JMIR Formative Research

Each year, family caregivers help the 2 million patients newly diagnosed with cancer manage their symptoms; however, few interventions have been developed and proven efficacious to enhance their symptom-support role. The recent expansion of electronic patient-reported outcomes (ePROs), including remote symptom monitoring, presents an opportunity to explore novel approaches to supporting caregiver symptom management by leveraging patient ePRO data.

Atrial fibrillation (AF) is one of the most common arrhythmias and is associated with multiple risk factors, including stroke and heart failure. Therefore, it is important that accurate and comprehensive educational resources on AF are available. Artificial intelligence (AI) platforms such as Google Gemini and ChatGPT are emerging tools for medical education. To our knowledge, this is the first study to assess the quality of these platforms for AF education.

Accessible health communication is essential for effective patient education, yet most medical information remains written at a level above the general population’s recommended reading level. Large language models (LLMs) can simplify complex medical information, but it remains unclear whether responses can be automatically adapted to match the linguistic complexity of user-generated questions seeking medical information.

In this 1-group pretest-posttest pilot of WISEBOx (Wise Intervention for Adolescents Who Purchase Loot Boxes) with adolescents who had previously purchased loot boxes (N=38), fewer participants reported purchasing loot boxes at the 1-month posttest than in the year before baseline. These findings support the feasibility of WISEBOx and the need for controlled efficacy testing.

Smartwatches can support mental health care by providing insights into activity and sleep, which are closely linked to mental health and daily functioning. However, their implementation in mental health care practice remains limited. Professionals can feel resistance toward digital mental health interventions if they feel their use is not aligned with therapeutic values, and report a need for guidance on how to use technologies in ways that do align with such values. Compassion, a core value in mental health care, may offer a meaningful frame for implementation. Therefore, we previously co-designed compassion-focused implementation materials: a card set offering practical suggestions of how smartwatch data can support group treatments in ways that counter the self-optimization logic of commercial devices and instead align with compassion.

Social desirability bias, the tendency to respond in ways that present the respondent favorably, is well documented in human self-report and has recently been observed in large language models (LLMs) completing personality inventories. Whether it also affects LLM responses to validated health screening instruments, where LLMs are increasingly proposed as simulated patients or synthetic respondents, has not been examined.

Adolescent mental health needs remain largely unmet, particularly among youth in geographically underserved communities. Digital peer support is a promising approach to expanding access to mental health support, yet real-world evidence on engagement patterns and clinical outcomes remains limited.

The use of generative AI in research and clinical practice is rapidly expanding, offering unprecedented capabilities in pattern extraction and data analysis. However, its application raises critical concerns regarding the accuracy and reliability of the information provided.

AI technologies have considerable potential to support mental health practitioners in identifying depression symptoms. However, existing tools are often developed with limited practitioner involvement and evaluated primarily on technical performance, leaving questions of clinical relevance, ethical design, and practitioner trust insufficiently addressed.

Body dysmorphic disorder (BDD) is a chronic, impairing condition for which cognitive behavioral therapy, including exposure and response prevention (ERP), is the first-line treatment, but few clinicians are trained in BDD-specific ERP. Constructing personalized exposure hierarchies is time-intensive and requires clinical expertise, particularly for patients with absent insight who may hold appearance beliefs with delusional conviction. Generative AI (GenAI)–powered chatbots, such as ChatGPT, have shown utility for clinical content, but their capacity to produce safe, appropriate ERP hierarchies for BDD has not been examined.

Long wait times and specialist shortages delay autism spectrum disorder (ASD) diagnosis, often beyond the period when early intervention is most effective. Digital diagnostic tools may help increase diagnostic capacity by supporting clinicians in delivering timely autism evaluations within routine outpatient care.

The integration of eHealth solutions into routine health care remains challenging despite their potential to improve care quality and efficiency. Living labs (LLs) have emerged as an approach to addressing these challenges by enabling co-design and iterative testing of eHealth tools. LLs promote collaboration among patients, health care professionals, developers, and policy makers. However, systematic insights into their practical implementation remain limited.