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Factors Influencing eHealth Literacy Worldwide: Systematic Review and Meta-Analysis
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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
JMIR Publications Inc.
Title: Factors Influencing eHealth Literacy Worldwide: Systematic Review and Meta-Analysis
Description:
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.
Related Results
Factors Influencing eHealth Literacy Worldwide: Systematic Review and Meta-Analysis (Preprint)
Factors Influencing eHealth Literacy Worldwide: Systematic Review and Meta-Analysis (Preprint)
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eHealth literacy has increasingly emerged as a critical determinant of health, highlighting the importance of identifying its influencing factors...
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