Journal of Medical Internet Research

Background: Social media videos have become one of the major sources of health information for individuals living with chronic diseases. Although numerous cross-sectional studies have evaluated the quality of health-related videos across different platforms, the overall quality of chronic disease-related videos and the determinants underlying quality variation remain unclear. Objective: To systematically evaluate the quality of chronic disease-related health videos across major global and Chinese social media platforms and to identify potential determinants of video quality using multivariable meta-regression. Methods: This systematic review and meta-analysis searched PubMed, Embase, and Web of Science from database inception to April 30, 2026, for cross-sectional studies evaluating Chinese-and English-language health videos. Scores from the DISCERN instrument, the Global Quality Scale (GQS), and the Journal of the American Medical Association (JAMA) benchmark criteria were standardized to a 0-100 scale and quantitatively synthesized using random-effects models. Prespecified subgroup analyses and multivariable meta-regression were conducted to explore potential sources of heterogeneity, including platform region, platform type, disease category, video duration, professional background of content creators, and audience engagement. Results: A total of 88 studies involving 18,688 videos were included. Overall methodological quality was suboptimal, with a pooled standardized DISCERN score of 50.61 (95% CI, 48.04-53.18), accompanied by substantial between-study heterogeneity (I2 = 99.3%). Videos hosted on international platforms achieved significantly higher quality scores than those on Chinese platforms (54.68 vs. 48.21; P = 0.008). Multivariable meta-regression demonstrated that conventional predictors-including video duration, the proportion of physician creators, and audience engagement-were not independently associated with video quality (P > 0.05). Importantly, the final model explained only 9.06% of the between-study heterogeneity (R2 = 9.06%), indicating that conventional content- and creator-level characteristics account for only a small proportion of the observed variability in video quality. Conclusions: Traditional predictors, including creator professionalism, video duration, disease category, and audience engagement, have limited ability to explain variation in the quality of online health videos. Although platform region emerged as the only significant moderator, the multivariable model explained only a small fraction of the observed heterogeneity, suggesting that the principal determinants of health information quality remain largely unexplained.

Background Promoting at-home tests (e.g., for COVID-19) using chatbots may be a novel and scalable way to improve uptake across underserved populations. Objective The objective of this study was to assess the navigational patterns (i.e., sequence of interactions) of underserved populations when using a chatbot designed to provide education on COVID-19 testing and free order access for at-home COVID-19 test kits. Methods The study was a descriptive analysis of the original data of the chatbot intervention of the SCALE-UP II trial, which compared different digital health modalities (i.e., chatbots versus simple text messages) to deliver free at-home COVID-19 test kits to minority populations in Utah. SCALE-UP II (registration numbers NCT05533918; NCT05533359) was a multisite, pragmatic clinical trial with patients randomized in a 2x2x2 factorial design (smartphone study) to receive (1) chatbot or text messaging, (2) the option to request patient navigation, and (3) intervention frequency every 10 or 30 days. All other participants were randomized in a 2x2 factorial design (nonsmartphone study) to receive the option to request patient navigation and intervention frequency every 10 or 30 days. Eligible patients (1) had an appointment at one of the participating community health centers (CHC) in the last 3 years, (2) were 18 years and older, and (3) had a valid cellphone number recorded in the CHC electronic health record (EHR). The trial enrolled 2117 in the smartphone study and 31,439 in the nonsmartphone study. In the smartphone study, the proportion of participants who requested test kits in the Chatbot arm was lower than in SMS text messaging. In the nonsmartphone study, test kits was higher if they were messaged every 10 days. Sources of funding included the National Institute on Minority Health and Health Disparities (NIMHD) of the US National Institutes of Health (NIH) grant number 5U01MD017421 and by awards from the National Cancer Institute of the NIH (P30CA042014) and the Huntsman Cancer Foundation. Results: Of 1,051 patients randomized to the chatbot intervention, 309 (29%) launched the chatbot, 196 (63%) interacted with it, and 186 (60%) started the COVID-19 test kit ordering process. Among those who launched the chatbot, 170 (55%) completed a test kit order. One patient (0.3%) accessed the chatbot educational content. The median age was 51, with 66% female, 54% Latino/a, 55% uninsured, and 86% located in an urban area. Conclusion: Ordering of COVID-19 test kits among underserved patients who interacted with the chatbot was high. Thus, chatbots may represent a viable approach to reach underserved populations as a part of public health response in a pandemic. All patients except one placed orders without reviewing educational content. Chatbot design should identify and minimize the number of steps for patients to achieve a specific goal.

