Applying an Extended UTAUT2 Model to Explain User Acceptance of Lifestyle and Therapy Mobile Health Apps: Survey Study (Preprint)

Abstract

BACKGROUND Mobile healthcare applications (mHealth apps) are a promising technology to monitor and control health individually and cost-effective with a technology that is widely used, affordable and ubiquitous in many people's lives. Download statistics show that lifestyle apps are widely used by young and healthy users to improve fitness, nutrition, and more. While this is an important aspect for the prevention of future chronic diseases, the burdened healthcare systems worldwide may directly profit from the use of therapy apps by those patients already in need for medical treatment and monitoring. OBJECTIVE Therefore, by comparing lifestyle and therapy apps, we aim at better understanding what influences potential users’ decisions to use (or not to use) mHealth apps. METHODS We apply the established UTAUT2 technology acceptance model to evaluate mHealth apps in an online questionnaire with n = 707 German participants. Additionally, trust and privacy concerns are added to the model and in a between-subject study design the influence of these predictors on behavioral intention to use is compared between lifestyle and therapy apps. RESULTS The results show that the model does only weakly predict the intention to use mHealth apps (R2 = .019). Only hedonic motivation is a significant predictor of behavioral intentions regarding both app types (lifestyle: .196, p < .01; therapy: .344, p < .001). Habit influences the behavioral intention to use lifestyle apps (.272, p < .001), while social influence (.185, p < .001) and trust (.273, p < .001) predict the intention to use therapy apps. A further exploratory correlation analysis of the relationship between user factors on behavioral intention was calculated. Health app familiarity shows the strongest correlation to the intention to use (r = .469, p < .001), stressing the importance of experiences. Also age, education level, app familiarity, digital health literacy, privacy disposition, and the propensity to trust apps correlate weakly to behavioral intention to use mHealth apps. CONCLUSIONS The results indicate that for the health care context, new and improved acceptance models need to be developed that also integrate user diversity, especially experiences with apps and mHealth.