JMIR Formative Research

Involving end-users in the development of an AI tool is an important facilitator to its implementation. Usability testing was therefore conducted with a prototype user interface of an Artificial Intelligence driven air-Safety Tool (AISaT) to capture the perspectives and user experiences of AISaT from 10 staff members across two hospitals working within estates, infection prevention and control, and clinical areas, to inform the development of next iterations of AISaT. The perspectives shared could be grouped under improvements to the understand-ability; content; navigation; visibility; usability; workflow; ownership; and frequency of use of the tool. There were key areas that can and will be easily improved within AISaT, however there were areas that required a deeper level of critical reflection, such as incorporating data on more existing variables in a room (i.e., existing ventilation) and whether all patients should be assumed as infectious and breathing heavily. The research team must consider if the target audience of end users and recommended frequency of AISaT use will be pre-defined by the tool developers, or whether this level of detail should be left to each individual hospital to decide.

Introduction: Auditory verbal hallucinations (AVH) affect 60-80% of people with schizophrenia, yet existing assessment tools inadequately capture their phenomenological complexity. Aim: To determine whether individuals with schizophrenia spectrum disorders could accurately match auditory verbal hallucination loudness to an external audio track, and to explore participant perspectives on this approach. Methods: Eight participants with schizophrenia spectrum disorders and active AVHs rated loudness via a Likert scale and by adjusting a headphone audio track to match their experience. Structured interviews and thematic analysis captured participant viewpoints. Results: No significant correlation was found between audio tool and Likert scale scores. Seven of eight participants reported the audio tool provided greater precision in quantifying AVH loudness and better enabled them to convey their internal experience to others. Discussion: Audio-matching tools may offer meaningful advantages over traditional scales for quantifying AVH loudness, even where statistical convergence with existing measures is absent. Limitations: Small sample size; loudness alone cannot fully capture the qualitative experience of hearing voices. Implications: This tool shows promise for longitudinal tracking of AVH loudness. Recommendations: Further investigation of digital approaches to AVH assessment is warranted.

Background: Children and young people (CYP) are particularly affected by mental health problems. Mobile apps provide a scalable and accessible approach to adolescent mental health support, and schools are well-positioned to address multiple risk factors and deliver large-scale interventions. By combining active (self-reported) and passive (sensor-derived) data, mobile apps can model mental states and deliver context-aware support. Artificial Intelligence (AI) enables adaptive, context-aware recommendations tailored to each user. However, there is limited research on AI-based mental health interventions in community CYP. MindCraft is a mobile app designed to monitor adolescents mental health using active and passive data and provide AI-informed recommendations ("nudges"). This study aims to investigate the effectiveness of personalised AI nudges delivered through MindCraft on improving mental health outcomes among adolescents in schools in the United Kingdom. Methods: The study is a three-arm RCT using a prospective cohort of secondary school students aged 14-19. Following informed consent, participants complete a baseline online assessment at school and download MindCraft. The primary outcome is the Strengths and Difficulties Questionnaire global and subscale scores. Secondary outcomes include the Eating Disorders Diagnostic Scale, the Sleep Condition Indicator Questionnaire, the Self-Injurious Thoughts and Behaviours Interview, the Self-Efficacy Questionnaire for Children and the World Health Organisation-Five Well-Being Index. Participants are randomised to: (1) an AI-informed intervention group receiving personalised nudges, (2) an active control receiving non-personalised nudges, or (3) a control group with self-monitoring only. Participants use the app for four weeks, with follow-up at one month. Repeated-measures analyses will assess changes across time points. Discussion: We hypothesise that AI nudges will have a greater positive effect on mental health outcomes at one month than general nudges and self-monitoring. Our findings will provide key evidence on the effectiveness of personalised mobile AI recommendations for adolescents mental health and inform school-based mental health prevention and early intervention. This study will contribute evidence on the ethical, acceptable, and scalable integration of AI-enabled digital mental health tools within public health and educational systems, with implications for the design of future digital public health interventions and policies supporting their safe integration in schools.

