JMIR Human Factors

Anxiety and depressive disorders remain highly prevalent and insufficiently treated, with many individuals experiencing persistent or untreated symptoms, limited access to evidence-based care, or insufficient support between clinical encounters. Adults with disabilities represent a particularly underserved subpopulation, often facing compounded barriers to mental health care and higher rates of anxiety and depression. Digital therapeutics offer a scalable opportunity to address these gaps by extending structured, evidence-based interventions beyond traditional care settings.

The incidence of type 2 diabetes (T2D) continues to increase, and the lack of individualized therapy strategies hinders patient engagement with and commitment to a healthy lifestyle. The PROTEIN project aimed to facilitate users in choosing healthy living, thereby improving their metabolism and T2D management.

Inflammatory bowel disease (IBD) requires ongoing monitoring and effective communication between patients and health care providers. Web-based patient portals may enhance engagement, but their association with disease activity remains unclear.

Approach bias modification (ApBM) aims to target maladaptive approach tendencies toward substance-related cues and has increasingly been examined as an adjunctive intervention for substance use disorders, including nicotine use. However, although participants’ subjective experiences of ApBM are likely to influence both its effectiveness and successful implementation, they have rarely been systematically investigated.

Population aging is associated with a rising number of people living with cognitive impairment (CI), straining traditional caregiving systems. More than 55 million individuals live with CI worldwide, while care delivery faces severe capacity constraints. China’s rapid aging provides a context for examining the acceptance and use of digital assistive technologies (DATs) in CI care. Although DATs offer the potential to address this care gap, systematic evidence on acceptance and use remains limited.

Pain and work-related musculoskeletal disorders are commonly seen in surgeons, significantly impacting quality of life and burnout. A questionnaire-based study was conducted to further investigate the nature and etiology of work-related pain among urologists in the state of Florida.

Artificial intelligence–based skin cancer screening apps (AISCSAs) offer diagnostic potential but face limited adoption. App store cues, such as ratings, may influence acceptance; yet, little is known about how users cognitively process app store information in high-stakes health contexts. To address this gap, eye-tracking was used to measure visual attention while participants evaluated a mock AISCSA app store listing.

Trauma-related mortality exhibits a marked social gradient, driven by access barriers and health inequities worldwide. These barriers jeopardize progress toward Sustainable Development Goals 3 and 4.

Digital therapeutics (DTx) are evidence-based software interventions with the potential to treat health conditions. However, uptake remains limited by low public awareness and overly complex patient education materials that exceed recommended readability levels. Large language models (LLMs) may simplify such content; however, their effects on users’ understanding have not been empirically demonstrated.

There is an unmet need for reliable medication stability information to avoid suboptimal administration of intravenous medications, which may lead to reduced efficacy of therapy and potential patient harm through medication degradation or incompatibilities that cause vascular access issues.

Contact-based interventions, where individuals share their lived experiences of mental health difficulties and recovery, appear effective in reducing stigma.

Nurses are required to perform multiple tasks concurrently, which leads to multitasking situations, and they have to continuously determine which tasks should be prioritized. This is particularly challenging for novice nurses. Although IT-based systems supporting prioritization have begun to emerge, research on the types of information required when prioritization is processed computationally is scant. Despite the clear need for a supportive information system to assist nursing task prioritization, such systems are not yet sufficiently developed.