Journal of Medical Internet Research

Patient-generated streaming data from wearable and digital technologies is increasingly promoted as a means of supporting mental health monitoring and clinical decision-making. While patient acceptance of these technologies has been reported, clinician perspectives remain underexplored despite their central role in determining whether streaming data are meaningfully integrated into routine care. This study explored clinicians experiences, as well as perceived facilitators and barriers, related to integrating patient-generated streaming data into routine mental health practice. A qualitative, exploratory interview study was conducted to examine clinicians experiences and perspectives on integrating patient-generated streaming data into mental health care. Semi-structured interviews were conducted with 33 clinicians, including family physicians (n=11), psychiatrists (n=12), and psychologists (n=10). Data were analyzed using reflexive thematic analysis guided by Braun and Clarkes six-step approach. Six themes were identified. Clinicians described variable use of digital and streaming technologies, ranging from routine engagement to deliberate non-use. Streaming data were viewed as clinically valuable when they provided longitudinal and objective insights, identified physiological and behavioural pattern changes, and supported patient engagement. However, clinicians emphasized that clinical usefulness was contingent on interpretability, contextual information, and relevance to decision-making. Major barriers included poor integration with electronic medical records, time constraints, data volume, limited organizational support, and uncertainty regarding data reliability and validity. Clinicians also expressed persistent concerns about privacy, governance, and regulatory oversight, highlighting the need for clear safeguards and accountability structures. Clinicians view patient-generated streaming data as a promising adjunct to mental health care, particularly for capturing longitudinal change between visits. However, meaningful clinical integration remains constrained by usability, workflow, organizational, and regulatory challenges, as well as limited confidence in data interpretation. Addressing these barriers through improved system integration, interpretive support, validation, and governance will be essential for translating the potential of streaming data into routine clinical practice.

BackgroundHwa-byung (HB) is a Korean culture-bound syndrome characterised by prolonged suppression of anger and somatic complaints. No evidence-based digital therapeutic (DTx) has been developed for HB. We evaluated the feasibility, user experience (UX), and preliminary clinical effect of an acceptance and commitment therapy (ACT)-based DTx application, Hwa-free, for HB. MethodsAdults aged 19-80 years diagnosed with HB were enrolled in a four-week app-based intervention with assessment at baseline (Week 0), Week 2, Week 4, and Week 8 follow-up. The primary outcome was UX assessed via a 22-item survey at Week 4. Secondary outcomes included HB-related symptom and personality scales, depression, anxiety, anger expression, psychological flexibility, health-related quality of life, and heart rate variability. ResultsOf 45 screened, 30 were enrolled and 28 constituted the modified intention-to-treat population. Mean app use was 19.9 {+/-} 7.9 days (71.2% adherence over 28 days). Adverse events were infrequent and unrelated to the intervention. Positive response rates exceeded 80% for video content (items 2-4: 82.8-89.7%), HB self-assessment (86.2%), meditation therapy (86.2%), and in-app guidance (85.7%). Pre-post improvements from baseline to Week 4 were observed in 11 of 18 clinical scales, including HB Symptom Scale ({Delta} = -9.8, Cohens d = -0.92), Beck Depression Inventory-II ({Delta} = -13.3, d = -1.11), and state anger ({Delta} = -7.8, d = -0.96). The HB screening-positive rate declined from 100% at baseline to 55.6% at Week 8. ConclusionsHwa-free demonstrated adequate feasibility, acceptable UX, and preliminary evidence of clinically meaningful improvement in HB-related symptoms. Future randomised controlled trial is warranted. Trial registrationCRIS, KCT0011105

