eHealth literacy correlates with online health information seeking but varies significantly by age and morbidityeHealth Literacy Linked to Online Health Information Seeking Habits

BACKGROUND: Online health information-seeking (OHIS) behavior shapes health self-management, and eHealth literacy-the ability to seek, appraise, and apply electronic health information-is regarded as its key driver. Previous reviews aggregated heterogeneous outcomes, focused on measurement properties, or examined single clinical populations, without isolating the eHealth literacy-OHIS link. OBJECTIVE: This study quantified the strength and heterogeneity of the eHealth literacy-OHIS association and identified its boundary conditions across generation, morbidity status, and information source credibility. METHODS: Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), we searched PubMed, Embase, Web of Science Core Collection, PsycINFO, Psychology and Behavioral Sciences Collection, and Library, Information Science, and Technology Abstracts (LISTA) up to March 15, 2026 (PROSPERO [International Prospective Register of Systematic Reviews] CRD420251088300). Eligible studies enrolled participants, measured eHealth literacy with validated instruments, and assessed OHIS. Risk of bias used the modified Newcastle-Ottawa Scale. Correlations were Fisher z-transformed and pooled under a random-effects model with the Hartung-Knapp-Sidik-Jonkman correction; subgroups were age cohort, morbidity status, and source type. Heterogeneity was quantified with I² and τ²; a univariate meta-regression examined temporal trends, and certainty of evidence was rated using GRADE (Grading of Recommendations, Assessment, Development, and Evaluation). RESULTS: Of 9249 nonduplicate records, 32 studies entered the qualitative synthesis, and 19 (20 effect sizes) the meta-analysis. The grand mean correlation was 0.27 (95% CI 0.15-0.38; P<.001) but is of limited interpretive value given extreme heterogeneity (I²=99%; τ²=0.064; 95% prediction interval -0.26 to 0.67). Correlations were stronger in non-Gen Z (k=12; r=0.39; 95% CI 0.27-0.50; P<.001) than in Gen Z (k=8; r=0.07; 95% CI -0.06 to 0.20; P=.23), in patients (k=3; r=0.58; 95% CI 0.01-0.86; P=.049) than in nonpatients (k=17; r=0.22; 95% CI 0.11-0.32; P<.001), and in professional (k=5; r=0.41; 95% CI 0.11-0.64; P=.02) than in nonprofessional (k=14; r=0.21; 95% CI 0.06-0.35; P=.01) sources. Meta-regression on collection year showed no significant temporal change (b=-0.005 per year; P=.55), and neither the Egger test (P=.60) nor trim-and-fill indicated small-study effects. CONCLUSIONS: The eHealth literacy-OHIS association is best understood through its boundary conditions, not the overall estimate. The association was robust in non-Gen Z and professional-source contexts but near-null in Gen Z, showing that the eHealth literacy scale's behavioral predictive validity is cohort- and platform-dependent. Interventions for Gen Z and nonpatient populations should pair literacy training with motivational cues and professionally curated information environments. GRADE certainty was very low, underscoring the need for longitudinal, performance-based research.