JMIR Formative Research

The rapid acceleration of large language models (LLMs) creates opportunities to expand the accessibility of mental health support; however, general artificial intelligence (AI) tools lack safety guardrails, evidence-based practices, and medical regulation compliance, which may result in misinformation and failing to escalate care in crises. In contrast, Ebb, Headspace’s conversational AI tool (CAI tool), was purpose-built by clinical psychologists and research experts using motivational interviewing techniques for subclinical guidance, incorporating clinically backed safety mechanisms.

The development of efficient, scalable, and precise tools to assess knowledge of evidence-based parenting strategies is critical, particularly as increased parenting knowledge is a core target of many intervention programs.

Healthcare providers (HCPs) in public health facilities in low- and middle-income countries (LMICs), including Nepal, often lack adequate training to manage mental health problems effectively.

Annotated bibliographies summarize literature, but training, experience, and time are needed to create concise yet accurate annotations. Summaries generated by artificial intelligence (AI) can save human resources, but AI-generated content can also contain serious errors.

Sleep disorders are common among older adults undergoing surgery and contribute significantly to postoperative complications, delayed recovery, and higher health care costs. The combined effects of age-related physiological changes and surgical stress further disrupt sleep in this vulnerable group. However, current tools for predicting surgical risk rarely account for the specific physiological, clinical, and psychological factors that affect older patients. While wearable devices are used to monitor sleep, most prediction models focus on general sleep quality in nonsurgical populations, leaving a gap in forecasting preoperative sleep disorders in older surgical candidates. Therefore, we developed and validated a tailored risk prediction model that integrates objective sleep data from wearable devices with comprehensive clinical and psychosocial evaluations for older adults preparing for surgery.

Digital health interventions (DHIs) have the potential to extend the reach of disease prevention interventions; however, few are evidence-based, theoretically grounded, or developed for high-risk youth and families. Co-design approaches engage the end user in the design and development of the DHI by applying an equity lens that can lead to increased accessibility and engagement.

Lately, big data studies have shown promise in using patient characteristics to rank the likelihood of retention of antiseizure medications (ASMs), a measure indicating tolerability as well as effect. How such results can be integrated into clinical practice has yet to be studied. We developed EPstat, a non-commercial tool that provides physicians with real-world treatment retention data from 33,998 epilepsy patients.

Physicians’ perceived compensation satisfaction enhances work enthusiasm, ensures health care system stability, and inspires health care quality. However, few studies have investigated the combined effect of multiple influencing factors on this perception.

Loneliness is a form of psychological distress associated with increased risk of depression, anxiety, and adverse health outcomes across the life span. This study evaluates an online gaming‑based community intervention that combines professionally facilitated groups, commercial video games, and skills‑focused workshops for adults who play video games.

Despite a 40-year prevalence of the human immunodeficiency virus (HIV) the acquired im-munodeficiency syndrome (AIDS) epidemic prevails. Effective AIDS treatment requires spe-cialist care and high adherence often hindered by structural issues in care access. Innovative eHealth solutions like a treatment-supportive mobile applications, can help address these is-sues. Successful implementation depends on user acceptance. Acceptance can be opera-tionalized as behavioral intention and measured through the Unified Theory of Acceptance and Use of Technology (UTAUT).

Effective diabetes management requires individualized treatment strategies tailored to patients’ clinical characteristics. With recent advances in artificial intelligence, large language models (LLMs) offer new opportunities to enhance clinical decision support, particularly in generating personalized recommendations.

This cross-sectional study examined presence of food stores and availability of fruits/vegetables in food stores with weight among urban public housing residents; while there was no association between average number of fruits/vegetables in food stores and weight, there were positive associations between number of convenience stores and weight and between number of general merchandise stores and weight.