BackgroundThe rapid proliferation of digital platforms has transformed health information access but has also led to increased exposure to misinformation. Existing research lacks standardized tools to quantify individual-level exposure to health misinformation in a comprehensive manner. ObjectiveTo develop a novel composite index--the Misinformation Exposure Index (MEI)--to measure multidimensional exposure to health misinformation among social media users. MethodsA questionnaire-based pilot study was conducted among a young adult population to assess patterns of health information exposure, source utilization, trust, and behavioural responses. The MEI was developed using a multi-domain framework comprising Exposure Frequency, Source Diversity and Risk, Trust in Information, and Behavioural Response. Responses were scored using Likert scales and weighted domain contributions to generate a composite score ranging from 0 to 100. ResultsParticipants demonstrated moderate to high engagement with digital platforms for health information, with reliance on both formal and informal sources. Variability in trust and verification behaviours was observed, with a proportion of participants reporting adoption of health-related practices without professional consultation. Composite MEI scores indicated that most individuals fell within the moderate exposure category, with a subset exhibiting high exposure characterized by frequent engagement with high-risk sources and behavioural influence. ConclusionThe MEI provides a novel and comprehensive framework for quantifying health misinformation exposure by integrating exposure patterns, source characteristics, trust, and behavioural outcomes. The index has potential applications in public health surveillance and intervention design. Further validation through large-scale studies is warranted to establish its reliability and generalizability.

BackgroundScalable, low-burden behavioral interventions are needed to address rising subclinical mental health symptoms. However, few randomized controlled trials have evaluated ultra-brief, remotely delivered, meditation using multimodal outcome assessment under real-world conditions. MethodsWe conducted a fully remote randomized controlled trial (ClinicalTrials.gov: NCT06014281) evaluating a focused-attention meditation intervention delivered via brief instructor training and independent daily practice. A total of 299 meditation-naive adults were randomized to immediate intervention or waitlist control in a delayed-intervention design. Participants practiced [≥]10 minutes daily for 8 weeks within a 16-week study. Outcomes included validated self-report measures, web-based cognitive tasks, and wearable-derived physiological metrics. ResultsAcross randomized and within-participant replication phases, the intervention was associated with significant reductions in anxiety and mind wandering, with effects remaining stable during 8-week follow-up. Improvements were greatest among participants with higher baseline symptom burden. Sleep disturbance improved selectively among individuals with poorer baseline sleep. Secondary outcomes, including rumination, perceived stress, social connectedness, and quality of life, also improved. Cognitive performance showed modest improvements primarily among lower-performing participants. Resting heart rate exhibited nominal reductions. ConclusionsAn ultra-brief, fully remote meditation intervention requiring 10 minutes per day was associated with sustained improvements in psychological functioning and smaller, baseline-dependent effects on cognition in a non-clinical population. These findings support digital delivery of low-dose meditation as a scalable preventive mental health strategy.

Background HIV remains a major public health challenge in South Africa, with gaps in early diagnosis and linkage to care driving onward transmission. Adolescent girls and young women face barriers to timely care, including stigma, privacy concerns, and limited clinic access, while healthcare providers work in resource-constrained settings with high client volumes. We evaluated the Self-Care from Anywhere (SCFA) toolkit, an AI-enabled intervention comprising an AI Companion for AGYW and a provider-facing Clinical Portal to support HIV prevention, testing, and linkage to care. The AI Companion is designed to complement and extend human-delivered services, particularly in resource constrained settings, rather than replace in-person counselling. Methods We conducted an exploratory study to assess the usability, feasibility, and acceptability of the SCFA toolkit in Gauteng Province (November 2024-May 2025). AGYW engaged with the AI Companion, and a subset completed a simulated HIV self-testing activity with AI-delivered counselling. Pre and post-intervention surveys, including the System Usability Scale (SUS), were administered. Usability testing of the Clinical Portal involved healthcare providers using the toolkit without formal training to capture first impressions. A subset of AGYW and healthcare providers participated in separate focus group discussions or in-depth interviews. Quantitative data were analysed using descriptive statistics, and qualitative data were analysed thematically. Results A total of 97 AGYW were enrolled; 75.3% had completed high school and 91.8% were unemployed or full time students. Most participants (85.6%) self-reported HIV-negative status, and 63.9% reported sexual activity in the past 12 months. The AI Companion demonstrated high usability (mean SUS 87.7, SD 12.7) and was perceived as acceptable and useful, particularly for its personalisation and confidentiality features. Healthcare providers had a mean age of 34 years (SD 6.5), with about half serving as HIV testing and screening counsellors. Most providers rated the Clinical Portal ease of use, comprehension, and client support as positive to very positive, though 23% expressed concerns regarding workflow efficiency and their ability to manage additional client volume. Providers also highlighted the Clinical Portal value for case management. Conclusion AI-powered digital health tools, such as the SCFA toolkit, show potential to enhance user engagement and support care delivery, with high usability and acceptability demonstrated among AGYW and healthcare providers. Continued user-centred refinement is essential to ensure these tools remain responsive to the evolving needs and care contexts of diverse user groups.