Background: Public services are increasingly delivered through digital platforms. Although digital health may improve access and scalability, they may also widen inequalities for people who lack reliable access, confidence, skills, affordability or trust. Objective: This study examined the prevalence of self-reported digital exclusion among UK veterans and assessed its association with depression, anxiety and loneliness. Methods: A cross-sectional online survey was conducted between July 2025 and March 2026. Participants were UK Armed Forces veterans and resident in the UK. The survey collected sociodemographic, military service, digital access and health data. Self-reported digital exclusion was defined as reporting feeling excluded or disadvantaged due to lack of digital access or skills. Probable depression, anxiety and loneliness were assessed using the PHQ-2, GAD-2 and three-item UCLA Loneliness Scale, respectively. Associations between digital exclusion and each outcome were examined using adjusted multivariable logistic regression. Results: Of 1,911 responses received, 1,607 were included after data quality exclusions. Among participants with valid responses to the primary digital exclusion item, 553 (41.7%) reported digital exclusion. Digital exclusion was more common among females, younger veterans and those with lower household income. Probable depression, anxiety and loneliness were more prevalent among digitally excluded participants than among non-excluded participants. In adjusted models, self-reported digital exclusion was associated with higher odds of probable depression (AOR 1.38; 95% CI 1.04 to 1.83; p=0.028), probable anxiety (AOR 1.63, 95% CI 1.23 to 2.16; p<0.001), and probable loneliness (AOR 1.85; 95% CI 1.43 to 2.40; p<0.001). Conclusion: More than two-fifths of veterans with valid exposure data reported digital exclusion, despite high reported device access and confidence. Self-reported digital exclusion was associated with poorer mental health and loneliness, although causality cannot be inferred from these cross-sectional data. Digital-first services for veterans should include routine digital needs screening, targeted support and clear non-digital routes to care.

Social media has an estimated global user base of 3.5 billion. Adolescents, constituting over 90% of this group, and thereby university students. are extensively involved in social media use, emphasizing its significance in their daily lives. Excessive use of social media poses risks such as depression, affecting academic performance. The objective of this study was to determine the association between social media use and depression among university students in Urban Northern Tanzania. This was a cross-sectional study that involved university students from three universities. Data was collected through an online close-ended questionnaire. Statistical analysis included the use of frequencies, percentages, chi-square and bivariate logistic regression at 95% confidence intervals (CIs) and significance at p value <0.05. A total of 384 participants were enrolled in the study. 36.7% reported that they used social media for 1 to 3 hours daily. 58.1% of all participants reported high frequency of social media use for social purposes. 29.2% of university students who participated in the study were screened to have depression. The prevalence of depression increased with longer daily duration of social media use. Those using social media for more than 5 hours daily, had 3.049 times higher odds of being depressed. Moderate frequency of social media use for social purposes was linked to reduced depression. Daily duration of social media was significantly associated with depression levels among university students. Significant associations were also found between moderate use of social media for socializing, with lower risk of depression. We recommend that universities develop targeted mental health interventions and promote balanced, purposeful social media use among students.

Vayu is a mobile breathwork system comprising an iOS companion app and Apple Watch application that delivers slow, resonant breathing using screen-free haptic cues, HRV-adaptive pacing, and reflective journaling grounded in Patanjali's five states of mind. The watchOS component provides tactile phase guidance and real-time biometric sensing (heart rate, HRV), while the iOS interface supports analytics and personalized recommendations. In a 4-6-week naturalistic pilot involving 199 adults (ages 22-65) across Canada, the United States, and India, participants engaged in daily 5-10-minute sessions guided by on-wrist haptics. Average adherence was 4.1 +/- 2.3 sessions per week, with 71% of active users maintaining at least 3 sessions per week. By week four, perceived stress (PSS-10) decreased by 2.5 points, resting heart rate declined by 7.4 bpm, and HRV increased by a median of 28.6% relative to baseline, accompanied by mood improvements. No adverse events were reported. HRV metrics are derived from Apple Watch PPG-based proxies and interpreted as relative trends. These findings suggest Vayu is effective and well-tolerated, demonstrating strong engagement and early efficacy signals.

