Background: African adolescents and young adults account for a disproportionate number of new HIV infections. There is an urgent need to identify scalable and cost-effective behavioral HIV prevention strategies for this population. Using a condom at first sex is associated with a higher likelihood of consistent use later. Tumaini (“Hope for the Future” in Swahili; Emory University) is a choose-your-own-adventure smartphone game that has been shown to reduce the risk of unprotected first sex by end line in a 45-month randomized controlled trial in western Kenya. Objective: This study aimed to assess the impact of Tumaini on proximal outcomes related to condom use at first sex (specifically, behavioral intentions, self-efficacy, attitudes, and knowledge) longitudinally across mid-adolescence in the above trial. Methods: Adolescent participants (n=996, mean baseline age 14, SD 0.56 years) were randomized 1:1 to receive either a smartphone loaded with Tumaini or an attention-control math game for 5 to 7 weeks at 3 time points (mean age 14.0, SD 0.56; 15.3, SD 0.55; and 16.0, SD 0.56 years, respectively). They completed a behavioral survey at 13 time points, through mean age 17.7 (SD 0.56) years. Using generalized estimating equations and controlling for age at baseline, we modeled mean scores (overall and stratified by gender) on a range of condom-related survey items over time to assess mean differences at specific time points. We applied appropriate Bonferroni corrections to inferences about cross-arm differences in mean changes relative to baseline at 4 time points (after each intervention period and at end line; α=.05/4) and within-arm mean changes relative to baseline at each of the 12 post-baseline time points (α=.05/12). Analyses were conducted as intent-to-treat. Results: At end line, 97.8% (n=974) of the sample had been retained. Participants in both arms dedicated a mean total of >30 hours to their assigned game. There was significant improvement across all condom-related proximal outcomes in the intervention arm relative to the control arm immediately after initial intervention exposure. For almost all outcomes, a significant cross-arm difference was also present at end line and for most outcomes at the 2 intervening comparison time points. Some outcomes saw stronger intervention effects on female participants (eg, self-efficacy to refuse unprotected sex) or male participants (eg, knowledge that condoms are an effective way to prevent HIV). In each arm, intention to use a condom at first sex was consistently higher among male participants; however, female intervention-arm scores overtook male control-arm scores following initial intervention exposure. Conclusions: Tumaini significantly improved theory-based proximal outcomes related to condom use, with effects sustained 45 months post initial exposure and 16 months post most recent exposure. Adolescents benefited from even short-term exposure, though repeated exposure generally sustained and reinforced intervention effects. As access to smartphones increases, Tumaini has potential for high scalability and impact on condom-related outcomes. Trial Registration: ClinicalTrials.gov NCT04437667; https://clinicaltrials.gov/study/NCT04437667

Background: Dementia prevention through the reduction of modifiable risk factors is gaining attention as a public health strategy. However, public knowledge of dementia risk and protective factors remains low. Web-based education offers a potential solution to raise awareness and promote risk-reduction behaviors. Objective: This randomized controlled trial evaluated the effectiveness of DementiaRisk.ca, an internet-based multimedia educational intervention, in increasing knowledge of dementia risk factors, intentions to engage in risk reduction behaviors, and changes in health behaviors. Methods: A 2-arm randomized controlled trial was conducted with 510 participants (265 in the intervention group and 245 in the control group). Participants were randomized to receive either the e-learning about dementia risk and promoting brain health, which included a multimedia lesson and microlearning emails, or a control intervention focused on mild cognitive impairment. Outcomes included knowledge of dementia risk factors, intentions to engage in risk reduction, and health behaviors, measured at baseline (T1), 4 weeks (T2), and 2 months postintervention (T3). Outcomes were analyzed using linear mixed effects models with fixed effects for group, time, and their interaction, and a random intercept for participants. Results: Of the 510 randomized participants, 405 (79.4%) completed all intervention components. Participants were predominantly female (n=309, 60.6%) and aged 55 years or older (n=284, 55.7%). Baseline mean dementia knowledge scores were 17.0 (SD 5.5) in the intervention group and 17.4 (SD 6.0) in the control group. At T2, scores increased to 25.8 (SD 4.5) and 23.6 (SD 5.1), respectively, yielding a between-group difference of 2.2 points (95% CI 1.2‐3.2;

