JMIR Human Factors

Health messages are integral to smoking cessation interventions. Common approaches to health message development include expert-crafted messages and audience-generated messages, which produce messages that can be monotonic, didactic, and limited in number. We introduce an alternative approach to health message development that relies on user-generated content available on open content platforms as a source of health messages.

Although various applications (apps) have been developed to support health behaviors, they are mostly commercial, possibly limiting the number of users. The ME-BYO index was developed by Kanagawa Prefecture in 2019 to comprehensively and numerically measure and visualize an individual's current health status and future disease risk by quantifying data. The ME-BYO index is free of charge, so it can be made available to as many prefectural residents as possible for health promotion. Effective online strength training programs are being developed that, when combined with ME-BYO index measurements, will help with both exercise habits and health management.

Chat Generative Pre-trained Transformer (ChatGPT) excels in natural language tasks, but its performance in the Chinese National Medical Licensing Examination (NMLE) and Chinese medical education remains underexplored. Meanwhile, Chinese corpus-based large language models (LLMs) such as ERNIE Bot, Tongyi Qianwen, Doubao, and DeepSeek have emerged, yet their effectiveness in the NMLE awaits systematic evaluation.

Digital mental health platforms often consist of many different forms of self-care exercises. To our knowledge, whether the number of choices presented to the users affects their uptake and experiences and poses negative consequences (i.e., not choosing any exercises, choice dissatisfaction) for users, especially those experiencing anxiety and depressive symptoms or unpleasant state emotions have not been empirically investigated.

Internet-based cognitive behavioral therapy (CBT) provides psychological interventions to individuals with mild depressive symptoms.

Needleless access devices are essential for intravenous therapy but can be a source of contamination and catheter-related bloodstream infections (CRBSIs) if not disinfected properly. The BD PosiFlush SafeScrub (Becton, Dickinson and Company) is designed to support aseptic nontouch technique (ANTT) by incorporating a built-in reminder to “scrub-the-hub” before flushing. This feature can help improve compliance with disinfection practices and may reduce the risk of microbial contamination.

The use of artificial intelligence (AI) methods in palliative care research is increasing. Most AI palliative care research involves the use of routinely collected data from electronic health records; however, there are few data on the views of palliative care health care professionals on the role of AI in practice. Determining the opinions of palliative care health care professionals on the potential uses of AI in palliative care will be useful for policymakers and practitioners to determine and inform the meaningful use of AI in palliative care practice.

Chronic obstructive pulmonary disease (COPD) affects approximately 16 million Americans and often results in avoidable readmissions due, in part, to medication errors and lack of education. Tele-health interventions can support medication reconciliation and inhaler education following hospital discharge for patients with COPD.

In blended care, digital mental health interventions (DMHIs) integrate with face-to-face psychotherapy provided in person or via telehealth. To incorporate DMHIs into routine care for depression and anxiety, it is important to understand the needs and expectations of mental health professionals for blended DMHIs.

Addressing the complex medical and psychosocial needs of older adults is increasingly difficult in resource-limited care settings. In this context, socially assistive robots (SARs) provide support and practical functions such as orientation and information delivery. Integrating large language models (LLMs) into SAR dialogue systems offers opportunities to improve interaction fluency and adaptability. Yet, in real-world use, acceptability also depends on minimizing both technical and conversational errors, ensuring successful user interactions, and adapting to individual user characteristics.

A large amount of data is generated in healthcare facilities, yet it is rarely made available for secondary research use. The reasons are manifold. Most importantly, different stakeholders´ needs must be balanced. However, there are currently hardly any feasible solutions for this.

Remote patient monitoring (RPM) has the potential to reduce in-clinic visits and promote proactive and preventive care for patients with chronic disease in primary care. However, a decentralized approach of RPM in the primary healthcare (PHC) setting has not met stakeholders’ expectation regarding scalability. This study introduces a centralized virtual ward (CVW) led RPM, utilizing a multidisciplinary team approach to monitor patients with chronic diseases by clinicians that do not belong to the patients PHC centre.