JMIR Formative Research

Difficulties with emotion regulation (ER) have widely been associated with the development and persistence of poor mental health, such as depression, anxiety, self-harmful thoughts, behaviors, and suicide attempts. Despite the importance of ER as a transdiagnostic risk factor, few interventions directly target ER. The impact of one such intervention, Purrble (a socially assistive robot; developed by Sproutel and Committee for Children), was recently evaluated when compared to treatment-as-usual (TAU) within a randomized controlled trial (RCT) with sexual orientation and gender identity minorities, that is, lesbian, gay, bisexual, transgender, queer, or questioning (LGBTQ+) youth, who had self-harm experiences. However, while RCTs are well suited to evaluating effectiveness, they offer limited insight into how participants engage with, integrate, and perceive the value of interventions, like Purrble, in their everyday lives. Consequently, complementary qualitative process evaluation is needed to fully understand participants’ experiences and contextualize trial outcomes.

Excessive screen time among school-aged children is a growing concern for child health and family routines. A previous systematic review of children aged 6 to 14 years found that excessive screen time is common in this age group, highlighting the need for feasible intervention strategies that can be implemented in daily family routines. Although many health education materials communicate the harms of excessive screen use, families often need practical support to translate risk messages into household rules, parent-child communication, alternative activities, and changes to the home environment.

Latino millennial caregivers comprise over one-quarter of millennial family caregivers in the United States. Latino millennial family caregivers face unique challenges due to cultural, structural, and economic burdens that may increase stress and stress-related health effects. These caregivers also report time limitations due to their multiple roles. Technology may be a way to deliver an accessible and flexible intervention to support stress management and emotional regulation in this population.

In recent years, digital interventions for smoking cessation have seen increasing use, but program completion rates vary widely. Therefore, understanding the factors associated with program completion in cessation programs is critical for evaluating the feasibility and acceptability of the digital intervention program.

Metabolic disease, including type 2 diabetes, is a major public health concern. Reducing sugar-sweetened beverage (SSB) consumption is a critical target for disease prevention and lifestyle modification. Workplace interventions offer a promising opportunity to address barriers to behavior change while strengthening motivation and self-efficacy.

Early identification of older adults with lower cognitive screening performance may facilitate appropriate clinical evaluation and intervention to delay the progress of cognitive decline and onset of dementia. Because facial information can be acquired noninvasively and unobtrusively during everyday life, it has attracted increasing attention as a potential digital biomarker. However, it remains unclear which facial regions are associated with cognitive decline under a neutral facial condition. Furthermore, it is unknown whether these facial characteristics primarily reflect static facial appearance or facial movements.

Shared decision-making (SDM) is a core competency in patient-centered care, yet it is challenging for physicians. Simulation-based education with standardized patients (SPs) offers valuable practice opportunities for undergraduate medical education (UME), but is limited by cost, scalability, and variable feedback quality. Advances in large language model (LLM) technology and virtual patient (VP) design present a promising pathway toward accessible, realistic, and high-quality SDM skill training.

Amid the current primary care crisis, Canadians are increasingly turning to online resources, where misinformation is common. Launched in 2023, the web application Icanbewell provides free, reliable, evidence-based information on preventive health care.

Axial spondyloarthritis (axSpA) is a chronic inflammatory rheumatic disease affecting the axial skeleton that requires regular physical exercise in addition to pharmacotherapy. However, a significant proportion of patients do not exercise frequently enough. Axia is a digital therapeutic (DTx) that offers a solution by providing a disease-specific and patient-tailored home-based exercise program for patients with axSpA. The app combines structured exercise with patient education and disease management features and incorporates gamification and behavioral conditioning to promote user engagement. However, it has remained unclear how Axia is perceived and evaluated by patients.

Mental illness is a leading cause of disability worldwide, affecting more than 1 billion people, and suicide accounts for 720,000-800,000 deaths annually. Although AI-native mental health chatbots have the potential to help address the substantial gap between treatment needs and workforce capacity, their rapid proliferation has outpaced the development of scalable procedures for identifying and responding to suicidal ideation. Although recommendations call for human oversight to support safety monitoring and intervention in AI-based care, research on human clinical oversight and crisis-response procedures remains limited.

Triple-negative breast cancer (TNBC) is an aggressive molecular subtype of breast cancer that needs to be identified accurately from pathology reports to inform decision-making. Large language models (LLMs) have been reported to show great potential in the automatic extraction of important oncology information from unstructured medical text.

User-centered design (UCD) processes often generate extensive lists of potential software features, necessitating prioritization methods to guide development. The MoSCoW method, categorizing features as “must have,” “should have,” “could have,” or “won’t have,” is widely used in software development for its simplicity and ease of adoption. Despite its popularity, limited evidence exists on its application within health informatics. The Decision Aid Navigator (DEAN) system, a clinical decision support tool, required prioritization of features for its administrative portal to ensure usability and alignment with health information technology (HIT) professionals’ needs.