Journal of Medical Internet Research

Increasingly, brief digital interventions are being implemented to combat alcohol misuse. While the utility of incorporating personalized feedback into these interventions has been explored, less work has concentrated on how consumer-driven research might be harnessed to better tailor personalized feedback. Critically, the relative efficacy of co-designed interventions vs those designed by research teams without stakeholder input has not typically been explored.

21st Century Cures Act information blocking regulations led to many organizations altering policies to electronically release test results to patients immediately upon their availability.

Embodied intelligence—artificial intelligence instantiated in physical or virtual bodies that can perceive, communicate, and interact with users and their environments—has been increasingly applied in health care. However, the evidence base remains fragmented because of inconsistent terminology, diverse embodiment forms, and limited synthesis of application domains, target populations, care settings, acceptability, and effectiveness. This fragmentation constrains conceptual clarity and translation into routine health care practice.

Noncommunicable diseases (NCDs) account for over 70% of global deaths, with hypertension and diabetes serving as major contributors. The COVID-19 pandemic disrupted traditional health care services for NCDs and highlighted telehealth as a crucial alternative. Telehealth—encompassing synchronous and asynchronous electronic communication to deliver clinical services remotely—can overcome geographical barriers and enhance patient engagement. However, telehealth usability among health care professionals (HCPs) remains under-studied across low-, middle-, and high-income countries.

Large language models (LLMs) are increasingly used by patients for health information and preliminary medical advice. In patient-facing consultations, users may present explicitly stated diagnostic preferences or symptom narratives emphasizing a preferred explanation. Such cognitively biased input constrains the diagnostic context available to the model and may systematically steer its reasoning during interactive LLM-supported health consultations.

Little is known about (1) sociodemographic, psychosocial, or smoking-related differences among individuals recruited to smoking cessation randomized controlled trials (RCTs) using in-person versus online recruitment methods or (2) the relative speed of recruitment using these 2 approaches. This secondary analysis is the first to examine these comparisons in a smoking cessation RCT for people experiencing food insecurity, a vulnerable special population for whom quitting is especially urgent.

The delivery of specialist stroke rehabilitation is undergoing a significant transformation, with telerehabilitation increasingly integrated into clinical practice and supported by guidelines and policy. There is a need for the pragmatic evaluation of telerehabilitation in service, which includes insights from clinical teams and people with stroke. This evaluation sought to address that need in the context of community stroke services in the East of England.

Sedentary behavior among older adults is a major public health concern, contributing to the increased risk of chronic diseases and functional decline. With aging populations worldwide, prolonged sitting time (averaging up to 13 h/d in older adults) has been independently associated with cardiovascular disease, metabolic disorders, cognitive decline, and all-cause mortality. Mobile health (mHealth) interventions offer a promising approach to address this issue. However, there remains a lack of evidence-based, systematically developed mHealth programs specifically targeting sedentary behavior in older populations.

Cyberbullying victimization is a significant risk factor for poor psychological well-being among college students. Existing tools fail to capture the distinct dimensions of victimization in social media contexts.

As the population ages, demand for health services among older adults and caregivers increases. Digital health services help meet this demand. However, acceptance and experiences differ between older adults and caregivers.

The use of Clinical Decision Support Systems (CDSS), such as clinical decision rules, algorithms, or machine learning-based applications, has gained attention in recent years. However, their adoption and effectiveness may vary across different health care systems and settings. For a CDSS to be adopted, it must effectively address the practical issues encountered by professionals; however, little research has been done to identify these needs and requirements.