This systematic review and meta-analysis was prospectively registered in PROSPERO (registration CRD420251009048), where the registered protocol is available. The review was conducted and reported in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 statement, and the literature search was reported following the PRISMA-S (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Literature Search Extension) [16,17]. The PRISMA 2020 checklist and PRISMA-S checklist are provided in Checklist 1 and Checklist 2, respectively. The study selection process is presented in the PRISMA 2020 flow diagram. Certainty of evidence for the main quantitative syntheses was assessed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach at the outcome level [18,19].

This study was approved by the Institutional Review Board of Capital Medical University (approval 2023SY081). As no individual-level data were collected and all analyses were conducted using publicly available aggregate data, additional informed consent was not required. Detailed information regarding the ethics approval process and supporting documentation is provided in Multimedia Appendix 1.

Several deviations from the registered protocol should be noted. The most important change was the introduction of a clearer time restriction, such that this review focused on studies with data collected during and since the COVID-19 period. In addition, the framework used to classify health behavior outcomes was refined, different effect-size metrics (grouped ORs, continuous ORs, and correlation coefficients) were synthesized separately, and the search strategy was updated, including changes to information sources, databases, and search terms. As a result of these methodological refinements, some aspects of the final study selection and results differed from those originally described in the registered protocol. These changes were made to improve conceptual clarity, search sensitivity, and the interpretability of the quantitative synthesis.

The study selection process is illustrated in the PRISMA 2020 flow diagram (Figure 1). A total of 2422 records were identified through database searches, including PubMed (n=542), Embase (n=565), Web of Science Core Collection (n=478), CINAHL Ultimate (n=155), and Scopus (n=682). After removal of 1153 duplicate records, 1269 records remained for title and abstract screening.

During the screening stage, 1092 records were excluded based on title or abstract, including conference abstracts or meeting abstracts (n=88), study protocols (n=51), reviews, meta-analyses, or scoping reviews (n=120), editorials, commentaries, letters, or opinions (n=25), studies not focused on eHealth literacy (n=90), studies not focused on health behaviors (n=197), studies not addressing the association between eHealth literacy and health behaviors (n=9), studies clearly outside the COVID-19 period or context (n=81), and other clearly irrelevant topics or populations (n=431).

A total of 177 reports were sought for full-text retrieval, of which 10 reports were not retrieved. The remaining 167 full-text papers were assessed for eligibility. Of these, 148 papers were excluded for the following reasons: outcomes not within predefined health behavior domains (n=27), no association analysis between eHealth literacy and health behaviors (n=4), inappropriate study design (n=7), data collected before 2020 or timing not reported (n=19), tool development or adaptation only (n=1), and effect estimates not eligible for direct quantitative synthesis (n=90), with detailed classifications provided in Multimedia Appendix 4.

Finally, 19 studies were included in the quantitative synthesis. Among these, 10 studies contributed to the correlation-based quantitative synthesis [14,26-34], and 9 studies contributed to the OR-based quantitative synthesis [35-43].

The distribution of modified NOS scores is presented in Figure 2, and detailed scoring results are provided in Tables S1 and S2 in Multimedia Appendix 6 [25-43]. The modified NOS scores ranged from 5 to 10. Among the included studies, 12 were rated as high quality [27,29,31,33,37,41-43], and 7 were rated as moderate quality [14,26,30,32,38-40]. No study was classified as low quality.

Most studies scored well in domains related to exposure and outcome assessment, reflecting the use of named eHealth literacy instruments and clearly defined health behavior measures. Lower scores were more commonly observed in domains related to sample representativeness and control of confounding, as most included studies used cross-sectional designs and convenience-based sampling strategies.

Using the GRADE approach, the certainty of evidence for the association between eHealth literacy and health behaviors was rated as low for the grouped OR-based synthesis and very low for the correlation-based and continuous OR-based syntheses. The full GRADE evidence profile and summary of findings table are provided in Multimedia Appendix 7.

Because all included studies were observational and predominantly cross-sectional, the initial certainty of evidence was low. For the grouped OR-based synthesis, the certainty was not rated down further because heterogeneity was moderate, the CI and prediction interval remained consistent with a positive association, and the additional concerns identified were not judged serious enough to warrant further downgrading beyond the initial low rating for observational evidence.

In contrast, the certainty of evidence for the correlation-based synthesis was rated down to very low because of substantial inconsistency across studies. For the continuous OR-based synthesis, the certainty was also rated down to very low because of very high heterogeneity and serious imprecision, mainly related to the small number of contributing studies and interval estimates compatible with no association. No synthesis was upgraded because the criteria for upgrading were not met, including a convincing large effect, a clear dose-response gradient, or plausible residual confounding that would reduce the observed association.

