JMIR mHealth and uHealth

Ecological momentary assessment (EMA) methods can provide assessment of alcohol-related beliefs and behaviors when people are in their natural environments. An increasingly common EMA approach involves the use of passive sensors (eg, continuous sharing of Bluetooth and GPS cell phone data) to collect rich data and trigger alcohol assessment in specific contexts. While collecting real-time assessments allows researchers to explore contextual factors impacting alcohol use, there is a lack of research examining compliance rates to alcohol-related EMAs that use daily questions for study periods over 2 weeks. EMAs over longer periods allow researchers to assess meaningful changes over time and sample low-base-rate events. Further, there is limited research exploring how EMA compliance is influenced by individual-level factors (eg, privacy concerns and alcohol use). Identifying factors associated with compliance can help inform EMA protocol design.

Mobile mental health (MH) apps are increasingly promoted as tools for self-care. However, less is known about how users integrate them into everyday life and how they combine with other forms of support.

Digital health technologies (DHTs) offer opportunities to support poststroke self-management and rehabilitation, but their implementation may be hindered by mismatches between technological design, stakeholder needs, and care contexts. Synthesizing stakeholder perspectives can clarify implementation priorities and gaps in the evidence.

Gender-affirming voice training (GAVT) has reduced gender incongruence for transgender and gender-nonconforming (TGNC) individuals, but remains limited by cost and scarcity of specialized providers. Mobile health (mHealth) is a bridge to these access gaps; yet, few high-quality, clinically informed mHealth apps exist within voice clinical sciences.

Young adults report high levels of anxiety, yet their access to timely treatment remains limited. Mobile-based digital therapeutics, particularly ecological momentary assessment and intervention (EMA/EMI), offer a promising approach to deliver context-responsive care. The integration of wearable-derived digital phenotypes with digital therapeutics may enhance the personalization of feedback in such interventions, but empirical evidence from real-world settings remains scarce.

Diabetes self-management education and support requires scalable digital solutions, making mobile health (mHealth) interventions increasingly vital. Despite their proven clinical efficacy, the real-world translation and scalability of these interventions are severely hindered by fragmented and opaque reporting. To address this methodological gap, the World Health Organization developed the mHealth evidence reporting and assessment (mERA) checklist to standardize reporting transparency.

Continuous glucose monitoring (CGM) can facilitate weight management and lower the risk of metabolic diseases by providing real-time feedback on glycemic responses, thereby enabling more informed lifestyle decisions. However, current CGM systems remain constrained by invasiveness, cost, and short sensor lifespan, limiting their practicality for guiding individualized postprandial low-glycemic diets.

Remote patient management (RPM) that supports patient self-monitoring of vital parameters and lifestyle factors may improve cardiovascular risk management (CVRM) in primary care. However, large-scale implementation remains limited, partly due to insufficient evidence on long-term value for money, budget impact, and implications for health care professionals' workload.

Ubiquitous smartphone access and statistical advances offer opportunities to continuously track affect intensity, which is central to various psychological processes and behaviors. Research demonstrated the potential of personalized predictions of momentary negative affect (NA) and positive affect (PA) using passive sensing. However, studies typically incorporated all available data sources without differentiating their added value, nor did they investigate whether refining location features with self-reported semantic location (eg, workplaces) improved personalized predictions.

Early warning systems (EWS) have the potential to reduce heat-related health risks, yet few are designed specifically for vulnerable populations. Pregnant women and infants, often cared for by postpartum women, are particularly susceptible to heat-related illnesses. The MotherHeat Alert mobile app is a heat EWS specifically developed for pregnant and postpartum women.

Childhood obesity remains a major public health challenge. Mobile health (mHealth) interventions offer a scalable approach to support behavior change, but their effectiveness may depend on participant adherence. While both caregiver and child adherence are important, few studies have jointly examined caregiver app-use adherence and child behavioral adherence within digital obesity interventions.

Family caregivers of children with disabilities frequently experience high levels of stress and psychological burden. Digital support platforms offer accessible channels for information and emotional support, but their long-term use patterns remain underexplored.