JMIR Formative Research

Foodborne diseases remain a significant global public health concern, imposing substantial health and economic burdens. The COVID-19 pandemic altered health care–seeking behaviors, infection control practices, and infectious disease epidemiology; however, its association with microbial foodborne pathogens remains incompletely characterized.

Precision medical education (PME) is an emerging concept that aims to improve physician education by tailoring curricula specifically to each learner. The basic framework behind PME requires collecting data on each individual learner, identifying gaps in knowledge, and then applying direct educational interventions. However, logistical difficulties in the delivery of such interventions hinder the implementation of PME in existing academic programs.

Teletherapy is gaining increasing importance in physiotherapeutic care but has so far been only marginally established in Germany. The current state of use, attitudes, and perceived barriers among physiotherapists remains insufficiently described.

Occupation-based practice (OBP) has been demonstrated to enhance occupational performance and goal attainment in stroke rehabilitation. However, existing evidence is predominantly derived from hospital-based settings, with limited investigations into OBP implementation in community contexts. In Thailand, particularly in rural areas, there remains a need to establish effective OBP service delivery models and to evaluate their outcomes across diverse client populations receiving community-based rehabilitation services.

Measurement of life space characterizes people’s engagement with their environment. Several studies have used GPS devices to capture life space. A challenge with device-based measures is that participants may forget to bring the device with them. Life-space measures that leverage the GPS data from participants’ mobile phones may address this challenge, but few studies have leveraged this approach to date.

Experiment in a Box (XB) is a guided, time-limited framework designed to help individuals test and adapt simple eating and movement heuristics within the context of everyday life. Rather than emphasizing external performance metrics alone, XB invites participants to evaluate practices through felt experience, including whether a practice helps them feel better, worse, or the same. Although prior XB studies have shown promise for supporting health behavior change, less is known about the experiential and interpretive processes through which participants use XB to connect felt experience with action, adaptation, and continued practice.

Rejoyn (CT-152) is a prescription digital therapeutic (DTx) adjunct to antidepressive medication authorized for patients with major depressive disorder. To better understand the patient benefit of DTx and other treatment modalities, current regulatory standards support the use of modern psychometric methods to interpret the clinical meaningfulness of treatment effects for patients. In the primary analysis of Rejoyn from the pivotal phase 3 Mirai trial (NCT04770285), Rejoyn showed a broad risk-to-benefit profile as demonstrated on multiple clinician- and patient-rated scales, including the primary efficacy outcome measure, the Montgomery-Åsberg Depression Rating Scale (MADRS). These findings supported US Food and Drug Administration authorization of Rejoyn as a prescription DTx. However, the clinical relevance of these changes in the MADRS score is not immediately interpretable in clinical practice. Here, we present the results from several post hoc analyses of the Mirai trial data to support the interpretation of clinically meaningful treatment differences on clinician- and patient-reported change in depressive symptoms.

Comprehensive geriatric assessment (CGA) is a widely recommended, multidimensional approach for guiding clinical decision-making and management in older adults. However, its implementation remains limited by a shortage of geriatric specialists, the complexity of geriatric care, and the increasing demands of an aging population. Mobile self-administered tools offer a potential strategy to expand access to multidimensional geriatric evaluation, particularly in resource-limited settings.

Inadequate postcare patient education contributes to preventable readmissions and adverse outcomes that disproportionately affect medically complex, high-need communities. Large language models (LLMs) show promise for generating personalized, plain-language patient education at scale. However, existing LLM evaluation frameworks prioritize technical accuracy over patient accessibility and alignment with health literacy, and few explicitly account for the attitudinal influences that clinician evaluators may introduce into the rating process. In this viewpoint, we introduce an evaluation framework that pairs the Technology Acceptance Model (TAM) with the Medical Condition Regard Scale (MCRS). We call it the TAM-MCRS LLM evaluation framework, a novel clinician-centered approach for comparing which LLMs produce the highest-quality postcare patient education across accuracy, appropriateness, clarity, and completeness. We intend for this framework to be used to evaluate LLM-generated patient education outputs through a 2-arm design that pairs an expert clinician panel with automated assessment methods, allowing for interarm comparison using clinical vignettes while accounting for measured evaluator attitudinal variance. The framework was developed through the National Institutes of Health Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) Clinicians Leading Ingenuity IN AI Quality (CLINAQ) fellowship program, in partnership with Ochsner Health and Xavier University of Louisiana. The TAM-MCRS framework integrates two theoretical lenses. TAM maps perceived usefulness onto accuracy and completeness, and perceived ease of use onto clarity and appropriateness. Clinicians rate each output with a TAM-based questionnaire, and we then administer the MCRS as a postscoring attitudinal covariate to see whether their regard for the conditions represented in the vignettes influences those ratings. Together, the 2 lenses are intended to produce evidence that is objective, theoretically grounded, clinically realistic, and disparity-responsive. Implications for clinician informaticists, health system governance, and responsible AI deployment are discussed. This viewpoint reflects the authors’ position and is written for clinician informaticists, health system AI governance leaders, implementation scientists, and investigators evaluating LLM-generated patient education.

Type 2 diabetes mellitus (T2D) is one of the most significant chronic health challenges in Michigan, especially for individuals with food insecurity. Improving nutrition, including lowering carbohydrate intake, is foundational to T2D prevention and management. Grocery delivery paired with low-carbohydrate education has the potential to reduce logistical barriers to dietary change but is not well understood in individuals with food insecurity.

Sleep is a core component of psychiatric assessment, yet inpatient monitoring typically relies on brief observational checks that are subjective, variable, and sometimes disruptive. Wearable devices offer a means of capturing continuous, objective sleep and activity data without disturbing patients. Although digital health technologies are increasingly used in psychiatric research, little is known about how wearable-derived data can be integrated into routine inpatient workflows or used meaningfully by clinicians.

Cultural competence is an essential dimension of contemporary health care delivery, particularly in diverse, multicultural societies. South Africa, with its rich linguistic and cultural plurality, presents a unique context in which culturally responsive care is critical for equitable health outcomes. Despite growing international attention to cultural competence in health professions education, little research has explored this domain within optometry education in sub-Saharan Africa.