Exploring the Acceptance of mHealth Applications - Do Acceptance Patterns Vary Depending on Context?

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 795)


In the present study, we investigate influencing factors on the acceptance of mHealth smartphone apps, using an extended UTAUT model. N = 165 participants evaluated use intention, performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), as well as privacy concerns for a fitness app (lifestyle context) and a diabetes app (medical context). Structural equation modeling is used to assess the relevance of influences on adoption intention in these contexts. Results show that acceptance factors indeed differ strongly between lifestyle and medical contexts. For the latter, only PE and SI determine intention to use, although privacy concerns are higher than in the lifestyle context. In contrast, intention to use the fitness app is predicted by PE, SI, FA, and privacy concerns. The extended UTAUT model showed very good predictive relevance for use intention in both contexts. These findings reveal that technology acceptance needs to be examined depending on context.


Technology acceptance mHealth Life-logging UTAUT Privacy concerns Human factors 



The authors thank all participants for sharing their thoughts and opinions and Niklas Kunstleben for research support. This research was funded by the German Ministry of Education and Research (BMBF) under the project MyneData (KIS1DSD045).


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Human-Computer Interaction CenterRWTH Aachen UniversityAachenGermany

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