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.

Background: Older adults with vision impairment often experience barriers to using digital technology. The indirect associations between vision impairment and digital access and skills via digital self-efficacy and frustration among older adults remain largely unknown. Objective: This study aimed to 1) explore factors associated with digital access, skills, self-efficacy, and frustration among older adults with vision impairment; 2) examine associations between vision impairment and digital access, skills, self-efficacy, and frustration among older adults; and 3) examine whether digital self-efficacy and frustration may help explain associations between vision impairment and digital access and skills among older adults. Methods: This was a cross-sectional study using nationally representative data from the Health Information National Trends Survey (HINTS) 2024. Respondents aged 60 and older were included. Vision impairment was assessed using a self-reported item. Outcomes included self-reported digital access, skills, self-efficacy, and frustration. Survey-weighted multivariable logistic regression and generalized structural equation modeling were conducted, adjusting for age, sex, race/ethnicity, education, and the number of comorbidities. Results: Among 3,149 older adults (mean [SD] age, 70.7 [10.0] years; 45.6% female), 7.1% (n=223) reported vision impairment. Among older adults with vision impairment, 65.6% (95% CI, 53.5% to 75.9%) used the internet daily, and 79.5% (95% CI, 66.8% to 88.2%) used a smartphone in the past 12 months. In multivariable logistic regression analyses among older adults with vision impairment, older age was associated with lower odds of daily internet use (OR, 0.84; 95% CI, 0.79 to 0.90), smartphone use (OR, 0.85; 95% CI, 0.75 to 0.97), wearable device use (OR, 0.88; 95% CI, 0.79 to 0.97), and using the internet to send a message to a healthcare provider (OR, 0.87; 95% CI, 0.80 to 0.93). Older adults who self-identified as racial and ethnic minority groups (e.g., Black/African American, Hispanic) had lower odds of daily internet use (OR, 0.15; 95% CI, 0.05 to 0.50) and using the internet to send a message to a healthcare provider (OR, 0.17; 95% CI, 0.04 to 0.73) compared with Non-Hispanic White older adults. Vision impairment was associated with lower odds of daily internet use (OR, 0.60; 95% CI, 0.37 to 0.99) and digital self-efficacy (OR, 0.53; 95% CI, 0.32 to 0.86). Digital self-efficacy was associated with higher odds of daily internet use (OR, 2.95; 95% CI, 2.04 to 4.26). Generalized structural equation modeling identified an indirect association between vision impairment and daily internet use via digital self-efficacy (coefficient, -0.68; 95% CI, -1.24 to -0.12). Conclusions: Findings suggest that reduced digital self-efficacy may help explain the observed association between vision impairment and daily internet use among older adults. Interventions targeting digital self-efficacy, including accessible interface designs, personalized coaching, and peer support, may help bridge the digital divide among older adults with vision impairment.

TikTok has become a significant source of health information, and concern has grown about AI-generated content (henceforth, 'AIGC') as a vehicle for health misinformation. Where AIGC presents realistic-appearing people giving health advice, disclosure labels are the viewer's only reliable cue that what they are watching is synthetic. This research letter compares AI label metadata across 128,016 mental health-related TikTok videos and 4,924 videos from a network of 50 profiles posting exclusively AI-generated mental health content to evaluate how much content reaches audiences undisclosed. In a keywords-based collection, fewer than a percent of TikTok videos about mental health carried an AI label, but in profiles containing purely AI-generated content, just over 9 in 10 videos (90.23%) were neither labelled by the creator nor identified by TikTok's automatic detection. Additionally, in the keyword collection, automatic detection produced the majority of labels, while in confirmed AI-generated content from 50 profiles, it accounted for just three of the 481 labelled videos. These findings highlight the challenging landscape of AI disclosure and labelling and raise questions about where automatic detection is failing.

