Journal of Medical Internet Research

Social media has become a critical channel for public health communication during the COVID-19 pandemic, yet how official health messaging aligns with broader public discourse remains insufficiently understood. This study develops an end-to-end info-veillance framework to examine the dynamic relationship between Centers for Disease Control and Prevention (CDC) communications and general public discourse on social media. We analyzed 17,524 CDC tweets and 67,895 public discourse tweets. Biterm Topic Model (BTM) was used to extract topics from each corpus, and a novel topic consistency scoring system integrating cosine similarity with daily public topic prominence was developed to quantify temporal alignment between official health communication and public discourse. Two complementary sentiment measures were incorporated: expected sentiment (average emotional tone) and net sentiment (overall emotional intensity). Temporal relationships were examined using autoregressive integrated moving average with exogenous variables (ARIMAX) models. Results show that topic alignment increased over time across CDC topics, while expected sentiment remained consistently negative. Higher alignment was associated with immediate and delayed changes in expected sentiment and stronger emotional intensity in net sentiment based on ARIMAX results. These findings suggest that topic alignment reflects public attention rather than agreement with official communications, and is associated with more negative emotional responses. This framework provides a scalable, generalizable approach to investigate and evaluate public engagement with official health communication.

BackgroundIrritable bowel syndrome (IBS) contributes disproportionately to gastrointestinal-related emergency department (ED) utilization in South Korea, yet evidence on population-level interventions informed by patient-generated digital discourse remains limited. Recent social media analyses have identified dominant thematic concerns among IBS patients, including dietary triggers, symptom management, psychosocial burden, and information-seeking, suggesting actionable targets for digital self-management tools. ObjectiveTo evaluate the population-level impact of the Jang Geongang (, "Gut Health") digital self-management platform, whose content architecture was informed by topic modeling of IBS-related social media discourse, on IBS-attributed ED visits and unplanned hospitalizations, using a controlled interrupted time series (CITS) design. MethodsWe analyzed monthly aggregate claims data from South Koreas National Health Insurance Service (NHIS) spanning January 2018 to December 2024 (84 monthly observations). The Jang Geongang platform was launched in four pilot metropolitan areas (Seoul, Incheon, Daejeon, Gwangju) in July 2021, with eight non-pilot metropolitan areas serving as concurrent controls. Segmented regression with Newey-West heteroskedasticity and autocorrelation consistent (HAC) standard errors was used to estimate changes in level and trend of IBS-attributed ED visits per 100,000 insured population. Sensitivity analyses included autoregressive integrated moving average (ARIMA) transfer function models, varying pre-intervention windows, and leave-one-out control exclusion. ResultsThe CITS model estimated an immediate level change of -3.42 IBS-attributed ED visits per 100,000 (95% CI: -5.18 to -1.66, p < 0.001) following platform launch, and a change in monthly trend of -0.19 visits per 100,000 per month (95% CI: -0.31 to -0.07, p = 0.003), compared to control areas. By December 2024, the cumulative estimated reduction was 10.5 ED visits per 100,000 (23.8% relative reduction). Effects were concentrated in younger adults (19-39 years; level change: -5.14, p < 0.001) and IBS-D subtype visits (level change: -4.87, p < 0.001). ARIMA transfer function models corroborated these findings (immediate impact: -3.28, p = 0.001). Unplanned hospitalizations showed a smaller but significant reduction (level change: -0.84 per 100,000, p = 0.018). ConclusionsA digital self-management platform designed using social media derived IBS patient discourse insights was associated with sustained population-level reductions in IBS-attributed emergency utilization. Controlled interrupted time series analysis provides robust evidence for the public health impact of translating social media analytics into scalable digital health interventions.

