identified during the thematic analysis. Conclusions: Findings support the effectiveness of MECBT in reducing depression severity. Qualitative benefits included providing structure, flexibility, and accessibility. Constructive feedback included problems with time restraints, the need for continuity of care, technical challenges, and the need for human contact. Clinical Trial: NCT04666974

• Digital Health Technologies for Community-Based Stroke Rehabilitation: A Scoping Review of Implementation Status, Barriers, and Facilitating Strategies

  Date Submitted: Nov 5, 2026

  Open Peer Review Period: Nov 5, 2026 - Dec 31, 2026

  • Peer Review Me

  Background: Stroke is a leading cause of long-term disability, and sustained, high-intensity rehabilitation is critical for functional recovery. Community- and home-based rehabilitation is increasingly supported by digital health technologies (DHTs), including telerehabilitation platforms, mobile applications, virtual reality (VR) systems, robotics, and wearable devices. However, evidence regarding real-world implementation of these technologies within community settings remains fragmented and no existing review has systematically mapped their implementation status, associated barriers, facilitators and implementation strategies. Objective: This scoping review aimed to (1) map the current implementation landscape of DHTs for community-based stroke rehabilitation, including geographic distribution, technology types, delivery settings, study designs, and follow-up assessment arrangements; (2) characterize the reporting of implementation-related outcomes and stratify included studies according to the depth of their implementation data; and (3) synthesize barriers, facilitators, and implementation strategies reported by stroke survivors, caregivers, and clinicians. Methods: We systematically searched PubMed, Web of Science, SinoMed, and CNKI for English- and Chinese-language original studies published between January 2016 and August 2026. Eligible studies evaluated DHTs for community- or home-based stroke rehabilitation and reported implementation-related outcomes. Screening, data extraction, and synthesis were undertaken in accordance with the PRISMA-ScR checklist. Implementation evidence was coded into eight predefined categories (Cat.1–8) and each study was assigned to one of three levels of evidence richness (L1–L3): Level 1 (basic-level evidence extraction), Level 2 (moderate-depth evidence extraction), and Level 3 (in-depth evidence extraction containing participant-reported qualitative material). Thematic synthesis of barriers, facilitators, and implementation strategies was restricted to the 22 Level-3 studies with formal qualitative data, guided by five predefined thematic domains. Results: Of 1,942 records identified, 111 studies met the inclusion criteria. The majority were conducted in North America (n = 45, 40.5%) and recruited participants with chronic phase stroke (n = 57, 51.4%); 76 studies (68.5%) delivered interventions exclusively at home. Mobile applications represented the most prevalent technology modality (n = 56). Only 26 studies (23.4%) reported follow-up assessments of ≥1 month post intervention. Adherence was the most frequently reported implementation outcome (n = 87, 78.4%), while safety (n = 25, 22.5%), health-economic (n = 4, 3.6%), and intervention fidelity (n = 3, 2.7%) outcomes were rarely reported. Sixty-four studies (57.7%) were assigned to Level 1, 25 (22.5%) to Level 2, and 22 (19.8%) to Level 3. The thematic synthesis of qualitative data identified barriers and facilitators spanning five predefined thematic domains: technology-system, individual-participant, caregiver and social context, clinical service delivery, and intervention content, and generated 28 actionable implementation strategies. Conclusions: DHTs for community-based stroke rehabilitation are expanding rapidly, yet the implementation evidence remains largely superficial. Data on safety, health-economics, and long-term follow-up are critically scarce, and only one-fifth of included studies contain in-depth formal qualitative enquiry. Future research should draw on implementation-science frameworks, standardize the reporting of implementation-outcomes, and prioritize rigorous qualitative evaluation to facilitate sustainable real-world uptake. Clinical Trial: digital health; telerehabilitation; stroke rehabilitation; community-based rehabilitation; implementation science; barriers and facilitators; mobile applications; scoping review

• Simulation-Derived Human Triage Data Improve Lightweight Large Language Model Performance in Mass-Casualty and Tactical Triage: A Multi-Dataset Prompting Study

  Date Submitted: Nov 4, 2026

  Open Peer Review Period: Nov 5, 2026 - Dec 31, 2026

  • Peer Review Me

  Background: Mass-casualty and operational triage require rapid decisions under uncertainty, yet real-world labeled decision data from these settings are scarce, heterogeneous, and difficult to collect at scale. Immersive virtual reality (VR) simulation provides a controlled way to generate structured human triage data in realistic high-consequence scenarios. Objective: To evaluate whether simulation-derived human triage examples improve the performance and safety profile of a lightweight open-weight large language model (LLM) beyond zero-shot prompting and explicit triage guidance alone, and whether the pattern is observed in simulation datasets organized around Sort, Assess, Life-Saving Interventions, Treatment/Transport (SALT) and Tactical Combat Casualty Care (TCCC). Methods: We performed a retrospective computational evaluation using structured cases derived from immersive VR triage simulations. Four prompting conditions were evaluated: zero-shot, context/guideline only, simulation-derived few-shot examples only, and few-shot examples plus context. In the TCCC-oriented analysis, few-shot examples were evaluated from both a matched TCCC-derived source and a cross-source SALT-derived source mapped to the four TCCC labels. Outcomes included accuracy; F1 score, which balances precision and recall, summarized as macro-F1 and weighted-F1; per-category F1; under-triage; critical under-triage; pooled failure modes; attribute-stratified performance; few-shot sensitivity; and prompt-elicited model confidence. TCCC row-level evaluations contain repeated presentations and are interpreted descriptively. Results: For SALT, test accuracy was 68.2% with zero-shot prompting, 68.4% with context alone, 71.1% with few-shot examples, and 79.1% with few-shot plus context; macro-F1 was 51.7%, 57.2%, 70.7%, and 83.8%, respectively. In TCCC, few-shot-plus-context accuracy was 87.0% with matched-source examples and 88.1% with cross-source examples; the corresponding context-only accuracies were 68.6% and 70.9%. The gain from adding examples beyond context alone was 10.7 percentage points for SALT, 18.5 points for matched-source TCCC, and 17.2 points for cross-source TCCC (pooled descriptive gain, 17.8 points). Category-level results showed large gains for EXPECTANT with simulation examples, while SALT DELAYED remained the weakest category. SALT critical under-triage decreased from 11.1% with few-shot prompting to 0.4% with few-shot plus context. TCCC under-triage decreased from 9.8% to 5.5% in the matched-source analysis and from 9.8% to 4.9% in the cross-source analysis; critical under-triage was 0% in both few-shot-plus-context source conditions. Prompt-elicited confidence remained high even when predictions were wrong and did not provide a reliable case-level safety filter. Conclusions: Simulation-derived human examples improved lightweight LLM triage performance beyond explicit framework guidance alone. The effect was observed with both matched-source TCCC examples and cross-source SALT-derived examples, and the strongest conditions reduced safety-relevant under-triage. These findings support immersive simulation as a practical source of reusable human decision data that preserve observed variation and experiential judgments not necessarily encoded in written guidance or reproduced by model-generated synthetic cases, with potential relevance to other high-consequence domains where real-world decision data are scarce. TCCC estimates are descriptive because many rows are repeated presentations of a much smaller number of patient identities, and the results do not establish readiness for autonomous or real-time clinical triage.

• Feedback and Outcome Measurement in Wearable-Supported Physical Activity Trials: Systematic Methodological Review

  Date Submitted: Nov 4, 2026

  Open Peer Review Period: Nov 4, 2026 - Dec 30, 2026

  • Peer Review Me

  Background: Wearable activity trackers can supply both behavioral feedback and trial outcomes. Interpreting their results requires a traceable account of how recordings become an outcome for a particular randomized group and assessment period. Objective: To quantify the co-reporting of valid-data rules and analysis samples across feedback–outcome acquisition arrangements, and to examine how source reports link these details to specific outcomes, randomized groups, and periods. Methods: Five databases were searched in September 2026; report reassessment ended on October 20, 2026. The register comprised 703 randomized trials. Ten domains retained four evidence states: relevant reporting, information not found, unable to judge, and not applicable. We mapped acquisition arrangements and co-reporting of valid-day rules, non-wear identification, and usable-data denominators. Sensitivity analyses reversed 33 domain updates and four classification updates and examined assessable subsets. Selected original reports illustrated outcome-specific measurement pathways. Results: Among 7030 domain assessments, 4806 (68.4%) contained relevant reporting, 762 (10.8%) had information not found, 1456 (20.7%) were unable to judge, and six were not applicable. Acquisition was shared in 305 trials, independent in 192, combined in 57, temporally separate in six, and unclear in 143. The three core domains co-occurred in 135/703 trials (19.2%); reversing all targeted updates yielded 133/703 (18.9%). At least one core domain was unable to judge in 267 trials. Source examples distinguished device-specific rules, observed and modeled samples, and different elapsed times behind the same follow-up label. Conclusions: Reporting was more frequent for measurement schedules and device roles than for processing rules and usable-data samples. Linking each recording stream, processing rule, and denominator to its outcome, randomized group, and assessment period would make wearable-derived trial results easier to interpret and reproduce. Clinical Trial: PROSPERO CRD420261512744

• Making Heterogeneous Clinical AI Workable: Qualitative Study of Physicians’ Coordination Practices Across System Boundaries

  Date Submitted: Nov 4, 2026

  Open Peer Review Period: Nov 4, 2026 - Dec 30, 2026

  • Peer Review Me

  Background: Artificial intelligence (AI) is increasingly embedded in clinical work, but implementation research has largely examined individual applications. Hospital physicians now often encounter several AI applications at once, some integrated with the electronic health record (EHR) and others stand-alone, and little is known about how relationships among these applications shape clinical work. Objective: This study aimed to examine how physicians make heterogeneous clinical AI repertoires workable in hospital practice, focusing on when boundaries among AI applications and existing clinical information infrastructure become consequential seams and how physicians span those seams through infrastructuring practices. Methods: We conducted a qualitative interview study with 30 hospital-based physicians from multiple specialties in a large not-for-profit health system in the Midwestern United States comprising 5 hospitals. Semi-structured interviews were conducted in person and virtually between June and September 2026. We used iterative thematic analysis with an abductive approach informed by infrastructuring, treating seams and practices of bridging, assembling, and circumventing as sensitizing concepts. One author coded the data, a second author checked the coding, and negative and contrasting cases were examined. Results: Of the 30 physicians, 22 described regular use of AI-enabled tools, 6 limited use, and 2 no use. Physicians encountered clinical AI as a heterogeneous repertoire of systems, some integrated with the EHR and others stand-alone, differing in function, technical connectivity, informational access, and organizational status. Multiplicity alone was not fragmentation, as applications often coexisted without consequence, and the EHR sometimes supplied adequate coordination. Boundaries became consequential seams when a clinical task required information, interpretation, or action to cross them, and existing arrangements did not support crossing. Three types of seams emerged: technical/informational, data-visibility, and epistemic. Physicians responded through 4 infrastructuring practices: representing patients for external AI, manually bridging systems, reconciling outputs, and rerouting or substituting. Representing also preserved boundaries for privacy or governance. Conclusions: The central implementation challenge of multi-AI clinical work is not disconnected technologies but managing relationships among heterogeneous systems and associated workflows. Physicians selectively constructed technical, representational, and epistemic connections at consequential seams, producing workable coordination rather than seamless integration, at the cost of added clinician labor. Health systems should govern AI repertoires, not only individual applications, attending to consequential seams, information provenance, and boundaries that should deliberately remain unbridged.