Seizure diaries are one of the most useful sources of information in the management of epilepsy, however patient engagement with them can be sporadic. Sustained participation with seizure diaries affects the completeness and reliability of self-reported data, so it is vital to be able to measure engagement. To facilitate this, we create a multidimensional engagement metric with which to characterize how patients interact with their seizure diary. We utilise data from the Helpilepsy, a seizure diary application, common features found in application engagement metrics in business settings, and well understood clinical features to do this. Clustering is then performed to isolate different user groups based on how engaged they are, and these groups are studied to understand what drives the differences in engagement. We found three groups emerge from the clustering: low, medium and highly engaged users. Investigating these groups further, we put together a ``profile" for highly-engaged users. We find that they tend to be older at the point of diagnosis, and have had epilepsy for longer than the other users. We also find they tend to have had more medications, have higher doses of common anti-seizure medications, and they have more medications typically given to those with refractory epilepsy. The implications for e-diary design are that more attention should be given to those newer to epilepsy in the onboarding phase. Also, engagement is not necessarily based on just the upload of seizures, with other features of an e-diary being important to be filled in.

Patients with a brain tumour receive evidence-based clinical care in Australia but a focus on supportive care, including social connection, is often deficient. Digital health platforms hold promise to support these patients and their carers. Existing platforms often lack end-user co-design, evidence-based development and rigorous evaluation. Recognising this unmet need, we co-designed Brain Tumours Online, a digital supportive care platform to streamline access to educational resources, symptom management tools, and peer support for patients, carers, and healthcare professionals. In this article, we present our evaluation approach for Brain Tumours Online to advance methodological thinking in the evaluation of multi-faceted, co-designed digital health platforms. In contrast to standardised procedures in clinical trials, digital health interventions such as supportive care platforms are more complex due to their interactive nature, no prescriptive protocols for usage and the dynamic content of web-based information. Thus, traditional evaluation approaches often fall short in evaluating such multi-faceted digital health supportive care platforms. To address these challenges, we developed a bespoke, logic-modelling based evaluation approach to assess the usability, engagement, impact, and economic value of our platform. Our pragmatic but rigourous evaluation approach required the adaptation of existing evaluation frameworks, subject-matter, and lived experience expert knowledge. Our implementation science and co-design approach are shared in different papers. Our study outcomes will also be shared in a separate paper. In the current paper, we share our approach to the evaluation of Brain Tumours Online and provide insights that may be of value for other researchers interested in the nuances of trialing multi-faceted digital health supportive care platforms.

Background: Physician clinical information-seeking behavior has been studied in high-income settings but remains poorly characterized in Latin America. Objective: To describe clinical information needs, geographic variation, and temporal usage patterns among physicians using a semantic clinical search platform across Latin America. Methods: We conducted a retrospective query-log analysis of physician searches performed between May 2025 and June 2026. The dataset included 235,803 queries generated by 9,443 physicians across 20 Latin American countries. Query topics were classified using a 10-category canonical taxonomy derived from platform metadata and validated through manual review of a stratified random sample of 200 queries. Results: The physician activation rate was 84.4%. Among categorized queries, Management Plan (35.6%), Differential Diagnosis (20.6%), and Work-up and Test Selection (11.4%) accounted for 67.6% of all searches. This category hierarchy was broadly consistent across the 17 countries included in country-level analyses despite differences in cohort size and healthcare settings. Query intensity was also similar across countries, with a mean of 29.6 queries per active physician over the study period. Manual validation confirmed agreement between reviewer assessment and taxonomy assignment in approximately 93% of sampled queries. Conclusions: Clinical information seeking among Latin American physicians was dominated by management planning, diagnostic reasoning, and test selection, with broadly consistent patterns across countries. These findings provide a regional behavioral baseline for understanding physician information needs in semantic clinical search systems.

