JMIR mHealth and uHealth

Mobile information technology (IT) is increasingly being used in the health care sector, and it can play a critical role in both the care of children with congenital heart disease (CHD) and the quality of life of their families.

Use of effective contraceptive methods (ECMs) reduces maternal mortality. Person-centered counseling increases uptake, but barriers to high-quality counseling persist. Telehealth may improve access to comprehensive contraceptive care, but its effectiveness remains unclear.

Depressive symptoms are common yet often underrecognized in routine care, underscoring the need for scalable screening approaches beyond episodic self-report assessments. Wearable actigraphy can passively and continuously capture daily activity and 24-hour rest–activity rhythms associated with depressive symptom burden. However, the performance of artificial intelligence (AI) models that leverage actigraphy data for depressive symptom screening remains insufficiently established.

Previous studies demonstrated the effectiveness of , a mobile phone–based life-skills training program for addiction prevention among adolescents. However, socially stratifying factors, such as educational level or migration background, were associated with lower program engagement and participation. To address these disparities, we optimized and tailored program elements, particularly for subgroups with low engagement, using qualitative interview data.

Breast cancer is a major public health challenge worldwide. Women at high risk for breast cancer are more likely to develop the disease; yet, screening participation remains. Mobile health interventions may improve breast health awareness and screening behaviors, but evidence in high-risk populations for breast cancer remains limited.

Skin neglected tropical diseases (NTDs) pose significant diagnostic and management challenges in resource-limited settings due to constrained dermatological expertise, frontline health worker (FHW) training, and limited access to diagnostic resources. Mobile health apps with artificial intelligence (AI)–enabled diagnostic imaging capabilities have the potential to enhance clinical decision-making and professional development at the primary care level. The World Health Organization (WHO) skin NTD mobile app uses convolutional neural networks to analyze images of skin lesions and generate differential diagnoses, intended to be used alongside clinical history and examination, to support FHWs in identifying 12 skin NTDs and 24 common skin conditions. Beyond clinical decision support, the app also aims to upskill FHWs in the recognition and management of these diseases. However, the success of such tools depends on understanding users’ needs and the realities of implementation in diverse clinical contexts.

Sufficient bowel preparation is critical for increasing the quality of colonoscopy. However, current bowel preparation guidelines have limitations. We used a constructed and validated convolutional neural network model to assist patients in assessing the adequacy of bowel preparation and guide the use of laxatives based on individual variations.

Sustained engagement is a central challenge for digital therapeutics. In routine operations, inactivity-triggered supports (eg, SMS reminders and telephone follow-up) are used to nudge patients back after lapses, but evidence in digital erectile dysfunction (ED) therapy is limited.

Although machine learning has increasingly been used to predict mental health symptoms and maladaptive behaviors, real-world prediction of addiction-related risk remains limited. Emotional and temperamental vulnerabilities are established correlates of alcohol-related problems, yet few studies have integrated these factors with wearable-derived biosignals in alcohol-risk prediction models.

Parents are increasingly using smartphone apps to help guide decision-making around aspects of caregiving, including safer sleep. However, it remains unclear whether the guidance provided aligns with best practice guidelines.

Digital health tools are increasingly used in mental health care to passively collect patient data and analyze health status outside of clinical settings. While technologies such as digital phenotyping, affective computing, and computational behavioral analysis offer new insights into symptom manifestation in daily life, they generate large volumes of potentially sensitive data that raise significant data privacy concerns, requiring high levels of patient awareness and consent. Empirical research is lacking on stakeholder understandings toward the sensitivity of these data and expectations for data stewardship, perspectives that are critical for developing robust informed consent and data protection policies for digital health data use.

Tobacco use remains a leading preventable cause of morbidity and mortality. Digital health tools and wearable technologies offer scalable opportunities for behavioral self-monitoring. However, real-world evidence characterizing long-term tobacco use trajectories and associated physiological changes during wearable adoption is limited.