• Comparing Open-Weight Large Language Models for Systematic Review Tasks: One Health Antimicrobial Resistance Case Study

  Date Submitted: Nov 3, 2026

  Open Peer Review Period: Nov 4, 2026 - Dec 30, 2026

  • Peer Review Me

  Background: Systematic reviews of antimicrobial resistance (AMR) across One Health domains require screening large numbers of records and extracting heterogeneous study characteristics. Evaluations of large language models (LLMs) for these tasks have predominantly focused on proprietary, cloud-hosted systems, raising concerns about cost, data governance and reproducibility. Locally hosted open-weight models offer greater control over these factors, but their performance and computational requirements remain insufficiently characterised. Objective: To benchmark locally hosted open-weight LLMs (1.5–141 billion parameters) for title and abstract screening and structured data extraction in a One Health AMR systematic review, assessing performance, computational efficiency and suitability for human-assisted workflows. Methods: The corpus comprised 5,535 records (2020-2025) from a PRISMA-guided review of gene-based AMR biomarkers. Two reviewers screened all records independently, resolving disagreements by consensus. Fourteen 4-bit quantised models, served with Ollama on institutional GPUs without fine-tuning and with one fixed prompt per task, screened every record. Thirteen extracted six fields (pathogen species, sampling location, sampling year, resistance genes, One Health classification and genotypic detection method) from the 3,305 records included at the first human screening stage, scored against dual-reviewer reference extraction. Screening was evaluated with recall, precision, F1-score and accuracy. Extraction was scored per field with token-set fuzzy similarity, Jaccard similarity or exact accuracy and summarised as an unweighted macro-average (0-100), retaining records without a usable response. Results: The five leading screening models (24-120 billion parameters) reached F1-scores of 0.83-0.85 and accuracy of 0.76-0.78, compared with 0.78 and 0.63 for an include-all baseline. They retained 93%-97% of eligible records but excluded only 40%-51% of ineligible ones. Extraction macro-averages ranged from 44.3 (deepseek-r1:1.5B) to 90.5 (nemotron-3-super:120b-a12b), with seven models of 4-120 billion parameters within 2.2 points (88.3-90.5). Year of sampling was the best-extracted field (maximum 99.3) and resistance genes the weakest (maximum 85.1). Between 0 and 242 extraction responses per model were unusable. The three leading 24-32B screening models used 9.1-11.1 GPU-hours, against 22.9-35.1 for the two leading 120B models. Conclusions: Locally hosted open-weight LLMs demonstrated promising performance for human-assisted systematic review workflows, but important limitations remain. Screening models achieved high recall at the expense of specificity, limiting their ability to reduce manual screening workloads without risking missed eligible studies. Autonomous screening is therefore not supported by these findings, while potential use as a second screener warrants prospective evaluation. For structured extraction, a 4B model performed within 2.2 points of the leading 120B model, although resistance gene extraction remained challenging. Model size alone was not a reliable indicator of performance, with 24–32B models achieving screening F1-scores comparable to 120B models at less than half the GPU-hours. These findings support further evaluation of locally deployed LLMs as computationally efficient, institutionally controlled tools for assisting evidence synthesis.

• Patient Experience with Ambient AI Scribes: A Mixed-Methods Quality Improvement Study

  Date Submitted: Nov 3, 2026

  Open Peer Review Period: Nov 4, 2026 - Dec 30, 2026

  • Peer Review Me

  Background: Documentation burden is a leading driver of clinician burnout. Ambient artificial intelligence (AI) scribes have emerged as a tool to reduce this burden by automatically generating clinical documentation from patient encounters. While research has focused largely on clinician outcomes, much less is known about how ambient AI scribes affect the patient experience. Objective: The objective of our study was to evaluate patient-reported experience during outpatient visits with and without AI scribes. We hypothesized a priori that AI scribe use would be associated with improved provider ratings and likelihood to recommend, due to greater provider attentiveness. Methods: This observational quality improvement study analyzed routinely collected patient experience survey data from outpatient visits across primary care and 21 specialties within a large integrated health system from September 23, 2025, to January 6, 2026. A convenience sample of 38,177 patients who completed post-visit surveys was included, out of which 6,767 encounters involved AI scribe use. Outcomes included patient-reported likelihood to recommend the provider, Net Promoter Score (NPS), ratings of provider listening, explanations, and respect, and qualitative feedback on the AI scribe. Proportional odds and top-box ordinal regression models assessed associations between AI scribe use and patient experience outcomes, adjusting for patient age, race, and visit type. Thematic analysis of free-text responses was performed, and exploratory logistic regression models examined associations between qualitative themes and likelihood to recommend. Results: The overall mean age was 62.5 (±17.9) years; 60% were female; 69% of patients identified as White, 28% as Black or African American, and 3% as other races. Patients exposed to AI scribes were more likely to recommend their provider (OR 1.09, 95% CI 1.01-1.19; P=.03), and to give their provider the highest rating for quality of explanations (OR 1.17, 95% CI 1.06-1.28; P=.001), listening (OR 1.11, 95% CI 1.01-1.22; P=.03), and respect (OR 1.16, 95% CI 1.04-1.29; P=.007). Among patients exposed to AI scribes, 91% were willing to use the technology again. Of 1,889 free-text responses, 74.4% were positive, 13.3% neutral, and 12.3% negative. Relational themes (e.g. improved connection and benefit to the provider) were more strongly associated with higher likelihood of recommending the provider, whereas perceived lack of consent and risk of inaccuracy were associated with lower likelihood to recommend. Conclusions: AI scribe use was associated with improvements across several dimensions of patient experience. The elements of patient experience that were most strongly associated with likelihood to recommend the provider were largely relational factors, underscoring the importance of communication, transparency, and clear consent in implementation.

• Message Signals and Peer Response in an Atrial Fibrillation Online Health Community: Observational Study of Reddit Posts Using Large Language Model Annotation

  Date Submitted: Nov 3, 2026

  Open Peer Review Period: Nov 3, 2026 - Dec 29, 2026

  • Peer Review Me

  Background: Online health communities enable patients to seek information, share experiences, and obtain peer support. Yet some messages prompt extensive discussion while others receive little response. Drawing on signaling theory and social-support research, this study examines how readers use message features to infer a poster’s situation and decide whether and how to respond. Objective: This study investigated how medical terminology density and emotional intensity, separately and jointly, relate to the volume and elaboration of peer responses in an online atrial fibrillation (Afib) community. Methods: This study analyzed 5028 posts and 71,592 comments from the r/AFIB subreddit, posted between February 2023 and November 2024 and collected using the Python Reddit API Wrapper. The analytic sample included only posts that received at least one comment. Discussion topics were identified using BERTopic. A large language model annotated post intention, topic, emotional intensity, disease outlook, and coping confidence. The label definitions were refined with an Afib domain expert and validated against a three-coder majority vote. Finally, medical terminology density was calculated using a curated Afib glossary. Drawing on signaling theory, medical terminology density was treated as a competence-threshold signal and emotional intensity as a need signal. Negative binomial regression estimated comment counts, and linear regression estimated mean comment length and comment richness. Four models were estimated for each outcome. Results: A higher density of medical terminology was associated with fewer comments (B=-0.494, 95% CI [-0.982, -0.006], incidence rate ratio [IRR]=0.610, P=.047), whereas emotional intensity was associated with more comments (B=0.007, 95% CI[0.005, 0.009], IRR=1.007, P<.001). Their positive interaction (B=0.029, 95% CI[0.009,0.049], IRR=1.029, P=.004) indicated that the negative association between technical language and response volume was weaker for more emotional messages. Medical terminology was not significantly associated with comment length or comment richness. Messages requesting peer experience (IRR=1.147, P<.001) and, as an exploratory finding, messages seeking or offering psychological support (IRR=1.088, P=.009) received more replies, while on the other hand messages asking for medical advice (IRR=0.886, P<.001) and messages about family (IRR=0.860, P<.001) received fewer. Conclusions: In online Afib communities, message features appear to function as signals that shape peer engagement. Medical terminology may limit participation by signaling a higher expertise requirement, whereas emotional expression may broaden participation by signaling a need for support. Response volume and response elaboration showed different patterns, indicating that engagement should not be treated as a single construct. These findings offer guidance for platform moderators and health communicators seeking to enhance accessible peer support.

• Comparing CT-Based Algorithms for Non-Small-Cell Lung Cancer Genotyping: Systematic Review and Bayesian Network Meta-Analysis

  Date Submitted: Oct 30, 2026

  Open Peer Review Period: Nov 3, 2026 - Dec 29, 2026

  • Peer Review Me

  Background: Routine computed tomography (CT) can quantify whole-tumour features. Clinical use requires reproducibility across scanners and populations. Objective: To compare five CT-based algorithm categories for non-small-cell lung cancer genotyping using studies that evaluated multiple categories within shared patient cohorts. Methods: PubMed, Embase, Web of Science, and the Cochrane Library were searched to 25 June 2026. Eligible studies compared at least two categories against a shared molecular reference standard. A Bayesian diagnostic accuracy network meta-analysis synthesised epidermal growth factor receptor (EGFR) results. The Prediction model Risk Of Bias ASsessment Tool for artificial intelligence assessed bias. Certainty was evaluated using the Grading of Recommendations Assessment, Development and Evaluation framework. The review was funded by the Hunan Provincial Natural Science Foundation and two Hunan pharmacy research programmes. Results: Twenty retrospective studies were included. Seventeen EGFR studies contributed 43 validation datasets and 2450 validation patients, counted once per study. Convolutional neural networks had the highest sensitivity (0.84; 95% credible interval [CrI], 0.78–0.88). Transformers had the highest specificity (0.91; 95% CrI, 0.85–0.96), based on two datasets with 219 participants. Their diagnostic odds ratio relative to convolutional networks was 1.94 (95% CrI, 0.78–4.80). Certainty was moderate at most. Three studies of other genotypes were synthesised narratively. Conclusions: Convolutional neural networks and transformers showed complementary sensitivity and specificity profiles. Their comparative accuracy remained uncertain, and the evidence did not support replacing molecular testing. Clinical Trial: PROSPERO CRD420261479713; https://www.crd.york.ac.uk/PROSPERO/view/CRD420261479713

• Changes in waist-to-height ratio risk category during the first six months of medication-supported digital weight loss treatment: a real-world cohort study

  Date Submitted: Nov 2, 2026

  Open Peer Review Period: Nov 3, 2026 - Dec 29, 2026

  • Peer Review Me

  Background: Waist-to-height ratio (WHtR) is a practical measure of central adiposity that suits remote care, but real-world evidence from medication-supported digital weight loss services (DWLSs) has focused on weight and BMI. Objective: To examine changes in WHtR risk category during the first six months of treatment at a real-world medicated DWLS. Methods: Retrospective cohort study of Australian patients who started tirzepatide or semaglutide with the Juniper DWLS between 1 January and 20 June 2026, and who submitted a self-reported baseline waist circumference and a valid measurement at 3 months (days 61–121) and/or 6 months (days 153–213). WHtR risk categories were low (<0.4), normal (0.4–0.49), increased (0.5–0.59) and high (≥0.6). Category shifts were tested with the McNemar–Bowker test. Logistic regression identified factors associated with improving by at least one category among patients starting in the increased or high-risk category. Results: Of 13,545 patients who started treatment, 2097 were included (1030 tirzepatide, 1067 semaglutide; mean age 44.7 years; 96% female); 44.7% were in the high-risk category at baseline. Among 1941 patients with a 3-month measurement, mean waist circumference fell by 8.0 cm (95% CI 7.7–8.2), the proportion in the high-risk category fell from 44.3% to 23.0%, and 787 (40.5%) improved by at least one category. Among 584 patients with a 6-month measurement, waist circumference fell by 11.5 cm (10.9–12.1), the high-risk proportion fell from 49.3% to 17.6%, and 337 (57.7%) improved. Only 0.9% moved to a higher-risk category at either time point, but 76.0% (3 months) and 68.1% (6 months) remained at or above 0.5. Improvement was more likely with high baseline risk category (OR 5.46 at 3 months) and a shorter distance above the threshold, with tirzepatide compared with semaglutide (semaglutide OR 0.53), and with more weekly weight tracking (OR 1.32 per 10 percentage points); it was less likely with older age, higher BMI and previous weight loss medication use. Conclusions: Waist circumference and WHtR fell over the first 3 to 6 months of medication-supported digital treatment, and many patients moved to a lower risk category, although most remained at or above the 0.5 threshold. Self-reported measurements, the absence of a control group, and low follow-up data rates (14.3% of available 3-month patients, and 8.1% of 6-month patients), limit the conclusions.

