JMIR Formative Research

Engaging in regular aerobic physical activity (PA) during midlife is associated with reduced risk for Alzheimer disease and related dementias. Yet, most midlife adults fail to meet national PA guidelines. Goal setting is a commonly used behavior change technique to increase PA, but limited empirical evidence exists regarding whether certain types of goal setting are more effective than others. This study served as an initial step toward understanding how different goal-setting strategies may enhance PA and promote adherence to national PA guidelines among insufficiently active midlife adults with obesity.

Artificial intelligence (AI) is increasingly applied in chronic disease management, including diabetes, where it has the potential to support real-time data interpretation, improve clinical decision-making, and enhance patient engagement. Although AI tools are often developed to increase efficiency and personalization, there is limited evidence on how patients perceive the role of AI in managing their condition, particularly in relation to shared decision-making (SDM) and the patient–provider relationship.

Although mobile health (mHealth) apps for tobacco cessation augment traditional cessation methods and have contributed to increases in cessation rates, Black adults are underrepresented in mHealth app studies for tobacco cessation. As a result, their mHealth app preferences are not well-known.

Death is a difficult topic to discuss for many. Notwithstanding, there is much to learn regarding the contemporary Japanese people’s views on a good (peaceful) death. Particularly, shifts in public perceptions of death following the beginning of the COVID-19 pandemic should be considered by health care staff who deliver end-of-life care.

The transition from acute to chronic pain often reflects a persistent dissociation between physical tissue damage and subjective reports. In alignment with the 2020 International Association for the Study of Pain definition, pain is a personal experience filtered through a latent “susceptibility architecture.” While clinical assessment currently relies on static, text-based questionnaires, these are often confounded by linguistic interpretation bias and cognitive literacy. We hypothesized that an individual’s internal psychological substrate—traditionally captured via text—can be characterized through real-time behavioral signatures during physical challenge.

Health care leaders face a strategic dilemma: traditional expert-led content development ensures safety but is too slow for digital innovation, whereas artificial intelligence (AI) automation offers speed but introduces risks from hallucinations. Resolving this tension requires governance frameworks that balance operational efficiency with rigorous accountability for patient safety.

Generative artificial intelligence (AI) is arriving in high-stakes assessment; however, governance, validity evidence, and faculty readiness remain uneven. From a Taiwan-Japan perspective, we outline a pragmatic, transferable approach to integrating AI into nursing objective structured clinical examinations (OSCEs) using a 5-AI-role model—learning assistant, AI‑augmented standardized patient, assessment assistant, case generator, and learning analyst—mapped across pre-OSCE, peri-OSCE, and post-OSCE workflows with human-in-the-loop final judgment. Taiwan contributes agile interdisciplinary development, staged pilots (practice, mock OSCE, and limited high-stakes stations), A/B comparisons, and explainability-by-design logging that links scores to time-stamped evidence. Japan contributes robust policy scaffolding (national AI use guidance in K-12, a revised nursing model core curriculum with outcomes and assessment blueprints, and institutional research cultures that support auditability and quality assurance). We distill 4 cross-cutting governance pillars—human oversight, learning process transparency, ethics and safety, and traceability—into implementable techniques (machine-readable rubrics, standardized patient persona cards, bias monitoring, and targeted faculty development). Aligning with international principles (International Advisory Committee for AI; Organisation for Economic Co-operation and Development; United Nations Educational, Scientific and Cultural Organization; World Health Organization; European Commission’s High Level Expert Group; and National Institute of Standards and Technology), we propose a joint road map and shared registry to benchmark reliability, validity, equity, and workload impact. This viewpoint targets OSCE directors, nursing educators, and institutional leaders and provides a phase-gated governance blueprint rather than reporting original trial outcomes. Taiwan-led agility, complemented by Japan’s standards-driven assurance, can form an Asia-Pacific reference model for trustworthy AI‑augmented OSCE in nursing education.

Visual patient avatars are an innovative patient monitoring technology that can be used to translate numerical and waveform data into intuitive, avatar-based representations of patient conditions. Previous research indicates that this technology improves health care providers’ situational awareness compared to conventional monitoring methods. As patient-worn continuous vital sign monitoring continues to evolve, we introduce the Visual Patient Wearable device to provide avatar-based visualization tailored to this application.

Suicide is the second leading cause of death for children and adolescents aged 6 to 18 years. Pediatric suicidality is underreported, which poses significant challenges for effective intervention and prevention strategies. Identifying populations at risk of suicidality can provide critical benefits in terms of study cohort selection, prevalence estimation, and clinical resource allocation.

Depression during the perinatal period poses significant risks to both maternal and infant health. Although transcranial direct current stimulation (tDCS) has shown promise as a safe and well-tolerated intervention for perinatal depression, empirical evidence remains limited, and no prior study has integrated clinical outcomes with continuous objective behavioral monitoring.

Health care has seen several new disruptive technologies. One such innovation is the introduction of blockchain smart contracts. These smart contracts are activated automatically once preprogrammed conditions are met. Smart contracts have improved patient outcomes, the efficiency of care delivery, and reduced costs. Despite their benefits, patients have had limited interactions with smart contracts in primary care; therefore, they may not trust blockchain-based smart contracts and may perceive them as risky or have concerns about their security.

Mental health providers (MHPs) face a significant administrative burden from documentation, which can contribute to burnout and reduce time available for direct patient care. Although artificial intelligence (AI)–powered scribes have shown promise in general medical settings, their utility has not been well explored in the specific context of mental health care. This study describes the development and preliminary observational evaluation of Smart Notes, a generative AI tool designed to assist MHPs with documentation on a commercial virtual mental health platform.