JMIR Formative Research

Young adults have high rates of mental health problems, such as mood or anxiety symptoms, and high rates of problematic drinking. Many young adults who undergo psychiatric hospitalization to address depression and anxiety symptoms also engage in risky drinking and tend to drink to cope with negative emotions. However, in many cases, treatment programs focusing on mood and anxiety symptoms often fail to adequately address problematic alcohol use in young adults.

Mobile health apps show promise for delivering physical activity interventions, but uptake remains low due to usability barriers. Beta testing is essential to optimize user experience before full implementation.

Large language models (LLMs) have gained increasing popularity in medical education, with evidence supporting their educational value when framed through the lens of cognitive load theory. Source-based LLMs, which explicitly ground responses in user-uploaded material via retrieval-augmented generation algorithms, may offer additional educational value by using student-developed materials to conceptualize new areas of learning within a familiar framework. This has applications for areas like medical education in dermatology, which could benefit from inclusive sources and enhanced education to alleviate health care gaps. However, no prior studies have examined whether the inclusion of student-authored notes alters the response characteristics of a source-based LLM when responding to medical questions.

Simulation-based training has established itself as integral to clinical education, particularly for high-stakes, low-frequency pediatric emergencies. Innovations incorporating virtual reality (VR) are rapidly gaining traction for offering scalable, repeatable, and immersive opportunities for scenario-based learning. Understanding its role and applicability in postgraduate pediatric training, however, remains limited, with further exploration required into how pediatric trainees perceive, conceptualize, and anticipate VR-based simulation within real-world training contexts.

Fragmentation of electronic health records in oncology hinders coordinated care, delays diagnoses, and limits therapeutic personalization. Blockchains promise to promote secure, interoperable, and patient-centered data governance; however, patient perceptions of blockchains remain underexplored, particularly in middle-income countries such as Brazil.

Generative artificial intelligence (GenAI) chatbots have the potential to provide personalized mental health support to individuals at scale.

Motivational interviewing (MI) is an effective counseling approach for promoting health behavior change, but its scalability is constrained by the need for highly trained human counselors. Large language models (LLMs) may provide a scalable way to support MI counseling, but evidence remains limited, especially for Chinese MI resources and evaluations based on standardized MI fidelity frameworks.

Mitral valve prolapse (MVP) is a common valvulopathy associated, in a minority of cases, with heart failure, severe mitral regurgitation (MR), and sudden arrhythmic death. Digital tools hold promise for faster and more efficient recruitment of study participants into a large-scale MVP Registry.

Predictive models increasingly support clinical decision-making, although imbalanced outcome distributions are common in health care datasets and can distort performance evaluation. The area under the receiver operating characteristic curve (AUROC) remains the most frequently reported metric, despite its limited ability to reflect clinically meaningful performance under class imbalance.

When confronted with ambiguous stimuli, the ability to utilize contextual information is crucial for survival. Context processing involves the ability to discriminate new information from previously encountered information and to recognize something as previously encountered, even briefly or partially. Deficits in context processing are a key feature across a number of psychiatric conditions. Existing tasks only examine the discrimination and recognition of cues as opposed to contextual information. Thus, new tasks using complex scenes are urgently needed.

Population aging has become a critical global challenge, with South Korea entering a super-aged society and facing rapidly increasing health care demands. In response, digital health care devices have emerged as promising tools for supporting personalized health management and improving health care accessibility among older adults. However, despite their potential, adoption rates among older adults remain relatively low. Prior research based on the Technology Acceptance Model (TAM) has largely relied on variable-centered approaches, overlooking substantial heterogeneity in acceptance patterns among older adults. A person-centered segmentation approach is therefore needed to identify diverse acceptance profiles. Few studies have integrated the augmented TAM with K-means clustering to identify acceptance-based segments in this population.

Enterovirus infections cause substantial pediatric morbidity worldwide, with severe cases requiring hospitalization. Accurate forecasting of hospitalization burden supports proactive resource allocation and clinical preparedness. During the postpandemic period (2023‐2024), Taiwan experienced a resurgence of enterovirus activity following COVID-19–related suppression, although at levels below prepandemic baselines, creating unique operational forecasting challenges.