Predicting mHealth Acceptance Using the UTAUT2 Technology Acceptance Model: A Mixed-Methods Approach

Abstract

Background: Mobile health (mHealth) apps are increasingly used in healthcare to support people with chronic diseases such as diabetes. mHealth acceptance is crucial for using them. Due to acceptance problems, however, mHealth apps are not used by all chronic disease patients. To predict user acceptance, technology acceptance models such as UTAUT2 are used. However, UTAUT2 was not explicitly developed for the mHealth context. Objectives: This study investigates if additional health-related constructs could increase the predictive power of the UTAUT2 model. Methods: A mixed-methods design, comprising an initial qualitative methods triangulation study that consisted of a literature search, expert interviews, and patient interviews, and a subsequent quantitative cross-sectional survey with 413 patients was used. Results: The mixed-methods study revealed and validated two new constructs relevant for predicting mHealth acceptance not represented in the UTAUT2 model: “perceived disease threat” and “trust”. Conclusion: The UTAUT2 model was successfully extended by two new constructs relevant to the mHealth context.