Introduction: Modeling when influenza epidemics typically occur can help countries optimize surveillance, time clinical and public health interventions, and reduce the burden of influenza. Methods: We used influenza virus detections reported during 2011-2024 by 180 countries to the Global Influenza Surveillance and Response System, excluding COVID-19 pandemic impacted years (2020-2023). We analyzed data by calendar year (week 1-52) or shifted year (week 30-29) time windows, based on when most influenza detections occurred in each country. For countries with sufficient data, we computed generalized additive models (GAMs) of each country's weekly influenza-positive tests to smooth and impute time series distributions. From these GAMs, we calculated each country's normalized weekly influenza burden. Country-specific normalized time series were grouped using hierarchical k-means clustering reducing the Euclidean distance between time series within clusters. We calculated cluster-specific GAMs to estimate average seasonal timing. Countries without sufficient data were assigned to a cluster based on population-weighted latitudinal distance to a cluster's mean latitude. Results: We identified five clusters, or epidemic zones, from 111 countries with sufficient data. The influenza burden in epidemic zones A and B was consistent with a northern hemisphere pattern, with most influenza detections occurring during October-April (A) and September-March (B), while epidemic zones D and E were characterized by southern hemisphere-like seasonal timing, with most influenza burden occurring during May-November. Epidemic zone C had most influenza burden occurring during September-March; most countries assigned to this cluster were in the tropics. Conclusion: Epidemic zones may serve as a useful tool to strengthen and optimize influenza surveillance for global health decision-making (e.g., during vaccine strain composition discussions) and to guide country preparedness efforts for seasonal influenza epidemics, including the timing of enhanced surveillance, as well as the procurement and delivery of vaccines and antivirals.

Objectives: Heart failure is a leading cause of mortality, necessitating identification of patients at increased risk needing intervention. In this study, we investigated if Fitbit data can reveal physiological trends associated with hospital visit risk. Materials and methods: Individuals with heart failure (n=249) were randomized into three arms for prospective 180-day monitoring. All arms received a Fitbit and wireless weight scale. Arm 1 received devices only; Arm 2 received a mobile app with surveys; Arm 3 received the app plus financial incentives. Results: 51 participants had hospital visits during the study period. These individuals took fewer steps (p=.002) and reported increased symptom severity (p=.044). Resting heart rate increased three days prior to a visit (p=.022). Baseline steps revealed a higher visit probability for less active participants (p=.003). Discussion and conclusion: Passive physiological monitoring can effectively identify individuals at risk of health exacerbation, demonstrating the potential of wearable devices for timely clinical intervention.

