JMIR Human Factors

Digital decision-support tools for labor care remain limited, with few technologies successfully addressing the complex, time-sensitive decisions required during labor triage. Fit4Labour is a clinician-facing, data-driven research tool, currently under development, that combines computerized cardiotocography interpretation with maternal and fetal risk factors to generate individualized risk scores at labor onset. Its primary aim is to support clinicians in identifying fetuses who may require closer monitoring or expedited delivery, while simultaneously providing reassurance in low-risk cases. By promoting consistent communication and timely escalation of care, the Fit4Labour tool seeks to strengthen clinical decision-making. Understanding and addressing usability and implementation barriers will be critical to its adoption in clinical practice.

Implementing digital mental health interventions (DMHI) for those with psychosis is a persistent challenge. A process evaluation, or studies conducted alongside trials, is one research method that may address this issue. However, a synthesis of process evaluation data in this area is missing.

The rapid expansion of mobile technology has accelerated the integration of health applications and conversational AI into clinical and public health practices. To ensure these tools are effective and sustainable, usability evaluations and early user engagement during development are essential. The Health Information Technology Usability Evaluation Scale (Health-ITUES) is a validated and flexible usability assessment instrument that is available in multiple languages and applicable across diverse contexts. However, a Japanese version of this scale has not yet been developed.

Video-algorithmic patient monitoring (VAPM) combines remote, noncontact sensors and algorithmic analysis and is increasingly trialed in acute psychiatric and other care settings. While promoted for improving safety and reducing risk, it raises ethical concerns regarding safety, privacy and surveillance. Little is known about how those encountering VAPM in mental health care contexts anticipate its use and potential impacts, including where it has not yet been implemented.

Assessing usability is important given the increasing use of technology in rehabilitation. While wearable robotic exoskeletons (WREs) are commonly incorporated into neurological rehabilitation in both clinical and community settings, there is a need to examine the applicability of different usability instruments in this context. The Assistive Technology Usability Questionnaire for People with Neurological Diseases (NATU Quest) was developed to evaluate the usability of assistive technology in individuals with neurological conditions. However, its reliability and validity have been established only for assistive devices such as wheelchairs and canes, restricting its generalizability.

Body image dissatisfaction, disordered eating, and eating disorders represent significant public health concerns; however, many affected individuals never access evidence-based support. We co-designed and developed a rule-based chatbot, JEM, which conducts conversations addressing evidence-based psychoeducation and psychotherapeutic microinterventions. We previously demonstrated the feasibility, acceptability, and preliminary satisfaction of the JEM chatbot in a research setting. However, broader satisfaction, experiences, and user-reported outcomes in real-world settings have not yet been investigated.

Web-based and mobile phone–based apps have become widely available for dietary self-monitoring; however, their use may increase the risk of disordered eating. College students frequently demonstrate poor nutrient intake despite consumption of sufficient calories. One way to improve diet quality may be via the use of a smartphone app that encourages intuitive eating.

Population aging is associated with a growing prevalence of neurocognitive disorders among adults aged 65 years and older. Digital health technologies offer promising opportunities to support cognitive health and well-being in this population. However, their effectiveness largely depends on users’ level of engagement. Despite the recognized importance of engagement in digital health, limited evidence exists on how engagement is conceptualized, measured, and related to intervention outcomes among older adults living with neurocognitive disorders.

The OA Coach mobile app was developed to support individuals with knee osteoarthritis in self-managing their condition. The app aims to fill a current gap in the osteoarthritis mobile app field by combining key features such as symptom tracking, objective activity tracking, educational modules, and encouragement notifications underpinned by behavior change theory.

Mobile health (mHealth) interventions with virtual coaches offer scalable and potentially cost-effective solutions for health behavior change. However, these interventions commonly present challenges, such as limited personalization and insufficient grounding in evidence-based strategies. Perfect Fit (PF; Perfect Fit consortium), a personalized mHealth intervention with a text-based virtual coach, supports adults in quitting smoking and becoming more physically active. By combining innovative techniques, including sensor technology, end user involvement, and evidence-based strategies, PF aims to address common challenges faced by mHealth interventions, including those with virtual coaches.

Insufficient engagement in moderate to vigorous physical activity (MVPA) is a significant risk factor for major noncommunicable diseases, including cardiovascular diseases (eg, coronary heart disease and stroke), type 2 diabetes, and several cancers. Physical inactivity accounts for an estimated 3.2 million deaths annually. Office employees, due to their sedentary and desk-based work patterns, are particularly vulnerable to low MVPA levels, which negatively affect health and work productivity. The COVID-19 pandemic exacerbated these issues, further reducing MVPA levels due to lockdowns and work-from-home policies. Although numerous interventions have aimed to promote MVPA, many lack theoretical grounding, stakeholder involvement, or systematic development frameworks. A theory- and evidence-informed approach is warranted to target modifiable determinants and mechanisms, improve coherence and replicability, and enhance effectiveness and scalability.

Alcohol use remains a major public health concern, and although preventive alcohol self-help interventions aim to support individuals in need of indicated prevention, they continue to face challenges related to low engagement and high attrition. Chatbots, also known as conversational agents (CAs), powered by artificial intelligence, may enhance engagement by offering personalized guidance and 24/7 availability. Yet, user perspectives on CAs in preventive alcohol self-help care remain largely unexplored.