Because included studies reported associations using different effect size metrics (correlation coefficients, grouped ORs, and continuous ORs), separate meta-analyses were conducted for each effect size framework to avoid inappropriate pooling of heterogeneous measures.

Reporting biases were assessed using funnel plots and Egger regression where appropriate. Visual inspection of funnel plots was performed for the 3 syntheses (Figures S1-S3 in Multimedia Appendix 8). The funnel plot for the correlation-based synthesis showed slight visual asymmetry. However, interpretation of the funnel plots for the grouped OR and continuous OR syntheses was limited because only 6 and 3 studies, respectively, were included.

Egger regression was conducted for the correlation-based synthesis, which included 10 studies. The test did not show statistically significant evidence of small-study effects (intercept=−1.37; SE 1.76; t₈=−0.78; P=.46). Because only 6 studies were available for the grouped OR synthesis, Egger regression was performed only as an exploratory analysis. This exploratory test also did not show statistically significant evidence of small-study effects (intercept=2.34; SE 1.16; t₄=2.03; P=.11). No formal funnel plot asymmetry test was interpreted for the continuous OR synthesis because only 3 studies were available, making such tests statistically underpowered and unreliable.

Overall, no clear statistical evidence of small-study effects was detected in the syntheses for which Egger regression was applied. Nevertheless, because the number of studies was limited, particularly for the grouped OR and continuous OR syntheses, reporting biases could not be ruled out.

Subgroup analyses were performed for the grouped OR-based synthesis and the correlation-based synthesis by income level, patient status, geriatric status, and health behavior domain. Subgroup analyses were not conducted for the continuous OR-based synthesis because only 3 studies were available, all of which contributed to the health-promoting behavior domain, which was considered insufficient for meaningful subgroup comparison. Detailed statistical parameters for subgroup analyses in the correlation-based and grouped OR-based syntheses, as well as the overall meta-analytic parameters for the continuous OR-based synthesis, are provided in Multimedia Appendix 9.

In the correlation-based synthesis, positive correlations were observed across all examined subgroups. For geriatric status, the pooled correlation coefficient was 0.40 (95% CI 0.33‐0.46) in older-adult populations and 0.44 (95% CI 0.37‐0.50) in younger or mixed-age populations. For patient status, the pooled r was 0.46 (95% CI 0.37‐0.53) in patient populations and 0.42 (95% CI 0.34‐0.50) in general populations. For income level, the pooled r was 0.46 (95% CI 0.39‐0.53) in high-income economies and 0.41 (95% CI 0.31‐0.51) in low- and middle-income economies. For the health behavior domain, the pooled r was 0.44 (95% CI 0.36‐0.52) for health-promoting behaviors and 0.42 (95% CI 0.31‐0.52) for health management behaviors. No correlation-based study contributed to the health decision-making behavior domain (Table 3; Table S1 in Multimedia Appendix 9). Formal tests for between-subgroup differences were not statistically significant for income level, patient status, geriatric status, or health behavior domain in the correlation-based synthesis, suggesting that these study-level characteristics did not explain a substantial proportion of the between-study heterogeneity.

In the grouped OR-based synthesis, positive associations between higher eHealth literacy and health behaviors were also observed across most subgroups. For geriatric status, the pooled OR was 1.59 (95% CI 1.21‐2.08) in older-adult populations and 2.55 (95% CI 1.85‐3.51) in younger or mixed-age populations. For patient status, the pooled OR was 2.31 (95% CI 0.89‐5.99) in patient populations and 2.18 (95% CI 1.68‐2.83) in general populations. For income level, the pooled OR was 2.25 (95% CI 1.58‐3.20) in high-income economies and 2.01 (95% CI 1.17‐3.45) in low- and middle-income economies. For the health behavior domain, the pooled OR was 2.70 (95% CI 1.74‐4.19) for health decision-making behaviors, 2.25 (95% CI 1.58‐3.20) for health-promoting behaviors, and 1.55 (95% CI 1.11‐2.17) for health management behaviors (Table 3; Table S2 in Multimedia Appendix 9). Formal tests for between-subgroup differences showed a statistically significant difference only for geriatric status (QM=4.70; P=.03), whereas income level, patient status, and health behavior domain were not significant moderators. This pattern suggests that geriatric status may be a potential source of heterogeneity, but this finding should be considered exploratory because it was based on a small number of studies and study-level classifications. The overall meta-analytic parameters for the continuous OR-based synthesis are reported separately in Table S3 in Multimedia Appendix 9.