While the public perceives e-cigarettes as less harmful than combustible tobacco, little is known about their specific health concerns regarding vaping. We demonstrate a data-driven strategy to discover the public's health concerns about vaping e-cigarettes expressed on social media. We obtained all public posts from the largest e-cigarette-related subreddit, r/electronic_cigarette, from its inception on September 17, 2008, through April 1, 2022 (N = 10,403,433). We identified health concerns attributed to vaping by (a) selecting all cause phrases containing "cause" and its inflections, (b) calculating the empirical frequency ratio of words and bi-grams occurring in these phrases relative to random phrases, (c) retaining the 10% of words with the greatest empirical frequency of occurring in cause phrases, and (d) annotating this sample for health-relevant concerns and their subjects. In total, 76,342 posts contained cause phrases, with increased volume over time. Of the 425 words most strongly associated with cause phrases compared to random phrases, 53.4% (95%CI, 48.7-58.1) were identified as health-relevant. The top health-related concern was lipoid pneumonia, cited in 5.9% (95%CI, 5.0-6.8) of all cause phrases, followed by pneumonia (4.1%; 95%CI, 3.3-4.9), and nausea (2.7%;95%CI, 2.0-3.4). The top health concern subjects were respiratory, representing 23.7% (95%CI, 18.5-29.5) of all cause phrases, followed by gastrointestinal (12.7%; 95%CI, 8.8-17.2) and cardiovascular (8.5%; 95%CI, 5.3-12.3) concerns. Other subjects included neurological, dermatological, oral health, sexual health, psychiatric, oncologic, addiction, and sleep concerns. Because our strategy relies on data-driven techniques, our analysis can be integrated into routine social media monitoring and applied across different types of social media and text data, potentially leading to more timely identification of emerging concerns and a broader understanding across platforms. As a result, experts can craft messaging that accounts for current perceptions held by the public using our method.

Medical discrimination may alter how patients relate to health information sources following adverse care encounters. We examined whether discrimination experience is associated with selective erosion of institutional health trust and with compensatory digital health engagement, using nationally representative data from the Health Information National Trends Survey (HINTS) 6 (2022; n=6,252) and HINTS 7 (2024; n=7,278). Survey-weighted modified Poisson regression estimated prevalence ratios (PRs) for binary high-trust outcomes, and survey-weighted ordinary least squares estimated coefficients for continuous outcomes; jackknife replicate weights (50 replicates) provided variance estimates. Discrimination was associated with substantially lower probability of high trust in the healthcare system (PR=0.39; 95% CI 0.30-0.52) and physicians (PR=0.85; 95% CI 0.77-0.94), with no significant association for trust in scientists, government, family, or religious organisations. The clinical-institutional pattern replicated in HINTS 6, which additionally showed reduced trust in scientists for race/ethnicity-based discrimination. Contrary to a disengagement hypothesis, discrimination-exposed adults showed higher probability of online health information seeking (PR=1.06), health app use (PR=1.11), and online provider messaging (PR=1.13); these associations persisted after adjustment for trust in physicians. Discrimination was independently associated with lower health self-efficacy (b=-0.271). Medical discrimination selectively erodes trust in clinical institutions while leaving broader epistemic trust largely intact. Despite this, discrimination-exposed patients engage more actively with digital health channels, consistent with compensatory reorientation toward non-clinical information sources. These findings describe engaged but institutionally alienated patients, with implications for restoring clinical trust and for equity-centred digital health design.

