The Effect of Individual Factors on Health Behaviors Among College Students: The Mediating Effects of eHealth Literacy

BACKGROUND: College students’ health behavior is a topic that deserves attention. Individual factors and eHealth literacy may affect an individual’s health behaviors. The integrative model of eHealth use (IMeHU) provides a parsimonious account of the connections among the digital divide, health care disparities, and the unequal distribution and use of communication technologies. However, few studies have explored the associations among individual factors, eHealth literacy, and health behaviors, and IMeHU has not been empirically investigated. OBJECTIVE: This study examines the associations among individual factors, eHealth literacy, and health behaviors using IMeHU. METHODS: The Health Behavior Scale is a 12-item instrument developed to measure college students’ eating, exercise, and sleep behaviors. The eHealth Literacy Scale is a 12-item instrument designed to measure college students’ functional, interactive, and critical eHealth literacy. A nationally representative sample of 525 valid college students in Taiwan was surveyed. A questionnaire was administered to collect background information about participants’ health status, degree of health concern, major, and the frequency with which they engaged in health-related discussions. This study used Amos 6.0 to conduct a confirmatory factor analysis to identify the best measurement models for the eHealth Literacy Scale and the Health Behavior Scale. We then conducted a multiple regression analysis to examine the associations among individual factors, eHealth literacy, and health behaviors. Additionally, causal steps approach was used to explore indirect (mediating) effects and Sobel tests were used to test the significance of the mediating effects. RESULTS: The study found that perceptions of better health status (t(520)=2.14-6.12, P<.001-.03) and greater concern for health (t(520)=2.58-6.95, P<.001-.003) influenced college students’ development of 3 dimensions of eHealth literacy and adoption of healthy eating, exercise, and sleep behaviors. Moreover, eHealth literacy played an intermediary role in the association between individual factors and health behaviors (Sobel test=2.09-2.72, P<.001-.03). Specifically, higher levels of critical eHealth literacy promoted students’ health status and their practice of multiple positive health behaviors, including eating, exercise, and sleep behaviors. CONCLUSIONS: Because this study showed that eHealth literacy mediates the association between individual factors and health behaviors, schools should aim to enhance students’ eHealth literacy and promote their health behaviors to help them achieve high levels of critical eHealth literacy. Although some of the study’s hypotheses were not supported in this study, the factors that influence health behaviors are complex and interdependent. Therefore, a follow-up study should be conducted to further explore how these factors influence one another.