JMIR Formative Research

The transition from child to adult mental health services is a vulnerable period marked by service disengagement, care gaps, and worsening mental health outcomes. Although planned, developmentally appropriate transition processes can improve functioning, youths report insufficient preparation, limited continuity of care, and unmet expectations for support. Existing transition supports remain underevaluated and require further adaptation for mental health contexts. Youth consistently report needing clearer information, concrete support, and sustained connection. Digital tools, particularly SMS text messaging, which is widely used, accessible, and acceptable to youth, offer a promising way to deliver timely transition supports. Yet most digital mental health tools are developed without meaningful youth involvement, highlighting the need for participatory approaches to ensure relevance, usability, and uptake.

Post–COVID-19 fatigue affects millions worldwide; yet, evidence-based management strategies remain limited. Activity pacing, regulating activity to match available energy and minimize symptom exacerbation, may support symptom management, although optimal pacing approaches remain unclear.

Emergency department (ED) revisits are critical quality indicators, particularly in medically underserved areas, where traditional prediction tools show limited performance. Machine learning (ML) approaches may offer improved predictive performance for identifying high-risk patients.

An FDA-cleared artificial intelligence tool demonstrated frequent discordance with radiologist reports for lung nodule assessment on chest CT.

Older Black adults remain underrepresented in dementia research, particularly in studies using speech-based methods for early cognitive assessment. Understanding how to effectively recruit and engage this population in research involving audio-recorded interactions is critical to ensuring equitable inclusion and developing culturally responsive study designs. However, recruiting older Black adults into dementia research remains a significant challenge.

Environmental factors account for 23% of global deaths and 25% of chronic diseases. In France, the Fourth National Environmental Health Plan prioritizes training health professionals in environmental health. Endocrine-disrupting chemicals (EDCs) are chemical substances that interfere with hormonal systems, contributing to a range of health effects. In 2024, the Primary Care and Environmental Health (PCEH) program at the University of Montpellier–Nîmes introduced an innovative e-learning module on EDCs for first-year family medicine residents.

Telemonitoring has been shown to improve glycemic control in type 2 diabetes, but the optimal design for effectively integrating self-management education remains unclear. Including patient feedback in the design process can enhance usability, increase engagement, and improve the feasibility and effectiveness of the intervention in real-world settings.

A prevalent and serious complication of diabetes mellitus is the development of diabetic foot ulcer (DFU). There is a need for effective solutions that help prevent DFU to support our increasingly stressed health care systems. The use of mobile health (mHealth) tools has been shown to improve awareness and effective self-care management skills in people at risk of developing diabetic foot ulceration.

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Endometriosis is a chronic gynecological condition affecting approximately 10% of women of reproductive age worldwide and is associated with chronic pelvic pain, infertility, and reduced quality of life. In Brazil’s Unified Health System (Sistema Único de Saúde [SUS]), diagnostic delays frequently range from 7 to 10 years and disproportionately affect socially vulnerable populations, including rural, low-income, Black, and Indigenous women. Digital health interventions have been proposed as scalable solutions; however, most available applications are developed in high-income settings and do not align with the structural and operational realities of low- and middle-income countries (LMICs).

Large language models (LLMs) such as ChatGPT are increasingly used to support health-related queries and decision-making. However, these models can be “jailbroken” through adversarial prompts that bypass safety filters and elicit harmful or medically inappropriate responses. In health care contexts, such vulnerabilities pose serious risks. Understanding how jailbreak susceptibility varies across languages is essential for developing robust safeguards and promoting equitable access to safe health information. This paper may contain examples that may be deemed harmful in terms of violence, self-harm, and drug abuse.

Ischemic heart disease (IHD) is becoming increasingly prevalent, with a rising trend significantly impacting the quality of life (QoL) of young Malaysians.