BackgroundTimely detection of seasonal influenza outbreaks is critical for healthcare system preparedness and public health response. Although numerous studies have examined short-term influenza forecasting, fewer have operationalized prediction as a binary early-warning problem linked to actionable surveillance thresholds. This study evaluated the performance of traditional and machine learning models for detecting national influenza outbreak weeks using U.S. Centers for Disease Control and Prevention (CDC) ILINet surveillance data. MethodsWeekly national ILINet data from 2010-2025 were analyzed. Outbreak weeks were defined as those in which weighted influenza-like illness (ILIPERCENT) exceeded the 90th percentile of the 2010-2017 training distribution (threshold = 3.3932%). Predictors included three-week lags of ILIPERCENT and percent positive laboratory specimens, along with seasonal harmonic terms. Models were trained on 2010-2017 data and evaluated on a temporally held-out 2020-2025 test period. Performance metrics included area under the receiver operating characteristic curve (AUC), precision-recall area under the curve (PR-AUC), sensitivity, specificity, precision, and F1-score. FindingsOn the 2020-2025 test set, logistic regression achieved an AUC of 0.9964 and PR-AUC of 0.9868, with sensitivity of 1.0000 and specificity of 0.9516. XGBoost achieved an AUC of 0.9946 and PR-AUC of 0.9812, with sensitivity of 0.8939 and specificity of 0.9798. Both models demonstrated near-perfect discrimination between outbreak and non-outbreak weeks under strict temporal validation. InterpretationNational influenza outbreak early-warning can be implemented using publicly available CDC surveillance data with high discriminatory accuracy. Framing prediction as a threshold-based outbreak detection problem strengthens operational relevance and supports integration of predictive analytics into routine influenza surveillance and preparedness planning. Author SummarySeasonal influenza places a heavy burden on hospitals and communities each year, yet public health officials often rely on surveillance reports that describe what has already happened rather than signaling when activity is about to intensify. We examined whether routinely collected U.S. influenza surveillance data could be used to detect outbreak conditions earlier and more clearly. Using national data from the Centers for Disease Control and Prevention (CDC) covering 2010 to 2025, we compared a traditional statistical model with a machine learning approach to determine how accurately each could identify weeks when influenza activity exceeded a predefined outbreak threshold. Both approaches performed extremely well when tested on recent seasons, correctly distinguishing outbreak from non-outbreak weeks with high accuracy. Importantly, this framework translates weekly surveillance data into a practical alert signal rather than simply producing numerical forecasts. By linking model output to a clear outbreak definition, health departments and healthcare systems could use similar tools to support timely planning, communication, and resource allocation during influenza season.

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

Timely identification of hospital inpatients at risk of deterioration facilitates interventions to support their recovery. Many hospitals implement early warning scores to detect abnormal patient vital signs, such as the National Early Warning Score 2 (NEWS2). However, these are typically based on a snapshot of the most recent vital signs, rather than exploiting trends overtime that clinical intuition suggests may also be informative. Multiple approaches, including recently described methods, have been developed to predict patient deterioration from time series. We therefore compared the effectiveness of different mortality prediction models, including clinical scoring systems, classical machine learning models and state-of-the-art deep learning models using both snapshot and time series vital sign data. No significant improvement in model performance was observed using predictions from time series compared to using the last observation of the time series and non-temporal features such as demographics. Our study comprehensively compares different model types, and provides recommendations for developing predictive models and guidance for what evaluation is needed before considering deploying such models in inpatient care.

BackgroundAmbient AI scribes are rapidly entering clinical workflows, yet end-user perspectives remain underrepresented in the peer-reviewed literature. Online clinician communities offer an unfiltered window into adoption barriers, perceived benefits, and product-level concerns. ObjectiveTo characterise themes and sentiment in clinician discourse on ambient AI scribes across professional Reddit communities. MethodsWe scraped posts from ten clinically oriented subreddits using twelve AI scribe related queries via the public Reddit JSON API. A two-tier keyword filter retained posts mentioning at least one AI scribe term and one clinical or workflow term. Texts were embedded with all-MiniLM-L6-v2, reduced via UMAP, clustered with HDBSCAN, and labelled using BERTopic with c-TF-IDF keyword extraction. Noise topics matching predefined off-topic patterns (for example, residency match, finance) were removed. Themes were assigned concise labels via Claude Sonnet 4. Sentiment was classified per post using cardiffnlp/twitter-roberta-base-sentiment-latest. ResultsAfter filtering, 176 unique relevant posts from seven active subreddits were retained, with r/FamilyMedicine (n = 64) and r/healthIT (n = 34) dominating. BERTopic produced 12 coherent themes spanning workflow integration, vendor comparison (DAX, Heidi, Freed, Abridge), HIPAA and privacy, mobile and device use, templates and formatting, and research versus clinical use. Overall sentiment was 61.4% neutral, 21.6% positive, and 17.0% negative. The most net-positive theme was DAX/Nuance/AI tools (about 55% positive); the most net-negative were charting fatigue and the freed-AI-scribes discussion thread (about 37 to 40% negative). Engagement (median upvotes and comments) was highest for tool-comparison and pricing themes, indicating salience of practical adoption questions. ConclusionsClinician sentiment toward ambient AI scribes is cautiously favourable but dominated by neutral, problem-solving discourse. Vendor selection, cost, HIPAA compliance, and EHR integration are the most actively debated issues. These insights can inform implementation strategy, vendor benchmarking, and policy guidance for ambient documentation tools.

