JMIR Formative Research

The flower strains of cannabis are an important attribute that determines product appeal and health impacts. However, there is a lack of surveillance of cannabis strains as the marketplace expands in response to the growing legalization of recreational and medical cannabis.

Smoking continues to be a leading cause of preventable morbidity and mortality, and more than 480,000 Americans die annually due to smoking-related illness attributable to smoking and secondhand smoke. More advanced, responsive, and tailored digital interventions using machine learning and artificial intelligence may be a valuable tool for successful smoking cessation referrals.

HIV pre-exposure prophylaxis (PrEP) is underused by cis- and transgender women despite a significant HIV burden. Smartphone technologies are promising tools to support HIV prevention but have yet to be assessed in women.

With the increasing burden of chronic diseases, self-management education (SME) is crucial. Traditional SME based on face-to-face delivery by clinicians is resource-intensive, and general digital tools such as web-based platforms often provide limited interactivity for patient learning. Although chatbots based on large language models (LLMs) show promise in interactivity, their real-world effectiveness lacks empirical evidence.

Despite increasing smartphone penetration worldwide, personalized mHealth (mobile health) care interventions remain largely untapped for the support of people with tuberculosis. An evidence-based multifeature smartphone platform for HIV care tailored and widely implemented in the United States may enhance treatment quality and completion in the Kilimanjaro context.

Fecal-oral diseases remain a major public health challenge in sub-Saharan Africa, where sanitation infrastructure is limited and cultural barriers hinder improved practices. Compost latrines are promoted as ecological solutions, but their acceptability is uncertain.

Chemotherapy-related toxicities often lead to unscheduled health care use and diminished quality of life. Digital health interventions, such as chatbots, offer a scalable solution for supportive care; however, evidence regarding their effectiveness in resource-limited, low- and middle-income settings remains limited.

Despite gender parity in medical school matriculants, women in academic medicine encounter ongoing challenges in career advancement and promotion.

Recent research has found that concurrent intimate partner violence (IPV) experience (ie, victimization) and use (ie, perpetration) may be more common than experiencing or using IPV in isolation. Therefore, screening for IPV experience and use concurrently is needed to provide resources and connect patients to care.

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.