Background: Digital health technologies, spanning mobile applications, telemedicine, and AI-driven platforms, are rapidly reshaping healthcare delivery globally. Although Generation Z university students are classified as digital natives, empirical data evaluating their eHealth literacy, technology acceptance, and specific trust barriers in developing South Asian nations like Sri Lanka remain scarce. Objective: This study evaluated eHealth literacy, technology acceptance, online health information-seeking behaviors, and adoption barriers among Gen Z undergraduates in Sri Lanka, focusing on the interplay between eHealth literacy, AI trust, and digital care preferences. Methods: A cross-sectional survey (N = 172) was conducted among Sri Lankan university undergraduates utilizing adapted, validated instruments: the eHealth Literacy Scale (eHEALS) and the Technology Acceptance Model (TAM). Statistical analysis included scale reliability validation (Cronbach's alpha), descriptive profiling, Chi-Square ({chi}{superscript 2}) contingency tests, Pearson correlations, and Multiple Linear OLS Regression models. Results: Participants demonstrated high overall eHealth literacy (Mean = 3.84 {+/-} 0.58) and strong endorsement of digital health utility (Mean = 3.99 {+/-} 0.59). Online health searches were reported by 86.6% of respondents. AI tools (e.g., ChatGPT, Gemini) emerged as the second most frequent source for health queries (57.6%), surpassing YouTube (44.2%) and social media (26.2%), with medical students showing significantly higher AI utilization ({chi}{superscript 2} = 8.70, p = .003). In multiple regression analysis, digital platform preference over physical clinic visits (R{superscript 2} = .352, p < .001) was significantly predicted by Perceived Ease of Use ({beta} = 0.371, p = .001) and Trust in AI Recommendations ({beta} = 0.370, p < .001), whereas face-to-face consultation preference (76.7%) and personal data privacy risks (50.0%) remained predominant adoption barriers. Conclusion: Gen Z students in Sri Lanka exhibit high digital health readiness and substantial reliance on AI-driven information seeking. However, institutional deployment must address privacy concerns and integrate hybrid clinical workflows to bridge the gap between high perceived utility and physical consultation preferences.

Background. General purpose generative AI (GenAI) chatbots are increasingly used by students for mental health support. Research on prevalence estimates vary widely, rarely link use to validated clinical measures, and have not been reported in a Canadian student population. We estimated the prevalence trends, patterns, perceived impact, and correlates of GenAI use for mental health support among Canadian university students. Methods. We analysed one year (May 2025 to April 2026) repeated cross-sectional data from the Canadian arm of the WHO World Mental Health International College Student survey (WMH-ICS) The primary outcome was past-year prevalence of GenAI use for mental health support. Specific use purposes, perceived impact, reasons for non-use, and future use intent were also analysed. Factors associated with GenAI use were assessed using modified Poisson regression. As a sensitivity analysis, an elastic-net penalised regression model was fitted to assess the robustness of findings to an alternative modelling approach. Results. The past-year prevalence of GenAI chatbot use for mental health support was 25.2% (95% CI: 22.7 - 27.9), with a lifetime prevalence of 30.2%. Use was mostly occasional and predominately for seeking mental health information, stress management, and emotional support/companionship. Students of Asian ethnicity, those with higher clinical burden, recent adverse life experiences, weaker social support, and prior digital help-seeking behaviours were more likely to use GenAI for mental health purposes. Conversely, 2SLGBTQ+ students and those with romantic partners were less likely. Nearly three-quarters (74.2%) of users perceived such use to have a positive impact on their mental health and emotional wellbeing. Non-users reported preference for human interaction, distrust of GenAI in mental health (67.4% each), and privacy/security concerns (50.3%). Non-use also reflected principled objections to AI, including ethical and environmental concerns, with most non-users indicating no future use intention. Conclusions. GenAI chatbot use for mental health support has become commonplace among Canadian university students and is concentrated among those with greater mental health needs and fewer social support resources. Although most users perceived these tools as beneficial, their clinical effectiveness and safety remain uncertain. Rigorous prospective studies are needed to determine whether perceived benefits translate into improved mental health outcomes and whether purpose-built GenAI mental health interventions offer greater clinical benefit and safety than general-purpose chatbots.

Public health professionals have access to more data than ever before. Yet answering a relatively simple epidemiological question often requires navigating multiple databases, formats, software tools, and reporting systems. As a result, valuable data often remain locked behind technical barriers, making it harder for public health professionals to turn information into decisions. We developed EpidBot to simplify this process. EpidBot is a platform that allows users to retrieve, analyze, visualize, model, and report epidemiological data through natural language interaction. By connecting multiple public health data sources within a single environment, the platform enables users to conduct analyses that would traditionally require several independent tools and specialized technical skills. Rather than functioning solely as a search interface, EpidBot supports complete analytical workflows. Users can explore surveillance data, compare trends across locations and time periods, generate maps and visualizations, construct epidemiological models, and produce structured technical reports while maintaining full visibility of data sources and analytical procedures. To show what this looks like in practice, we present representative use cases, including the automatic generation of a mathematical model for Ebola virus disease in the Democratic Republic of the Congo. From a single user request, EpidBot assembled evidence from published sources, generated and calibrated a compartmental transmission model, identified key transmission drivers, evaluated intervention scenarios, and produced a technical report with quantitative findings and policy-relevant recommendations. EpidBot shows how natural language interaction can reduce the technical barriers that often separate public health professionals from the analyses they need to perform. By bringing data access, analysis, modeling, visualization, and reporting into a single environment, the platform helps transform information into evidence while preserving transparency and reproducibility.

