The Relation Between eHealth Literacy and Health-Related Behaviors: Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: With widespread use of the internet and mobile devices, many people have gained improved access to health-related information online for health promotion and disease management. As the health information acquired online can affect health-related behaviors, health care providers need to take into account how each individual's online health literacy (eHealth literacy) can affect health-related behaviors. OBJECTIVE: To determine whether an individual's level of eHealth literacy affects actual health-related behaviors, the correlation between eHealth literacy and health-related behaviors was identified in an integrated manner through a systematic literature review and meta-analysis. METHODS: The MEDLINE, Embase, Cochrane, KoreaMed, and Research Information Sharing Service databases were systematically searched for studies published up to March 19, 2021, which suggested the relationship between eHealth literacy and health-related behaviors. Studies were eligible if they were conducted with the general population, presented eHealth literacy according to validated tools, used no specific control condition, and measured health-related behaviors as the outcomes. A meta-analysis was performed on the studies that could be quantitatively synthesized using a random effect model. A pooled correlation coefficient was generated by integrating the correlation coefficients, and the risk of bias was assessed using the modified Newcastle-Ottawa Scale. RESULTS: Among 1922 eHealth literacy-related papers, 29 studies suggesting an association between eHealth literacy and health-related behaviors were included. All retrieved studies were cross-sectional studies, and most of them used the eHealth Literacy Scale (eHEALS) as a measurement tool for eHealth literacy. Of the 29 studies, 22 presented positive associations between eHealth literacy and health-related behaviors. The meta-analysis was performed on 14 studies that presented the correlation coefficient for the relationship between eHealth literacy and health-related behaviors. When the meta-analysis was conducted by age, morbidity status, and type of health-related behavior, the pooled correlation coefficients were 0.37 (95% CI 0.29-0.44) for older adults (aged ≥65 years), 0.28 (95% CI 0.17-0.39) for individuals with diseases, and 0.36 (95% CI 0.27-0.41) for health-promoting behavior. The overall estimate of the correlation between eHealth literacy and health-related behaviors was 0.31 (95% CI 0.25-0.34), which indicated a moderate correlation between eHealth literacy and health-related behaviors. CONCLUSIONS: Our results of a positive correlation between eHealth literacy and health-related behaviors indicate that eHealth literacy can be a mediator in the process by which health-related information leads to changes in health-related behaviors. Larger-scale studies with stronger validity are needed to evaluate the detailed relationship between the proficiency level of eHealth literacy and health-related behaviors for health promotion in the future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.036 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".