Purpose: Early recognition of deterioration in patients with suspected infection at the emergency department (ED) is important. Current clinical scoring systems show limited discriminative performance for early deterioration. Continuous electrocardiogram (ECG) recordings may offer additional dynamic physiological information that can enhance early prediction of deterioration in patients with suspected infection. Methods: We developed a multimodal, ECG-derived spectrogram-based pipeline to predict deterioration within 48 hours of ED admission. We used the first 20 minutes of ECG recordings for the spectrograms. We compared the model with the National Early Warning Score (NEWS), quick Sequential Organ Failure Assessment (qSOFA), a baseline model with vital parameters, sex, and age, and a Heart Rate Variability (HRV) derived model. Results: In this study, 1321 patients were included, of whom 159 (12%) deteriorated. The multimodal model combining baseline data with spectrograms showed the best overall performance, with an Area Under the Receiver Operating Characteristic (AUROC) of 0.788, followed by the baseline model (age, sex, triage vitals) alone, with an AUROC of 0.730. The HRV-only model and the qSOFA showed the lowest performance (AUROC 0.585 and 0.693, respectively). Conclusion: This study shows that ECG-derived multimodal spectrogram models outperform those based solely on vital signs and HRV features, as well as established clinical scores such as NEWS and qSOFA. Spectrogram analysis represents a promising approach to enhance early risk stratification and support clinical decision-making for patients with suspicion of infection in the ED.

Abstract Background People with disabilities use technology, like search engines, to seek health information online. This health information includes information on coronavirus disease, or COVID-19. COVID-19 remains a public health concern. Research shows that people with disabilities encounter frustrations, or "pain points," when seeking online information, but little is known about these specific pain points and who encounters them. Objective The goals of this study are to determine pain points for people with disabilities who seek health information online, and to assess how pain points impact the experience of technology use and information seeking. Methods Ten participants recruited from a prior quantitative survey completed the concurrent think-aloud study over a month-long period. Participants completed four online search tasks and narrated their experiences in real-time while doing so. Transcripts were stored in Taguette; thematic analysis was performed on these transcripts. Findings Participants were predominantly white, with three identifying as Asian. All ten participants reported having disabilities. Participants with attention deficit hyperactivity disorder (ADHD) reported distracting webpage layout, whereas participants with physical disabilities reported physical fatigue while navigating online information. All participants encountered AI-generated information; only one participant indicated trust in the AI-generated information. Other common sources of information included hospital and governmental webpages, peer-reviewed articles, and news and advertising results. News and advertising results were especially common with respect to search results for "COVID-19 vaccine." Themes identified included the following: accessibility/usability, AI-generated information, government/hospital and related sources of information, peer-reviewed articles, news and advertising, and sentiment and trust. Conclusions Information can be fatiguing, distracting, or otherwise difficult to navigate for people with diverse disabilities searching for COVID-19 related information online. Further work should incorporate user feedback from people with disabilities when designing online content.

This study evaluated the feasibility of collecting passive and active digital phenotyping data using the OverSight iOS application in individuals with inherited retinal diseases (IRDs), and explored associations between digital behavioural markers, visual function, and mental health. Participants with IRDs were recruited from Moorfields Eye Hospital (UK) and followed for 12 months. OverSight passively captures mobility data through HealthKit and typing-derived metrics through SensorKit. Participants completed patient-reported outcome measures (EQ-5D, NEI-VFQ-25, HADS, and MRDQ) within the app. Passive data included step count, walking speed, typing speed, total words typed, autocorrections, and sentiment word categories (anxiety, down, and health-related terms). Feasibility indices included enrolment, retention, and completeness of passive datastreams. Twenty-five participants were enrolled and 92% were retained at 12 months. Seventeen participants met the validity threshold for HealthKit data and 16 also met SensorKit thresholds. Median daily step count was 6,087, walking speed 1.18 m/s, and typing speed 2.19 characters/s. Age was negatively correlated with typing speed and anxiety-related word use, and photopic peripheral visual difficulty was negatively correlated with anxiety- and down-related word use. Digital phenotyping using OverSight was feasible over 12 months. Exploratory analysis suggest mobility, typing behaviour and sentiment markers may represent useful adjunctive indicators of functional vision and psychological outcomes in patients with IRDs.