Background: Large language models (LLMs) are increasingly used in telehealth, but their safety in antibiotic prescribing remains uncertain, particularly in the presence of patient misinformation. Methods: A cross-sectional analytical study evaluated 5,000 responses from five chatbot models using 1,000 primary-care vignettes of mild infections. Guideline adherence, overprescribing, misinformation effects, and safety behaviors were assessed. Inappropriate prescriptions were classified using the WHO AWaRe framework. Results: Overall, 76.2% of responses were guideline-concordant, while 6.6% showed unprompted overprescribing and 17.2% were influenced by misinformation. Some models were more vulnerable to misinformation than others. Although most responses correctly noted that antibiotics do not treat viral infections, fewer advised consulting a doctor, and warnings against self-medication were rare. Many inappropriate prescriptions involved broad-spectrum antibiotics. Conclusion: LLMs show potential in telehealth but remain prone to misinformation and inappropriate prescribing. Stronger guideline integration and clinical oversight are necessary to ensure safe use. Keywords: antimicrobial stewardship; large language models; telehealth; antibiotic prescribing; misinformation; clinical safety

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.

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. Author SummaryMental health symptoms can change between appointments yet care often depends on periodic visits and patient recall. Devices such as smartwatches and other digital tools can continuously collect information, from mood and sleep to activity and related measures, offering a possible way to support care outside the clinic. While patients are often seen as the main users of these tools, clinicians play a central role in deciding whether such technology is implemented in care. This study interviewed 33 mental health clinicians, including family physicians, psychiatrists, and psychologists, about their views on using patient-generated streaming data in routine care. Clinicians saw promise in these data as they help track changes over time, support discussions with patients, and provide additional insight between visits. However, they also described important barriers, including managing large amounts of data, limited integration with health record systems, uncertainty about data quality, and concerns about privacy and regulation. These findings suggest that successful implementation of streaming data in mental health care will depend on designing systems that are clinically relevant, easy to interpret, and supported by appropriate safeguards and infrastructure.

BackgroundOnline professional learning offers a scalable alternative to traditional face-to-face learning, but there are doubts regarding how well it works and when it works best. This review assessed the effectiveness of online professional learning interventions on healthcare professionals knowledge and skill acquisition. MethodsWe conducted a systematic review and meta-analysis of randomised controlled trials that compared online professional learning against static controls or face-to-face controls. We searched MEDLINE Complete, Scopus, Embase, CENTRAL, and PsycINFO from inception to January 31, 2025. Eligible studies included practising healthcare professionals in any clinical setting that measured knowledge or skill acquisition related to patient care. Data was extracted in duplicate, with disagreements resolved through discussion or by a third reviewer. We used multilevel meta-analyses to estimate the overall effect size and conducted moderation analyses for pre-specified factors. The study protocol was pre-registered on the Open Science Framework (OSF; https://osf.io/46zav). FindingsOf 55,376 records; 171 studies (391 effects, 25,412 participants) met the inclusion criteria. Online learning significantly improved knowledge and skill acquisition compared to static controls (g = 0.93, 95% CI [0.78,1.07], p < 0.001; I{superscript 2} = 89.8%), with larger effects in lower-middle income countries (g = 1.30, 95% CI [0.88, 1.72]) than in high income (g = 0.75, 95% CI [0.63, 0.86]). Online learning also significantly improves outcomes compared to face-to-face instruction (g = 0.45, 95% CI [0.31,0.59], p < 0.001; I{superscript 2} = 85.92%), with larger effects for knowledge outcomes (g = 0.46, 95% CI [0.33, 0.59]) than skills outcomes (g = 0.20, 95% CI [0.04, 0.36]). Effects did not differ significantly by profession, clinician experience, clinical setting, intervention characteristics or the learning design features (all p > 0.05). No studies had low overall risk of bias, and some evidence of publication bias was found. InterpretationFrom this body of evidence, we identified that online learning appears to improve healthcare professionals knowledge and skill acquisition, exceeding traditional teaching methods. Healthcare organisations can be confident implementing or expanding online professional learning to improve knowledge and skill acquisition. FundingNo funding