Background Human papillomavirus (HPV) vaccination is an effective cancer prevention strategy, yet HPV vaccine awareness remains uneven across sociodemographic groups. In the current digital information environment, awareness may be shaped not only by access to health information but also by exposure to false or misleading health information, difficulty evaluating information accuracy, and echo-chamber dynamics on social media. Objective This study examined associations between perceived exposure to false or misleading health information on social media, difficulty determining whether social media health information is true or false, perceived echo-chamber exposure, and HPV vaccine awareness among U.S. adults. Methods We analyzed nationally representative Health Information National Trends Survey data using survey-weighted descriptive statistics and logistic regression models. The analytic sample included 2,371 respondents, representing a weighted population of 49.2 million U.S. adults. The outcome was HPV vaccine awareness. Primary predictors included perceived exposure to false or misleading health information on social media, difficulty determining whether social media health information was true or false, and perceived same-view health network exposure on social media. Models adjusted for age, sex, race/ethnicity, education, household income, rurality, and Census division. Results Overall, 60.37% of respondents reported HPV vaccine awareness. Most respondents reported encountering false or misleading health information on social media, with 45.57% reporting "some" and 32.83% reporting "a lot." In unadjusted models, greater perceived exposure to false or misleading health information was associated with higher odds of HPV vaccine awareness. After adjustment, respondents reporting "some" false or misleading health information had significantly higher odds of HPV vaccine awareness compared with those reporting none (AOR=2.40, 95% CI: 1.06-5.43), while the association for "a lot" was marginal (AOR=2.29, 95% CI: 0.97-5.38). Difficulty identifying true versus false social media health information and perceived echo-chamber exposure were associated with HPV vaccine awareness in unadjusted models but were attenuated after adjustment. HPV vaccine awareness was substantially higher among females and respondents with higher educational attainment, and lower among Hispanic, non-Hispanic Asian, and non-Hispanic other respondents compared with non-Hispanic White respondents. Conclusions HPV vaccine awareness is associated with both digital health information exposure and persistent sociodemographic inequities. Greater perceived exposure to misleading health information may reflect broader engagement with health-related content on social media, where accurate and inaccurate information coexist. Public health communication strategies should address misinformation vulnerability while expanding accurate, culturally responsive HPV vaccine messaging across digital platforms.

Objective. To evaluate the perceived usability, acceptability, and user experience (rather than the clinical effectiveness) of Boora, an AI-assisted, human-supervised digital platform prototype for longitudinal overweight and obesity care, among users and health professionals in Brazilian primary care. Design. Convergent mixed-methods formative evaluation. Perceived usability was measured with the System Usability Scale (SUS) and summarised descriptively; semi-structured interviews conducted after hands-on use were analysed with codebook thematic analysis (Braun and Clarke); the two strands were integrated through a joint display. Qualitative reporting followed the Consolidated Criteria for Reporting Qualitative Research (COREQ). Setting. Primary health care network of Ananindeua, Para, within the Brazilian Unified Health System (January to February 2026). Participants. Fifteen adults with overweight or obesity (BMI at least 25 kg/m2, confirmed via electronic health records) who used the patient application on their own smartphones for 24 hours, and eight primary care professionals (nurses, physicians, and a dietitian) who used the professional dashboard for approximately 20 minutes on predefined tasks with synthetic data. Main outcome measures. SUS scores and qualitative themes addressing usability, acceptability, perceived usefulness, barriers, and perceived clinical and workflow fit. Results. Boora showed good perceived usability in both cohorts (users mean 76.5, SD 10.3; professionals mean 77.5, SD 4.6; both above the SUS normative average of 68). Four themes emerged per cohort. Users valued an accessible interface and visible progress but described daily logging burden, fragile anticipated engagement, and digital-literacy and accessibility barriers. Professionals valued a clear interface and the prospect of panel-managed, proactive follow-up, while requiring training, AI governance, protected time, and interoperability with the national record. Integration indicated that the disengagement users anticipated was the risk professionals perceived the dashboard could help identify, whereas the educational AI assistant was the weakest and most ambiguous component for both groups. Conclusions. Boora was perceived as usable and acceptable, with perceived value concentrated in human-supervised, longitudinal follow-up rather than autonomous self-tracking or AI advice. These findings concern perceived usability and acceptability, not clinical effectiveness or sustained engagement. Real-world adoption would depend on accessibility refinements, electronic-record integration, and clear AI governance aligned with the principles of Brazil's proposed risk-based AI framework and the LGPD.