BackgroundRecruitment remains a major barrier to timely clinical trial completion. Trialshub is an LLM-powered, chat-based platform intended to help users identify relevant trials and connect with coordinators to streamline recruitment workflows. ObjectiveTo evaluate the perceived usability and operational value of Trialshub, and identify implementation considerations for real-world deployment. MethodsA usability test was conducted at Morehouse School of Medicine for the Trialshub application. Purposively selected participants included clinical research coordinators and individuals with and without clinical trial search experience. Participants completed a pre-test survey assessing demographics, digital health information behaviors, and familiarity with AI tools, followed by a moderated usability session using a Trialshub prototype. Users completed scenario-based tasks (locating a breast cancer trial, reviewing results, and initiating coordinator contact) using a think-aloud protocol. Task ratings, screen recordings, and transcribed feedback were analyzed descriptively and thematically, and reported. ResultsParticipants reported high comfort with using digital tools and moderate-to-high familiarity with AI. Trialshubs chat-first design, guided prompts, and checklist-style eligibility display were perceived as intuitive and reduced cognitive load. Fast access to trials and the coordinator-contact workflow were viewed positively. Key usability issues included uncertainty at step transitions, insufficient cues for selecting results and next actions, and inconsistent system reliability (loading delays, errors, and broken trial detail pages). Participants also noted redundant questioning due to limited conversational memory, requested improved filtering/sorting, and clearer calls-to-action. All participants indicated that Trialshub has strong potential to meaningfully improve clinical trial processes. ConclusionsTrialshub shows promise for improving trial discovery and recruitment workflows, with identified design implications for real-world deployment.

Study objectives Consumer sleep technologies (CSTs) enable low-burden longitudinal sleep monitoring, and their output measures are often interpreted as equivalent to polysomnography (PSG) measures. We applied a measurement reliability-aware approach to determine whether CST-derived 'sleep' measures (1) are interchangeable or device-specific, (2) can reliably assess trait-like sleep characteristics of an individual, (3) can be reduced to latent principal components of sleep, and (4) can be used for classification and biomarker discovery. Methods Data from 74 older adults (20 people living with dementia [PLWD]) were collected at-home (upto 14 nights; Total=752nights) using four tools simultaneously: research-grade actigraphy (Axivity), a wearable (Withings Watch), a nearable (Withings Sleep Analyzer) and Sleep Diary, followed by one in-lab PSG assessment. We used repeated-measures correlation analyses, intraclass correlation coefficients (ICC), principal component analysis (PCA) and binary classification models to address our objectives. Results Single-night between-device correlations and correlations with PSG were moderate (0.3[&le;]r<0.7) for some duration- and timing-related measures, but other associations were weak (r<0.3). Seventy-one percent of sleep measures reached acceptable reliability (ICC[&ge;]0.7) within seven nights of aggregation, but the required aggregation window varied across measures, tools and between PLWD and Controls. Reliability-filtered PCA yielded stable and interpretable principal components, but Duration was the only component showing moderate between-device association. Principal components were successfully used to classify PLWD vs Controls but feature importance varied across devices. Conclusions Aggregation of CST derived measures across 7-14 nights, yielded reliable measures, most of which were device-specific, with duration being the only essential aspect transferable between devices.

