JMIR Publications

ABSTRACT

Background:

The popularization of the Internet and rapid development of mobile devices lead to an increased inclination and opportunities to obtain health-related information from the Internet. The eHealth Literacy Scale (eHEALS) is a first developed and widely used tool to measure eHealth literacy, and is employed to assess an individual’s ability to search, understand, appraise, and utilize eHealth information. However, the Chinese version of eHEALS multiple factors model remains to be validated and the correlation between eHEALS and health-promoting lifestyle profile (HPLP) among university students is rarely explored in Taiwan.

Objective:

This study aimed to examine the fit, validity and reliability of the Chinese eHEALS multiple factors model and clarify the potential sociodemographic predictors of eHEALS among university students as well as the predictive effects of eHEALS on HPLP.

Methods:

University students in Taipei, the capital of Taiwan were recruited and a two-stage sampling was performed from March to April 2021. Firstly, nine universities were randomly selected based on the ratios of various types of universities. subsequently, 39-50 students were randomly selected from each university to complete the self-administered questionnaire which includes sociodemographic characteristics, eHEALS, and HPLP. A total of 406 valid questionnaires were collected. Confirmatory factor analysis, independent sample t-test, one-way ANOVA, multiple regression analysis, Pearson product-moment correlation analysis, and binary logistic regression analysis were conducted for statistical analysis.

Results:

The Chinese eHEALS exhibited optimal fit when delineated into the search, utilization, and evaluation three-factor model (comparative fit index=.991, Tucker-Lewis index=.984, root mean square error of approximation=.062), and the validity and reliability were confirmed. The mean eHEALS score of university students was 3.17/4.00 points, and the score for evaluation subscale was the lowest (3.08/4.00 points). eHEALS was relatively poor in female students, those who acquired information by themselves, and those with average or poor perceived health status (adjusted R2=0.072, P˂.001). Furthermore, after adjusting for sociodemographic variables affecting HPLP among students, eHEALS remained an independent predictor of HPLP. Compared with students with good eHEALS, those with poor and moderate eHEALS had 20.96 times (adjusted odds ratio 20.96, 95% CI 4.69-93.60) and 2.97 times (adjusted odds ratio 2.97, 95% CI 1.31-6.76) risk of negative HPLP, respectively.

Conclusions:

Appropriate eHealth literacy training programs are recommended and should be prioritized for students with poor eHEALS. Additionally, good campus eHealth literacy learning and support channels should be established to equip students with the ability to critically assess and validate eHealth information. The three-factor model of the Chinese eHEALS in this study results in more definite scale content, which can increase the practicality and applicability of this scale in health-promoting-related studies.