The impact of post-adoption beliefs on the continued use of health apps

Int J Med Inform. 2016 Mar:87:75-83. doi: 10.1016/j.ijmedinf.2015.12.016. Epub 2015 Dec 30.

Abstract

Background: Recently, there has been a rapid increase in the development and use of health apps on smartphones. In spite of research on such technologies, there exist considerable gaps between health app use and our understandings of such technology. Therefore, this study explored the process of leading people to keep using health apps, mainly based on the post-acceptance model (PAM).

Purpose: Despite significant previous research on health apps, few studies have focused on the post-adoption behaviors of using these technologies. To address and fill the gaps in health app research, this study has developed and tested a model to explain the micro-mechanism that determines the continuance intention to use health apps, theoretically relying on the post-acceptance model (PAM) and the technology acceptance model (TAM).

Methods: A sample consisting of 343 Korean adults who were currently using health apps on smartphones participated in an online survey. A path analysis was conducted to test the proposed model composed of the main factors from PAM and TAM.

Results: The results from the path analysis indicated that the following perceptual and emotional factors-perceived usefulness, perceived ease of use, confirmation, and satisfaction-were significantly associated with the continuance intention to use health apps on smartphones.

Discussion/conclusion: Main findings from this present study contribute to developing and empirically testing a model of explaining the basic process of motivating health app users to keep using those apps. This model will be helpful for researchers to further examine health-related technologies, particularly mHealth-oriented ones.

Keywords: Continuance intention; Health apps; Post-acceptance model (PAM); Technology acceptance model (TAM).

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Attitude to Computers*
  • Computer Literacy
  • Female
  • Health Information Systems / statistics & numerical data*
  • Humans
  • Information Seeking Behavior*
  • Male
  • Mobile Applications / statistics & numerical data*
  • Models, Theoretical*
  • Physicians / standards*
  • Surveys and Questionnaires
  • Telemedicine*