Support from peers has long been considered an alternative support resource than professional healthcare ones. Despite the inconclusive findings of previous studies regarding the effects of peer support, the integration of Peer Support Networks (PSNs) for youth and adolescents appears to offer promising outcomes. However, many existing digital peer support systems operate as proprietary platforms, lacking transparency in monitoring the efficacy of support and in understanding how personality traits influence outcomes. However, many existing digital peer support systems operate as proprietary platforms lacking transparency in monitoring the effect of peer support and understand the influence of personality traits on its outcomes. To address these limitations, we utilized our research platform, Peer2S, a digital PSN designed to facilitate connections based on shared lived experiences while simultaneously monitoring users mental well-being and personality traits. We conducted a four-week within-subjects experiment with 28 Japanese university students to examine the PSN systems impact on anxiety and perceived social support. Following a two-week baseline control period, participants interacted with the system for two weeks. Pre- and post-intervention assessments utilized generalized anxiety and multidimensional social support measures, alongside personality evaluations. The results indicated that participants experienced a significant reduction in anxiety after using the system, whereas no significant changes occurred during the control period. Perceived general social support showed a borderline significant increase, though specific college-context support dimensions remained unchanged. Furthermore, multiple regression analysis revealed that personality traits moderated anxiety outcomes. Contrary to typical protective associations, higher agreeableness significantly predicted increased anxiety during the intervention, which may reflect cultural tendencies toward conflict avoidance and over-accommodation in Japan. Conscientiousness demonstrated a marginally significant protective effect against anxiety, while personality traits did not predict changes in perceived social support. These findings suggest that short-term, algorithmically mediated peer support can yield measurable improvements in mental well-being, particularly in reducing anxiety. Moreover, the varying impacts of personality traits highlight the necessity of considering sociocultural contexts when designing and deploying digital mental health interventions. Authors summaryThe formation of social bonds is often selective, established through shared values, cultural interests, or significant life experiences among "peers." In some populations such as adolescents and young adults, peer support is regarded as a promising source of empathy, understanding, and psychological support. We report a study conducted using our customized digital peer matchmaking system with Japanese university students to examine if this novel approach to peer support impacts mental well-being. We found that after just two weeks of using the system, participants experienced a significant reduction in their anxiety levels. We also dove deeper to look at if individual personality traits influence their use outcomes. Interestingly, our results revealed that highly agreeable individuals actually experienced increased anxiety while using the system. In a Japanese cultural context, this may occur because agreeable users tend to avoid conflict and over-accommodate others at their own expense. Ultimately, our research demonstrates that matchmaking algorithms can effectively facilitate digital peer support to improve mental well-being, provided we carefully consider how different personality traits and cultural backgrounds shape user experiences.

