{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T07:22:05Z","timestamp":1790666525641,"version":"4.1.0"},"reference-count":66,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Med Internet Res"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec sec-type=\"background\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Online health information\u2013seeking (OHIS) behavior shapes health self-management, and eHealth literacy\u2014the ability to seek, appraise, and apply electronic health information\u2014is regarded as its key driver. Previous reviews aggregated heterogeneous outcomes, focused on measurement properties, or examined single clinical populations, without isolating the eHealth literacy\u2013OHIS link.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"objective\">\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study quantified the strength and heterogeneity of the eHealth literacy\u2013OHIS association and identified its boundary conditions across generation, morbidity status, and information source credibility.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      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\u2013transformed 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\n                      <jats:italic>I<\/jats:italic>\n                      \u00b2 and \u03c4\u00b2; a univariate meta-regression examined temporal trends, and certainty of evidence was rated using GRADE (Grading of Recommendations, Assessment, Development, and Evaluation).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      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;\n                      <jats:italic>P<\/jats:italic>\n                      &lt;.001) but is of limited interpretive value given extreme heterogeneity (\n                      <jats:italic>I<\/jats:italic>\n                      \u00b2=99%; \u03c4\u00b2=0.064; 95% prediction interval \u22120.26 to 0.67). Correlations were stronger in non\u2013Gen Z (k=12; r=0.39; 95% CI 0.27-0.50;\n                      <jats:italic>P<\/jats:italic>\n                      &lt;.001) than in Gen Z (k=8; r=0.07; 95% CI \u22120.06 to 0.20;\n                      <jats:italic>P<\/jats:italic>\n                      =.23), in patients (k=3; r=0.58; 95% CI 0.01-0.86;\n                      <jats:italic>P<\/jats:italic>\n                      =.049) than in nonpatients (k=17; r=0.22; 95% CI 0.11-0.32;\n                      <jats:italic>P<\/jats:italic>\n                      &lt;.001), and in professional (k=5; r=0.41; 95% CI 0.11-0.64;\n                      <jats:italic>P<\/jats:italic>\n                      =.02) than in nonprofessional (k=14; r=0.21; 95% CI 0.06-0.35;\n                      <jats:italic>P<\/jats:italic>\n                      =.01) sources. Meta-regression on collection year showed no significant temporal change (b=\u22120.005 per year;\n                      <jats:italic>P<\/jats:italic>\n                      =.55), and neither the Egger test (\n                      <jats:italic>P<\/jats:italic>\n                      =.60) nor trim-and-fill indicated small-study effects.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The eHealth literacy\u2013OHIS association is best understood through its boundary conditions, not the overall estimate. The association was robust in non\u2013Gen Z and professional-source contexts but near-null in Gen Z, showing that the eHealth literacy scale\u2019s 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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/93578","type":"journal-article","created":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T07:10:06Z","timestamp":1780557006000},"page":"e93578-e93578","source":"Crossref","is-referenced-by-count":1,"title":["The Relation Between eHealth Literacy and Online Health Information\u2013Seeking Behavior: Systematic Review and 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