IntroductionFamily Constellation Therapy (FCT) has been widely disseminated in clinical, public health, and judicial settings despite persistent concerns regarding its theoretical basis, safety, and the limited availability of rigorous randomised evidence supporting its clinical use. ObjectiveThe aim of this systematic review is to assess the effects of FCT across all clinical conditions, explicitly considering both benefits and harms; and summarise the characteristics of studies and intervention settings used in randomised controlled trials of FCT. MethodsFollowing a prospectively registered protocol (CRD420251136190), we conducted a systematic search of seven databases (PubMed, EMBASE, APA PsycInfo, CENTRAL, BVS, Web of Science, and CINAHL) and grey literature (ICTRP and ProQuest database) without language or date restrictions to identify published and unpublished randomised controlled trials of FCT. Study selection, data extraction, risk of bias (RoB 2), and certainty of evidence (GRADE) were performed in duplicate. Statistical analyses followed a prospectively registered analysis plan with prespecified criteria for data pooling and for handling analytical limitations. ResultsNo reliable evidence was found to support the use of FCT for any condition across both clinical and non-clinical samples. All trials included were judged to be at high risk of bias and all comparisons were rated as very low-certainty evidence. Concerns regarding potential adverse effects were identified, and the available data was insufficient to establish the effectiveness of the intervention, precluding any clinical recommendation. ConclusionClinicians, policymakers, and consumers should reconsider adopting FCT while reliable evidence is not available.

The goal of this work was to leverage a large corpus of text based psychotherapy data to create novel machine learning algorithms that can identify suicide risk in asynchronous text therapy. Advances in the field of natural language processing and machine learning have allowed us to include novel data sources as well as use encoding models that can represent context. Our models utilize advanced natural language processing techniques, including fine-tuned transformer models like RoBERTa, to classify risk. Subsequent model versions incorporated non-text data such as demographic features and census-derived social determinants of health to improve equitable and culturally responsive risk assessment, as well as multiclass models that can identify tiered levels of risk. All new models demonstrated significant improvements over our previous model. Our final version, a multiclass model, provides a tiered system that classifies risk as "no risk," "moderate," or "severe" (weighted F1 of 0.85). This tiered approach enhances clinical utility by allowing providers to quickly prioritize the most urgent cases, ensuring a more accurate and timely intervention for clients in need.

Early childhood mental health problems are common and difficult to detect due to reliance on caregiver reports of often unobservable symptoms. This study quantified threat response movement patterns during a 30-second laboratory threat induction task using wearable inertial sensors. Movement patterns were used to examine (1) changes in stimuli response across the task (task validity) and (2) associations with symptom severity (clinical validity). Sacral accelerometer and gyroscope data were analyzed from 91 children aged 4-8 years during the brief task, 48.4% of whom had a mental health diagnosis. Consistent with task validity, Turning Speed varied across task phases differing in potential threat intensity. Consistent with clinical validity, internalizing symptoms were associated with smaller Turning Angle, possibly indicating vigilance. This effect was moderated by comorbid externalizing symptoms, such that children with high internalizing and high externalizing symptoms exhibited larger Turning Angles, possibly indicating avoidance. Findings demonstrate that brief wearable-enabled tasks can capture subtle objective behavioral markers of threat responses and underscore the importance of considering comorbid symptom dimensions in early childhood mental health screening.

Background: Digital phenotyping using wearable devices and ecological momentary assessment (EMA) enables continuous, real-world monitoring of physiological and emotional states, but identifying high-risk stress states in real time remains challenging. We examined day-level associations between emotional distress and heart rate variability (HRV), and assessed whether daily physical activity modifies this relationship using longitudinal wearable and EMA data. Methods: The Smart Momentary Interactive Longitudinal Evaluation Study (SMILES) was a prospective cohort study conducted among STEM graduate students in the U.S. in 2025. Participants wore an Oura Ring Generation 3 continuously for five months and completed daily EMA surveys assessing emotional distress. The primary outcome was nightly HRV measured as the root mean square of successive differences and log-transformed for analysis. Quantile regression within a quadratic inference function framework was used to estimate associations at the 25th, 50th, and 75th percentiles of HRV, accounting for within-participant correlation and time-varying covariates. Findings: Thirty-one participants contributed 1,724 person-days of observation. High emotional distress was associated with lower HRV across the HRV distribution, with the strongest association observed at the lower HRV quantile ({beta} = -0.094, 95\% CI: [-0.111, -0.078]). A significant interaction between daily step count and emotional distress was observed across quantiles, such that higher physical activity was associated with higher HRV on high emotional distress days but not on low-to-moderate distress days. Interpretation: Integration of wearable-derived physiological data with EMA enables real-time identification of high-risk stress states in naturalistic settings. The observed buffering effect of physical activity during periods of elevated emotional distress suggests that wearable-guided, personalized just-in-time adaptive interventions, such as physical activity prompts, could be deployed to improve autonomic regulation and mental health.