Introduction: Understanding the factors influencing perceptions of cancer-related information is crucial for improving public health communication. This study explores the association between perceived difficulty in understanding information related to cancer (Cancer info Hard to Understand) and concerns about the quality of cancer-related information (Concern about Cancer Info Quality) with the extent of difficulty in comprehending medical statistics information (Understanding Medical Statistics). Methods: Data came from the 2022 Health Information National Trends Survey (HINTS). The cross-sectional study included 1972 participants with a response rate of 67.36% for Cancer info Hard to Understand, and 65.31% for Concern about Cancer Info Quality. We investigated the effect of Understanding Medical Statistics on Cancer info Hard to Understand, and Concern about Cancer Info Quality using univariate and multivariable logistic regression models with survey weights. The multivariable logistic regression model was adjusted for age, gender, ethnicity, marital status, education level, employment history, confidence in internet health resources, and social media. The chi-square test was used to measure the association between the predictors and the outcome. Results: Individuals finding medical statistics hard to understand were more likely to be concerned regarding the quality of the cancer-related information (AOR=1.74, 95% CI: [1.20, 2.52]) and also found cancer-related information difficult to comprehend (AOR=1.89, 95% CI: [1.19, 3.00]). Also, the influence of social media on health information seeking was significantly associated with Concern about Cancer Info Quality (AOR=2.24; 95% CI: [1.33, 3.76]), and Cancer info Hard to Understand (AOR=2.84; 95% CI: [1.61, 5.03]). Conclusion: This study highlights the critical role of understanding medical statistics in shaping perceptions of cancer-related information. From an epidemiological perspective, enhancing statistical literacy is essential for making informed health decisions, addressing health disparities, and designing effective, targeted cancer communication strategies.

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 [&ge;]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.

Background Home Test-to-Treat (HTTT) programs deliver timely antiviral treatment for acute respiratory infections, including COVID-19 and influenza, through at-home testing and telehealth. Because access is often measured by visit occurrence, variation in how and when care is delivered may be overlooked. We hypothesized that telehealth access follows distinct process-based patterns. Methods We analyzed de-identified encounters from the national HTTT program (September 2023-July 2024); 6,213 of 8,160 eligible individuals remained after exclusions for missing data. Phenotypes were derived by k-means clustering of standardized variables capturing encounter timing, modality preference, process duration, and sociodemographic and digital access attributes. Ten-day surveys assessed symptom duration and healthcare utilization. Results Three phenotypes emerged: Delayed/Disrupted Access (n = 1,537; 24.7%), Digitally Engaged but Socioeconomically Vulnerable (n = 1,460; 23.5%), and Mainstream Access and Efficient Utilization (n = 3,216; 51.8%). Mean process duration differed (15.93 [SD 3.84] vs 3.69 [3.31] vs 2.87 [2.41] hours; p < 0.001). Synchronous preference was lowest in the Digitally Engaged group (22.9%); antiviral prescribing was high (88.6%-91.9%). Among 10-day respondents (n = 1,023), symptom duration did not differ. Emergency department visits were most frequent in the Digitally Engaged group (2.3% vs 0.0% and 0.5%; p = 0.02) and urgent care in the Delayed/Disrupted group (5.8% vs 4.1% vs 2.0%; p = 0.02). Conclusions Telehealth use in a national HTTT program formed distinct phenotypes defined by timing, modality, and care-process efficiency. Evaluating equity requires attention to how and when care is delivered, not simply whether it occurred.

Background: Statins are key to preventing atherosclerotic cardiovascular disease and lowering low-density lipoprotein cholesterol and cardiovascular events. However, skepticism regarding their safety and value persists and is increasingly influenced by social media. TikTok has emerged as a major source of health information, but its content varies in quality and accuracy. This study evaluated the quality, attitudes, misinformation, and engagement of statin-related content on TikTok. Methods: Public TikTok videos were collected using predefined search terms and coded by creator type, thematic content, and overall attitude. Video quality was assessed using the DISCERN instrument, the Patient Education Materials Assessment Tool for Audiovisual Materials, and the Global Quality Score. False or misleading claims were independently reviewed by two cardiology fellows. Associations between engagement and quality were also examined. Results: Of 1,349 screened videos, 258 met inclusion criteria. Most were educational (91.0%), with non-physician healthcare providers (34.5%) as the largest creator group. Risks or negative effects were discussed more often than benefits (63.2% vs 42.2%), and 39.5% contained at least one false or misleading claim, most often from complementary and alternative medicine providers and wellness promoters. Quality differed by creator type across all instruments, with physician-created content scoring highest. Video popularity showed minimal association with informational quality. Conclusion: Statin-related TikTok content frequently emphasizes harms, often contains misinformation, and varies substantially in quality by creator type. Greater involvement of healthcare professionals on social media may help improve digital health literacy and counter misleading information about statin therapy.

