Digital media and communication technologies have markedly altered how people access health information, and many internet users have been reported to retrieve health-related information online [1]. Survey evidence has indicated widespread online health information seeking (OHIS) and self-diagnosis practices, and the internet has been shown to enable rapid retrieval of abundant and up-to-date material as well as bidirectional exchange between providers and consumers [2]. The affordances of social networking platforms, messaging services, video streaming, and intelligent conversational systems have further expanded opportunities for both one-to-many dissemination and many-to-many cocreation of health knowledge [3,4]. However, online health information originates from heterogeneous providers, and quality-control challenges persist, a situation that increases the risk of exposure to biased or inaccurate content [5,6].

OHIS behavior is a multifaceted construct encompassing dimensions such as thematic breadth, engagement depth, source selection, and search strategies [7]. Nevertheless, a significant portion of the literature has reduced this complexity to unidimensional metrics, such as search frequency or self-reported intensity [8], thereby oversimplifying the phenomenon and potentially masking the nuanced associations between eHealth literacy and search practices. Population-based surveys, for instance, often rely on frequency-based measures without distinguishing between preventive, diagnostic, or treatment-oriented inquiries [9]. Furthermore, while previous reviews have documented substantial heterogeneity in measurement frameworks, from self-efficacy-based instruments to performance-oriented assessments, there remains a critical gap in systematically linking these operational definitions to underlying cognitive mechanisms and observable behavioral outcomes. Such conceptual and methodological inconsistencies hinder a comprehensive understanding of how eHealth literacy correlates with distinct facets of OHIS, including information evaluation and application.

Prior research has identified significant generational variations in digital nativity and information-seeking preferences; whereas younger cohorts tend to exhibit more prevention-oriented behaviors, older adults often display lower objective literacy despite reporting higher perceived empowerment through digital engagement [9]. Generation Z (Gen Z) digital natives may possess strong technical skills, but their search behavior is often driven by intuition [10]. Furthermore, health status is closely associated with distinct information needs; patients and individuals with chronic conditions typically engage in more intensive searches across a diverse array of sources, yet they concurrently face an elevated risk of information overload [11]. Information source types also appear to interact with literacy levels, as individuals with higher eHealth literacy tend to prioritize professional portals and exercise more critical evaluation of social media content [12]. Meanwhile, the emergence of generative AI and chatbots has introduced new complexities regarding accessibility and information accuracy [13]. Despite the burgeoning literature, the theoretical pathways linking eHealth literacy to OHIS remain insufficiently delineated. While previous studies have established correlations between eHealth literacy and distal health outcomes such as medication adherence [14], the extent to which it relates to the frequency, scope, and selectivity of online searches remains unclear [7]. Specifically, the moderating roles of age, health status, and source characteristics have not been systematically quantified. Because a positive eHealth literacy–OHIS correlation is broadly expected, the key scientific value lies in identifying when and for whom this association weakens and what such attenuation implies for the validity of instruments such as the eHealth literacy scale (eHEALS) in contemporary digital environments.

Several systematic reviews have synthesized evidence on eHealth literacy, but their scope and analytical focus differ substantially from this study. One review [14] meta-analyzed the correlation between eHealth literacy and health-related behaviors (pooled r=0.31; n=14 studies), yet their outcome category subsumed health-promoting behaviors, medication adherence, and preventive actions as a whole, precluding any estimate specific to OHIS. Neter and Brainin [15] reviewed the literature on health literacy, eHealth literacy, and health outcomes among patients with long-term conditions, documenting associations with health service use and self-management, but did not pool effect sizes for information-seeking behavior or examine cross-population variability. Lee et al [7] systematically evaluated the psychometric properties of 7 eHealth literacy instruments using the Consensus-based Standards for the selection of health Measurement Instruments (COSMIN) methodology, confirming that eHEALS was the most widely validated scale while recommending content updates to reflect the evolving digital health landscape; their measurement-focused scope, however, did not extend to estimating how literacy levels relate to behavioral outcomes. Milanti et al [16] mapped the determinants and outcomes of eHealth literacy in healthy adults, identifying positive associations with general health-promotion behavior and health service use, yet treated information seeking only as an incidental component of broader outcome categories. Collectively, these reviews either aggregated heterogeneous behavioral outcomes without disaggregating information seeking, concentrated on measurement rather than behavioral correlates, or restricted their scope to specific clinical populations without cross-group comparisons. No prior synthesis has isolated the eHealth literacy–OHIS link as its primary focus, nor has any examined whether this association is moderated by generational cohort, morbidity status, or information source credibility. The present meta-analysis fills this gap.

