JMIR Formative Research

Many individuals use audiobooks at bedtime to unwind and facilitate sleep, yet little is known about how this practice relates to sleep health. Understanding these patterns may inform behavioral strategies to support sleep.

Accidents and injuries are the leading causes of preventable death among adolescents and are often related to substance use. About 60% of US high school students have tried alcohol and 22% report current alcohol use. Preventing and reducing adolescent alcohol use would contribute to substantial health benefits and prevent major health morbidity and mortality. Advances in interactive narrative learning technologies hold promise for designing games for health that effectively deliver age-appropriate and personalized behavior change interventions. The Interactive Narrative System for Patient-Individualized Reflective Exploration (INSPIRE) is designed to serve as an extension to clinical preventive care, engaging adolescents in a theoretically grounded alcohol prevention intervention by leveraging the dual mechanisms of interactive narrative and 3D game technologies.

Electronic medical record (EMR) systems are now ubiquitous in Ontario primary care, with near-universal adoption among family physicians. EMRs have become critical pieces of clinic infrastructure that can affect nearly every aspect of clinical and administrative work. As EMR vendors consolidate and functionality evolves, some clinics undertake EMR migrations—complex transitions that require data conversion, workflow redesign, training, and change management. Despite their importance and wide adoption, there is a gap in understanding how community-based primary care organizations in Canadian settings navigate migration in practice, particularly from the perspective of frontline clinicians and staff.

Real-world psychiatric care is marked by wide heterogeneity in clinical presentations and outcomes, underscoring the need for systematic approaches to outcome measurement. The Clinical Global Impression–Severity (CGI-S) scale is a brief, clinician-rated measure of overall illness severity that is widely used in psychiatric research, but rarely documented in routine care. Large language models (LLMs) may enable automated extraction of CGI-S scores from narrative clinical notes, thereby providing scalable outcome measures for real-world clinical care and research.

Medical students experience sustained academic, clinical, and psychosocial pressures. Online forums provide an informal space where students seek information, express concerns, and exchange peer support. However, few studies have analyzed these interactions longitudinally based on real-world data from online social platforms.

Digital phenotyping—the use of continuous data streams from digital devices such as smartphones to assess behavioral, psychological, and physiological states—holds transformative potential for health monitoring and personalized care. However, real-time analysis of large multimodal data often exceeds mobile devices’ computational resources, leading most platforms to rely on sequential processing and cloud-based computation.

Lack of diversity in clinical trials reduces the generalizability of treatment efficacy across different demographic groups, thereby diminishing the relevance, quality, and equity of research findings.

Continuous ambulatory HF-HRV (high-frequency heart rate variability) monitoring using a chest-worn ECG (electrocardiogram) device met a priori feasibility benchmarks (≥75% compliance and data quality) in adolescents at high risk for suicide over approximately two to three weeks, including during school days and sleep.

Digital media literacy (DML) may help adolescents to handle online risks, misinformation, digital violence, and problematic media use more reflectively. However, structured interventions for adolescents in child and adolescent psychiatry (CAP), particularly in inpatient and day-treatment settings, remain scarce.

Systematic collection of social determinants of health (SDoH) data remains inconsistent across health care settings, despite its critical impact on patient outcomes. Large language model–powered chatbots offer promise for scalable SDoH data collection, but rigorous, feasible evaluation methods for patient-facing applications are lacking.

The development of robust medical AI for knowledge discovery and decision support commonly necessitates large-scale datasets from multiple institutions. However, such data aggregation is severely constrained by privacy regulations and the inherent risk of sensitive information leakage, making it difficult to navigate the utility-privacy trade-off.

As information technology becomes increasingly pervasive in early childhood education, the impact of digital gameplay on developmental outcomes has emerged as a critical research frontier.