Managing BMI and Emotional Distress Using mHealth in a Nationally Representative Sample: A Multiple-Mediator Path Model (Preprint)

Background: Mobile health (mHealth) technologies, including smartphone health apps and wearable trackers, are increasingly used to promote health behaviors. However, their impact on physical and mental well-being remains complex, with both benefits and potential unintended negative consequences. Objective: This study aimed to examine the relationship between mHealth use (i.e., health app, wearable tracker) and two health outcomes (body mass index (BMI) and emotional distress), as well as the mediating roles of healthy eating, sleep, and physical activity based on a representative sample. Methods: We analyzed data from a nationally representative sample of U.S. adults aged 33–43 (N = 1,931). Chi-square tests and one-way ANOVA were used to compare demographic differences between mHealth users and non-users. A path model examined the relationship between mHealth use (i.e., smartphone health apps, wearable trackers) and health outcomes (i.e., BMI, emotional distress), with lifestyle factors (i.e., healthy eating, physical activity, sleep) as mediators. Mediation analyses tested indirect effects through these lifestyle factors. Results: mHealth users are more likely to be female, married, have higher levels of education and income, and have health insurance. The primary use of mHealth is the management of physical activity. The use of health apps positively correlates with the use of wearable trackers (β = .408, p < .001). Surprisingly, health app use predicts greater BMI (β = .058, p = .019). However, the use of health apps, as well as wearable trackers, predicts more healthy eating (βhealth_app = .097, p < .001; βwearable = .081, p < .001) and physical activity (βhealth_app = .125, p < .001; βwearable = .105, p < .001), both of which link to lower BMI (βhealthy_eating = -.075, p =.001; βphysical_activity = -.147, p < .001). For emotional distress, wearable tracker use directly predicts lower emotional distress (β = -.089, p < .001); a path also mediated by healthy eating (β = -.120, p <.001) and physical activity (β = -.077, p = .001). Although health app use does not predict emotional distress directly, the mediated path via healthy eating and physical activity remains significant. Notably, the use of wearable trackers, not that of health apps, connects with reduced sleep hours (β = -.077, p = .001), which in turn correlates with higher BMI (β = -.109, p < .001) and greater emotional distress (β = -.137, p < .001). Conclusions: mHealth technologies can promote healthier behaviors, but their impact depends on users taking the initiative toward sustained lifestyle changes. While wearable trackers may aid in mental well-being, their association with reduced sleep warrants further investigation.