IMPORTANCE With a growing global user base of 3.5 billion and users spending nearly as much time gaming as on social media, video gaming's effects on mental well-being have attracted scholarly and public interest. Despite the WHO's inclusion of gaming disorder in ICD-11 and government-mandated restrictions in multiple countries, the causal evidence supporting such policies remains limited. OBJECTIVE To investigate the causal effect of video gaming on mental well-being in the post-COVID period. DESIGN A natural experiment of game console lottery was used to identify the causal effect of video gaming on mental well-being. The intention-to-treat effect was estimated using multivariate regression and propensity score matching. Causal effects of game engagement were estimated using the instrumental variable method (two-stage least squares) and a causal machine learning algorithm, instrumental forest. SETTING Online surveys were conducted between September 2022 and March 2023, covering all 47 prefectures in Japan. PARTICIPANTS A total of 71,435 participants aged 10-69 answered the surveys. 6,911 individuals participated in the natural experiment. EXPOSURES Video game engagement, including video game console ownership, use of the console in the last 30 days, and video gaming duration. MAIN OUTCOMES AND MEASURES Psychological distress and life satisfaction. RESULTS The intention-to-treat effects of winning game console lotteries on mental well-being were positive (0.1 SD). Game console ownership improved mental well-being by 0.1-0.2 SD, and past-month play improved it by 0.2-0.3 SD. An extra hour of daily video game play led to 0.3-0.5 SD improvements in mental well-being. CONCLUSIONS AND RELEVANCE This study found that video gaming had a positive effect on mental well-being in the post-COVID period. The consistency of the effect size with that of a related COVID-period study adds robustness to our findings. Our findings add to the growing evidence that digital media screen time has diverse effects on well-being and support public health policies that recognize the potential mental well-being benefits of appropriate levels of video gaming.

Importance: AI chatbots are increasingly used for mental health support, but little is known about how adults with lived experience of mental health condition use and perceive these tools. Objective: To characterize the use and perception of AI chatbots, including for mental health purposes, among adults connected to a large US mental health organization. Design: Cross-sectional online survey conducted from March to May 2026. Setting: Adults recruited through email newsletters from the National Alliance on Mental Illness (NAMI), the largest grassroots mental health organization in the US. Participants: Adults aged 18 years older with English proficiency. Affiliation with NAMI or a diagnosis of mental health disorder was not required. Results: Of 454 participants, 316 (69.6%) reported having used an AI chatbot. Use was more common among younger participants and those with a current mental health diagnosis. Among AI users, 133 (42.1%) reported using a chatbot for mental health purposes. Mental health-related use was typically brief and focused on information gathering and in-the-moment emotion regulation. Most users rated chatbots as helpful for their mental health. Among the 95 participants with a mental health provider, only 14 (14.7%) had openly discussed their AI use with their provider. Higher frequency of AI use was associated with greater odds of disclosure (OR, 1.67; 95% CI, 1.20-2.38; P = .003). Conclusion and Relevance: In this survey of adults connected to a large mental health organization, AI chatbots were widely used but engagement for mental health purposes was typically brief and focused. Most use occurred without clinician awareness, suggesting a need for proactive conversations about AI use in routine mental health care.

