JMIR Formative Research

Patient-reported outcomes, including ecological momentary assessments (EMAs), are acquired from patients via repeated self-reports of their perceived momentary physical and emotional states before and after medical procedures. Patient-reported outcomes are used to measure health outcomes and quality of care. However, certain observable states or behaviors (eg, moods such as fatigue, hope, or medication adherence), or behaviors suggestive of health decline (eg, depression, cognitive decline), are not easily measured via self-reports in certain situations (eg, patients undergoing certain medical procedures, patients with dementia, and others). The peer-ceived momentary assessment (PeerMA) method involves support persons or peers (eg, family members and friends) to report their perception of a patient’s subjective physical and emotional states and has been validated in healthy populations.

Emerging adulthood is a high-risk period during which many with type 1 diabetes (T1D) demonstrate suboptimal diabetes management and glycemic control. There is a need for effective and scalable interventions designed specifically for this population. Technology-based approaches are readily accessed by this age group. Further, interventions that are consistent with self-determination theory (SDT) – which posits the fulfillment of psychological needs for autonomy, self-efficacy, and relatedness promote intrinsic motivation for change – may resonate well with emerging adults’ (EAs) developmental needs for establishing independence, autonomy, and growing their social network.

Digital platforms, particularly social media including Instagram, present unique opportunities for health promotion among adolescents due to their widespread use with interactive features supporting high user engagement. However, the feasibility of effectively utilizing platforms like Instagram for health interventions requires careful consideration of adolescent engagement patterns.

This viewpoint explores the role of conversational AI in educating the public on breast self-examination, using an interaction with DeepSeek AI as an example, where 6 AI generated responses to commonly asked questions were compared with guidelines published by organisations such as WHO. While the AI provided clear, accessible, and evidence-aligned responses consistent with professional guidance, limitations included oversimplification and absence of multimedia resources. These findings suggest that AI can support public health education but should be complemented by physician oversight and evidence-based resources for responsible use.

Diabetic foot complications are among the most severe and costly outcomes associated with diabetes, with high prevalence particularly in the Middle East and North Africa region. Current screening tools are often limited by subjectivity, invasiveness, or scalability challenges, underscoring the need for innovative approaches.

Academic institutions face increasing challenges in grant writing due to evolving federal and state policies that restrict the use of specific language. Manual review processes are labor-intensive and may delay submissions, highlighting the need for scalable, secure solutions that ensure compliance without compromising scientific integrity.

Diabetes, cardiovascular disease, and chronic kidney disease are associated with high morbidity and costs of care. Medications can reduce long-term complications but may contribute to complications such as hypoglycemia and acute kidney injury during acute illnesses. Sick day medication guidance (SDMG) could help prevent these adverse events, but evidence for effective strategies to deliver this guidance is lacking.

Intensive longitudinal designs support temporally granular study of processes making methods like ecological momentary assessment (EMA) increasingly common in medical and behavioral science. However, the repetitive and intensive measurement strategies associated with these designs increase participant burden which limits the breadth and precision of EMA surveys. This is particularly problematic for complex clinical phenomena, such as suicide risk, which research has shown is multidimensional and fluctuates over narrow time intervals (e.g., hours). To overcome this limitation, we proposed the Computerized Adaptive Test for Suicide Risk Pathways (CAT-SRP) which supports the simultaneous assessment of multiple empirically informed risk domains and facilitate personalized survey content.

Direct-to-consumer (DTC) digital health companies, offering services such as on-demand prescriptions, mental health apps, fertility tracking, and at-home diagnostics, have become more common in the United States. These companies represent a shift in healthcare delivery by engaging consumers directly and operating largely outside of traditional healthcare systems. Despite their increasing presence, little is known about the populations these companies serve, the health domains they address, and the technologies they employ. Understanding these characteristics is critical for evaluating the quality of services provided, implications for healthcare costs, and impact on health equity.

Large Language Models (LLMs) have great potential to improve and make the work of clinicians more efficient. Previous studies have mainly focused on web-based services such as ChatGPT, often with simulated cases. For the processing of personalized patient data, web-based services have major data protection concerns. Ensuring compliance with data protection and medical device regulations therefore remains a critical challenge for adopting LLMs in clinical settings.

The integration of Electronic Health Records (EHRs) with telehealth platforms represents a transformative approach in healthcare, providing critical accessibility and engagement solutions, especially during the COVID-19 pandemic. In Riyadh's hospitals, the adoption of EHR-integrated telehealth has significantly increased and offers enhanced patient care options. However, there is a need to examine its continued relevance, effectiveness, and challenges in a post-pandemic context.

Reducing patient harm and improving patient safety is a central objective in global healthcare. Effective communication and meaningful patient engagement are considered essential strategies to achieve this goal. However, implementation of structured and strategic patient engagement at the organizational level remains limited, particularly in the context of patient safety. Patient and family advisory councils (PFACs) offer a promising model to enhance organizational-level patient engagement, yet guidance on implementation and targeted training for PFAC members is scarce.