Introduction Social media is a central part of young people's lives, yet research on its mental health effects remains mixed. We posit that these inconsistencies stem in part from treating youth as a homogeneous group, obscuring distinct behavioral patterns associated with divergent mental health and wellbeing trajectories. Objectives This study aimed to: (1) explore heterogeneity in social media engagement styles among U.S. youth aged 15-24; and (2) examine how these engagement styles are embedded within a broader system of mental health, wellbeing, emotional regulation, belonging, family and neighborhood context, and stress and adversity. Methods Data were drawn from a 2024 nationally representative cross-sectional survey of 2,563 U.S. youth, conducted as part of the Youth Mental Health Tracker initiative. We employed unsupervised clustering to identify five distinct social media engagement profiles. Subsequently, we used Bayesian network-based causal discovery to examine (a) upstream factors that emerge as drivers of engagement styles and (b) downstream outcomes influenced by profile membership in the learned system. Results Five profiles were identified: the Perpetually Plugged-In (31.3%), characterized by near-constant multifaceted social media use, for both positive and negative purposes across multiple domains of life; the Burned-Out Browsers (21.9%), with high exposure to negative and comparison-based content with frequent attempts to disengage; the Practical Navigators (20.7%) who engage in structured, goal-oriented use focused on learning, hobbies, and maintaining connections; the Positive Engagers (13.6%) with high social and identity-driven engagement; and the Light Touch Users (12.5%) who have low overall engagement and limited reliance on social media for connection, identity, or support. Causal analyses revealed that the Perpetually Plugged-In and Burned-Out Browsers had the worst mental health and wellbeing, with their engagement driven by different reasons. While both engagement profiles were influenced by similar psychosocial risk factors, they were distinguished by their dominant drivers: contemporaneous social stressors (bullying, discrimination, and emotional dysregulation) for Perpetually Plugged-In youth, versus adverse childhood experiences for Burned-Out Browsers. In contrast, Positive Engagers reported high social media engagement alongside the highest levels of social wellbeing, using social media for identity exploration and social support within a context of low cumulative stress and adversity. Conclusions Findings suggest that youth social media risk is not driven by intensity of use alone, but by the interaction between engagement style and offline emotional and social conditions. Policies focused solely on restricting access risk overlooking these differences and may inadvertently sever important sources of connection for many youth. Strategies should identify experiential risk signals while strengthening supportive contexts that enable healthier engagement. Overall, youth social media use is best understood as part of a broader psychosocial system, and recognizing this heterogeneity is essential for designing more targeted, equitable, and evidence-based interventions.