Across subgroup strata, the overall direction of association remained positive, but several subgroup estimates were based on a small number of studies and should therefore be interpreted cautiously. In addition, although measurement heterogeneity was considered, its likely impact was limited because the vast majority of included studies assessed eHealth literacy using eHEALS, with only 1 study using a different instrument. Therefore, differences in eHealth literacy measurement were unlikely to be a major driver of the observed heterogeneity, which more plausibly reflects variation in study populations, settings, and behavioral outcomes. Accordingly, the pooled estimates, particularly those from the correlation-based and continuous OR-based syntheses, should be interpreted as average effects across heterogeneous study contexts rather than as single common effects applicable to all settings.

This systematic review and meta-analysis examined whether eHealth literacy was associated with health behaviors in studies conducted during and since the COVID-19 period and whether this association varied across behavioral domains and analytic frameworks. The findings were interpreted according to 3 considerations: heterogeneity, risk of bias, and certainty of evidence [18,19,21].

Overall, higher eHealth literacy was generally associated with more favorable health behaviors in the correlation-based and grouped OR-based syntheses, whereas evidence from the continuous OR-based synthesis was less certain after Knapp-Hartung adjustment. In the correlation-based synthesis, the pooled average association was moderate and positive, but heterogeneity was substantial. In the grouped OR-based synthesis, the association was also positive and showed moderate heterogeneity, providing the most consistent quantitative evidence among the 3 effect-size frameworks. In contrast, although all 3 continuous OR studies reported positive study-level associations, the pooled average effect was not statistically significant after Knapp-Hartung adjustment and heterogeneity was very high. These heterogeneity patterns indicate that the pooled estimates should be interpreted as average associations across heterogeneous study contexts, rather than as evidence of a single stable relationship between eHealth literacy and health behaviors. This interpretation is consistent with conceptual work, emphasizing that digital and eHealth literacy are context-dependent constructs shaped by informational, social, and service environments [44,45].

In subgroup analyses, geriatric status showed a statistically significant subgroup difference in the grouped OR-based synthesis, whereas country income level, patient status, and health behavior domain were not statistically significant moderators. This suggests that age structure may partly explain differences in the association, but this finding should be interpreted cautiously because subgroup analyses were based on a limited number of studies and study-level classifications. Although no included study was rated as low quality, several NOS domains indicated remaining methodological limitations, particularly in sample representativeness, nonresponse, and confounding control. These limitations may have introduced selection-related bias and residual confounding in some studies. They are especially relevant because most included studies were cross-sectional, a design that limits temporal ordering and causal inference [46,47]. In accordance with GRADE guidance, the certainty of evidence was low for the grouped OR-based synthesis and very low for the correlation-based and continuous OR-based syntheses. The grouped OR-based synthesis was not downgraded beyond the initial low certainty assigned to observational evidence. The correlation-based synthesis was downgraded because of substantial inconsistency, whereas the continuous OR-based synthesis was downgraded because of very high heterogeneity and serious imprecision. Accordingly, the findings should be interpreted as suggestive evidence of a generally positive but context-dependent association, rather than as definitive evidence of a uniform or causal effect.

First, all included studies were observational and predominantly cross-sectional. Because cross-sectional studies measure exposure and outcome at the same time, they cannot establish temporal ordering or causal direction [46]. Therefore, these findings cannot determine whether higher eHealth literacy leads to healthier behaviors, whether healthier individuals develop higher eHealth literacy, or whether both reflect shared determinants. This interpretation is consistent with STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidance on transparent reporting of observational designs rather than causal claims [47].

Second, heterogeneity remained substantial in the correlation-based synthesis and very high in the continuous OR-based synthesis. This heterogeneity was not fully explained by prespecified subgroup variables and likely reflects variation in outcome definitions, health behavior measurement approaches, digital health contexts, and unmeasured study-level factors. Accordingly, the pooled estimates should be interpreted as average associations across heterogeneous study contexts rather than as stable effects applicable to all populations or behavioral domains.

Third, although no study was rated as low quality, several modified NOS domains indicated remaining methodological limitations, particularly in sample representativeness, nonresponse, and confounding control, as shown in Multimedia Appendix 6. These limitations may have introduced selection-related bias and residual confounding in some studies.