• Assessing Autism Spectrum Disorder and Other Neurodevelopmental Conditions in Preschoolers Through the Strengths and Difficulties Questionnaire (SDQ) With Remote Data Collection: A Retrospective Observational Study

  Date Submitted: Nov 2, 2026

  Open Peer Review Period: Nov 2, 2026 - Dec 28, 2026

  • Peer Review Me

  Background: Screening is now worldwide recognised as essential for early detection of neurodevelopmental divergences, and telemedicine is increasingly proving to be a valuable resource in this area. Our retrospective observational study aimed to assess the ability of the Strengths and Difficulties Questionnaire (SDQ 2-4) as a measure to distinguish autism spectrum disorder from other neurodevelopmental disorders in clinical and typically developing populations of preschoolers by remote data collection. Objective: This study aimed to assess the validity of the digitally administered Strengths and Difficulties Questionnaire for ages 2–4 (SDQ 2–4), delivered remotely through a clinical web-based platform, as a screening tool for identifying neurodevelopmental conditions in preschoolers. Specifically, it examined the ability of the SDQ 2–4 to differentiate children with autism spectrum disorder (ASD) from those with other neurodevelopmental conditions, including developmental language disorder (DLD) and developmental delay (DD), as well as from typically developing children. Methods: Data from 343 preschoolers, including 93 children with autism spectrum disorder (ASD), 28 neurotypical children (NT), 167 children with developmental language disorder (DLD), and 55 children with developmental delay (DD), were collected through the MEDea Information and Clinical Assessment on-Line (MedicalBIT) platform. Results: Our results showed higher scores on all SDQ 2-4 scales for the ASD group vs the NT group, except for a scale scored in reverse (Prosocial Behaviour Scales) that had lower scores in children with ASD than NT children. Total Problems, Peer Problems, Hyperactivity, and Prosocial Behaviour Scales could more significantly differentiate the ASD group from the NT group. When comparing ASD group with other neurodevelopmental conditions (DLD, DD), the most significant results were found for the Total Problems, Peer Problems and Prosocial Behaviour Scales. Conclusions: We concluded that these scales were more effective in differentiating children with autism spectrum disorder from children with developmental language disorder and from children with developmental delay, as well as from neurotypical children. We proved the SDQ 2–4 to be a valid short screening tool for use in preschoolers, to differentiate between ASD and other conditions by remote data collection.

• Modelling timing of digital behaviour: A tutorial using social media data

  Date Submitted: Oct 30, 2026

  Open Peer Review Period: Nov 1, 2026 - Dec 27, 2026

  • Peer Review Me

  Digital data contains important information about both the behaviours people perform, and the time-of-day they perform these. For example, data collected from social media platforms can include precise measures of when individuals engage and interact with these platforms. Given the political and public interest around the impact of nighttime use of social media platform, effectively collecting and analysing this data is essential. This requires approaches which can take large-scale and complex social media data, and produce accurate and useful measures of the time-of-day these platforms were used. However there is little guidance available to researchers around which approaches are currently available, and when they should be used. The purpose of this tutorial is to demonstrate and evaluate the variety of approaches that have been used to quantify the time-of-day individuals use social media platforms, and how such research can be carried out using real-word data. Our tutorial aims to be a detailed guide for researchers interested in using social media data to explore questions around the timing of behaviour. In this tutorial, we used data collected from TikTok, which contains around 10,000 TikTok videos, watched by a single user between 2025 and 2026, and the precise time-of-day when each was watched. In our tutorial, we provide detailed instructions on how such data can be collected and prepared for analysis. Next, we use five different approaches to calculate estimates of the time-of-day TikTok videos were watched by this user. These approaches focus splitting hours into pre-determined time windows, using methods from the field circular statistics or calculating the seconds that pass between two consecutive social media behaviours. We provide detailed instructions on how each approach can be implemented, and evaluate their strengths and limitations. Finally, we provide recommendations on which approaches should be used for particular types of social media data. Ultimately, these recommendations depends on the type of data collected, and which research questions are of interest.

• Aligning Decentralised and Digitally Enabled Clinical Trial Methods with the Needs of Complex Populations: A NASSS-Informed Systematic Review and Thematic Synthesis

  Date Submitted: Oct 18, 2026

  Open Peer Review Period: Oct 31, 2026 - Dec 31, 2026

  • Peer Review Me

  Background: Decentralised and digitally enabled clinical trials (DCTs) have expanded rapidly through telehealth, mobile health applications, wearable sensors, electronic patient-reported outcomes, remote monitoring, and virtual trial infrastructure. Although these approaches promise improved accessibility and reduced participant burden, whether they meet the needs of people with complex health conditions remains uncertain. Objective: To examine how decentralised and digitally enabled trial methods align with the needs of complex populations using the Nonadoption, Abandonment, Scale-up, Spread and Sustainability (NASSS) framework. Methods: A systematic review was conducted following PRISMA and registered on the Open Science Framework (10.17605/OSF.IO/AMSRG). PubMed, Scopus, Web of Science, ScienceDirect and CINAHL were searched from January 2000 to March 2026. Eligible studies evaluated decentralised or digitally enabled trial methods in adults with chronic disease or features of clinical complexity older age, frailty, multimorbidity, cognitive impairment, or mental health conditions. Risk of bias was assessed using RoB 2 and ROBINS-I. A JBI convergent integrated mixed-methods synthesis combined deductive NASSS coding with inductive thematic analysis and cross-domain synthesis. Results: Sixty-seven studies met the inclusion criteria, predominantly randomised controlled trials alongside non-randomised, feasibility, pragmatic and implementation designs. Most enrolled adults with chronic disease, whereas relatively few specifically recruited older, frail, multimorbid or cognitively impaired participants. Risk of bias was predominantly low to moderate. Harmonised analysis generated 1,904 coded excerpts, with Technology the largest domain (649; 34.1%), followed by Condition (396; 20.8%), Value Proposition (234; 12.3%), Adopter System (209; 11.0%), Organisation (185; 9.7%), Wider System (144; 7.6%) and Embedding and Adaptation over time (87; 4.6%). Twenty-seven studies addressed all seven NASSS domains and 51 addressed at least six, indicating that successful DCTs depend on coordinated technological, behavioural, organisational and system-level mechanisms. In cognitive impairment and mental health, established assessments including the CDR, PHQ-9, PHQ-ADS, MADRS, EPDS and web-based neuropsychological batteries were feasibly captured remotely; however, no included study evaluated remote administration of the MMSE or MoCA, whether clinician-led or automated, providing reference standards for future Composite Digital Cognitive Outcomes derived from wearable sensors, ambient sensing, conversational artificial intelligence, speech and language analysis, and patient-connected outcome measures. Conclusions: DCTs can enhance accessibility, engagement, remote monitoring and personalised care when technologies are matched to the clinical, functional, behavioural and cognitive needs of complex populations. However, current provision is not yet sufficiently adapted or validated for people with cognitive impairment and other complex needs. The value of DCTs depends on alignment between technological design, clinical context, participant capability, organisational readiness and wider health-system support, not decentralisation alone. Future trials should prioritise accessible hybrid models, caregiver-supported participation, sustained technical and clinical support, and validation of emerging Composite Digital Cognitive Outcomes against established cognitive reference standards to advance equitable participation and long-term impact. Clinical Trial: N/A

• Selective Large Language Model Support for Patient-Message Triage in Remote Care: A Cost-Aware Hybrid Approach

  Date Submitted: Oct 29, 2026

  Open Peer Review Period: Oct 29, 2026 - Dec 24, 2026

  • Peer Review Me

  Background: Patient-facing conversational artificial intelligence (AI) agents are increasingly deployed across health care to sustain two-way patient communication and collect patient-reported data at scale. Their workflows include a classification layer that reads each free-text message and routes it to an appropriate response. Performing this triage with a large language model (LLM) is accurate but carries recurring cost and latency at scale and sends sensitive patient text to an external service; a smaller, locally hosted model is cheap, private, and controllable but is less reliable on ambiguous or out-of-scope (out-of-distribution) messages and can make clinically consequential errors. F1 metrics compound the problem by treating every error as equivalent, while in this context misclassifying a safety-critical message is far more harmful than confusing two benign categories. We evaluate triage errors using an asymmetric clinical cost matrix. Objective: We aimed to develop and retrospectively evaluate a two-stage triage system that preserves LLM-level quality while reducing how often the LLM is called. We evaluate it by clinical consequence rather than by raw error count. Methods: A compact local classifier handled routine messages, and an LLM was invoked only when accepting the local prediction carried high expected clinical cost. The routing rule is the system’s central design choice: each message is escalated by its expected clinical cost under an asymmetric, expert-elicited cost matrix, rather than by generic confidence or uncertainty. The local classifier is a fine-tuned text-embedding encoder trained with outlier exposure and a geometry-aware output head (a Semantic-Anchor Equiangular Tight Frame, SA-ETF, that aligns maximally separated class directions to each category’s clinical description). We studied two programs at one academic health system: 1) a hypertension remote-monitoring program, and 2) a type 2 diabetes self-management study, with 21 and 39 in-scope categories plus a Beyond Scope category. In both programs, the analyzed messages were authored by researchers and staff testers exercising the assistant. Five experts curated the cost matrix, penalizing clinically consequential undertriage more heavily than benign misclassification or overtriage. Gold-standard test sets comprised 114 hypertension and 95 diabetes messages that were independently labeled and adjudicated. Results: Across both settings the hybrid matched the clinical cost of LLM-only triage while escalating only 57%-59% of messages. On hypertension its balanced accuracy exceeded LLM-only by a small, consistent margin (+0.026 across seeds; significant in the pooled paired bootstrap, p<0.05); on diabetes the two were comparable. Routing by expected cost also escalated 97%-100% of out-of-scope messages to the LLM instead of letting the local model silently assign them an in-scope label, and it reduced LLM use and external message transmission by 41%-43%. Conclusions: Selective, clinically cost-aware escalation can preserve LLM-level triage quality while reducing operational and data-governance burdens. Because the evaluation was retrospective, this is a development-stage evaluation; prospective validation is needed before deployment.

• Effectiveness of Telemedicine for Patients with Psoriasis: A Systematic Review and Meta-Analysis

  Date Submitted: Oct 29, 2026

  Open Peer Review Period: Oct 29, 2026 - Dec 24, 2026

  • Peer Review Me

  Background: Psoriasis is a chronic inflammatory skin disease requiring long-term management. Telemedicine may improve continuity of care, treatment adherence, and self-management; however, evidence regarding its effectiveness across clinical and patient-centered outcomes remains inconsistent. Objective: To systematically evaluate the effectiveness and safety of telemedicine-based interventions for patients with psoriasis across multiple clinical, psychological, behavioral, and patient-reported outcomes. Methods: We searched 10 databases, including PubMed, Embase, the Cochrane Library, Web of Science, Scopus, CINAHL, PsycINFO, CBM, CNKI, and Wanfang, from inception to March 31, 2026. Randomized controlled trials comparing telemedicine-based interventions with routine care or conventional follow-up were eligible. Two reviewers independently screened studies, extracted data, and assessed risk of bias using the Cochrane Risk of Bias 2 tool. Meta-analyses were conducted using mean differences (MD), standardized mean difference (SMD), or risk ratio (RR), with corresponding 95% confidence intervals (CI). Subgroup analyses were performed to explore potential sources of heterogeneity. Sensitivity analyses were conducted using the leave-one-out method, in which each study was sequentially excluded to assess the robustness of the pooled estimates. Publication bias was assessed using Egger’s and Begg’s tests. Results: Twenty-four articles involving 2546 patients were included. Risk of bias assessment identified some concerns in 11 studies and high risk in 9, with no studies rated as having a low overall risk of bias. Telemedicine significantly reduced PASI scores (MD=−2.97, 95% CI −5.69 to −0.25; P=.03; I²=98.6%; P<.001) and DLQI scores (MD=−2.29, 95% CI −3.79 to −0.79; P=.003; I²=84.0%; P<.001). Self-management ability (SMD=1.21, 95% CI 0.44 to 1.98; P<.001; I²=93.6%; P<.001) and psoriasis-related health behaviors (SMD=1.46, 95% CI 0.81 to 2.11; P<.001; I²=85.7%; P=.01) also improved significantly. Treatment adherence rates increased (RR=1.17, 95% CI 1.07 to 1.27; P<.001; I²=27.9%; P=.24), whereas adherence scores (SMD=3.88, 95% CI: 0.23 to 7.54; P=.04; I²=98.8%, P<.001) showed substantial heterogeneity and limited robustness. Anxiety assessed using the Hospital Anxiety and Depression Scale-Anxiety subscale improved significantly (MD=−1.56, 95% CI −2.73 to −0.39; P=.01; I²=0%; P=.45). Depression and patient satisfaction did not improve significantly overall (SMD=−0.66, 95% CI: −1.59 to 0.27; P=.16; I²=95.4%; P<.001). However, Social functioning-related outcomes improved (SMD=−2.45, 95% CI −4.49 to −0.41; P=.02; I²= 98.1%). The pooled incidence of adverse events was lower in the intervention groups (RR=0.74, 95% CI 0.56 to 0.99; P=.04; I²=0.0%, P=0.41), although sensitivity analysis indicated limited robustness. One study suggested potential cost savings with online care. Conclusions: Telemedicine may improve psoriasis severity, quality of life, self-management, health-related behaviors, and treatment adherence. However, its effects on psychological outcomes and patient satisfaction remain uncertain, and evidence regarding safety and cost-effectiveness is limited. Substantial heterogeneity and methodological limitations warrant cautious interpretation. Future trials should use standardized outcome measures, longer follow-up, and rigorous assessments of safety and economic outcomes. Clinical Trial: International Prospective Register of Systematic Reviews CRD420261294958; https://www.crd.york.ac.uk/PROSPERO/view/CRD420261294958

