JMIR mHealth and uHealth

Mobile health (mHealth) interventions show promise for supporting diabetic foot self-care; however, sustained engagement remains a challenge, particularly in low- and middle-income countries. While educational strategies are commonly used to address diabetic foot ulcers, they often fail to translate knowledge into long-term self-care. Understanding behavioral mechanisms underlying mHealth use, including the role of caregivers, is critical for improving implementation and effectiveness.

Approximately 1 in 100 children worldwide are diagnosed with autism spectrum disorder (ASD), and 46% to 89% of those diagnosed experience significant feeding difficulties. Mobile health (mHealth) apps are a potential tool for scalable support. However, there is nascent literature on the quality and relevance of mHealth apps for managing ASD-related feeding challenges.

Emergency department (ED) crowding compromises patient safety and increases clinical workload. Existing crowding metrics capture structural indicators but not how clinicians subjectively experience crowding in real time. Clinician perception may provide a complementary measure.

Smartphone-delivered travel-record and health-code systems were deployed across China during the COVID-19 pandemic to classify risk based on travel history and regulate access to venues and travel. The geographic classification rule is a key design choice for these systems. A city-boundary rule (city-cut) is administratively convenient but may group areas with different mobility patterns under one risk label, whereas a fixed-radius rule (radius-cut) offers greater spatial precision but may exclude more distant areas connected by routine travel. Whether a classification rule aligns with observed mobility is a design question distinct from evaluating a deployed system’s epidemiological effects.

Peripheral artery disease (PAD) is prevalent in hemodialysis (HD) patients but remains difficult to diagnose due to unreliable ankle-brachial index (ABI) measurements caused by vascular calcification. We hypothesized that monitoring foot temperatures and their changes during HD, provoked by hemodynamic shifts, could serve as a novel diagnostic tool for detecting severe PAD.

Mobile health interventions have the potential to motivate healthy behaviors. Optimal strategies for engaging patients in cardiac rehabilitation (CR) through mobile health interventions remain unclear.

Poor medication adherence is a critical impediment to effective chronic obstructive pulmonary disease (COPD) management and remains challenging to track in routine care. Digital therapeutic interventions may improve chronic disease management through remote monitoring and patient education. However, most existing digital interventions focus on individual components of disease management, and evidence from longer-term pragmatic randomized controlled trials evaluating integrated digital therapeutics in routine clinical practice remains limited.

Smartphone-based self-assessment methods, such as ecological momentary assessment (EMA), are increasingly used to measure perceived stress in research and emerging clinical applications. However, the proliferation of studies and methods has made it difficult for researchers and practitioners to maintain an overview and make informed methodological choices. An updated synthesis of smartphone-based self-report methods is therefore needed.

Discriminating mild cognitive impairment (MCI) from subjective cognitive decline (SCD), and healthy controls (HC) remains challenging. Digital tools may overcome paper-and-pencil screening’s limitations.

Early childhood caries (ECC) is a prevalent yet preventable disease that significantly impacts young children’s health and quality of life. The World Health Organization’s highlights the potential of mobile health programs to enhance engagement and support sustained oral health behavior change.

Chronic disease and comorbid mental health problems disproportionately affect rural and underserved populations. Digital self-management programs are potentially scalable, but evaluations often focus on narrow outcomes.

Recovery after total hip arthroplasty (THA) and total knee arthroplasty (TKA) is typically assessed with infrequent clinic-based evaluations that may not reflect patients’ everyday mobility. Smartphone inertial sensors enable scalable, repeated gait measurement in real-world settings, but the extent to which prompted walking tests reflect daily life walking during recovery is not well defined.