JMIR Formative Research

The integration of eHealth solutions into routine health care remains challenging despite their potential to improve care quality and efficiency. Living labs (LLs) have emerged as an approach to addressing these challenges by enabling co-design and iterative testing of eHealth tools. LLs promote collaboration among patients, health care professionals, developers, and policy makers. However, systematic insights into their practical implementation remain limited.

As Taiwan enters a super-aged society, the prevalence of dementia continues to rise, highlighting the need for scalable, noninvasive approaches to identify behavioral alterations associated with cognitive decline. AI-based analysis of spontaneous affective facial responses has emerged as a potential digital phenotyping tool, but its feasibility for assessing cognitive performance remains underexplored.

Hypertension is a common chronic condition associated with substantial morbidity globally. In Jordan, many patients struggle with sustained medication adherence and self-management, highlighting a need for accessible care strategies. Low-cost platforms such as WhatsApp may support telehealth education and remote follow-up, although formative pilot data are needed to assess feasibility in this context.

Traditional brick-and-mortar pulmonary rehabilitation (PR) programs remain substantially underused, with only 2.7% of Medicare beneficiaries completing PR within 12 months following hospitalization for a COPD exacerbation. Given the critical role of PR in improving exercise tolerance, quality of life, and reducing readmissions, innovative delivery models are needed to address this massive utilization gap. Virtual PR platforms have emerged as a potential solution, offering scalable, and home-based rehabilitation.

There is evidence of rapid adoption of large language models (LLMs) in undergraduate medical education, with medical students using them to support their learning. This raises concerns about the accuracy of LLM-generated information that students are learning from. Retrieval-augmented generation (RAG), which grounds an LLM in curated materials, may be a strategy to improve the accuracy of this information.

Millions of people now use general-purpose large language models (LLMs) for mental health support, yet how these models respond to emotional disclosures and whether their responses functionally resemble established therapeutic practices remain uncharacterized.

Little is known about the composition and function of the social networks of adolescent and young adult survivors of cancer, despite the importance of social health for adolescent and young adult development. For adolescent and young adult survivors of cancer, social networks may be particularly important sources of support and coping. Understanding this group’s social health through understanding their social networks can inform intervention development to improve various facets of health and life productivity.

We recently completed the efficacy testing of an instructor-led yoga program for patients with thoracic cancer undergoing radiotherapy and their family caregivers. Translating an instructor-led intervention to a mobile app–based delivery model may facilitate the implementation of this yoga program in routine oncological clinical care.

Childhood obesity remains a public health concern in the United States, disproportionately affecting Hispanic/Latino and rural populations. Despite this disparity, there is a lack of culturally relevant programs specifically designed to reach this population. To address this gap, our team culturally adapted a technology-based childhood obesity intervention, Hispanic Family Connections (HFC), targeting Hispanic/Latino families living in rural areas. The intervention comprises 2 main components: interactive voice response (IVR) calls and a workbook with at-home activities to promote family engagement.

Discharge mobility after osteoporotic vertebral fractures is heterogeneous and difficult to predict at admission. Machine learning offers structured approaches for multiclass functional outcome prediction, but existing models predominantly address binary end points and report global performance metrics without explicit analysis of the error structure.

Online platforms are increasingly used to deliver training and implementation resources for large-scale, school-based physical activity initiatives, and understanding behavioral engagement with these platforms is important because it provides an early indicator of initiative uptake; yet, few studies have examined engagement during real-world scale-up.

Climate-related disasters increasingly affect community health, health care access, and disaster preparedness, particularly in disaster-prone Gulf Coast communities. Although climate dashboards have expanded access to environmental data, many function primarily as static repositories with limited integration of health data or operational decision support capabilities.