Background: As the population ages, older adults face an increasing risk of physical inactivity and related health complications, highlighting the need for scalable interventions. Smartphone-based programs have emerged as a promising strategy to support sustained physical activity among older adults. Objective: This study aimed to evaluate whether a smartphone lecture program incorporating a digital peer support app would increase physical activity among older adults, compared to a conventional smartphone lecture program. Methods: This 2-arm, 1:1 parallel-arm, cluster-randomized trial was conducted in 2 urban regions of Japan (Sumida Ward, Tokyo, and Chiba City, Chiba). Eligible participants were community-dwelling adults aged ≥60 years, able to walk independently, and smartphone users; exclusion criteria included prior use of the peer support app or medical restrictions on walking. Participants were recruited offline during community smartphone lectures (closed-group recruitment). The intervention combined face-to-face lectures with app-based peer support, while outcomes were assessed both objectively (via smartphones) and through self-administered paper questionnaires. All participants received a baseline smartphone lecture. Intervention participants attended 2 additional sessions using a digital peer support app (Minchalle; A10 Lab Inc), which included features such as daily step goals, peer sharing, and group encouragement. Control participants attended 2 standard follow-up smartphone lectures. The primary outcome was the change in weekly average daily step count from baseline to Week 12. Secondary outcomes included total metabolic equivalent of task (MET)–minutes per week (assessed via the International Physical Activity Questionnaire), walking time (≥30 minutes per day), daily smartphone use, and number of smartphone use purposes. Results: A total of 156 community-dwelling older adults were grouped into 40 clusters and randomized (20 intervention clusters, n=80 and 20 control clusters, n=76). In total, 124 participants (79.5%) completed the follow-up, and valid step data were available for 117 participants, with missing data ranging from 5.1% to 29.1%. Baseline daily steps averaged 3951 (SD 1686) in controls versus 4583 (SD 1973) in the intervention arm. An unadjusted mixed model for repeated measures showed significantly higher step changes for intervention participants at Week 12 (difference=579, 95% CI 36-1123; =.04). No significant differences emerged for total METs (difference=646 MET-min per week, 95% CI –12 to 1303; =.054) or walking ≥30 minutes per day (odds ratio [OR] 1.56, 95% CI 0.63-3.90; =.33). However, the intervention arm demonstrated a significant increase in daily smartphone use (OR 4.10, 95% CI 1.15-14.6; =.03) and in the number of smartphone use purposes (difference=0.58, 95% CI 0.12-1.05; =.01). Conclusions: A smartphone lecture program integrated with app-based peer support led to modest but meaningful improvements in step counts among older Japanese adults, at Week 12 of the 12-week intervention. Future research should investigate long-term maintenance, additional measures of physical activity, and subpopulation responses to optimize digital health programs for older adults. Trial Registration: UMIN-CTR UMIN000051904; https://tinyurl.com/4m4zm99v

Background: Mammography screening uptake in Singapore remains below 40% despite campaigns and subsidies. Natural language processing (NLP) can extract nuanced attitudes from free text that fixed response options miss, revealing latent factors influencing breast cancer (BC) screening behavior. Objective: This study characterized women’s attitudes toward mammography using mixed methods data, examined associations between BC awareness and screening willingness, and identified barriers and facilitators through NLP of free-text responses. Methods: We conducted a cross-sectional study within the multicenter cohort in Singapore (October 2021-December 2023). In total, 4169 women aged 35‐59 years (median 48, IQR 43‐54) were recruited via convenience sampling (3 hospitals and 2 polyclinics). Participants completed online structured questionnaires on demographics and screening history, then a BC education quiz with feedback. Participants answering >80% correctly were classified as “BC-aware.” Posteducation, participants reported screening willingness (motivated or neutral) with optional free-text explanations. Logistic regression models (adjusted for study site, age, ethnicity, marital status, housing, and education) examined the associations with willingness. For 3819 English-language respondents, biterm topic modeling identified themes and sentiment analysis quantified emotional tone. Statistical significance: =.05. Results: Overall, 79% (3287/4169) were BC-aware, and 94% (3908/4169) reported increased motivation posteducation. BC-aware women had higher screening motivation than BC-unaware women (adjusted odds ratio [aOR] 2.88, 95% CI 2.19‐3.80;

Background: Antimicrobial resistance (AMR) poses a critical global health threat, with inappropriate antibiotic use being a major driver. Timely microbiological specimen submission before initiating antibiotic therapy is a cornerstone of antimicrobial stewardship (AMS), enabling pathogen-directed therapy and reducing unnecessary broad-spectrum exposure. However, suboptimal compliance remains common due to workflow interruptions, technological barriers, and behavioral factors. Failure Mode and Effects Analysis (FMEA), a proactive risk-assessment method widely used in health care quality improvement, provides a systematic framework to identify process vulnerabilities and prioritize corrective actions. Despite its increasing application, few studies have integrated FMEA with hospital informatization to optimize microbiological specimen submission workflows in routine AMS practice. Objective: This study aimed to systematically identify workflow risks affecting preantibiotic microbiological specimen submission and to design, implement, and evaluate informatization-enabled interventions using an FMEA-based framework. Methods: FMEA was conducted at a tertiary hospital in China. A multidisciplinary team identified potential failure modes across 4 domains: health information systems, personnel, administration, and external support. Risk Priority Numbers (RPNs) and Action Priority (AP) indices were calculated for each failure mode. Targeted interventions were implemented, including dual-verification barcode scanning, artificial intelligence-driven clinical decision support alerts, EHR-integrated training modules, and automated compliance dashboards. Pre- and postintervention specimen submission rates (January 2024-December 2024) were analyzed using the Mann-Kendall trend test. Results: The top 5 failure modes included PDA barcode scanning failures (RPN=175), inadequate clinical decision support (RPN=140), insufficient clinician awareness (RPN=56), suboptimal oversight mechanisms, and patient-related barriers. Postintervention, significant upward trends were observed in overall specimen submission rates (

