Influencing Factors of eHealth Literacy Worldwide: A Systematic Review and Meta-analysis (Preprint)
Authors: Zhong Hua , Liu Qianwen , Chen Hong 
Published: June 2023

BACKGROUND

eHealth literacy has increasingly become a new determinant of health. and it is essential to know what the exact influencing factors of it. Numerous studies have investigated among different populations. This offered us an opportunity to systematically review and synthesize evidence from the literature to gain a better understanding of eHealth literacy and its influencing factors.

OBJECTIVE

This study aims to provide a systematic review of eHealth literacy and its influencing factors.
Methods: The standardized Joanna Briggs Institute (JBI) critical appraisal tool prepared for cross-sectional studies was used for methodological validity assessment and data extraction. We demonstrated the meta-analytic measure of association using pooled standardized beta coefficient (β), 95% confidence interval (CI) and heterogeneity by I2 tests, Q test and Ƭ2. Effect of moderators (sample groups, developed/ developing country) was measured via meta-regressions. Predictors of eHealth literacy were aggregated and integrated according to the Literacy and Health Conceptual Framework and the Technology Acceptance Model (TAM).

METHODS

The standardized Joanna Briggs Institute (JBI) critical appraisal tool prepared for cross-sectional studies was used for methodological validity assessment and data extraction. We demonstrated the meta-analytic measure of association using pooled standardized beta coefficient (β), 95% confidence interval (CI) and heterogeneity by I2 tests, Q test and Ƭ2. Effect of moderators (sample groups, developed/ developing country) was measured via meta-regressions. Predictors of eHealth literacy were aggregated and integrated according to the Literacy and Health Conceptual Framework and the Technology Acceptance Model (TAM).

RESULTS

17 studies met the search criteria for the meta-analysis. The significant influencing factors of higher eHealth literacy including actions: internet usage (β = 0.14, 95% CI 0.102~0.182, I2 = 80.4%); determinants including: (i) age (β = -0.042, 95% CI-0.071~-0.020, I2 = 80.3%),(ii) ethnicity (β = -2.613, 95% CI -4.114~ -1.112, I2 = 80.2%), (iii) income (β = 0.206, 95%CI 0.059~0.354, I2 = 64.6%), (iv) employed status (β = -1.629, 95% CI -2.323~-0.953, I2 = 99.7%), (v) education (β = 0.154, 95% CI 0.101~0.208, I2 = 58.2%); personal capabilities: perceived usefulness(β= 0.832, 95%CI 0.131~1.522, I2 = 68.3%) and self-efficacy (β=0.239, 95%CI 0.129~0.349, I2 = 0.0%); health status factor: disease (β= -0.177, 95%CI -0.298~ -0.055, I2=26.9%).

CONCLUSIONS

Based on the Literacy and Health Conceptual Framework model, studies have explored the impact of various factors on user eHealth literacy across three dimensions: actions, determinants, and health status factors. This research found that users' eHealth literacy is influenced by factors such as internet usage, age, ethnicity, income, employment status, education, perceived usefulness, self-efficacy, and disease. The findings provide guidance for designing interventions to enhance eHealth literacy.

CLINICALTRIAL