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.

Digital mental health tools (DMHTs) often fail to be successfully implemented in clinical settings. While user- and human-centred design frameworks are frequently proposed for developing effective tools, they are insufficient to address the sociotechnical complexity of healthcare environments. This paper addresses this limitation by detailing the application of a more-than-human design framework to incorporate wider contextual factors into design decisions. To demonstrate the application of this more-than-human design framework, we present a case study showcasing the design of one specific feature within a DMHT intended to support Health Improvement Practitioners (HIPs) in New Zealand's Integrated Primary Mental Health and Addictions (IPMHA) service. Our process blends usage-context storyboards with interface prototypes, using think-aloud interviews to test the contextual fit of our prototypes. The initial design concept failed due to contextual factors such as inconsistent wait times and the administrative burden on clients and clinic staff. This led to a pivot to a more context-appropriate, practitioner-focused, in-session concept for digital psychometric administration and automated scoring. This case study demonstrates that for DMHTs to be viable within complex healthcare environments, design must focus on more than the needs of a single user, incorporating multiple stakeholders and contextual variables across the wider service-delivery context.

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.

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. 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.

Background Traumatic brain injury (TBI) affects 69 million individuals globally each year, yet care remains fragmented across complex, multi-specialty pathways and settings. Digital health technologies offer potential to bridge care gaps, particularly in resource-limited settings, yet existing frameworks do not adequately address the complexities of the TBI care pathway or the diverse global contexts in which care occurs. Methods A cross-sectional qualitative study using critical realist-informed thematic analysis was conducted with practising neurosurgeons recruited internationally via National Institute for Health and Care Global Health Research Group on Acquired Brain and Spine Injury (NIHR ABSI) collaborating centres, social media, and society newsletters. Semi-structured interviews were conducted by a single researcher (OM) via Microsoft Teams (March-July 2024), exploring technology availability, healthcare infrastructure, clinical pathways, and contextual challenges, with a systems thinking approach guiding identification of current and potential technology integration points. Fourteen neurosurgeons from twelve countries participated, representing six lower-middle, two upper-middle, and four high-income countries. Results Six inductive themes emerged: Availability, Acceptability, Applicability, Capability, Feasibility, and Possibility- forming a novel conceptual framework visualised as a hexagonal chart for guiding digital health technology design and implementation in TBI care. Marked disparities in technology availability and utilisation were identified across urban/rural settings and income levels. Conclusions This framework offers a practical, context-sensitive tool for researchers, policymakers, and clinicians developing or implementing digital health technologies in TBI care globally. Visualisation in a similar style to a radar-chart enables simultaneous consideration of factors- including digital literacy, infrastructure, and cultural attitudes- whose neglect frequently underlies implementation failures.

Objectives. To assess how digital inclusion factors and physical access barriers are associated with user trust in smartphone-based remote photoplethysmography (rPPG) hypertension screening, and to identify implications for digital health pol- icy, procurement and implementation in low-resource settings. Methods. Cross-sectional mixed-methods survey in five outpatient clinics in Kebbi State, northern Nigeria (N =287). Trust was measured using comfort, confidence and perceived usefulness Likert scales. Primary analyses used binary logistic models with HC3 robust standard errors; sensitivity analyses are reported in supplementary material. Free-text responses were thematically analysed. Results. Smartphone ownership was 51.2%; Transsion-brand devices comprised 56.5% of owners. Greater distance to a blood pressure facility was independently associated with lower perceived usefulness (OR 0.51, 95% CI 0.30-0.87; p=0.013) and lower comfort (OR 0.61, 0.37-0.98; p=0.042). Among owners, Transsion versus Samsung showed higher confidence odds (OR 3.82, 1.02-14.27; p=0.046). Qualitative themes supported the implementation interpretation: platform-fit and device speed requests among Transsion owners; connectivity and offline-first concerns among those with greater travel distance. No brand contrast achieved FDR-adjusted significance; brand findings are exploratory. Conclusions. Digital health policy and health technology assessment for smartphone-based screening should incorporate local device ecology, connectivity constraints, physical access burden and trust-calibration safeguards. Pre-implementation assessment of these factors is necessary for equitable and safe rPPG adoption in low-resource health systems.

