JMIR Human Factors

There are around 20,000 mental health apps available in app stores. The Organisation for the Review of Care and Health Apps (ORCHA), a United Kingdom digital health compliance company, has assessed a number of digital mental health apps with regard to their quality, professional and clinical assurance, data privacy, and user experience. This study analyzes the data that were collected by ORCHA when they assessed mental health apps.

Digital interventions for mental health and well-being are increasingly moving beyond screen-based applications toward more embodied approaches, necessitating design methodologies that emphasize bodily experiences. Soma design offers a distinctive interaction design approach that integrates bodily awareness with aesthetic appreciation, viewing the mind and body as an inseparable whole.

It is unclear why some smartphone apps designed for people with psychosis are engaging, while others are not. One possible explanation is the apps’ persuasive features and the operationalization and implementation of these features.

The utility of online engagement in enhancing quality of life and mitigating social isolation among older adults is well documented. However, its relationship with cognitive functioning, particularly through online engagement, requires further exploration.

Digital health interventions can be effective at changing behavior, but achieving long-term adherence remains a challenge. One psychological barrier to health behavior change is , or the tendency to prefer smaller, short-term rewards over larger, long-term rewards. Episodic Future Thinking (EFT) can disrupt future discounting and is a promising technique for improving health behavior, but such interventions have not been co-designed to address end user needs.

More than 20% of perinatal women experience depression, with suicide being a leading cause of maternal death in the United States. Professional societies emphasize the need to identify those at risk of developing perinatal depression to better target preventive care delivery during pregnancy.

Digital remote monitoring using smartphones and wearable devices is a promising solution for psychosis management, where precise, time-sensitive intervention is crucial. Combining active symptom monitoring (ASM) and passive sensing (PS) can support self-management by allowing remote, low-burden mental health monitoring.

Despite their benefits, digital health tools often face adoption barriers because of the digital divide. Identifying the fundamental user skills required to effectively navigate these tools and the usability barriers is essential to addressing disparities in use.

In Bangladesh, as well as throughout the world, children’s screen time has significantly increased. Children spend a lot of time on the internet and digital screens for entertainment, education, and communication, which has increased their daily screen time. However, the potential detrimental impacts of excessive screen time on children’s mental, physical, and social health have drawn attention.

Artificial intelligence (AI)–driven clinical decision support (CDS) tools offer promising solutions for health care delivery by optimizing resource allocation, detecting deterioration, and enabling early interventions. However, adoption remains limited due to insufficient validation and a lack of transparency and trust. Explainable AI (XAI) seeks to improve user understanding of AI outputs; however, how clinicians interpret and integrate these explanations into their decision-making remains underexplored. Furthermore, discrepancies in explanations, known as the “disagreement problem,” can undermine trust and, at worst, lead to poor clinical decisions.

Navigation programs for patients with cancer improve access and continuity of care, yet their digital transformation is often limited by poor usability and inadequate uptake. Applying user-centered and human-centered design (UCD/HCD) principles may close this gap, but the extent to which such design methods are used and evaluated in oncology navigation tools remains unclear.

Computerized clinical decision support (CDS) has the potential to improve patient outcomes by offering evidence-based guidance at the point of care—enhancing guideline adherence and diagnostic accuracy—and supporting system-level outcomes by enabling predictive analytics for more efficient resource planning. Prior work has identified factors that affect adoption, such as clinicians’ expectations of usefulness, ease of use, alignment with workflows, and resources to support utilization. However, CDS adoption is not static and changes according to dynamic systems of behaviors and workflows, requiring a deeper understanding of how evolving conditions affect implementation and outcomes.