JMIR Publications

ABSTRACT

Background:

Specifying the determinants of using health apps have been an important research topic for health scholars as health apps have proliferated during the past decade. Some predictors of using health apps, such as socioeconomic status (SES), have been revealed. Nevertheless, why those determinants are associated with health app use is still unknown.

Objective:

This study aims to examine the cognitive mechanisms underlying the relationships between two important potential determinants of health app use – SES and use of health information sources other than health apps (other source use) – and use of health apps, applying the Integrative Model of Behavioral Prediction.

Methods:

This study tested a comprehensive model of predicting health app use, which hypothesizes the indirect influences of SES and other source use on intentions to use health apps, which in turn predict actual use of health apps. The relationships of SES and other source use with intentions to use health apps were assumed to be mediated by proximal variables (attitudes, perceived behavioral control (PBC), injunctive norms, and descriptive norms). To examine this model, we conducted path analyses using data from a two-wave opt-in panel survey of Korean adults. The number of respondents was 1,718 at baseline and 1,304 at follow-up (recontact completion rate = 82.3%). We compared our model with two alternative theoretical models based on modified IM, to further clarify the roles of determinants of health app use.

Results:

Attitudes, PBC, and injunctive norms were positively associated with intentions to use health apps, which in turn were positively related to actual uses of health apps. Income was positively associated with intentions to use health apps and it was mediated by attitudes and PBC. Education was positively associated with descriptive norms, but descriptive norms was not significantly related to intentions to use health apps. Use of other health information sources was related to intentions via injunctive norms. We also found that PBC interacted with attitudes and jointly influenced intentions to use health apps, whereas the results did not support direct influences of education, income, other source use, and PBC on health app use.

Conclusions:

We found that PBC over using health apps may be the most important factor in predicting health app use. This indicates the necessity of designing and promoting health apps in a user-friendly way. Our findings also implied that socioeconomic inequalities in using health apps may be reduced by increasing positive attitudes towards and boosting PBC over health app use among individuals with low income. Lastly, the results suggested that existing health information channels have probably been distributed effective promotional information about health apps.