JMIR Publications

ABSTRACT

Background:

Aging populations globally face challenges in adopting mobile health (mHealth) technologies despite their potential for chronic disease management. Japan, with 29.1% of its population aged ≥65 years, the world's highest proportion　provides a critical case study for understanding mHealth acceptance mechanisms in super-aged societies. Despite government digital transformation initiatives, utilization remains limited at 21.6%, with pronounced age and gender disparities mirroring patterns observed internationally.

Objective:

This study investigates factors influencing mHealth acceptance by extending the Unified Theory of Acceptance and Use of Technology (UTAUT) to integrate eHealth literacy, self-efficacy, perceived risk, and distrust. Multi-group analyses examined age-stratified and gender-based differences to inform globally applicable intervention strategies for aging populations.

Methods:

A cross-sectional survey (November 2023) involving 960 adults equally distributed across seven age cohorts (18–27 to ≥78 years) assessed behavioral intention, performance expectancy, effort expectancy, social influence, facilitating conditions, distrust, perceived risk, self-efficacy, and eHealth literacy. Structural Equation Modeling tested hypothesized relationships; multi-group analyses examined demographic differences; binary logistic regression identified usage predictors across application types.

Results:

The model demonstrated good fit (χ²/df = 2.36, CFI = 0.947, RMSEA = 0.052). Performance expectancy (β = 0.339), facilitating conditions (β = 0.336), and effort expectancy (β = 0.274) predicted behavioral intention. Social influence exerted indirect effects through eHealth literacy (β = 0.490) and self-efficacy (β = 0.451). Six age-based differences emerged: eHealth literacy paradoxically increased distrust among young adults (β = 0.370, p < 0.001) but reduced perceived risk among middle-aged and older adults. Distrust negatively affected intention among younger cohorts but not older adults. Three gender differences emerged: social influence reduced distrust among males but not females; eHealth literacy reduced perceived risk among females but not males; performance expectancy more strongly predicted intention among females (β = 0.388) than males (β = 0.200).

Conclusions:

mHealth acceptance mechanisms differ markedly across age and gender, indicating universal strategies may fail in aging populations globally. Age-tailored interventions—peer-driven education for young adults, workplace privacy assurance for middle-aged adults, family-assisted training for older adults—combined with gender-responsive approaches addressing privacy concerns and infrastructural needs are essential. These findings offer evidence-based guidance for aging societies worldwide facing similar demographic transitions. Clinical Trial: Not applicable