Fourth, the certainty of evidence was limited. According to the GRADE assessment, certainty was low for the grouped OR-based synthesis and very low for the correlation-based and continuous OR-based syntheses [18,19]. The grouped OR-based synthesis was not downgraded beyond the initial low certainty assigned to observational evidence. The correlation-based synthesis was downgraded mainly because of substantial inconsistency, whereas the continuous OR-based synthesis was downgraded because of very high heterogeneity and serious imprecision related to the small number of studies and interval estimates compatible with no association. Therefore, the findings should be interpreted as suggestive and hypothesis-generating rather than as definitive evidence of a causal relationship.

Finally, the quantitative synthesis was limited to studies that reported extractable correlation coefficients or ORs, as meta-analysis requires effect estimates that can be computed on a common and interpretable scale [48,49]. Studies examining relevant associations but not reporting effect estimates suitable for direct synthesis were therefore excluded from the meta-analysis, which may have affected the comprehensiveness of the quantitative evidence [48,49]. In addition, although most studies used eHEALS or closely related instruments to assess eHealth literacy [5,6], health behavior outcomes were measured heterogeneously, suggesting that outcome-side variation may have contributed substantially to between-study heterogeneity [22,24].

Taken together, these findings suggest that eHealth literacy may be an important supportive capability for health-related action in contemporary digital health settings, but it should not be interpreted as a sufficient stand-alone determinant of behavior change. This interpretation is consistent with behavior change theory, which emphasizes that capability must interact with opportunity and motivation to produce behavior change [8]. In practice, the behavioral relevance of eHealth literacy is likely to depend on whether digital health information and services are accessible, understandable, usable, and connected to practical opportunities for action [50-53]. It is also shaped by broader digital health inequalities, implementation barriers, patient portal contexts, access to electronic health records, and continuity of digitally supported care [54-57]. Therefore, interventions aiming to improve health behaviors should not rely solely on improving eHealth literacy. Instead, they should combine eHealth literacy enhancement with behavior-specific strategies, accessible and user-centered digital services, clinician support, continuity of care, and structural resources that help individuals translate digital information into sustained health-related action [52-57].

Future research should use more robust study designs to clarify the temporal ordering and potential causal pathways between eHealth literacy and health behaviors. Longitudinal and intervention studies are particularly needed to determine whether improvements in eHealth literacy lead to subsequent and sustained changes in behavior. Researchers should report sufficient data for effect-size synthesis, including adjusted and unadjusted estimates where possible, and should clearly specify whether eHealth literacy is modeled as a continuous or categorical exposure. Future studies should also distinguish health behavior domains more explicitly, because eHealth literacy may operate differently across decision-making, health-promoting, and health management behaviors [52,53]. More evidence is needed from diverse geographic regions, patient and nonpatient populations, older and nonolder adults, and underrepresented behavioral domains, given persistent digital health inequalities and implementation challenges across settings [54-56]. Larger numbers of studies within each subgroup would allow more reliable examination of whether age structure, behavior type, patient status, country income level, or measurement approach explains heterogeneity.

Higher eHealth literacy was generally associated with more favorable health behaviors in the correlation-based and grouped OR-based syntheses, whereas evidence from the continuous OR-based synthesis was inconclusive after Knapp-Hartung adjustment. Because the evidence base was predominantly cross-sectional, heterogeneous, and limited by risk-of-bias concerns, and because the certainty of evidence was low to very low, this association should be interpreted as suggestive, contextual, and noncausal rather than uniform or definitive.

The innovation of this review lies in synthesizing evidence from the COVID-19 and post–COVID-19 period, applying a functional classification of health behaviors, and separating correlation coefficients, grouped ORs, and continuous ORs rather than combining statistically distinct effect measures into a single pooled estimate. This distinguishes this review from earlier syntheses that examined eHealth literacy and health-related behaviors more broadly but did not provide the same separation by behavioral function and effect-size framework. By combining functional behavioral classification, separate quantitative syntheses, prediction intervals, and GRADE assessment, this review provides a more cautious and context-specific interpretation of the evidence: eHealth literacy is generally associated with more favorable health behaviors in the correlation-based and grouped OR-based syntheses, but the strength and certainty of this association depend on the effect-size framework, behavioral outcome, and study population.

The practical implication is that eHealth literacy should be treated as an important supportive capability, not as a stand-alone solution for behavior change. Interventions aiming to improve health behaviors should not only raise eHealth literacy but also ensure that digital health information and services are trustworthy, usable, accessible, and connected to clinical and social support. Clinician guidance, continuity of care, behavior-specific tools, and feasible opportunities for action are needed to help individuals translate digital information into sustained health-related behavior. Future longitudinal and intervention studies should clarify temporal ordering, test causal pathways, and determine which combinations of eHealth literacy support and service-level conditions produce durable improvements across decision-making, health-promoting, and health management behaviors.