Background Stroke remains one of the leading causes of mortality and long-term disability worldwide, with low- and middle-income countries bearing a disproportionate share of the global disease burden. In India, delays in risk identification, fragmented referral pathways, and limited continuity of preventive care present significant challenges, particularly in rural communities. As a frontline health worker Accredited Social Health Activists (ASHAs) are strategically positioned to support community-based stroke prevention; however, existing workflows are frequently constrained by multi-tasking, predominantly paper-based documentation and fragmented digital systems. Advances in mobile health, artificial intelligence along with digital health ecosystem provided by Ayushman Bharat Digital Mission (ABDM) provide an opportunity to strengthen community healthcare through integrated digital platforms. Objective This protocol describes the design, system architecture, and prospective evaluation framework of ASHA Assist India, an integrated AI-assisted mobile health platform intended to support community-based stroke prevention by connecting citizens, ASHA workers, Primary Health Centres (PHCs), and higher levels of healthcare facilities within a unified digital ecosystem. Methods ASHA Assist India has been designed as a modular, cloud-based digital health platform supporting standardized data collection, longitudinal health monitoring, referral management, and AI-assisted clinical decision support. The proposed system comprises four user-facing applications corresponding to citizens, ASHA workers, PHCs, and referral hospitals, integrated through a centralized backend providing authentication, secure data management, interoperability, analytics, and notification services. The AI framework includes three planned analytical modules: (i) population-level stroke risk stratification, (ii) longitudinal stroke risk prediction, and (iii) acute stroke symptom recognition. A prospective implementation study is planned to evaluate platform usability, feasibility, workflow integration, implementation outcomes, and operational performance within routine community healthcare settings. Future validation of the AI modules will be conducted using prospectively collected longitudinal datasets. Expected Impact The proposed platform aims to strengthen community-based stroke prevention by improving digital workflow integration, facilitating coordinated referral pathways, and supporting longitudinal monitoring through the existing healthcare providers at health and wellness centres like ASHA, Community Health Officers (CHOs), ANM, etc. Beyond stroke prevention, the modular architecture is intended to provide a scalable framework for future digital health programmes addressing multiple non-communicable diseases within primary healthcare systems. Publication of this protocol establishes a transparent implementation and evaluation framework that may guide future research, digital health innovation, and implementation science in resource-constrained settings.

Objective: Since 2020, medication abortion provision in the U.S. has been reshaped by changing abortion policies and expanded telehealth access, yet little is known about how medication abortion service delivery has evolved. We examined national trends in service delivery from 2020 to 2025, including changes in abortion facility types, telehealth provision, and gestational limits. Study Design: Using ANSIRHs Abortion Facility Database, a national census of publicly advertising abortion facilities (2020 to 2025), we analyzed trends in medication abortion service delivery. Systematic web searches and mystery shopper calls gathered data on facility types, telehealth provision, and gestational limits. Data analysis included frequencies and comparisons across regions and states. Results: Medication abortion-only facilities increased nationally, from 35% of facilities in 2020 to 65% in 2025, with substantial growth in abortion-restrictive regions such as the Midwest and South. By 2025, 99% of facilities provided medication abortion. Telehealth provision expanded from 7 facilities in 2020 to 606 facilities by 2025, driven by growth in both brick-and-mortar facilities offering telehealth care and new virtual clinics. Overall, 46% of all facilities offered medication abortion by telehealth in 2025. Gestational limits for medication abortion increased nationally, from <1% of facilities offering medication abortion after 11 weeks in 2020 to 38% in 2025. Conclusions: Medication abortion service delivery has adapted to legal and logistical challenges by increasing telehealth options and expanding gestational limits. These changes improve access for abortion seekers, especially those living in restrictive environments. Sustaining abortion access will require ongoing provider adaptation and supportive policy environments.

