JMIR mHealth and uHealth

AI-powered digital mental health interventions (DMHIs) are a promising approach to address barriers to traditional mental health care. However, real-world evidence of their immediate and sustained benefits remains limited.

Individuals with cardiometabolic disease typically average 10 to 14 hours of daily sedentary time, which increases the risk of cardiovascular disease.

Cardiovascular diseases remain a major global health burden, highlighting the need for long-term physiological monitoring. Photoplethysmography (PPG) is widely used in wearable devices for noninvasive monitoring of heart rate (HR), rhythm, and oxygen saturation in mobile health (mHealth) apps. However, the reliability of wearable reflectance PPG depends on sensing conditions, including sensor-skin contact force and pressure.

Young adults experience disproportionately high levels of stress, creating significant health challenges. This demographic often underuses traditional stress management methods due to perceived ineffectiveness, inconvenience, or stigma. Given these challenges, mobile virtual reality (VR) offers a novel, engaging, and accessible alternative stress management tool that may be more appealing to young adults.

Mindfulness interventions are considered an effective strategy for preventing mental health problems among health care workers (HCWs). While prior reviews have often included facilitator-guided or multicomponent interventions, it is unclear whether self-guided digital mindfulness interventions can also be effective without professional or in-person support.

Unintentional childhood injuries represent a major public health issue affecting the lives and health of children worldwide, with the risk and injury type dynamically changing with age and developmental stage. To effectively prevent unintentional injuries, intervention measures must be adjusted based on actual circumstances and kept up to date in real time. Mobile health (mHealth) technologies can meet this need, but their specific functions and intervention methods remain unclear at present.

The increasing prevalence of chronic illness presents a significant global health challenge due to growing end-of-life suffering. Palliative care is now an essential health service under Universal Health Coverage. Its integration into primary health care and use of mobile health (mHealth) have been recommended to improve access.

Accurate assessment of physical behaviors (PBs) and activity intensity is essential for public health research and digital health monitoring. Wearable accelerometers combined with machine learning (ML) or deep learning (DL) enable objective behavior assessment, but most existing models are trained on laboratory data, limiting generalizability to free-living conditions.

Suicide is a leading cause of preventable mortality worldwide, with more than 700,000 deaths annually. Although suicidal ideation fluctuates rapidly, conventional risk assessments rely on retrospective self-report collected infrequently, and the detection of short-term suicide risk remains limited. Passive digital sensing using smartphones and wearable devices enables continuous monitoring of behavioral and physiological signals associated with suicide-related outcomes. However, current evidence remains fragmented, without a clear framework for translation into clinically interpretable risk indicators.

Physical activity (PA) alleviates many treatment-related side effects in gynecologic cancer survivors, yet long-term PA levels remain low. Mobile health interventions can support self-management and increase PA levels; however, evidence from real-world, year-long engagement with smartphone apps in this population is still limited.

On a population level, mental health apps are accessible and effective. However, nondigitally native adults with chronic pain are a large and growing population who have been neglected during the development process of these interventions. Although technology use is rapidly growing among this population, their engagement with mobile health–related apps is lagging because usability is often not optimized for their needs and preferences.

Emerging data suggest that text message–based mobile health interventions may enhance physical activity levels in patients with cardiovascular disease enrolled in cardiac rehabilitation. The optimal characteristics of texts that lead to maximal patient engagement and drive meaningful behavioral change are not well understood.