Background: Adverse events (AEs) after hospitalization are common and disproportionately affect adults with multiple chronic conditions (MCC). Capturing patient-reported symptoms and self-assessed health may enable earlier detection of post-discharge AEs. Objective: To identify and test user requirements for an automated remote monitoring system to enhance AE surveillance during the transition home following discharge. Methods: We conducted a mixed-methods study using an iterative, user-centered design approach. Semi-structured interviews with patients and clinicians informed system requirements, followed by real-world field testing in 20 patients who used the system for up to 7 days after discharge. The prototype leveraged interoperable electronic health record data services, delivered automated post-discharge check-ins using a combined questionnaire assessing new or worsening symptoms and patient-reported outcomes (PROs), provided risk-stratified health advice (when and with whom to initiate contact), and escalated high-risk symptoms to clinicians in real-time. Descriptive statistics assessed feasibility and utilization; conventional content analysis identified user needs and implementation considerations. Results: Thirty-seven patients with MCC and 23 clinicians participated. Key requirements for patients included clear communication of personalized risk based on red-flag symptoms, and actionable guidance aligned with discharge instructions. Key requirements for clinicians included explicit delineation of responsibility across inpatient and outpatient setting, and selective escalation to minimize burden. Field testing patients completed 60% of the combined questionnaires. Seven patients received Level 2 or Level 3 health advice after reporting new or worsening symptoms. Three patients triggered Level 3 alerts, resulting in one-time, secure escalation emails to clinicians. Four of the 7 patients who received Level 2 or 3 health advice had chart-confirmed emergency department visits within 1 week of discharge. Patients found the system understandable and helpful, while clinicians noted challenges interpreting PRO trends. Conclusions: These observations support the feasibility and acceptability among patients and clinicians of collecting patient-reported symptoms and PROs during the early post-discharge period. Future iterations should prioritize clear risk communication, role clarity, and interpretable patient-reported data. Formal validation is required to assess predictive performance and clinical utility of symptom-based escalation for post-discharge AE surveillance.

Influenza vaccine effectiveness (VE) is estimated from a limited number of clinics using a test-negative design. These standard estimates face geographic, temporal, and operational constraints. Using Twitter/X data, we applied few-shot chain-of-thought prompting to identify self-reported vaccination status and influenza test results, then implemented a test-negative-like design to estimate VE. Our estimates fell within the range of interim reports and could complement current systems, improving feasibility, timeliness, and scalability.

Background: Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) free-text comments contain actionable feedback, but timely, scalable, and affordable sentiment analysis remains challenging for health systems that rely on third-party vendors. Objectives: To evaluate cost-performance tradeoffs between a cost-optimized and a flagship large language model (LLM) for aspect-based sentiment analysis of HCAHPS comments, using human inter-rater agreement as a reproducibility benchmark. Methods: We analyzed 512 free-text HCAHPS comments collected from two community hospitals in calendar year 2023. Six trained reviewers (medical students, recent medical graduates, and practicing internists) independently assigned positive, negative, or neutral labels to each comment-aspect pair; the majority label among three reviewers formed the consensus reference standard. Two OpenAI models - GPT-5-nano (cost-optimized) and GPT-5 (flagship) - were prompted in a zero-shot setting via the OpenAI API. We calculated pairwise Cohen's {kappa} to establish a human inter-rater baseline, then compared each model's labels to the consensus using Cohen's {kappa}, accuracy, weighted F1, and per-call cost and latency. Results: Mean human inter-rater agreement was {kappa} = 0.79 (substantial). Both LLMs exceeded this baseline (cost-optimized {kappa} = 0.85; flagship {kappa} = 0.85) with nearly identical accuracy (0.92) and weighted F1 (0.93 vs. 0.93). Performance was strong on positive (F1 ~ 0.97) and negative (F1 ~ 0.90) classes but poor on the underrepresented neutral class (F1 <= 0.19). The cost-optimized model processed all 512 comments for $0.04 versus $0.18 for the flagship - a 4.2-fold cost difference without measurable performance gain. Conclusions: Commercially available LLMs can perform aspect-based sentiment analysis on HCAHPS comments at human-level reproducibility, with the cost-optimized tier sufficient for routine classification. This offers health systems a rapid, scalable, low-cost alternative to vendor-based patient-experience analytics.