Background: Mobile health (mHealth) apps target diverse health behaviors, but engagement may vary by purpose. Objective: This study examined the prevalence, usage patterns, and user characteristics of mHealth apps among Czech adults with internet access, focusing on sociodemographics, digital knowledge and use, and health indicators predicting wellness- and illness-related app use. Methods: Overall, 4775 Czech adults (2365/4775, 49.53% women) aged 18-95 (mean 45.37, SD 16.40) years completed an online survey. Sociodemographic factors included age, gender, education, and income. Digital knowledge and use were measured using the eHealth Literacy Scale and the passive/active use of social networking sites (SNS) for health information. Health indicators covered symptom severity, physical activity, BMI, and eating disorder–related risk propensity (body dissatisfaction, dietary restraint, and weight/shape overvaluation). Participants reported app use for sports, number of steps, nutrition, vitals, sleep, diagnosed conditions, reproductive health, diagnosis assistance, mood and mental well-being, and emergency care guidance. Multivariate hierarchical binary logistic regression analysis identified characteristics of app users. Exploratory structural equation modeling (ESEM) clustered apps into “promoting wellness” and “managing illness” and examined the predictors of frequency of use. Results: Of 4440 respondents, 2172 (48.92%) used mHealth apps. Users were younger (odds ratio [OR] 0.98, 95% CI 0.98-0.99, P<.001), had a monthly income more than 50,000 CZK (1 CZK=US $0.048; vs ≤20,000 CZK: OR 0.54, 95% CI 0.41-0.7, P<.001; 20,001-35,000 CZK: OR 0.78, 95% CI 0.65-0.93, P=.006; 35,001-50,000 CZK: OR 0.83, 95% CI 0.7-0.99, P=.03), were more eHealth literate (OR 1.17, 95% CI 1.06-1.3, P=.003), used SNS passively for health information (OR 1.35, 95% CI 1.21-1.51, P<.001), and had higher eating disorder risk (OR 1.18, 95% CI 1.12-1.25, P<.001) and physical activity (OR 1.18, 95% CI 1.13-1.23, P<.001) than nonusers. Step-counting apps were most common; 65.99% (1430/2167) used them daily or several times a day, followed by apps for sleep (691/2163, 31.95%), vitals (611/2165, 28.22%), and sports (407/2158, 18.86%). ESEM confirmed a 2-factor structure (“promoting wellness” and “managing illness”; χ²26=71.9, comparative fit index=0.99, Tucker-Lewis index=0.99, root-mean-square error of approximation=0.03, and standardized root-mean-square residual=0.03). Frequent use of wellness apps was associated with younger age (standardized β=–0.22, P<.001), higher eHealth literacy (standardized β=0.10, P<.001), and physical activity (standardized β=0.15, P<.001). Illness-management app use was associated with active use of SNS for health information (standardized β=0.62, P<.001) and eating disorder risk (standardized β=0.11, P<.001). Digital knowledge, digital use, and health indicators mediated the association between age and mHealth app use. Conclusions: mHealth app engagement reflects broader social, digital, and psychological inequalities rather than individual preferences alone. Encouraging digital inclusion and addressing body image- and diet-related use may help ensure that mHealth technologies do not exacerbate existing health inequalities across age and user groups. Trial Registration:
