Deakin University

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Factors influencing acceptance and continued use of mHealth apps

conference contribution
posted on 2017-01-01, 00:00 authored by H O Woldeyohannes, Ojelanki Ngwenyama
By 2018, mHealth apps would have been downloaded by 50% of the more than 3.4 billion global smartphone and tablet users. As existing challenges to adoption are allayed, empirical evidence for factors that most predict successful adoption of mHealth apps will be useful to inform and guide the trajectory of mHealth development. To date, most research has looked into clinician perception of mHealth apps. However, only 2% of mHealth apps target healthcare providers/insurance, while the remainder target patients and other consumers [1]. This study was conducted to examine the following: What factors predict adoption of mHealth apps? Participants (n = 11) between ages of 18 to 65 were recruited. A cross-sectional, qualitative interview methodology was used to investigate the research question. The UTAUT2 model for technology adoption and continued use was used to inform the interview guide. Closed coding, thematic analysis and co-occurrence analysis were performed to identify factors. Performance expectancy, effort expectancy and habit were the most relevant constructs that predict adoption of mHealth apps. Flexibility of app to personal preferences positively contributes to performance expectancy. Usage of a specific feature is influenced by user’s assessment of relevance to subjective overall health, or interest. Perception of limited features/value may lead to user boredom and use discontinuation. Social influence and hedonic motivation were the least directly implicated factors. Most participants were unwilling to purchase apps before a trial period. Emergent factors include trust for technology/information, required time for interaction with app, privacy of personal information/data, and app-generated feedback.



10293 LNCS


239 - 256




Vancouver, B.C.

Place of publication

Cham Switzerland

Start date


End date








Publication classification

E3 Extract of paper

Title of proceedings

HCIBGO 2017 : Proceedings of the 19th International Conference on Human-Computer Interaction