• Organizational Coaching to Increase Uptake, Engagement, and Sustainment of a Digital Substance Use Disorder Recovery App: A Cluster Randomized Trial

  Date Submitted: Oct 28, 2026

  Open Peer Review Period: Oct 29, 2026 - Dec 24, 2026

  • Peer Review Me

  Background: Evidence-based digital interventions have demonstrated benefits for individuals with substance use disorders (SUDs), yet uptake within routine clinical practice remains limited. Implementation strategies that help treatment organizations integrate digital tools into clinical workflows may help address this implementation gap. Objective: This cluster-randomized trial evaluated the effectiveness of the Network for the Improvement of Addiction Treatment–Technology Implementation (NIATx-TI) organizational coaching model for increasing uptake, engagement, and sustainment of the ACHESS recovery support application (ACHESS) in specialty SUD treatment settings. The trial evaluated uptake (account creation), engagement (days of app use), and sustainment (continued account creation after coaching ended). Methods: The study population was 40 clinical sites from 10 specialty SUD treatment organizations in Iowa. Sites were randomly assigned to either standard digital health implementation support (control) or NIATx-TI organizational coaching (intervention). The primary implementation outcomes were (1) uptake, measured as the number of new ACHESS accounts relative to the number of eligible outpatient admissions, (2) engagement, measured as days of ACHESS use, and (3) sustainment, measured as account creation during a 10-month post-intervention sustainment period. Results: During the 18-month intervention period, intervention sites achieved substantially greater uptake of ACHESS than control sites, with an account creation rate of 17.0% relative to eligible outpatient admissions, compared with 3.9% at control sites, an absolute difference of 13.1 percentage points (p < .01). Model-predicted ACHESS engagement, measured by mean app-use days, was higher in the intervention arm than in the control arm across the intervention period (p < .01), with the magnitude of the between-arm difference varying over time. During the 10-month sustainment period following withdrawal of active coaching, intervention sites continued to outperform control sites, with account creation rates of 17.1% and 2.6%, respectively (p < .01). Conclusions: The NIATx-TI implementation strategy significantly improved uptake, engagement, and sustainment of an evidence-based digital recovery support application in specialty SUD treatment settings. These findings suggest that organizational coaching and implementation support can help bridge the gap between evidence-based digital interventions and routine clinical practice. Even with this gain, absolute uptake remained modest, suggesting that integration with electronic health records and reimbursement structures that incentivize use may be needed alongside coaching to achieve broader penetration of patient-centered digital tools in SUD treatment. Clinical Trial: ClinicalTrials.gov NCT03954184.

• Healthcare Stakeholder Transformation Mapping by 2036: A Qualitative Foresight Study

  Date Submitted: Oct 28, 2026

  Open Peer Review Period: Oct 29, 2026 - Dec 24, 2026

  • Peer Review Me

  Background: Healthcare transformation is reshaping stakeholders’ roles, relationships, responsibilities, and influence. Current stakeholder mapping approaches focus mainly on existing configurations, offering limited insight into how they may evolve. A foresight perspective could help address this gap. Objective: This study aimed to examine how the healthcare stakeholder ecosystem might evolve by 2036 and develop a Stakeholder Transformation Map showing anticipated changes in stakeholder roles, relationships, influence, decision-making authority, and responsibilities, along with emerging functions. Methods: An international qualitative foresight study was conducted from June to September 2026 with 30 experts across 16 countries, recruited through purposive and complementary snowball sampling. An 11-item online questionnaire included both structured-response and open-ended questions. We analyzed structured responses descriptively and examined qualitative data using inductive reflexive thematic analysis. Findings were then integrated through a foresight-oriented synthesis. Results: We identified four themes comprising 12 subthemes: redistribution of healthcare work and expertise; increasing patient agency without equivalent decision-making power; growing integration of technology actors; and the distribution of influence, authority, control, and accountability. Although 29/30 participants (96.7%) identified potential emerging stakeholders, 23/30 (76.7%) selected changes in roles or responsibilities as the most significant dimension of transformation. The findings suggested that new stakeholders may emerge alongside the reconfiguration of established functions, while future relationships and decision-making arrangements remain uncertain. Conclusions: Healthcare stakeholder transformation may involve both the reshaping of established roles and the emergence of new actors who challenge existing boundaries. The Stakeholder Transformation Map provides a foresight-oriented approach to exploring continuity, transformation, potential discontinuity, and uncertainty, moving stakeholder analysis beyond static representations to consider interconnected possibilities for 2036. Clinical Trial: -

• Hearing Her Concerns: Gender and Radiation Discourse Among Cardiovascular Interventionists in China: Social Media Analysis

  Date Submitted: Oct 28, 2026

  Open Peer Review Period: Oct 28, 2026 - Dec 23, 2026

  • Peer Review Me

  Background: Women remain underrepresented in radiation-based cardiovascular procedures, and occupational radiation concerns are a leading career deterrent. How these concerns surface in everyday professional discourse, particularly in China, is poorly documented. Objective: We aimed to compare radiation-related discourse by author gender among cardiovascular interventional professionals on Xiaohongshu (RedNote), China's largest female-dominant social platform, and to characterize themes that are rarely captured by workforce surveys. Methods: In October 2026, we collected public Xiaohongshu posts through systematic keyword searches (59 query groups in 3 iterative waves) and full sampling of 12 key opinion leader accounts, yielding 2,300 deduplicated posts and 27,881 comments with nested replies. Posts were coded for author role, 9 radiation-related themes, and affective valence (cost, neutral, empowerment) using rule-based pre-screening followed by human-supervised large language model (LLM)-assisted adjudication, with full-text review of all practitioner-authored posts. Author gender was ascertained from platform profile fields with in-text self-presentation as fallback. Proportions were compared with χ² tests, with author-level analysis to address clustering. Results: Of 2,300 posts, 1,027 were authored by health care practitioners (405 female, 376 male, 246 unknown gender; 576 unique authors). Cost-valence discourse was twice as prevalent among women as among men (21.5% vs 10.6%; χ²₁=16.84; P<.001) and persisted at author level (32.9% vs 20.1%; P=.007). Among posts explicitly addressing radiation, cost content was expressed in 35.8% of female-authored versus 21.6% of male-authored posts (P=.07). Fertility and pregnancy concerns (10.4% vs 1.1%; P<.001) and gendered workplace culture (9.1% vs 0.8%; P<.001) were almost exclusively female-authored and spanned all career stages, from specialty selection to pregnancy and return to the table. Empowerment content was nonsignificantly more common among women (14.3% vs 10.4%; P=.09). Comment-layer analysis (341 retained comments) added partner fertility anxiety, pregnancy-related scheduling penalties, operator health effects, and gatekeeping voiced mainly by women (10 of 15 known-gender comments). Posts by patients and family members were predominantly female-authored (86.7% of 255 known-gender authors). Conclusions: On China's largest female-dominant social platform, female cardiovascular interventional professionals described the costs of occupational radiation at twice the rate of male peers, while also producing more empowerment content. The fertility-radiation conflict was voiced already at the specialty-selection stage and extended to operators' partners and patients, identifying actionable targets for protection, staffing, and counseling.

• Prediction Error as a Marker of Unexpected and Preventable Trauma Deaths: Nationwide Register-Based Cohort Study

  Date Submitted: Oct 27, 2026

  Open Peer Review Period: Oct 28, 2026 - Dec 23, 2026

  • Peer Review Me

  Background: Trauma is a leading cause of death and disease burden worldwide. Multidisciplinary review of patient deaths is a standard method to identify preventable deaths and improve trauma care, but selecting which deaths to review is challenging. Whether the prediction error of a mortality prediction model can identify unexpected deaths that are judged preventable on review remains unknown. Objective: To develop prediction models for 30-day mortality in trauma patients, and to evaluate the association between model prediction error and preventable death. Methods: We conducted a register-based cohort study using the Swedish trauma registry SweTrau. We trained three LightGBM models with increasingly comprehensive predictor sets (static, physiological and interventions models) on data from all hospitals in SweTrau except the Karolinska University Hospital in Solna. Data from the excluded hospital, where a multidisciplinary board had reviewed deaths for preventability, served as evaluation data. The prediction error was defined as observed 30-day mortality minus predicted probability. We estimated the association between prediction error and preventable death using logistic regression among reviewed deaths that occurred within 30 days, and assessed the discrimination of the prediction error for preventable death using the area under the receiver operating characteristic curve (AUC). The study is reported according to TRIPOD+AI. Results: The training data included 78,566 patients, of whom 3,586 died within 30 days. The evaluation data included 14,913 patients; of the 864 who died within 30 days, 709 had been reviewed and 42 were classified as preventable deaths. The internal validation AUCs for 30-day mortality were 0.938 (95% CI 0.930 to 0.947) for the static model, 0.966 (0.960 to 0.972) for the physiological model and 0.967 (0.961 to 0.974) for the interventions model. Prediction error was positively associated with preventable death, with an odds ratio per unit of prediction error of 4.44 (95% CI 1.48 to 14.29) for the static model, 10.48 (4.10 to 28.15) for the physiological model and 10.14 (3.97 to 27.10) for the interventions model. The prediction error discriminated preventable from non-preventable deaths with an AUC of 0.62 (95% CI 0.54 to 0.69), 0.73 (0.65 to 0.80) and 0.71 (0.63 to 0.79), respectively. Conclusions: Deaths that were unexpected according to a mortality prediction model were more likely to be judged preventable on expert review. Prediction error may be useful for selecting trauma deaths for peer review.

• A FHIR-Based Semantic Data Model for Automated Multicenter Digital Mental Health Studies: Design and Implementation Study

  Date Submitted: Oct 26, 2026

  Open Peer Review Period: Oct 27, 2026 - Dec 22, 2026

  • Peer Review Me

  Background: Digital mental health interventions for children, adolescents, and young people, including gamified cognitive behavioural therapy (CBT) and experience sampling, show promise for scalable, low-threshold care, but their evaluation is constrained by fragmented data infrastructures and limited interoperability support for multicentre studies. Existing common data models address either retrospective research or clinical records, and none fully support automated intervention-study orchestration combined with multimodal digital biomarker collection in youth mental health. Objective: This study aimed to design, implement, and evaluate the SMILE Common Semantic Data Model (CSDM), a Fast Healthcare Interoperability Resources (FHIR) R5–based semantic data model supporting automated protocol execution and multimodal real-world data collection for a gamified digital mental health intervention delivered to children, adolescents, and young adults in community settings across 7 European countries. Methods: The CSDM was specified using the HL7 FHIR R5 information model and implemented as a HAPI FHIR 6.10.0 repository integrated with a serious game, a companion app, an identity provider (Keycloak), and an attribute-based authorization service (SAPL). Study structures, protocol orchestration, and multimodal data streams were mapped to standard FHIR resources, and automated workflows were driven by CDS Hooks. The framework was deployed across 7 study sites and evaluated with respect to interoperability and workflow automation, system performance, and data quality. Results: During 284 days of operation, the platform enrolled 1,713 participants across 7 study sites and 1 testing site (for research and development), generating 1,334 protocol definitions, 7,358 individualized care plans, and 85,426 executable tasks without manual intervention after enrolment. The repository contained 32,814 participant-related data resources, including 26,894 questionnaire responses, 5,617 multimodal compositions, and 303 document references, with an overall completeness score of 96.6% and reference integrity of 99.1%. Stress testing supported up to 5,000 concurrent simulated users, with REST API response times of 40-60 ms under high load and a 1-week endurance test recording 1,108,989 error-free interactions. Prior to deployment, load testing of the integration and questionnaire subsystem sustained up to 5000 concurrent simulated users, with mean questionnaire completion times of approximately 1.3 seconds and REST API response times of 40-60 ms under the highest load. A 1-week endurance test with 500 simulated users processed 9,614,925 queries across 1,108,989 error-free interactions, alongside 4000 HTTP 503 and 11 TLS errors. Conclusions: Standard FHIR resources can orchestrate multicentre digital mental health intervention studies and unify structured assessments with digital biomarker streams in a single unified repository. The SMILE CSDM offers a reusable template for multicentre, real-world digital intervention studies in child and adolescent mental health. Clinical Trial: Trial Registration: ISRCTN32523126 (prospectively registered on April 29, 2025).