BACKGROUND Peripheral Artery Disease (PAD) is a leading cause of lower extremity amputation. Health literacy is essential for disease awareness, but PAD awareness remains low, particularly in minoritized populations. The goal of the presented study is to investigate the PAD awareness and media preference for health information in Chinese-speaking communities in California. METHODS Anonymous 14-question surveys in Mandarin and English was designed to gauge basic knowledge of PAD, preferred methods for obtaining health information and general media preferences were collected from health fairs in San Francisco (SF), Oakland, and Los Angeles (LA) in the Chinese-speaking communities. Associations between the above variables and demographics were compared between groups. RESULTS A total of 180 responses included 94 from SF, 57 from Oakland and 29 from LA. PAD awareness was low across all cohorts. LA and SF cohorts shared similar patterns to receive health information as they preferred radio (p = 0.021, SF 49/94 and LA 8/29) while the Oakland cohort favored video/media (p = 0.024, 18/57). SF and LA cohorts showed a stronger preference for newspapers (p = 0.471, SF 32/94 and LA 12/29) and television (p = 0.244, SF 28/94 and LA 12/29), while the Oakland cohort favored video (Oakland 20/57). CONCLUSION To our knowledge, this is the first study to survey multiple US Chinese-speaking communities to assess PAD knowledge and learning preferences. The awareness of PAD among the selected Chinese-speaking communities was dismal and preferred learning methods varied from surveyed communities. Implementing community-based health education strategies will be necessary.

Background: In two large studies conducted in Bangladesh, our recently developed artificial intelligence (AI)-based models for assessing dehydration severity in children under five years (DHAKA models) and patients over age five (NIRUDAK models) were significantly more accurate and reliable than the WHO IMCI and IMAI guidelines for diarrhea management. We incorporated these models into a novel mobile health (mHealth) clinical decision support tool (CDST), called FluidCalc, with the potential to improve acute diarrhea management by frontline health workers worldwide. Our objective was to assess the barriers and facilitators to uptake and use of our mHealth CDST in both a low-resource setting (Tanzania) and high-resource setting (United States (US)) among healthcare providers and stakeholders. Methods: Qualitative data were collected through focus group discussions (FGDs) with healthcare providers and in-depth interviews (IDIs) with stakeholders and policymakers from February - July 2025 in Tanzania and February - March 2026 in the US. The Consolidated Framework for Implementation Research (CFIR) was used to guide discussions and elicit participant feedback. Audio recordings were transcribed and translated from Swahili to English where applicable, and data were analyzed using framework matrix analysis. Results: 35 providers from different cadres participated in FGDs, and 13 stakeholders participated in IDIs. Facilitators to implementation included FluidCalc's simplicity, ease of use, and offline functionality. Participants reported that the app could streamline clinical workflows, promote adherence to diarrhea management guidelines, facilitate task shifting, support antibiotic stewardship, and reduce errors in fluid rehydration calculations. FluidCalc was also viewed as a valuable teaching tool, and for supporting less experienced healthcare providers and trainees, and as useful during diarrheal disease outbreaks. Perceived barriers included the need for reliable digital infrastructure, including access to mobile devices, internet connectivity, and dependable electricity and lengthy institutional approval processes. Endorsement and approval from the Ministry of Health and health facility leadership were perceived as essential for successful implementation. Conclusion: Healthcare providers and stakeholders believe FluidCalc has the potential to improve care for patients with acute diarrhea in both high- and low-resource settings. Addressing identified barriers and ensuring reliable digital health infrastructure are needed to support effective integration into patient care.

