%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 27
%N 
%P e50313
%T Factors Influencing eHealth Literacy Worldwide: Systematic Review and Meta-Analysis
%A Hua,Zhong
%A Yuqing,Song
%A Qianwen,Liu
%A Hong,Chen
%+ Department of Nursing, West China School of Nursing, West China Hospital, Sichuan University, No 37, Guoxuexiang, Wuhou District, Sichuan, Chengdu, 610041, China, 86 18980601733, 1366109878@qq.com
%K meta-analysis
%K eHealth literacy
%K eHealth
%K Technology Acceptance Model
%K Literacy and Health Conceptual Framework
%K social determinants of health
%K digital health
%K consumer
%D 2025
%7 10.3.2025
%9 Review
%J J Med Internet Res
%G English
%X Background: eHealth literacy has increasingly emerged as a critical determinant of health, highlighting the importance of identifying its influencing factors; however, these factors remain unclear. Numerous studies have explored this concept across various populations, presenting an opportunity for a systematic review and synthesis of the existing evidence to better understand eHealth literacy and its key determinants. Objective: This study aimed to provide a systematic review of factors influencing eHealth literacy and to examine their impact across different populations. Methods: We conducted a comprehensive search of papers from PubMed, CNKI, Embase, Web of Science, Cochrane Library, CINAHL, and MEDLINE databases from inception to April 11, 2023. We included all those studies that reported the eHealth literacy status measured with the eHealth Literacy Scale (eHEALS). Methodological validity was assessed with the standardized Joanna Briggs Institute (JBI) critical appraisal tool prepared for cross-sectional studies. Meta-analytic techniques were used to calculate the pooled standardized β coefficient with 95% CIs, while heterogeneity was assessed using I2, the Q test, and τ2. Meta-regressions were used to explore the effect of potential moderators, including participants’ characteristics, internet use measured by time or frequency, and country development status. Predictors of eHealth literacy were integrated according to the Literacy and Health Conceptual Framework and the Technology Acceptance Model (TAM). Results: In total, 17 studies met the inclusion criteria for the meta-analysis. Key factors influencing higher eHealth literacy were identified and classified into 3 themes: (1) actions (internet usage: β=0.14, 95% CI 0.102-0.182, I2=80.4%), (2) determinants (age: β=–0.042, 95% CI –0.071 to –0.020, I2=80.3%; ethnicity: β=–2.613, 95% CI –4.114 to –1.112, I2=80.2%; income: β=0.206, 95% CI 0.059-0.354, I2=64.6%; employment status: β=–1.629, 95% CI –2.323 to –0.953, I2=99.7%; education: β=0.154, 95% CI 0.101-0.208, I2=58.2%; 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%), and (3) health status factor (disease: β=–0.177, 95% CI –0.298 to –0.055, I2=26.9%). Conclusions: This systematic review, guided by the Literacy and Health Conceptual Framework model, identified key factors influencing eHealth literacy across 3 dimensions: actions (internet usage), determinants (age, ethnicity, income, employment status, education, perceived usefulness, and self-efficacy), and health status (disease). These findings provide valuable guidance for designing interventions to enhance eHealth literacy. Trial Registration: PROSPERO CRD42022383384; https://www.crd.york.ac.uk/PROSPERO/view/CRD42022383384 
%R 10.2196/50313
%U https://www.jmir.org/2025/1/e50313
%U https://doi.org/10.2196/50313