• From Autism Classification to Precision Intervention: A Biologically Anchored Artificial Intelligence Framework

  Date Submitted: Oct 25, 2026

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

  • Peer Review Me

  Autism encompasses heterogeneous genetic, neurobiological, developmental, and medical pathways. Artificial intelligence (AI) has advanced autism research through multimodal analysis of eye tracking, EEG, neuroimaging, and molecular data; beyond autism, AI is also accelerating therapeutic target identification and drug discovery. Yet translating these advances into individualized, biologically informed clinical decisions remains a major challenge. We propose biologically anchored AI as a decision-oriented framework linking genetic etiology, molecular and microbiome-associated pathways, neurophysiology, digital observations, and developmental context to explicit clinical questions. We distinguish etiological characterization, exploratory mechanistic stratification, and treatment-specific response prediction; these are not interchangeable forms of validation. Examples include individualized genetic therapies, SYNGAP1-directed drug development, heterogeneous responses in fragile X and oxytocin trials, stool metabolomics, sleep EEG, and prenatal MRI. Rather than requiring comprehensive testing for every person, the approach asks whether a selected measurement improves a defined decision beyond standard assessment. Candidate subgroups and biomarkers must be replicated, tested prospectively for incremental value and, where treatment selection is proposed, evaluated for differential benefit from a specified intervention. The goal is not a new universal autism subtype or an automated treatment recommendation, but clinically meaningful, equitable, and developmentally responsive precision care.

• From Social Media Use to Social Media Exposure Events: A Conceptual Framework and Its Application to Allergic Disease

  Date Submitted: Oct 21, 2026

  Open Peer Review Period: Oct 25, 2026 - Dec 25, 2026

  • Peer Review Me

  Social media exposure is commonly approximated by platform use, frequency, or time spent online, measures that reveal little about the health-related content presented to individuals or the conditions of presentation. Although digital exposome and digital determinants of health frameworks recognize the relevance of digital environments to health, individual-level social media exposure remains difficult to operationalize. We propose the social media exposure event as a candidate epidemiological unit: a discrete occasion on which specific health-related content is presented to an individual through a social media interface or function within its social, algorithmic, commercial, and temporal context. A four-level framework distinguishes platform ecology, delivery, individual-level presentation, and separately assessed responses and outcomes. Relevant event characteristics include content and source attributes, visible social signals, emotional framing, repeated presentation, commercial relationships, and timing relative to health-related decisions. Perceived expertise, emotionally salient narratives, and repeated encounters may shape interpretation, but presentation alone does not establish attention, belief, or action. Allergic disease illustrates potential applications to diagnostic testing, allergen avoidance, dietary decisions, treatment adherence, and psychosocial burden. An example involving food-specific IgG testing on a Chinese social media platform illustrates the limits of inferring individual exposure and health consequences from public posts and comments. The framework is conceptual, not a validated measurement instrument. Future research should establish reliable event definitions, develop ethically acceptable methods to reconstruct individual encounters, and test whether event-level measures add explanatory or predictive value beyond conventional measures of social media use.

• The Use of Information Technology Tools in the Self-Care of People Undergoing Cancer Treatment: a scoping review.

  Date Submitted: Oct 22, 2026

  Open Peer Review Period: Oct 23, 2026 - Dec 18, 2026

  • Peer Review Me

  Background: Cancer is one of the two leading causes of death before the age of 70 in more than half of the world's countries and prevents the growth of global life expectancy. Over the years, the incidence rate has increased, mainly due to the development of new diagnostic tools. Empowerment regarding self-care is one factor that can improve the quality of life of people with cancer. Currently, mobile applications, including social media, are the primary tool for literacy regarding the disease, treatment, and its consequences. However, there are no defined criteria regarding the accuracy, safety, and quality of the information provided, requiring verification with the healthcare team to prevent an increased risk of clinical deterioration in users. Objective: To map the usage of information technology tools in self-care for people with cancer. Methods: This is a scoping review grounded in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews (PRISMA-ScR) and the Joanna Briggs Institute (JBI) methodology, structured using the PCC (Population, Concept, and Context) acronym. The search was conducted across multiple databases (MEDLINE/PubMed, Scopus, EMBASE, ScienceDirect, ProQuest, and BVS), with screening performed in the Rayyan software by three independent reviewers, resulting in the inclusion and analysis of 41 studies. Results: The findings demonstrated that the usage of digital technologies offers significant positive aspects, such as increased adherence to treatment and monitoring, promotion of patient autonomy, real-time optimization of symptom management, strengthened engagement with the healthcare team, and continuous monitoring of toxicities. Conversely, relevant limitations were identified regarding the studies (small and homogeneous samples, lack of sustained adherence due to low usability or lack of digital literacy, and scarce robust evidence on the improvement of clinical outcomes and quality of life) and the tools (interface flaws, connectivity issues, limited personalization, technical instability, and cybersecurity and privacy vulnerabilities). Conclusions: Information technology tools represent promising and complementary resources to enhance care and empower oncology patients. However, they require user-centered development, greater digital inclusion, rigorous data security, and future multicenter studies to consolidate their implementation into clinical practice.

• Recruiting Women With Overactive Bladder Into a Primary Care Digital Therapeutic Trial (RiSolve) Using a Fully Remote Direct-to-Patient Strategy: Descriptive Study

  Date Submitted: Oct 22, 2026

  Open Peer Review Period: Oct 23, 2026 - Dec 18, 2026

  • Peer Review Me

  Background: Participant recruitment is a persistent barrier to clinical trials, particularly in primary care, where fixed funding timelines intensify pressure to enrol rapidly and traditional clinician-mediated and community-based methods can be slow and resource-intensive. Direct-to-patient (DTP) digital recruitment through social media offers a potential alternative, but real-world evidence in primary care trials requiring rapid enrolment remains limited. Objective: To evaluate the effectiveness and efficiency of a multi-modal recruitment strategy combining primary care network outreach with DTP social media advertising in achieving enrolment targets within a constrained timeframe in a fully remote primary care digital therapeutic trial, and to describe its impact on recruitment performance, participant retention, and engagement. Methods: The RiSolve study was a single-arm, fully remote primary care trial of a digital therapeutic for women with bothersome overactive bladder symptoms, delivered through the Irish primary care clinical trials network with a target of approximately 45 participants within an eight-week window. Recruitment combined traditional outreach (approximately 150 general practices, urogynaecology units, community and pharmacy promotion, and snowball referral) with DTP paid advertising on Meta (Facebook and Instagram) and Google. The recruitment funnel, campaign metrics (impressions, click-through rate, cost per click, and cost per enrolled participant), and retention (Week 8 completion) were summarised descriptively. Results: Of 208 expressions of interest, 204 (98.1%) were generated through social media; none of the 46 enrolled participants were recruited via traditional pathways. The enrolled cohort's mean age (60 years) and body mass index (27.8 kg/m²) were consistent with the population subgroups reported to carry the highest burden of OAB. The target was achieved three weeks ahead of schedule (47 participants consented over five weeks; mean 9.4 per week), with an 83.9% pre-screening-to-consent conversion. The Meta campaign achieved a click-through rate of 1.80% and a cost per click of €0.13 on a spend of €879.08, at €19.11 per enrolled participant. Engagement closely tracked campaign delivery, with 94.7% of expressions of interest recorded while advertising was live. Retention was 82.6% (38/46). Conclusions: A direct-to-patient digital recruitment strategy enabled rapid, cost-efficient enrolment in a fully remote primary care trial, meeting the target ahead of schedule while traditional methods contributed no enrolments. Recruited participants showed high retention, countering concerns that digitally recruited cohorts are difficult to retain. These findings support direct-to-patient digital recruitment as a viable strategy for time-constrained, decentralised primary care trials. Future work should incorporate end-to-end conversion tracking and directly compare recruitment pathways across a range of other therapeutic areas. Clinical Trial: ClinicalTrials.gov NCT07206446; https://clinicaltrials.gov/study/NCT07206446

• Machine Learning for Predicting 12-Month Asthma Attack Risk in UK-Wide Primary Care: Development, Validation, and Clinical Utility Analysis

  Date Submitted: Oct 21, 2026

  Open Peer Review Period: Oct 22, 2026 - Dec 17, 2026

  • Peer Review Me

  Background: Asthma attacks remain a significant burden on primary care systems, yet predicting individual risk remains challenging. While machine learning shows promise, most existing models are derived from secondary care data or lack robust validation and assessment of clinical utility. Objective: This study aims to develop and validate machine learning models for predicting 12-month asthma attack risk using UK-wide primary care data, adhering to TRIPOD-AI and RECORD reporting guidelines. Methods: We conducted a retrospective cohort study using the Optimum Patient Care Research Database (OPCRD), comprising more than 600,000 patients with active asthma across England, Scotland, and Wales. Data were split into a training set (England) and a geographical validation/test set (Scotland and Wales). We compared five algorithms: Logistic Regression (LR) as the benchmark model, Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and neural networks (NN) (a combination of fully connected network [FCN] and long short-term memory [LSTM]). We assessed model performance using the Area Under the Receiver Operating Characteristic (ROC) curve (AUC), calibration plots, and Decision Curve Analysis (DCA) to quantify net benefit. Results: XGBoost emerged as the top-performing model on both the validation (AUC = 0.816) and test (AUC = 0.788) sets. It achieved 5% higher sensitivity than LR (95% vs 90%) at 62% specificity. At the same specificity (62%), XGBoost identified about five more patients per 100 who had an asthma attack within 12 months than LR, indicating a higher net benefit. Additionally, all other models outperformed LR on the test set (LR AUC: 0.754; DT: 0.770; RF: 0.783; NN: 0.786). Model calibration confirmed reliable probability estimates, and the model interpretation highlighted key predictors, including prior asthma attacks and medication use. Conclusions: These findings indicate that advanced ML approaches substantially improve asthma risk prediction over a conventional regression model. Future work should focus on prospective evaluation and implementation science to determine how to integrate these models into primary care workflows to improve preventive intervention targeting and reduce asthma attacks.

• Disclaim and Diagnose: Consumer-Facing Artificial Intelligence Chatbots and Medical Device Criteria: Cross-Sectional Content Analysis

  Date Submitted: Oct 20, 2026

  Open Peer Review Period: Oct 21, 2026 - Dec 16, 2026

  • Peer Review Me

  In a cross-sectional content analysis of 24 consumer-facing artificial intelligence chatbots responding to 3 clinical vignettes, most responses provided diagnostic (91.7%) and treatment (70.8%) content despite frequent disclaimers (76.4%), meeting a key Food and Drug Administration criterion for software as a medical device.