Background: Type 2 diabetes (T2D) is emerging as a growing global public health crisis. Early and effective interventions can reduce T2D incidence among at-risk populations. Compared with traditional approaches, digital health technologies offer promising opportunities for prevention, with eHealth literacy (eHL) emerging as a critical determinant of digital prevention outcomes. Objective: This systematic review aims to synthesize and explain the pathways and mechanisms through which eHL supports T2D prevention among at-risk populations. Methods: We searched Scopus, Web of Science, and PubMed databases for English-language original research published between January 1, 2000, and August 14, 2025. Studies included were prevention research involving eHL engagement among populations at risk for T2D. Nonoriginal literature, such as editorials and abstracts, as well as research protocols, was excluded. The findings were synthesized using a thematic analysis approach, integrating the Theoretical Domains Framework with the eHL model. Two reviewers independently screened literature and extracted data, and discrepancies were resolved by a third reviewer. The Mixed Methods Appraisal Tool was used to assess risk of bias. Results: This review included 28 studies (n=13,100), mostly quantitative and published within the past decade, targeting people with prediabetes, prior gestational diabetes, and overweight/metabolic risk. Study quality was moderate to high (Mixed Methods Appraisal Tool 60%‐100%) with no high risk of bias. eHL supported prevention mainly through knowledge (28/28), behavioral regulation (16/28), social influences (15/28), environmental resources (12/28), and goals (11/28), while emotions, memory, attention, decision process, and beliefs about competence were rarely addressed. Health literacy (27/28), information literacy (20/28), and communicative eHL (20/28) were most common; critical eHL and media literacy were not addressed. Studies reported positive outcomes: high engagement, weight loss (≥5%), improved glycemic markers, and enhanced lifestyle behaviors. Conclusions: This is the first systematic exploration of eHL mechanism pathways in T2D prevention via theoretical mapping. We found interventions yield positive effects despite highly uneven mechanism application: extant research relies excessively on knowledge and behavioral pathways while underemphasizing emotional support, autonomy, and critical evaluation—factors linked to long-term adherence. We provide a mechanism-based framework and identify critical gaps, including the absence of focus on critical eHL and media literacy. This review is limited by substantial variation across studies that did not allow for meta-analysis and by the limited evidence base on eHL. Future interventions should explore and test emotional and autonomy support, information discernment training, and accessibility optimization in T2D prevention. These comprehensive, equity-focused intervention approaches will help ensure that eHL becomes a truly effective public health tool that benefits everyone, especially at-risk and vulnerable populations. Trial Registration: PROSPERO CRD42025630395; https://www.crd.york.ac.uk/PROSPERO/view/CRD42025630395

Background: Telecare is seen as a promising technology aimed at enhancing the accessibility and efficiency of health care services. Although focus on quality has been highly prioritized within the health care services, there is a need to explore the quality of telecare services in general and municipal telecare call centers (CCs) in particular, as health and assistive technologies are increasingly being implemented in patients’ homes. Objective: The study sought to explore which factors influence the quality of telecare services provided by municipal telecare CCs in Norway, evaluated through the anchored, realistic, cocreated, human, integrated, and evaluated (ARCHIE) framework. Methods: The study had a multiple-case design. Interviews were the main source of data from 15 informants from 5 municipal telecare CCs across Norway. Observation and document studies were used for background and contextualization. To explore and evaluate quality, a combined deductive–inductive analysis was conducted. Results: Evaluated against the ARCHIE framework, none of the quality criteria were fully met. Due to the telecare service not being sufficiently anchored for all patients, it was challenging to provide realistic technologies. The collaborative work was difficult, with challenges in recruiting patients. The human principle was characterized by variation of knowledge and national guidelines. Municipal telecare CCs were not integrated into the health care services, and data must be used to a greater extent for evaluation and learning than is currently the case. Conclusions: The findings suggest that municipal telecare CC services have several shortcomings in providing high-quality health care. Relating the quality principles identified by the ARCHIE framework to normalization process theory constructs indicates that the CC service remains in a transitional phase of normalization. To improve the telecare CC services and enhance communication and integration, policymakers need to reduce fragmentation in the broader health care system. Further national standardization to professionalize the telecare CC services should be developed. The telecare CCs need to improve their service related to all indicators of the ARCHIE framework. Training for telecare operators should be prioritized.

