JMIR Formative Research

Postoperative follow-up after brain tumor surgery is typically limited to intermittent clinic visits, leaving subtle neurological or general deterioration between visits underrecognized. Digital self-monitoring platforms may help fill this gap, but evidence in neuro-oncology is scarce, particularly regarding how patient-reported symptom trajectories can feed into future data-driven early warning systems.

Stress, sleep deprivation, and burnout are significant safety risks for acute care surgeons, negatively impacting performance, well-being, and clinical outcomes.

Effective collaboration throughout the full cycle of care is essential for value-based health care. In the Netherlands, occupational health care and curative health care traditionally operate as 2 separate sectors. As a consequence, effective communication and robust collaboration between professionals working in these sectors are lacking. Digital collaborative care platforms (ie, digital systems that facilitate communication and collaboration between health care professionals) are recognized as a promising solution to address the fragmentation of work-focused health care (health care that supports people on long-term sick leave in staying at or returning to work). A human-centered design (HCD) approach can help ensure that such platforms align with professionals’ needs by involving them throughout the design process.

Alzheimer disease (AD) affects cognition, treatment adherence, family connections, and health care resource allocation. Most patients with AD have low adherence to medication therapy due to the limitations associated with cognitive impairment. Therefore, increasing the involvement of patients and their family members in medication management is important to improve treatment outcomes and reduce the burden of care.

Hypertension remains a leading global health challenge, particularly in low- and middle-income countries (LMICs), where limited health care infrastructure and resources restrict effective management. Community health workers (CHWs) are critical in delivering care in these settings, and when equipped with mobile health (mHealth) apps, they can greatly enhance chronic disease management. Involving CHWs in the design and development at all stages is essential for the success of such programs. However, relatively little research discusses CHW feedback on mHealth interventions.

Pediatric chronic pain affects up to one-third of youth and is associated with significant disruptions in social, emotional, and behavioral functioning. Although behavioral treatments are effective, access remains limited due to geographic, financial, and systemic barriers. Digital behavioral health interventions offer a promising solution, but many lack user-centered design, iterative refinement, and implementation-informed development strategies that support usability and scalability.

The prevalence of obesity is a global health challenge, as obesity is associated with various comorbidities, reduced quality of life, and increased mortality. Providing effective treatment to improve health and quality of life for people with obesity is a major health care concern. Internet-delivered treatment (IDT) is an alternative treatment that increases patient accessibility and reachability; however, pilot testing is required before such interventions are evaluated in full-scale studies or implemented.

Prostate cancer progression exhibits significant variability influenced by biological and racial factors. DNA methylation profiling has shown potential in early cancer detection, but its integration with machine learning across racially diverse populations remains limited.

A survey of primary care clinicians suggests that a clinical decision support tool to support sexual risk assessment and prescribing of HIV pre-exposure prophylaxis was appropriate and useful for identifying at-risk patients, but uptake was hindered by workflow and usability barriers, which underscores the importance of postimplementation clinician feedback to improve the use of clinical decision support tools.

The National Health Service 10-year health plan emphasizes an increasing shift toward digital health care delivery. However, there is limited research on how best to support, engage, and include individuals who are digitally excluded. As health care services become more digitally driven, evidence-based interventions are needed to address digital exclusion and ensure equitable access to care, particularly for people living with long-term conditions.

Physical inactivity is a modifiable and significant trait in people with chronic obstructive pulmonary disease (COPD). While traditional exercise-based pulmonary rehabilitation (PR) improves symptoms and exercise tolerance, its impact on physical activity (PA) levels remains limited. Digital health (eHealth) interventions may help address this gap.

Large language models use machine learning to produce natural language. These models have a range of potential applications in health care, such as patient education and diagnosis. However, evaluations of large language models in health care are still scarce.