• TRAIL-AI Reporting Framework for AI-Mediated Evidence and Scientific Provenance: A Special Communication

  Date Submitted: Oct 20, 2026

  Open Peer Review Period: Oct 21, 2026 - Dec 16, 2026

  • Peer Review Me

  Artificial intelligence (AI) is increasingly used in literature retrieval, evidence synthesis, data analysis, interpretation, and manuscript development. Current guidance from journals and professional organizations emphasizes disclosure of AI use, transparency, and human accountability. However, disclosure alone may not reveal how AI-mediated processes influence evidence selection, reasoning pathways, or the scientific claims ultimately reported. Given growing concerns regarding fabricated citations, unsupported assertions, inconsistent terminology, and nontransparent evidence transformations, additional mechanisms are needed to ensure scientific trustworthiness. To propose TRAIL-AI (Transparent Reporting and Audit of AI-Mediated Literature, Evidence, and Inference), a reporting framework designed to improve transparency, traceability, and accountability for AI use that materially influences scientific evidence, analysis, interpretation, or We reviewed emerging recommendations and reporting guidance addressing generative AI in scientific publishing and biomedical research and identified gaps related to scientific provenance and evidence traceability. Building on these observations, we developed TRAIL-AI as a conceptual framework focused on consequence-sensitive reporting of AI-mediated scientific activities. The framework consists of 13 reporting items organized into five domains: (1) task and threshold, (2) resources and retrieval, (3) AI-mediated actions, (4) inspection and verification, and (5) lineage and responsibility. TRAIL-AI introduces a proportional reporting model with three levels: Level 1 for expressive or mechanical assistance, Level 2 for evidence-influencing activities, and Level 3 for inference-influencing activities requiring claim-level provenance documentation. The framework additionally incorporates semantic provenance through the use of recognized clinical terminologies and emphasizes human verification and investigator accountability. TRAIL-AI extends existing AI reporting approaches by linking AI-mediated operations to the evidence and claims they influence. The framework introduces a structured provenance statement for evidence-influencing use and a claim-level ledger for selected high-consequence scientific assertions. Key reporting elements include documentation of AI systems and accessible resources, retrieval and screening processes, affected evidence and claims, verification of source existence and claim support, management of evidentiary uncertainty, terminology normalization, preservation of audit records, and designation of accountable investigators. The framework prioritizes reconstructability, allowing reviewers and readers to inspect consequential AI-assisted scientific processes without requiring exact reproduction of nondeterministic model outputs. TRAIL-AI proposes a practical approach for strengthening transparency and accountability in AI-assisted scientific research. By moving beyond simple disclosure toward provenance-based reporting, the framework seeks to ensure that material scientific claims remain traceable to identifiable evidence, documented AI-mediated actions, standardized clinical concepts, and accountable human judgment. Future work should evaluate the framework through consensus development, pilot implementation, and prospective studies assessing validity, reliability, feasibility, and its impact on the identification of unsupported or inadequately documented scientific claims.

• Assessing Physician Workload in Primary Care: A Scoping Review of Methods, Metrics, and Opportunities for Automation

  Date Submitted: Oct 20, 2026

  Open Peer Review Period: Oct 21, 2026 - Dec 16, 2026

  • Peer Review Me

  Background: Objective measurement of physician workload is essential for understanding how clinical and administrative activities are distributed in primary care and evaluating changes associated with digital technologies. However, measurement approaches differ in the activities they capture and often require substantial manual observation and coding. Objective: This scoping review aimed to map the methods and metrics used to objectively measure physician workload in primary care using primary data and identify components of workload measurement potentially suitable for automation. Methods: Following Joanna Briggs Institute guidance and the PRISMA extension for Scoping Reviews, we searched MEDLINE via PubMed, Scopus, Web of Science, and CINAHL for English-language, peer-reviewed studies published between January 1, 2016, and June 30, 2026. Reference lists and forward citations were also searched. Eligible studies reported objective workload measures derived from direct observation, recordings, study-specific digital activity capture, or prospectively timestamped activity records. Studies based exclusively on subjective measures or secondary analyses of routinely collected electronic health record (EHR) audit logs were excluded. Reviewers independently screened studies, and data extracted by one reviewer were verified by two others. Findings were synthesized descriptively without formal risk-of-bias appraisal. Results: Thirteen studies across six countries were included. Eight used direct observation, and 12 reported at least one time-based measure. Measures captured consultation and direct patient care, EHR and documentation activities, task volume and workflow, and total working time, including work outside scheduled hours. Task counts, sequences, transitions, and operational failures provided information beyond activity duration. Two studies examined AI-assisted documentation. Measurement remained heavily dependent on manual activity classification and temporal coding. Differences in task definitions, observation boundaries, and outcome measures limited comparability across studies. Potential automation targets included activity classification, identification of task boundaries and workflow transitions, and calculation of workload metrics from observational data. These applications represented proposed methodological extensions rather than validated approaches. Conclusions: Objective workload measurement in primary care remains dominated by time-based indicators that requires substantial human effort. Standardized task definitions and reporting are needed to improve comparability. Future research should evaluate whether automated coding can reduce measurement effort while preserving temporal accuracy and clinical context, using independently human coded observations as reference standards and retaining human oversight for ambiguous activities.

• Cognitive-Motor Dual Tasks Using Immersive Extended Reality for Cognitive Rehabilitation: Scoping Review

  Date Submitted: Oct 20, 2026

  Open Peer Review Period: Oct 21, 2026 - Dec 16, 2026

  • Peer Review Me

  Background: Cognitive-motor dual-task (CMDT) training and immersive extended reality (XR) have each shown promise for cognitive rehabilitation by enhancing motivation and ecological validity. Research on XR-based CMDT systems is growing rapidly, but there is little guidance on how such systems should be designed and evaluated. Objective: This scoping review aimed to map how existing XR-based CMDT systems for cognitive rehabilitation synthesize cognitive and motor tasks, support user interaction, deliver feedback, and are evaluated, and to derive a design framework for future systems. Methods: Following the PRISMA-ScR guidelines, we searched Web of Science, Scopus, IEEE Xplore, the ACM Digital Library, and PubMed for studies published from 2019 to July 9, 2025. Eligible studies described a head-mounted display–based XR system that delivered CMDT training for cognitive rehabilitation and reported a user-based evaluation. Data were charted across 4 dimensions: dual-task synthesis, XR interaction design, feedback, and evaluation. Results: Of 4615 records identified, 25 studies were included. Target populations included general aging populations (n=8), multiple sclerosis (n=4), mild cognitive impairment (n=3), and 7 other conditions. Rigid-timing tasks were the most common synthesis pattern (12/25, 48%), and more than half of the systems (13/25, 52%) were built from basic cognitive and motor tasks rather than contextualized or real-activity scenarios. Most systems (16/25, 64%) confined interaction to a fixed space around the user (narrow virtual reality); only 2 allowed free navigation and 4 used mixed reality. All but 2 systems (23/25, 92%) provided feedback, and 14 adapted difficulty, of which 11 relied on manual level selection, 2 on game metrics, and 1 on heart rate. Twelve studies evaluated efficacy (5 randomized controlled trials, 1 nonrandomized controlled trial, and 6 pre-post studies), whereas the remaining 13 were feasibility or formative studies; most studies (19/21, 90%) involved fewer than 50 participants. Conclusions: Diverse CMDT implementations in XR appear feasible across a range of cognitive impairments, but spatial fusion and dynamic adaptive difficulty remain underdeveloped, and evidence of efficacy from controlled trials is limited. We propose a design framework with 3 layers (goals, design elements, and constraints) and 8 design elements across 4 dimensions as an extensible foundation for future XR-based CMDT research. Future work should prioritize advancing spatial fusion, developing personalized adaptive difficulty algorithms, and conducting controlled clinical studies to establish efficacy.

• Preoperative mixed reality comprehensive cognitive training reduces the incidence of postoperative delirium in older adults undergoing orthopedic surgery: a randomized controlled trial

  Date Submitted: Oct 18, 2026

  Open Peer Review Period: Oct 20, 2026 - Dec 15, 2026

  • Peer Review Me

  Background: Older surgical patients are frequently affected by postoperative delirium (POD), a complication with serious short- and long-term consequences. Although cognitive prehabilitation holds promise for prevention, its conventional delivery formats suffer from low engagement and poor adherence. Objective: We sought to determine whether preoperative mixed reality comprehensive cognitive training (MRCC) lowers the incidence of POD in older adults undergoing orthopedic surgery. Methods: In this prospective randomized controlled trial with blinded outcome assessment, 144 patients aged ≥65 years scheduled for elective orthopedic surgery under general anesthesia were allocated in a 1:1 ratio to receive MRCC—delivered through mixed reality (MR) glasses in 20-minute daily sessions on the 2 consecutive days before surgery (n=72)—or standard perioperative care (n=72). The primary outcome, the incidence of POD within 7 days after surgery or until hospital discharge (whichever came first), was assessed with the Confusion Assessment Method (CAM). Secondary outcomes comprised delirium characteristics, perioperative electroencephalography (EEG) biomarkers, postoperative pain trajectories, preoperative sleep quality, postoperative cognitive performance (paper-based and MR-based), recovery quality, and patient satisfaction. Results: POD occurred in 27 of 144 patients (18.8% overall): 6 of 72 (8.3%) receiving MRCC versus 21 of 72 (29.2%) under standard care, an absolute risk reduction of 20.9% (IRR 0.286, 95% CI 0.123-0.666; P=.004). Among the patients in whom delirium developed, episodes lasted a median of 1 day with MRCC versus 2 days in controls (P=.048). MRCC further improved preoperative sleep quality, attenuated movement-evoked pain, and enhanced postoperative recovery (all P<.001). EEG analyses revealed attenuated pathological delta slowing and preserved complexity of brain signaling. Conclusions: Brief preoperative MRCC significantly lowered the occurrence and duration of POD in older orthopedic patients and was accompanied by improvements in pain, sleep, and neurophysiological function. MR-based cognitive prehabilitation therefore emerges as a promising nonpharmacological option for preventing delirium in this population. Clinical Trial: Chinese Clinical Trial Registry ChiCTR2400093319; https://www.chictr.org.cn/showproj.html?proj=252634

• Modeling Re-Engagement Patterns in a Digital Alcohol Reduction App: Recurrent Event Analysis

  Date Submitted: Oct 19, 2026

  Open Peer Review Period: Oct 20, 2026 - Dec 15, 2026

  • Peer Review Me

  Background: Maintaining engagement with digital apps is a key concern when developing effective digital health interventions. Previous research typically examines engagement through a single-event outcome, such as time to first disengagement. However, users may repeatedly stop using and later return to the app, experiencing multiple disengagement and re-engagement cycles. Understanding the factors associated with re-engagement is valuable and may help inform the optimization and personalization of digital intervention strategies. Objective: This study aimed to characterize re-engagement with Drink Less, an alcohol reduction app, as a recurrent-event process and to examine its associations with baseline characteristics and recent app-use history. Methods: We analyzed data from 19,233 users of the Drink Less app who had a baseline Alcohol Use Disorders Identification Test (AUDIT) score of at least 8 on the 0 to 40 scale, indicative of drinking at hazardous or harmful levels. Disengagement was defined as 7 or more consecutive days without app use and a disengagement episode began when this threshold was reached. Re-engagement was defined as the first recorded app-use session after entry into that episode. The Prentice-Williams-Peterson Gap-Time (PWP-GT) model was used to examine associations between baseline characteristics, recent app-use history, and re-engagement. Recent app-use history was represented by previous gap duration, measured as the number of days from entry into the immediately preceding disengagement episode to the corresponding re-engagement that ended it; and previous engagement duration, defined as the number of days from that re-engagement to entry into the current disengagement episode. Results: In the baseline characteristics model, users in the Harmful AUDIT category (16-19) were more likely to re-engage after disengagement than those in the Hazardous category (8-15; HR 1.0431, 95% CI 1.0026-1.0852; P=.04). Older age was also associated with a greater likelihood of returning to the app (HR 1.0043 per year, 95% CI 1.0029-1.0058; P<.001), whereas users in manual employment (HR 0.8437, 95% CI 0.8000-0.8899; P<.001) and other employment categories (HR 0.9436, 95% CI 0.9016-0.9876; P=.01) were less likely to re-engage than those in non-manual employment. There was no clear evidence of a difference by sex. In separate lagged variable models, longer previous gap duration was negatively associated with re-engagement (HR 0.9921 per day, 95% CI 0.9913-0.9930; P<.001), whereas longer previous engagement duration was positively associated with re-engagement (HR 1.0078 per day, 95% CI 1.0067-1.0089; P<.001). All comparisons accounted for the other covariates in the respective model and were conditional on disengagement episode order. Conclusions: Re-engagement with Drink Less was significantly associated with age and employment, as well as with users’ recent app-use history. Together, these findings could inform the development and testing of targeted support both at registration and as the app learns about users’ behavior.

