JMIR Formative Research

Large language models (LLMs) are increasingly used to support digital health communication, yet their reliability in patient-facing cardiovascular imaging education remains uncertain. Cardiovascular imaging involves complex terminology and procedural details that many patients struggle to understand, creating a need for accurate, clear, and reassuring explanations. While prior evaluations of conversational AI have focused primarily on diagnostic reasoning or clinician-oriented tasks, few studies have systematically compared contemporary LLMs in their ability to communicate effectively with patients.

Home spirometry has been widely adopted in the delivery of cystic fibrosis (CF) care. While existing literature largely supports its feasibility and positive outcomes, behavior around home disease monitoring remains poorly understood. Inaccurate assumptions about home spirometry behavior may affect resource prioritization and influence clinical decisions and follow-up.

Young Black and Latino men who have sex with men and transgender women who have sex with men (YBLMT) experience disproportionate HIV-related health disparities in the United States. Digital health interventions offer scalable HIV prevention and support services for these populations. However, recruitment strategies may influence both sample demographics and participant retention, which is critical for intervention effectiveness.

Rural communities continue to experience behavioral health disparities associated with workforce shortages, digital exclusion, and fragmented coordination between trusted community-based supports and formal behavioral health systems. Although telehealth has expanded opportunities for care, less is known about how key implementation factors interact within hybrid behavioral health systems—coordinated systems that integrate trusted community-based support with formal digital behavioral health services—or how these interactions influence implementation and engagement across community and formal care settings.

Workday happiness is associated with workplace performance and burnout, but frequent questionnaire-based assessment is burdensome in real-world workplace settings. Free-text daily reports may provide a lower-burden way to monitor day-to-day changes in workday happiness.

While large language model (LLM)–assisted qualitative analysis could improve the efficiency and scalability of feedback-driven curricular refinement in medical education, how best to leverage LLMs for qualitative analysis while ensuring quality outputs remains an open question. Prior work has demonstrated the feasibility of using LLMs for inductive and deductive coding tasks, but more needs to be known about how LLM-assisted thematic coding can best be deployed in a medical education context to maximize its strengths and guard against its weaknesses.

Smartphones and wearables can continuously capture behavioral and physiological data in everyday life. Such mobile-sensing data may help track depressive symptoms more closely than occasional retrospective questionnaires, but prior findings have been mixed. Inconsistent findings do not preclude the presence of predictive relationships in specific individuals or time periods. In addition, it remains unclear which broader sensor domains, rather than single features, contribute most to prediction at the individual level.

As the global population ages, Parkinson disease (PD) has emerged as the second most prevalent neurodegenerative condition after Alzheimer disease. People with PD often experience speech problems, including reduced volume, monotone pitch, breathiness, and word slurring. Interventions such as speech therapy through mobile apps provide reassurance and easier access to care for patients having this neurodegenerative condition. Mobile apps offer patients with PD greater access to care and the reassurance of being able to manage their condition at home. However, we do not know the quality of these apps. A systematic evaluation of these mobile apps is necessary to ensure their effectiveness and suitability for use by patients with PD.

Advance care planning (ACP) is an ongoing dialogical process. Despite growing policy-level recognition, ACP engagement remains limited in Japan, and many individuals remain unfamiliar with the concept. Beyond knowledge deficits, insufficient psychological readiness may also impede engagement. Evidence on fully online narrative- and game-based approaches to supporting such readiness remains limited.

Obesity imposes a substantial economic burden, accounting for an estimated 10% of total health care expenditures in Germany. Digital health apps (DHAs) have demonstrated effectiveness in supporting weight management among individuals with obesity; however, evidence regarding their long-term economic impact remains limited.

Natural language processing and large language model systems are increasingly used to support mental health documentation, screening, and follow-up planning. In counseling contexts, model outputs may influence diagnostic framing, risk recognition, and clinical record content. Static performance metrics and fluent generated summaries are not sufficient to support safe implementation without governance, safety gating, human review, and monitoring.

Cesarean section (C-section) is the most common surgical procedure in the United States, yet its use varies widely across regions and institutions. Although clinical risk factors are central to delivery decisions, geographic context, health system capacity, and local practice patterns may also influence C-section use. Understanding both the determinants and predictability of C-section delivery is important for improving obstetric quality and equity.