Decomposition and Comparative Analysis of eHealth Literacy Differences among Urban and Rural University Students in China (Preprint)

Author:

Yu YaoORCID,Liang ZhenningORCID,Zhou QingpingORCID,Tuersun YusupujiangORCID,Liu SiyuanORCID,Wang ChenxiORCID,Wu ZhuotongORCID,Qian YiORCID

Abstract

BACKGROUND

E-health is growing at a rapid rate, and e-health literacy is receiving increasing academic attention.

OBJECTIVE

This study aims to investigate the differences in eHealth literacy between university students in urban and rural areas of China, with a focus on analyzing the influencing factors and their contributions to these disparities.

METHODS

The eHealth Literacy Scale (eHEALS) was administered to evaluate the eHealth literacy levels of 7,230 college students from various schools and majors across ten different regions, including Guangdong Province, Shanghai Municipality, and Jiangsu Province. A binary logistic regression model was developed to identify the main influencing factors of eHealth literacy among university students. The Fairlie decomposition model was utilized to analyze the factors and their contributions to the observed differences in eHealth literacy between urban and rural students.

RESULTS

The average eHealth literacy score among Chinese university students was 29.22, with 57.19% of students scoring below the passing mark. The findings revealed that the proportion of rural students with inadequate eHealth literacy (62.90%) was significantly higher than that of urban students (47.72%). The Fairlie decomposition analysis indicated that 71.42% of the eHealth literacy disparity could be attributed to urban-rural factors and unobserved variables, whereas 28.58% was due to observed factors. The primary contributing factors included per capita monthly household income (13.44%), exercise habits (11.66%), PHQ-9 scores (2.12%), religious beliefs (1.43%), academic performance (0.58%), and GAD-7 scores (0.52%). Among these, per capita monthly household income and exercise habits were the most significant explanatory variables for urban-rural differences.

CONCLUSIONS

Rural university students exhibit lower eHealth literacy levels compared to their urban counterparts, mainly due to differences in socioeconomic status, personal lifestyle, and health conditions. To address these disparities, targeted and precise intervention strategies should be developed. These strategies may include enhancing the dissemination and accessibility of health information resources, strengthening psychological health support and services, promoting physical activity and healthy lifestyles, and conducting regular health literacy training and assessments to improve the overall health literacy of this key population.

Publisher

JMIR Publications Inc.

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