Study ObjectivesTo evaluate wearable sleep staging across sleep apnea severity, including very severe sleep apnea defined as an apnea-hypopnea index (AHI)[&ge;] 50 events/h, and to assess how training-set composition affects performance in this subgroup. MethodsWe analyzed 552 overnight recordings, 318 from the Sleep Lab Dataset and 234 from the Hospital Dataset. In the Hospital Dataset, 26.5% had very severe sleep apnea. We developed a deep learning model for sleep staging using RR intervals from wrist-worn photoplethysmography and three-axis accelerometry. Baseline performance was assessed by cross-validation under 5-stage and 4-stage staging. We examined night-level associations with AHI severity. We also compared the baseline model with an ablation model trained on the same number of recordings but with more Sleep Lab Dataset and lower-AHI Hospital Dataset recordings, evaluating both models in the very severe subgroup. ResultsIn 5-stage classification, Cohens kappa was 0.586 in the Sleep Lab Dataset and 0.446 in the Hospital Dataset. Under 4-stage staging, the gap narrowed, with kappa values of 0.632 and 0.525, respectively. In the Hospital Dataset, performance declined with increasing AHI severity. Among 62 recordings with very severe sleep apnea, reducing high-AHI representation in training lowered kappa from 0.365 to 0.303. ConclusionsWearable sleep staging performance declined across greater sleep apnea severity in this clinical cohort. Clinical utility may benefit from training data that better represent the target severity spectrum and from selecting staging granularity to match the intended use case. Statement of SignificanceRepeated laboratory polysomnography is impractical for long-term sleep apnea management. Wearable sleep staging could support scalable monitoring, yet its reliability in clinically severe sleep apnea has remained unclear. This study developed and evaluated a wearable sleep staging approach in both sleep-laboratory and hospital cohorts. The hospital cohort included many severe and very severe cases. Performance was lower in the hospital cohort and declined with greater sleep apnea severity. A coarser staging scheme reduced the gap between cohorts, and models trained without representative very severe cases performed worse in this target population. These findings highlight the value of severity-aware model development and motivate future multi-night home validation with reliability cues.

Aims: Youth loneliness is a prevalent global health concern with lifelong health ramifications. Schools, as children's primary peer environments, are promising settings for loneliness interventions. However, school-based interventions are highly heterogeneous and no review to date has evaluated their effect on loneliness specifically. Methods: A systematic review was conducted to identify studies of school-based interventions measuring loneliness as an outcome in children and young people aged up to 18. Meta-analyses were conducted using a random-effects model to pool effect sizes and examine the significance of intervention characteristics and study design. Reported implementation factors were extracted and narratively synthesised. Results: Thirty-eight studies were included in meta-analysis, of which 19 were randomized controlled trials, ten were non-randomized controlled, and nine were single group studies. A small-to-moderate effect estimate was found, Hedges' g = -0.42 [95% CI: -0.71, -0.13], p = .006, and sub-group analyses indicated that differences in study design and quality did not result in significantly different effect estimates. Psychological interventions, followed by social and emotional skills training, produced significantly higher effects estimates compared with other intervention types. Conclusions: Findings indicate that school-based interventions are effective in reducing youth loneliness. However, study heterogeneity, reporting inconsistencies, and a wide prediction interval indicates this finding should be interpreted with caution. Future research may benefit from improved measurement and reporting of implementation factors, particularly dosage and fidelity.

ObjectiveLong term monitoring of obstructive sleep apnea (OSA) severity may be relevant for several clinical applications. We developed a method for estimating the apnea-hypopnea index (AHI) using wrist-worn, reflective photoplethysmography (PPG). ApproachA neural network was developed to detect respiratory events using PPG and PPG-derived sleep stages as input. The development database encompassed retrospective data from three polysomnographic datasets (N=3111), including a dataset with concurrent reflective PPG recordings from a wrist-worn device (N=969). The model was pre-trained with (transmissive) finger-PPG signals from all overnight recordings and then fine-tuned to wrist-PPG characteristics using transfer learning. Validation was performed on the test portion of the development set and on a fourth, external hold-out dataset containing both wrist-PPG and PSG data (N=171). Performance was evaluated in terms of AHI estimation accuracy and OSA severity classification. Main ResultsThe fine-tuned wrist-PPG model demonstrated strong agreement with the PSG-derived gold-standard AHI, achieving intra-class correlation coefficients of 0.87 in the test portion of the development set and 0.91 in the external hold-out validation set. Diagnostic performance was high, with accuracies above 80% for all severity thresholds. SignificanceThe study highlights the potential of reflective PPG-based AHI estimation, achieving high estimation performance in comparison with PSG. These measurements can be performed with relatively comfortable sensors integrated in convenient wrist-worn wearables, enabling long-term assessment of sleep disordered breathing, both in a diagnostic phase, and during therapy follow-up.