JMIR Formative Research

In the UK, there was an increased demand for young people’s mental health helpline services during COVID-19, when face-to-face services were often inaccessible. Despite this, there is scant research examining young people’s experiences with these helplines during the pandemic and post-pandemic periods.

The integration of artificial intelligence (AI) into clinical decision support systems (CDSSs) for mechanical ventilation in intensive care units (ICUs) holds great potential. However, the lack of transparency and explainability hinders the adoption of opaque AI models in clinical practice. Explanation user interfaces (XUIs), incorporating explainable AI algorithms, are considered a key solution to enhance trust and usability. Despite growing research on explainable AI in health care, little is known about how clinicians perceive and interact with such explanation interfaces in high-stakes environments such as the ICU. Addressing this gap is essential to ensure that AI-supported CDSS are not only accurate but also trusted, interpretable, and seamlessly integrated into clinical workflows.

Poor sleep is a concerning public health problem in the United States. Previous sleep interventions often face barriers such as high costs, limited accessibility, and low user engagement. Recent advancements in artificial intelligence (AI) technologies offer a novel approach to overcoming these limitations. In response, our team developed a prototype AI sleep chatbot powered by a large language model to deliver personalized, accessible sleep support.

Although progress has been made in improving the efficacy of Diabetes Self-Management Education (DSME) programs, there remains a dearth of research on culturally adapted, evidence-based DSMEs for Chinese Americans (CAs) with type 2 diabetes.

Kazakhstan has lacked validated tools to comprehensively assess physicians’ perceptions, usability, and perceived effectiveness of telemedicine services. International frameworks such as the Telehealth Usability Questionnaire (TUQ) and the Model for Assessment of Telemedicine (MAST) have not previously been adapted to the national clinical and organizational context

Hospital-based violence intervention programs (HVIPs) have shown promise in mitigating the effects of violence, but their impact is limited by time constraints and inefficient practices faced by the Violence Prevention Professionals (VPPs) who function as case managers. Mobile health (mHealth) applications (apps) offer the potential to enhance communication and service delivery between VPPs and clients, but few have been specifically designed for vulnerable populations.

Digital Health Tools (DHTs), including wearables and mobile apps, offer promising avenues for personalized care and real-time monitoring, but user engagement and clinical utility—especially in pediatric populations—remain unclear. Li-Fraumeni syndrome (LFS) is a genetic mutation in the TP53 tumor suppressor gene, predisposing individuals to cancer, requiring lifelong surveillance and associated psychological stress.

Depression, anxiety, and cognitive decline are prevalent concerns among older adults and can negatively affect their well-being. Pet ownership has been proposed as a potential protective factor, but inconsistencies remain in the current literature.

Point-of-care ultrasonography (POCUS) is a test performed by any physician, as an adjunct to physical examination, to identify the presence or absence of specific findings of medical conditions. This skill is not yet taught in undergraduate medical education in Peru.

Caffeine consumption is a common strategy to enhance alertness, particularly among medical students managing intense academic demands. This study examines caffeine intake across different stages of medical training—first-year, second-year, and third-year medical students—to determine whether intake increases as students progress.

Serum protein electrophoresis (SPE) is routinely interpreted through visual assessment of electropherogram images by medical laboratory scientists. We introduce an efficient tabular data–based machine learning approach that directly leverages numerical SPE profiles, offering a robust and interpretable alternative to image-based deep learning methods.

Eating disorders (EDs) are severe mental health conditions driven by psychological, social, and emotional factors and have the highest mortality rate of any psychiatric disorder. Although evidence-based, theory-driven behavior change interventions are the gold standard, access to treatment remains limited. Digital interventions, such as apps, may offer accessible support for individuals with mild to moderate EDs; however, their development has rarely been guided by systematic behavior change frameworks. Consequently, many interventions inadequately target the mechanisms underlying ED behaviors and commonly lack involvement of people with lived experience.