JMIR Formative Research

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.

Leisure-time physical activity (LTPA) participation among Chinese college students remains insufficient despite growing public health concerns regarding sedentary lifestyles and obesity. Social media platforms have increasingly become important channels for delivering social support related to physical activity (PA); however, different dimensions of social support on social media may exert distinct psychological influences on exercise intentions.

The quality of online information regarding the risks associated with meat consumption could play a crucial role in shaping consumers’ behavior.

Community-based mobile health (mHealth) services are increasingly used to support chronic disease management in underserved rural populations facing workforce shortages, geographic isolation, and rapid aging. South Korea entered a super-aged society in December 2024, intensifying pressures in rural regions where multiple mHealth programs are embedded within primary care and public health systems. However, evidence on sustained use in real-world settings remains limited.

Long COVID is a clinical condition that significantly influences quality of life, productivity, and morbidity in the individuals affected. Much of the research to date has examined medical comorbidities and their associations with long COVID, but there remains a substantial need to understand the social and behavioral factors associated with long COVID.

Use of in-home video telehealth rapidly expanded in response to the COVID-19 pandemic, including at Veterans Affairs (VA), a forerunner in telehealth. Despite this uptick, differences in use by patient age and rurality created a digital divide that persists to this day. While clinicians frequently cite patients’ older age and lack of technical skills as barriers to in-home video telehealth, it remains unclear how clinicians decide whether to offer video visits to patients and to what extent these beliefs may hinder offering video visits to older adults. Gathering perspectives from clinician users of in-home video telehealth may illuminate opportunities to ensure continued access to care through solutions such as telehealth.