Traditional epidemic intelligence relies heavily on human epidemiologists for data interpretation and reporting, which makes it resource intensive, slow to respond, and vulnerable to variability in professional expertise. To overcome these limitations, we propose an expanded conceptual epidemic intelligence quadripartite framework that extends the classical trinity of (1) surveillance, (2) risk evaluation, and (3) early warning with a fourth pillar, (4) decision support and intervention optimization through AI agents. Acting as 24/7 digital epidemiologists, multiagent systems can integrate heterogeneous signals from multisource surveillance systems, conduct contextual risk evaluation and adaptive forecasting, generate tailored early warnings, and provide actionable recommendations for targeted control—closing the loop between detection and response. Embedding interpretability and mandatory human-in-the-loop oversight enhances trust and accountability. Nonetheless, real-world deployment requires addressing context-specific challenges of data quality, interoperability, robustness, governance, circular reporting, and equity. If designed with transparency, inclusiveness, and resilience, AI agents have the potential to transform epidemic intelligence into a continuously adaptive and globally connected system.

Background: Diabetes mellitus (DM) continues to be a critical public health issue in Hong Kong. Although self-care behaviors help promote health among patients with DM, adherence remains suboptimal. More attention should be paid to eHealth literacy with the development of modern technologies. Objective: This study aims to assess the level of eHealth literacy among patients with DM and examine its association with self-care and health outcomes. Methods: A cross-sectional study was conducted among patients with type 2 DM from the DM clinic of a public hospital in Hong Kong. Data on eHealth literacy, self-care, self-care self-efficacy, diabetes distress, glycated hemoglobin (HbA) control, and sociodemographic information were collected. Multivariable regression analyses were performed, adjusting for relevant sociodemographic and medical variables. Results: Among the 427 patients with DM recruited, around two-thirds (65.1%) were classified as having a high level of eHealth literacy. Compared to those with lower eHealth literacy, participants with higher eHealth literacy demonstrated significantly higher levels of self-care (

Background: Although smoking-cessation aids such as support groups and nicotine replacement therapy (NRT) can help people quit, quit rates remain low. Mobile health interventions can boost accessibility and engagement, especially with NRT, but require ongoing effort to deliver timely responses. Accurate intent detection is crucial for identifying user needs and delivering timely, appropriate chatbot responses. Recent large language model advancements in natural language processing and artificial intelligence (AI) have shown promise. However, these systems often struggle with many intent categories, complex language, and imbalanced data, reducing recognition accuracy. Objective: The main goal of this study was to develop an AI tool, a large language model that could accurately detect people’s message intents, despite dataset imbalances and complexities. In our application, the messages came from a smoking-cessation support-group intervention and often involved the use of NRT provided as part of that intervention. Methods: We consistently used a state-of-the-art public domain large language model, Llama-3 8B (8 billion parameters) from Meta. First, we used the model off-the-shelf. Second, we fine-tuned it on our annotated dataset with 25 intent categories. Third, we also downsampled the predominant intent category to reduce model bias. Finally, we combined downsampling with corrected human annotations, creating a cleaned dataset for a new round of fine-tuning. Results: Without fine-tuning, the model achieved unweighted and weighted -scores (overall performance) of 0.41 and 0.38, respectively, on the downsampled corrected test dataset, and 0.29 and 0.35 on the full test dataset. Fine-tuning improved performance to 0.77 and 0.80 on the downsampled corrected dataset, and 0.72 and 0.86 on the full dataset. Fine-tuning with downsampling attained the best -scores, 0.88 and 0.91 on the downsampled corrected dataset, though performance dropped on the full test dataset (0.58 unweighted, 0.66 weighted) due to the predominance of the off-topic intent category, while unweighted recall remained high (0.80). The final method combining fine-tuning, downsampling, and error correction achieved 0.86 unweighted and 0.90 weighted -scores on the downsampled corrected dataset, and 0.57 and 0.65 on the full dataset with unweighted recall improving to 0.82. Conclusions: Large language models performed poorly without fine-tuning, highlighting the need for domain-specific training. Even with fine-tuning, performance was limited by a highly imbalanced dataset. Downsampling before fine-tuning moderately improved performance but still left room for improvement and concerns about dataset noise. A careful review of model-human disagreement cases helped identify human annotation errors. After error correction, the method without error correction still achieved slightly higher precision and -score on the corrected test dataset. While error correction slightly improved recall on noisy data, automated downsampling alone may be sufficient, making manual correction a more resource-intensive option with limited added benefit.