• Lexical Entity Detection and Negative Review Burden in Online Reviews of a Public Health Care System: Content Analysis and Lexicon Validation Study

  Date Submitted: Oct 19, 2026

  Open Peer Review Period: Oct 20, 2026 - Dec 15, 2026

  • Peer Review Me

  Background: Online reviews provide unsolicited accounts of health care experiences and may complement conventional patient-experience surveys. However, analyses based on overall sentiment can obscure the specific service domains discussed, while aspect-level approaches require distinction between detecting an entity and attributing sentiment to it. Objective: This study aimed to characterize service entities and lexical discourse patterns in online reviews of a regional public health care system, compare primary and hospital care, identify where negatively rated feedback was concentrated across facility types, and evaluate the lexical detection instrument. Methods: We conducted a retrospective observational analysis of 47,589 Google Maps reviews from 812 publicly funded health care facilities in Andalusia, Spain. Six service entities and seven complaint- or praise-related lexical markers were detected using a prespecified rule-based lexicon. Sentiment was assigned from review-level star ratings. Negative review burden was defined as the proportion of all reviews that both mentioned an entity and received a negative rating. Lexical-marker differences were estimated using facility-clustered logistic regression within sentiment strata. The final lexicon was validated against manual annotation of 400 reviews. Results: System/Management was the most frequently detected entity (42.6%) and had the highest negative review burden overall (37.3%, 95% CI 35.1%-39.5%) and across all nine facility types. Among negatively rated reviews, access-barrier formulations were more frequent in primary than hospital care (12.5% vs 2.2%; OR 6.47, 95% CI 4.57-9.15), whereas waiting-and-delay formulations were less frequent (5.6% vs 12.0%; OR 0.43, 95% CI 0.37-0.51). Among positively rated reviews, professionalism showed no statistically detectable difference (OR 0.99, 95% CI 0.86-1.13), while gratitude (OR 0.45, 95% CI 0.38-0.54) and humanistic-care formulations (OR 0.33, 95% CI 0.25-0.45) were less frequent in primary care. System/Management burden was highest in outpatient consultation centers (52.5%), health centers (46.0%), and local health posts (36.2%), forming a descriptive pattern of higher burden in ambulatory consultation settings. Five of six entities achieved precision between 0.91 and 1.00; Facilities showed insufficient validation performance and was excluded from substantive interpretation. Lexical markers generally showed high precision but limited recall. Conclusions: Online reviews revealed distinct patterns in organizational, access, waiting, and positive-care discourse across settings. Organizational processes showed the greatest concentration of negatively rated feedback, particularly in ambulatory consultation settings. These findings describe reviewer discourse rather than population-representative patient experience or entity-specific sentiment. A transparent, empirically evaluated lexical approach can provide a reproducible means of identifying domains of negatively rated feedback warranting closer organizational investigation.

• Developing a super landmark, multivariate prediction model to predict early warning score alerts in hospital inpatients: a proof-of-concept study

  Date Submitted: Oct 18, 2026

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

  • Peer Review Me

  Background: Many hospitals have established frameworks, such as the National Early Warning Score 2 (NEWS2), to detect and respond to patient deterioration. The best way to use data from wearable vital signs sensors (WVSS) within these frameworks is unclear. Objective: Develop a clinical prediction tool which predicts NEWS2 5+ alerts recorded by nurses using WVSS data from adult inpatients in the United Kingdom. Methods: Data was collected from 57 patients participating in two clinical studies of WVSS: 44 with Covid-19 and 13 with haematological malignancy receiving chimeric antigen receptor T-cell therapy (CAR-T). Patients wore three WVSS measuring heart rate/respiratory rate (HR/RR), temperature and oxygen saturations (SpO2) every minute. Contemporaneous vitals were recorded by nurses. A mixed-effects, Cox proportional hazards (CPH) model was developed using a stacked, super landmark dataset derived from vitals recorded during the first 49 hours of WVSS application. This included a 1-hour, run-in period to establish a baseline. The dataset was divided into 1-hourly landmark times followed by 1-hour-long prediction horizons. The outcome of interest was a National Early Warning Score 2 (NEWS2) 5+ alert from nurse-recorded vitals during each horizon. The CPH model was trained using a “ground truth” dataset. This comprised all landmark times when an outcome event was known and when WVSS-derived predictors were available in the preceding hour. The model was internally validated using patient-level bootstrap sampling (200 samples). Calibration was inspected visually and discrimination assessed using Harrell’s C-index. Rates of NEWS2 5+ alerts from nurse-recorded vitals in each horizon were compared with alerts predicted by the model. They were also compared with alerts expected if WVSS vitals were used to determine NEWS2 directly. This analysis was completed for the ground truth data and for the entire super landmark dataset after WVSS data gaps were filled by multiple imputation. Results: The super landmark dataset contained 2577 hourly landmarks. In 425/2577, the outcome (NEWS2 alert during horizon) and the predictors (WVSS data from the preceding hour) were known. This ground truth subset contained 129 NEWS2 5+ alerts and the model predicted 111/129 (sensitivity: 86.0%) with a specificity of 240/296 (81.1%). The optimism corrected C-index was 0.773 (95% CI: 0.705 to 0.833). In the ground truth subset, the nurse-recorded alert rate was 129/425 (30.4%), the model alert rate was 167/425 (39.3%), and the wearable rate was 373/396 (94.2%). In the imputed, super landmark dataset, the nurse-recorded alert rate was 191/693 (27.6%), the model alert rate was 1015/2577 (39.4 [95% CI: 37.4-41.4]%), and wearable rate was 2478/2577 (96.2 [95% CI: 95.3-96.9]%). Conclusions: This proof-of-concept study suggests that prediction models may help reduce alert rates from WVSS. It illustrates a route by which such models could help to standardise alerting, enabling nurse-recorded and wearable vitals to contribute to the same alerting system. Clinical Trial: The COSMIC-19 study was registered with clinicaltrials.gov (registration: NCT04581031) and was approved by Yorkshire and the Humber – Bradford Leeds Research Ethics Committee, UK (reference 20/YH/0156). The EMBRaCE-GM study was registered with clinicaltrials.gov (registration: NCT05099237) and was approved by the Wales 6 Research Ethics Committee, UK (reference 21/WA/0251).

• Prediction Models for Immune Checkpoint Inhibitor Efficacy in Gastric Cancer: A Systematic Review and Meta-analysis

  Date Submitted: Oct 18, 2026

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

  • Peer Review Me

  Background: Treatment efficacy varies substantially among patients with gastric cancer receiving immune checkpoint inhibitors (ICIs). Although prediction models are increasingly being developed, their performance, methodological quality, and clinical applicability have not been systematically evaluated. Objective: We aimed to comprehensively assess existing models for predicting the efficacy of ICI-based therapy in gastric cancer. Methods: PubMed, Embase, the Cochrane Library, Web of Science, MEDLINE via EBSCOhost, and Ovid were searched from inception to June 25, 2026. Reporting followed PRISMA 2020, extraction was guided by CHARMS, and risk of bias was assessed using PROBAST. AUCs were synthesized in an exploratory record-level Sidik–Jonkman random-effects analysis with Knapp–Hartung adjustment. Standard errors were approximated from AUCs and extracted analysis sample sizes. Subgroup and leave-one-record-out analyses were conducted. Results: Twelve reports contributed 80 model-performance records. Seven records with a reported AUC of 1.000 were summarized descriptively; 73 records were pooled. The pooled AUC was 0.80 (95% CI, 0.77–0.82; I²=88.29%). Pooled AUCs were 0.83 (95% CI, 0.79–0.86) for machine-learning models, 0.82 (95% CI, 0.78–0.87) for logistic regression models, 0.80 (95% CI, 0.66–0.94) for deep-learning models, and 0.68 (95% CI, 0.65–0.71) for single-biomarker ROC analyses. The seven-category comparison was significant (P<0.001), but categories were uneven and performance records were correlated. Leave-one-record-out estimates ranged from 0.79 to 0.80. All 12 reports had high overall risk of bias. Calibration was explicitly reported for 2 records, and complete matched contingency tables were available or reconstructable for 19. Conclusions: Published models provide preliminary evidence on discrimination among patients receiving ICI-based therapy, but high risk of bias, heterogeneous outcomes, correlated performance records, approximate standard errors, and limited external validation constrain interpretation. The evidence does not establish clinical utility or identify a model suitable for selecting ICIs over alternative treatments. Clinical Trial: PROSPERO CRD420261526885

• Optimizing Disaster Triage with Large Language Models: Simulation-Based Comparative Study of the Effects of Prompt Design and Model Version on Triage Accuracy

  Date Submitted: Oct 18, 2026

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

  • This manuscript needs more reviewers Peer Review Me

  Background: Mass casualty incidents require rapid decisions under high pressure, and experts are often not the first on site. Large Language Models (LLM) like ChatGPT could serve as a readily available expert system to aid decision-making. There is a lack of scientific studies investigating how the output depends on systematically modified prompts and the version model. Objective: This study aims to compare the triage accuracy of five ChatGPT versions using 30 simulated pre-hospital disaster cases and to examine to what extent systematically extended prompts affect the outcome. Additionally, over- and undertriage rates were investigated, as well as the creation of a meaningful ranking of all patients. Methods: 30 fictious case vignettes were triaged using five different versions of ChatGPT: GPT-3.5, GPT-4, GPT-4o, GPT-o1, and GPT-5. Initially, an openly formulated prompt was used, requesting triage, including an explanation. Subsequently, ChatGPT was asked to use the mSTART, then the PRIOR algorithm. Finally, the prompt was extended to include a detailed description of the respective algorithm. Each constellation of case vignette, model version, and prompt was repeated five times separately, and a final response was selected for further analysis based on majority vote. Accuracy, over-, and undertriage were examined as outcomes and paired comparisons with and without a detailed description of an algorithm were tested for significance using the exact McNemar test. Finally, ChatGPT was asked to create a treatment ranking of multiple patients. Results: With an openly formulated prompt, ChatGPT achieved accuracy values ranging from 56.7% (GPT-4o) to 90% (GPT-5). Mentioning an algorithm did not result in consistent improvements. A detailed description of the PRIOR algorithm considerably increased accuracy. ChatGPT 5, prompted to use the PRIOR algorithm, achieved the best results without a detailed description, reaching 93.3% (28/30). ChatGPT 4 and GPT-4o, with the specific PRIOR algorithm included in the prompt, achieved the highest overall accuracy, at 96.7% (29/30) each. These two cases showed a significant improvement in the exact McNemar (GPT-4: P = .016; GPT-4o: P = .008). While older versions tend towards undertriage, modern versions showed a more balanced rate. When tasked with ranking ten patients, rate of correctness ranged from 80% to 86.7%. When all 30 patients were ranked together, only ChatGPT 5 remained equally good at 86.7%, while GPT-3.5 correctly classified 36%. Conclusions: Triage performance using ChatGPT depends on the model version and can be influenced by specifically tailored prompts. Newer models achieve higher accuracy but benefit less from a detailed algorithm description. However, this approach has shown a significant increase in accuracy with selected older versions. Despite good triage accuracy, LLMs may currently be more suitable as supervised decision support than autonomous triage systems. To further assess triage competence, validation through realistic simulations is indispensable.

