Evidence map›Paper›PMID 42456044›Full record

SynthesisJournal of medical Internet research2026

The Relation Between eHealth Literacy and Online Health Information-Seeking Behavior: Systematic Review and Meta-Analysis.

Xi Wang, Tian Shen, Xi Chen, Kejia He, Yuxiang Chris Zhao

Abstract readSystematic ReviewMeta-Analysis
In one paragraph

Synthesis in Journal of medical Internet research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Xi WangSchool of Information Management, Nanjing University, Nanjing, Jiangsu, China.ORCID http://orcid.org/0000-0002-9675-4311
Tian ShenSchool of International Education, Nanjing University of Chinese Medicine, 138 Xianlin Ave, Nanjing, Jiangsu, 210023, China, 86 25 86562982.ORCID http://orcid.org/0000-0002-1949-7040
Xi ChenSchool of Business, Nanjing University, Nanjing, Jiangsu, China.ORCID http://orcid.org/0000-0002-2634-8641
Kejia HeSchool of Information Management, Nanjing University, Nanjing, Jiangsu, China.ORCID http://orcid.org/0009-0003-7383-5690
Yuxiang Chris ZhaoSchool of Information Management, Nanjing University, Nanjing, Jiangsu, China.ORCID http://orcid.org/0000-0001-9281-3030

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Health LiteracyInformation Seeking BehaviorTelemedicineDigital HealthHumansInternetdigital health literacyeHealth literacygenerational differencesGeneration Zmeta-analysisonline health information seeking behavior

Identifiers

PMID42456044
PMCPMC13372218

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.