JMIR mHealth and uHealth

Health care professionals must consider multiple dimensions of prevention when consulting with adolescents. Identifying risky behaviors early in adolescence is crucial for reducing both morbidity and mortality. General practitioners are increasingly eager to incorporate digital tools for prevention into their consultations with adolescents; however, the relevance and clinical validity of these digital tools are not always established or well-known. Consequently, primary care professionals require guidance and support in selecting relevant mobile health (mHealth) tools.

Radiographic knee osteoarthritis (ROA) is associated with abnormal plantar loading and altered gait coordination. Insole-based plantar-pressure sensing offers a practical wearable approach to ROA identification, but existing methods typically use either conventional biomechanical descriptors or end-to-end temporal models. An interpretable subject-level framework that captures both local abnormalities and distributed coordination is, therefore, needed.

Infections are a major health concern in hospitalized patients. Fever is an early sign of infection, making temperature monitoring essential for infection surveillance. Wearable devices are increasingly being explored for continuous temperature monitoring in acute care hospitals, but how temperature data from wearables are monitored, presented, and used to support fever or infection management in clinical practice remains poorly understood.

Reliable measurement of smartphone screen time is important for research examining associations between digital media use and health outcomes. Although objective monitoring tools reduce bias associated with self-report, the number of monitoring days required to reliably estimate typical smartphone use remains unclear. This question is particularly important for study design because longer monitoring periods increase participant burden and the likelihood of missing data.

Mobile health (mHealth) and online video are increasingly central to cardiology education and point-of-care decision support. However, little is known about how simple design choices, such as mobile-first web layouts and captioned videos, translate into real-world practice across countries with different income levels.

Work-related stress has been widely associated with an increased risk of various mental disorders and poor mental well-being. The fast-growing mobile health services industry has provided new opportunities for workplace mental health.

Intensive longitudinal data (ILD) include frequent and dense repeated measures captured over acute timescales (eg, every second, hour, or day) that are used to investigate within-person processes both within and across days. ILD are collected via wearable sensor data, ecological momentary assessments, or daily diaries and provide unique insights into within-person processes under ecologically valid conditions that can strengthen understanding of temporal relationships among variables and potential causal processes, while also informing the development of just-in-time adaptive interventions. Advancements in mobile and sensor technology have facilitated an explosion of ILD studies that have outpaced formal training in ILD study design. When designing ILD studies, researchers need to make careful decisions about the frequency (ie, how often) and timing (ie, when) of measurements. Decisions about the frequency and timing of measurement are influenced by issues such as variability across individuals and constructs, the purpose of the assessment, concerns about recall biases, saliency, or missing information, and participant needs. The interpretation of study results, causal inferences, and the predictive value of ILD are also impacted by decisions related to the timing of assessments, temporal lags between measures, how a “day” is defined, and data aggregation choices. Due to the increased interest in and adoption of ILD studies, and a lack of formal training, researchers can benefit from guidance on how to design ILD studies. Therefore, this paper aims to provide practical guidance to researchers on how to plan ILD studies using a step-by-step decision-making tutorial with applied health behavior research examples examining phenomena (eg, physical activity and alcohol use) that vary over acute time scales.

Temporomandibular disorders (TMDs) are common chronic conditions involving orofacial pain and functional limitations. Digital therapeutics (DTx) have demonstrated efficacy in TMD management; yet, the behavioral and clinical mechanisms underlying treatment response remain poorly characterized, particularly whether behavioral modification or DTx engagement intensity drives therapeutic benefit.

Delays in completing cancer screening diminish the preventive benefits of early detection, particularly among women receiving care in Federally Qualified Health Centers (FQHCs). Although many patients receive SMS reminders and complete screening, less is known about how quickly they complete testing or which patient-level and structural factors are associated with delays.

Prediabetes is highly prevalent and increasing globally, yet lifestyle interventions remain underused. AI-driven mobile health (mHealth) tools can help scale diabetes prevention efforts, but the key factors driving their success are not well understood.

Digital interventions offer scalable alternatives to traditional face-to-face diabetes education, but often face challenges related to inconsistent clinical effectiveness, and declining user engagement. However, whether a digital structured education program integrated with behavioral nudge tools can improve metabolic, behavioral, and psychological outcomes in adults with type 2 diabetes remains unclear.

Optimal bowel preparation (BP) is crucial for a successful colonoscopy. Although multiple factors influence BP quality, including patient adherence to laxatives and dietary instructions, the stool state during BP should be properly evaluated to perform a colonoscopy of sufficient quality. Therefore, we developed a smartphone app to evaluate a patient’s stool state during BP and a viewer to enable real-time monitoring by medical staff.