Background: Concerns about "AI psychosis" have swirled in the media since ChatGPT's release, but few systematic analyses exist. We therefore conducted an electronic health record (EHR) analysis to identify the frequency, clinical characteristics, and quality of AI interactions in patients experiencing psychosis treated in a medical center. Methods: AI keywords (e.g., ChatGPT, AI) were used to search Vanderbilt University Medical Center's EHR from 12/1/2022-4/1/2026. Records were discarded if they were not AI-related or if the primary diagnosis did not include psychosis. Three raters read notes to determine if a patient was experiencing AI psychosis and classified the interactions using 4 a-priori categories (Catalyst, Amplifier, Co-Author, Object) formulated to explain how AI-related negative outcomes emerge. Findings: 73 patients met our criteria. 28 patients were rated as experiencing AI psychosis, 17 had neutral interactions, and 28 expressed delusional content related to AI without documented evidence of conversational AI use. ChatGPT was the matching keyword for 53.6% patients experiencing AI psychosis. The majority of AI psychosis cases were documented after ChatGPT's "4o" model was released in May 2024. Notably, the AI Psychosis group had significantly more patients experiencing a first psychotic episode (60.7%) compared to the other two groups. Amplifier was the most common (64.3%) qualitative rating in the AI Psychosis group. Interpretation: "AI psychosis" is an infrequent but real phenomenon observed in clinical practice. Most affected patients were experiencing their first psychotic episode and presented with AI psychosis following the release of the more sycophantic GPT-4o. Among the affected patients, AI most often exacerbated an existing condition by reinforcing distorted ideas.

Introduction Dysmenorrhoea is highly prevalent globally and interferes with engagement in education, work, social participation, and quality of life. Although evidence suggests that sociocultural beliefs influence how menstrual pain is understood and managed, relatively little research has explored dysmenorrhoea-related knowledge and beliefs within South Africa. This study aimed to (1) determine the frequency of dysmenorrhoea, (2) assess dysmenorrhoea-related knowledge and compare knowledge between menstruating and non-menstruating individuals, and (3) explore commonly held generational, cultural, and religious beliefs related to dysmenorrhoea in a South African university cohort. Methods We analysed data collected as part of a cross-sectional survey conducted among staff and students at a South African university. Participants completed demographic questions, items assessing dysmenorrhoea-related knowledge, and an adapted Working Ability, Location, Intensity, Days of Pain, Dysmenorrhoea (WaLIDD) questionnaire. Participants were also invited to provide free-text responses describing generational, cultural, and religious beliefs about dysmenorrhoea. Quantitative data were analysed descriptively and compared between menstruating and non-menstruating participants. Free-text responses were analysed using reflexive thematic analysis. Results A total of 863 participants completed the survey, including 578 current or past menstruators. The frequency (95%CI) of dysmenorrhoea was 75.4% (71.7-78.9). Most participants were classified as having moderate (53%) or severe (31%) dysmenorrhoea on the WaLIDD scale. Awareness of dysmenorrhoea was higher among participants who had menstruated than among those who had never menstruated (80.4% vs 55.3%, p<0.001). Most participants (85.1%) reported wanting more education about dysmenorrhoea and its impact. Reflexive thematic analysis of 246 free-text responses identified five themes: (1) menstrual pain is normalised, dismissed, and expected to endure, (2) reproductive meanings attached to menstrual pain, (3) moral, spiritual, and cultural interpretations of menstrual pain, (4) negotiating competing explanations for menstrual pain, and (5) managing and controlling menstrual pain symptoms. Across themes, dysmenorrhoea was interpreted through social, cultural, reproductive, spiritual, and biomedical frameworks that shaped how pain was understood, communicated, and managed. Conclusion Dysmenorrhoea is common in this South African university cohort, and is rarely understood as a purely biological symptom. Instead, menstrual pain is understood and managed through broader social, cultural, reproductive, moral, and biomedical narratives, which shape how pain is recognised, disclosed, legitimised, and treated. These findings highlight the importance of considering sociocultural beliefs alongside clinical factors when developing menstrual health education, support strategies, and healthcare services.