• A Digital Behavior Change Intervention to Reduce Secondhand Smoke Exposure During Pregnancy: Qualitative Process Evaluation Nested Within a Pilot Randomized Controlled Trial

  Date Submitted: Oct 19, 2026

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

  • Peer Review Me

  Background: Pregnant women who live with a partner who smokes remain at high risk of secondhand smoke (SHS) exposure, a preventable cause of adverse pregnancy outcomes. Digital interventions offer a scalable route to promoting a smoke-free home (SFH), yet little is known about the mechanisms through which they act, and previous programs have rarely engaged the partner who smokes. Objective: This study aimed to explore participants' experiences of “First Breath,” a digital behavior change intervention to reduce prenatal SHS exposure, and to identify the mechanisms, barriers, and facilitators that shaped engagement with the intervention and behavior change. Methods: Pregnant women who did not smoke and were exposed to SHS at home, and expectant fathers who smoked, were randomized to “First Breath” or to a static online information page. “First Breath” provided knowledge and practical content to enhance motivation and self-efficacy to create an SFH, a personalized self-reported visual SHS exposure index, goal setting and action planning, text reminders, and guidance on nicotine replacement therapy (NRT) for temporary abstinence within the home. Women in both arms provided urine samples for cotinine analysis (a biomarker for SHS exposure), and biochemical feedback was provided for women in the intervention arm during the trial. Semistructured exit interviews were conducted with 27 of the 68 trial participants (15 women and 12 men; 13 intervention and 14 control) and analyzed using reflexive thematic analysis, with themes mapped deductively onto the capability, opportunity, motivation-behavior (COM-B) model. Results: Participants described the trial procedures and the intervention as acceptable. Exposure feedback was the most influential component and operated through 2 separate channels: the urinary cotinine results, described as more influential because they were external and biochemical, and the self-reported visual exposure index, which carried greater weight among the men, for whom it was the only personalized feedback available. Compared with the control arm, participants in the intervention arm reported broader knowledge acquisition, greater awareness, stronger attitudinal change, more couple communication, and more comprehensive behavioral adaptation. Change in the control arm was more limited and was prompted mainly by repeated questionnaire completion, which functioned unexpectedly as a behavioral cue. Most participants resisted NRT, engagement declined over time (most reported 3 to 6 entries in total), and travel to the study site for urine cotinine samples was the most significant external barrier. Conclusions: Personalized exposure feedback appears to be the principal mechanism through which “First Breath” promotes the reduction of prenatal SHS exposure, and should remain at the core of the program. A redesigned NRT component should be considered before a fully powered effectiveness trial. Clinical Trial: ClinicalTrials.gov NCT07003282; https://clinicaltrials.gov/study/NCT07003282

• Wearable Measures of Sleep and Sedentary Behaviour in Lewy Body Disease: A Systematic Review

  Date Submitted: Oct 16, 2026

  Open Peer Review Period: Oct 18, 2026 - Dec 13, 2026

  • Peer Review Me

  Background: Sleep disturbances, sedentary behaviour/inactivity, and circadian dysregulation are prevalent and clinically significant features of Lewy body disease (LBD) and idiopathic rapid eye movement sleep behaviour disorder (iRBD). Wearable devices are promising tools for continuous, objective, real-world assessment of these outcomes; however, no consensus exists on optimal protocols or digital outcome measures for LBD. Objective: This systematic review aimed to characterise the use of wearable devices for sleep, sedentary behaviour/inactivity, and circadian rhythm assessment in LBD and iRBD. Methods: Following PRISMA guidelines, seven databases were searched from inception to December 2024. We included quantitative original research using movement-based wearable devices to assess sleep, sedentary behaviour/inactivity, and/or circadian rhythm in LBD, or iRBD. Quality was assessed, and data were synthesised narratively. Results: Eighty-one studies met inclusion criteria; 89% focused on Parkinson’s disease (PD), with sleep being the most frequently assessed outcome (66.7%). Thirty-eight devices were identified, used across 13 different monitoring periods, and placed mostly on the wrist (69.1%). Sixty-seven outcome measures were identified across sleep assessment (44.8%), sedentary behaviour/inactivity (29.9%), and circadian rhythm (25.4%). Thirty-three studies examined associations between digital outcomes and clinical features, predominantly in PD. Conclusions: We found that wearable devices assessing sleep, sedentary behaviour/inactivity and circadian rhythm are widely used and feasible in PD, but the evidence was limited in iRBD, and Lewy body dementias. Methodology and derived digital outcomes were heterogeneous, with limited consistency or standardisation across studies, and insufficient evidence to establish the clinical validity of these measures. Nevertheless, wearable devices offer a feasible, non-invasive approach for continuous, real-world monitoring in this population. Clinical Trial: PROSPERO CRD420251046984; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251046984

• Geographically Explicit Ecological Momentary Assessment in Mental Health and Health Behavior Research: A Scoping Review

  Date Submitted: Oct 15, 2026

  Open Peer Review Period: Oct 15, 2026 - Dec 10, 2026

  • Peer Review Me

  Background: Geographically explicit ecological momentary assessment (GEMA) offers a novel approach to addressing this limitation by integrating momentary assessment with geolocation and geospatial data to examine these person–environment relationships in daily life, yet its applications and methodological characteristics have not been comprehensively synthesized. Objective: This scoping review summarized the purposes, methodological characteristics, environmental exposures, outcomes, and strengths and challenges of GEMA in mental health and health behavior research. Methods: Eight databases (Web of Science, PubMed, Embase, CINAHL Complete, Cochrane Library, CNKI, VIP, and Wanfang) were searched from inception to May 17, 2026. Studies were eligible if they integrated ecological momentary assessment (EMA) with objective geolocation and geospatial environmental data. Data on study characteristics, EMA and geolocation methods, environmental exposures, health outcomes, and reported strengths and challenges were extracted and synthesized using descriptive statistics and narrative analysis. Results: Fifty-one studies were included, predominantly involving general populations (47, 92.2%). Three primary purposes of GEMA use are identified: (1) examining exposure–momentary outcome associations (n = 49, 96.1%); (2) exploring underlying mechanisms (n = 22, 43.1%); and (3) methodological assessments (n = 21, 41.2%). Time-contingent EMA predominated (n = 36, 70.6%), smartphones were the primary GPS device (n = 45, 88.2%), EMA and GPS collection configuration was often unclear (n = 33, 64.7%), monitoring usually lasted 1 to14 days (n = 35, 68.6%) and EMA assessed three to four per day (n = 20, 39.2%). Environmental exposures spanned built (n = 34, 66.7%), natural (n = 22, 43.1%), and social environments (n = 13, 25.5%). Mental health outcomes were more commonly assessed (n = 38, 74.5%) than health behaviors (n = 16, 31.4%), particularly mood and stress. Dynamic and fine-grained spatiotemporal assessment was the commonly reported strength (n = 47, 92.2%), whereas major challenges involved EMA design and participant implementation, geolocation data quality, and exposure assessment and spatiotemporal specification. Conclusions: GEMA offers a promising approach for exploring dynamic person–environment relationships in real-world settings. Future research should improve methodological standardization, strengthen temporal and exposure linkage, balance measurement intensity with feasibility. Clinical Trial: OSF Registries 10.17605/OSF.IO/Y852Z; https://doi.org/10.17605/OSF.IO/RZYBW.

• Large Language Model Assistance for Diagnostic Reasoning in Rare Neurological Disease: Randomized Controlled Trial

  Date Submitted: Oct 14, 2026

  Open Peer Review Period: Oct 15, 2026 - Dec 10, 2026

  • This manuscript needs more reviewers Peer Review Me

  Background: Rare neurological diseases are frequently misdiagnosed, particularly among early-career physicians. Although large language models (LLMs) perform well on medical examinations, their effect on clinicians’ diagnostic reasoning remains unknown. Objective: To assess the effect of LLM assistance on diagnostic reasoning among resident and attending neurologists evaluating rare neurological diseases. Methods: A randomized, single-blind trial at 7 hospitals in China. Neurologists were recruited May 25 to June 10, 2026, and completed the assessment remotely or in person on June 14-15, 2026. Participants were randomly assigned 1:1, stratified by career stage, to complete 8 written cases of rare neurological disease using conventional resources with or without DeepSeek-V3. A nonrandomized exploratory arm ran the same cases through the model alone. The primary outcome was the case-level total rubric score, expressed as a percentage of points available. Secondary outcomes included the score in each of 5 rubric domains, whether the reference diagnosis was ranked first, whether it was included in the differential diagnosis, and completion time. All analyses followed the intention-to-treat principle. Results: Sixty physicians were randomized (30 per group) and included in the primary analysis (480 observations). Model-estimated mean scores were 81.4% (95% CI, 76.5-86.4) with LLM assistance vs 55.7% (95% CI, 50.8-60.6) with conventional resources, a difference of 25.7 percentage points (95% CI, 18.7-32.7; P<.001). The reference diagnosis was more often ranked first with LLM assistance (61.5% vs 29.9%; difference, 31.7 percentage points; 95% CI, 22.5-40.8) and more often included in the differential diagnosis (91.3% vs 58.7%; difference, 32.6 percentage points; 95% CI, 23.9-41.3) (both P<.001). Completion time was shorter with LLM assistance (median, 32.0 [IQR, 22.0-49.1] vs 57.6 [IQR, 45.2-77.9] minutes; difference, −25.6 minutes; 95% CI, −44.1 to −8.2; P<.001). Post hoc adjustment for response length attenuated the primary difference to 14.5 percentage points (95% CI, 6.5-22.4; P<.001). The model alone scored a mean of 81.8% (SD, 6.4). Conclusions: In this trial, access to a freely available LLM improved diagnostic reasoning and shortened completion time among resident and attending neurologists evaluating rare neurological diseases. Whether this benefit extends to real clinical encounters warrants further investigation. Clinical Trial: Chinese Clinical Trial Registry; ChiCTR2600125237. https://www.chictr.org.cn/bin/project/edit?pid=274923

• Sociotechnical consequences of secure messaging and LLM integration: an interview study with patients and clinicians

  Date Submitted: Oct 14, 2026

  Open Peer Review Period: Oct 15, 2026 - Dec 10, 2026

  • Peer Review Me

  Background: LLMs in secure messaging have been targeted as a potential solution to clinicians’ growing burnout related to EHR work outside of clinical encounters. Unfortunately, evaluations of this tool focus on the content of individual messages rather than how its use could influence patient and clinician perspectives of the therapeutic relationship. Objective: To understand how patients and clinicians view the use of LLMs in secure messaging and identify potential implications for the patient-clinician relationship. Methods: This qualitative study used semi-structured interviews and vignette-based prompts that mirrored real messaging exchanges between a patient and their clinician. The study setting included a purposive sample of patients and outpatient clinicians. Participants included patients and clinicians. A purposive sample of 20 adult patients was recruited from across the United States. Recruitment sought increased representation from minoritized racial and ethnic groups and older patients. A purposive sample of 11 clinicians were recruited from health systems concentrated in the San Francisco Bay Area. Data were collected between August 2024 and April 2025. One-time interviews conducted via videoconference. Interviews included questions about messaging use, perspectives on AI in healthcare, clinicians’ use of LLMs in patient messaging, and two vignette-based messaging scenarios. We examined patient and clinician perspectives on (1) the role of secure messaging and (2) the impact of LLM-drafted messages on the therapeutic relationship. Results: Participants expressed the importance of secure messaging for fostering the patient-clinician relationship and concerns that LLM-generated messages could detract from such relationships. They expressed concerns that, while these tools should help clinicians focus more on patient-centered care, they could instead increase work burdens and limit the care patients expected to receive with messaging. Some expressed further concerns that such tool use could harm rapport and trust. Conclusions: This qualitative interview study found that some patients and clinicians perceive LLM use in secure messaging could detract from the therapeutic relationship by harming patients’ rapport and trust. Implementation decisions should consider how patients’ expectations of their clinician and their use of messaging could be reshaped by the technology.

• 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

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  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.

• 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: An integration of key findings from a national research program

  Date Submitted: Oct 7, 2026

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

  • Peer Review Me

  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. This viewpoint synthesises key findings across a major program of work in Practice Analytics, describe implications for practice, and identify opportunities for future research and development. A modified integrative review of all selected research outputs 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. Through analysis of 45 research outputs form the Practice Analytics Program it was identified that 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

  • This manuscript needs more reviewers 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

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  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

  • This manuscript needs more reviewers 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

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  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

  • This manuscript needs more reviewers 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

• 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

  • Peer Review Me

  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

  • This manuscript needs more reviewers Peer Review Me

  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.

• 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.

• 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

  • Peer Review Me

  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

• 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.

• 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

  • Peer Review Me

  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

  • Peer Review Me

  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.

• 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

  • Peer Review Me

  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

  • Peer Review Me

  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

• 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

  • This manuscript needs more reviewers Peer Review Me

  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.

• 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.

• 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

  • This manuscript needs more reviewers 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.

• 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

  • 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)

• 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.

• 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.