JMIR Publications

ABSTRACT

Background:

The rapid development of digital technology has provided sufficient technical support for the dissemination of health information through the web. The elderly, who are the primary group seeking health information, often have limited proficiency in using digital technology. Exploring predictors of eHealth literacy can help propose targeted interventions to enhance eHealth literacy among the elderly.

Objective:

The aim of this study was to explore the predictors of eHealth literacy among community-dwelling older adults from a multidimensional perspective, based on the social-ecological systems theory.

Methods:

This study used a multistage sampling method. Information was collected on sociodemographic characteristics, health-related factors, internet-related factors, social support, and attitudes toward aging and eHealth literacy. Data were analyzed using descriptive statistics, correlation analysis, univariate, and stepwise regression analysis.

Results:

A total of 263 patients completed the questionnaire. The score of eHealth literacy in older adults was 20.74±6.72. Predictors of eHealth literacy among older adults include age (β=-.160, P<.001), education(β=.306, P<.001), residential status(β=.134, P=.002), medical insurance(β=.108, P=.009), self-rated health(β=.114, P=.013), frequency of health information search (β=.158, P=.001), attitudes toward health information (β=-.117, P=.013), family support (β=.166, P<.001), friends support (β=.231, P<.001), and psychological growth(β=-.167, P<.001). Predictors of the ability to apply electronic health information to health problems among older adults include age (β=-.152, P=.001), education (β=.261, P <.001), residence status (β=.103, P=.015), medical insurance (β=.109, P=.009), duration of internet use (β=.101, P=.023), frequency of health information search (β=.123, P=.008), family support (β=.142, P=.002), friends support (β=.229, P <.001), and psychological growth (β=.124, P=.008). Predictors of evaluation ability among older adults include age (β=-.142, P=.006), education (β=.244, P<.001), pre-retirement occupation (β=.129, P=.016), residence status (β=.106, P=.030), self-rated health (β=.117, P=.031), family support (β=.124, P=.021), friends support (β=.201, P<.001), and psychological growth (β=.146, P=.008). Predictors of decision-making ability among older adults include age (β=-.124, P=.019), education (β=.201, P<.001), duration of internet use (β=.135, P=.011), family support (β=.168, P=.002), other support (β=.198, P<.001), and psychological growth (β=.187, P=.001).

Conclusions:

Despite the gradual increase in internet penetration, eHealth literacy among older adults in China is relatively low. This study explores the factors contributing to eHealth literacy from a multidimensional perspective and identifies further influencing factors of eHealth literacy, which can serve as a foundation for the targeted enhancement of eHealth literacy among older adults in the future.