BackgroundDigital health dashboards can enhance health system performance by transforming routinely collected data into actionable insights for decision-making. In low-resource settings, however, their effectiveness depends not only on the relevance of indicators but also on system reliability within constrained digital infrastructure. Neonatal mortality remains a major global health challenge, with the highest burden in low- and middle-income countries, where many deaths are preventable through timely, evidence-based interventions. Continuous monitoring of care processes and outcomes is therefore essential. To support this need, we developed the NEST360 Implementation Tracker (NEST-IT) using R Shiny to support quality improvement across more than 100 hospitals in sub-Saharan Africa. As the platform scaled to over half a million records and increasing concurrent users, performance constraints emerged, particularly in hospitals with limited computing resources, threatening timely access to critical information. ObjectiveThis study aimed to describe optimization strategies applied to the NEST-IT dashboard and evaluate their impact before and after implementation. MethodsA structured optimization process was implemented following established R Shiny performance principles. Dashboard profiling was first conducted to identify key bottlenecks, after which targeted improvements were applied to improve efficiency and responsiveness. A quasi-experimental pre-post evaluation (December 2023-August 2024) assessed performance using three indicators: server processing time, visualization rendering time (VRT), and Time to First Byte (TTFB). Metrics were measured repeatedly during one-month baseline and post-optimization periods and summarized using mean values. ResultsFour primary bottlenecks were identified: delayed server responses, slow visualization rendering, inefficient data handling, and inconsistent device performance. Following optimization, interactive plot load time decreased from 10.1 to 2.7 {+/-} 0.6 seconds (73.3% improvement). Visualization rendering improved from 3.61 to 1.62 seconds, while server processing time fell from 2.3 {+/-} 0.7 to 0.8 {+/-} 0.3 seconds. TTFB improved from 1.9 {+/-} 0.4 to 0.6 {+/-} 0.2 seconds, and system uptime increased from 92.5% to 99.2%. ConclusionPerformance optimization substantially improved dashboard responsiveness, enabling timely access to critical neonatal information in resource-constrained hospital settings. The findings provide practical, evidence-based framework for improving the performance of R Shiny dashboards and demonstrate scalable strategies for delivering reliable digital decision-support tools in low-resource health systems.

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.

Withdrawal StatementThis article has been withdrawn by medRxiv because it was submitted with false information.

BackgroundUniversity students often struggle to maintain healthy sleep, physical activity, and screen usage due to academic pressures and irregular schedules. Ecological momentary assessments (EMAs) and interventions (EMIs) offer real-time, context-aware opportunities to monitor and promote healthier behaviors. This pilot study aimed to evaluate the feasibility of a hybrid study design combining continuous monitoring with sequential randomized controlled trials (RCTs) evaluating EMIs targeting movement behaviors among university students. MethodsThe MOVE@NUS pilot study (September 2024 - January 2025) embedded three sequential RCTs, each targeting one behavior: sleep, physical activity, or screen time. For each RCT, participants were randomized on a 1:1:1 schedule (control, intervention 1, intervention 2). Eligible participants were first-year undergraduates, aged 18-25 years, who regularly used an iPhone and an Apple Watch. Smartwatches (primary) and smartphones (supplementary) passively and continuously tracked behaviors. EMAs (eight 3-day bursts) and web-based surveys captured self-reported behaviors and participant experience. All assessments were self-administered, and no provider assistance was involved. ResultsOf 229 students who met screening criteria, 65 enrolled (mean age 20.4 {+/-} 1.5 years; 53.8% female). Questionnaire completion was high (baseline: 100.0%, midway: 89.2%, endpoint: 86.2%). EMA engagement decreased from 88.7% (first burst) to 49.2% (final burst). Passively monitored data were obtained from 62 participants (95.4%) with a mean tracking duration of 67.8 days (range: 11 to 114). Data completeness was highest for passively captured measures of physical activity, while more participant-dependent measures, such as manually uploading screen time screenshots, showed greater attrition. Overall satisfaction was 78.9% for sleep, 70.6% for physical activity, and 60.0% for screen time. ConclusionsThis hybrid study design is feasible and acceptable among university students, with successful integration of self-reports and passive tracking. Variations in engagement and data completeness highlight areas for optimization in future large-scale digital cohort studies. Trial registrationClinicalTrials.gov ID NCT06597890 First Posted: 2024-09-19.