Journal of Medical Internet Research

Wearable and connected digital devices continuously generate large volumes of real-world behavioral, physiological, and environmental data, offering new opportunities for health monitoring and personalized care. Digital phenotyping has emerged as a promising paradigm; yet, there is limited consensus regarding how such data should be analyzed. Inconsistent analytical practices and insufficient methodological reporting may compromise reproducibility, comparability, and the validity of findings.

Stigmatizing language (SL) in electronic health records (EHRs) can influence clinical decision-making, propagate bias across care encounters, and undermine patient trust. Gender-expansive patients (GEPs) may be particularly vulnerable to documentation-based stigma; however, large-scale quantitative evidence and fairness-aware evaluation of automated SL detection methods remain limited.

Real-world persistence with antiobesity pharmacotherapy is suboptimal: only 32% to 50% of patients persist with glucagon-like peptide-1 receptor agonists at 12 months, and fewer than 15% reach the 2.4 mg/week target dose. Digital platforms may support engagement, but their role remains poorly characterized.

Lightfoot and colleagues explored patient perspectives on the implementation and sustainability of My Kidneys & Me, a digital self-management intervention for chronic kidney disease, identifying key factors that may influence successful adoption into routine care. The study shows that generating evidence of effectiveness alone is insufficient for successful mobilization into practice, highlighting the importance of embedding implementation evaluation in concert with clinical trials to identify practical strategies that support real-world uptake and sustained use.

Patient portals are essential infrastructure, reinforced by the 21st Century Cures Act, yet adoption remains inequitable. The COVID-19 pandemic accelerated portal adoption as telehealth and remote result delivery made electronic access integral to care, but racial and ethnic disparities persisted. Understanding activation determinants is critical for addressing digital health disparities, particularly among neurology patients, for whom cognitive, speech, and mobility impairments can complicate portal use.

Large language models (LLMs) are increasingly used to generate health education materials, yet questions remain about whether LLM-generated content can balance professional accuracy with public accessibility and what ethical challenges may arise during deployment.

Refugees and forcibly displaced populations experience elevated rates of mental health conditions, including posttraumatic stress disorder, depression, and anxiety, while facing substantial barriers to mental health care. AI has emerged as a promising approach for mental health detection, intervention, and decision support; however, no review has specifically examined AI-based approaches to refugee mental health care.

Rehabilitation clinical practice guidelines (CPGs) have increased rapidly, but inconsistent methodological quality limits their implementation. Although Appraisal of Guidelines for Research and Evaluation II (AGREE II) and Reporting Items for Practice Guidelines in Health Care (RIGHT) provide standardized appraisal frameworks, their application is time-consuming. Large language model (LLM)–based AI agents may offer a scalable alternative with uncertain reliability.

Social media use among older adults continues to grow. Many people use social media to establish meaningful social ties and discuss their mental health. Depression in midlife and older adults is a critical public health concern, yet scalable, sensitive methods for early detection remain limited. Natural language processing offers new opportunities to examine sentiment and mental health through online language in typically understudied populations.

Unstructured electronic health records (EHRs) hinder the monitoring of intestinal infections. Large language models (LLMs) enable automated symptom extraction. However, their clinical validation is limited by a lack of systematic multimodel comparisons, unclear prompting strategies, and the privacy risks of cloud-based models (eg, data leakage and cross-border data transfer).