This meta-analysis aimed to estimate the average association between eHealth literacy and OHIS and, more importantly, to quantify its heterogeneity across populations and information contexts. We specifically tested whether the association is attenuated in Gen Z and whether stronger health motivation and credible information cues amplify the translation of perceived literacy into active seeking.

Conceptually, the association between eHealth literacy and OHIS can be understood through the Motivation-Ability-Opportunity (MAO) framework (Figure 1), which also guided the selection of moderators in this meta-analysis. In this lens, eHealth literacy primarily reflects the perceived ability to locate and appraise online health information, whereas OHIS represents a coping-oriented behavior that is more likely to be enacted when health motivation is salient and when the information environment provides accessible and credible opportunities for seeking. Accordingly, a weak eHealth literacy–OHIS association may suggest that ability is no longer the primary constraint on behavior, or that commonly used self-report instruments capture general confidence rather than actionable competence in contemporary digital settings.

No date-of-publication limits were applied; each database was searched from inception. Searches were restricted to English-language publications because the validated eHealth literacy instruments central to this review (eg, eHEALS) have been studied predominantly in English-language literature, and the inclusion of non-English studies without access to validated translations could introduce measurement inconsistency. No published search filters (eg, study design filters) were applied.

To supplement the database searches, backward citation searching was performed by screening the reference lists of all included studies. We also searched Google Scholar; the first 200 results were screened by title. No study registries were searched, as this review focused on published observational studies rather than registered trials. No additional data were sought by contacting individual authors or experts.

The search strategies were developed de novo and peer reviewed by a second reviewer (TS) prior to execution, with reference to the Peer Review of Electronic Search Strategies (PRESS) guideline [25]. All retrieved records were imported into Zotero (Corporation for Digital Scholarship), and duplicates were removed using the software’s automated detection function followed by manual verification.

For the systematic review, retrieved studies were selected according to the following inclusion criteria:

Exclusion criteria were (1) studies that did not use validated or well-defined eHealth literacy measures; (2) qualitative research, review articles, or conference abstracts; (3) experimental studies where baseline observational data could not be isolated; (4) studies that did not report active OHIS; (5) non-English publications; and (6) studies where the full text was inaccessible.

The literature search and selection process was performed independently by 2 reviewers (XW and Xiaohan Wang). Any discordance among the reviewers during the process of literature selection was resolved through mutual agreement or by involving a third researcher (XC) in a discussion. If 2 or more studies were performed on the same set of participants, the studies were considered duplicates, and only 1 comprehensive study was selected for further analysis.

The following data were extracted from the selected literature using a standardized form by 2 reviewers (XW and Xiaohan Wang): the characteristics of the studies (first author, publication year, country or location, study design, participants, and sample size); types of eHealth literacy scales; mean eHealth literacy score; types of OHIS whose correlations with eHealth literacy were verified; methods of measuring OHIS; statistical analysis methods; types of outcome indicators; and values of outcome indicators. Any inconsistency or ambiguity was resolved by discussion with other reviewers (XC and TS).

The risk of bias in the selected studies was assessed using the modified Newcastle-Ottawa Scale (NOS). While the NOS was developed for assessing the risk of bias in nonrandomized observational studies [26], our study used the modified NOS [27] that was developed for cross-sectional studies. The NOS uses a star system to assess the risk of bias in studies, whereby a lower score is associated with a higher risk of bias: high risk, 0‐3 stars; unclear risk, 4‐6 stars; and low risk, 7‐9 stars. Studies evaluated to have a high risk of bias were excluded from the meta-analysis.

Of 9249 identified nonduplicate studies, 8316 studies were excluded after a review of the studies’ titles and abstracts. The remaining 933 studies were assessed for eligibility through full-text review. Finally, 32 [33-64] studies, which presented the association between eHealth literacy and OHIS, were selected for qualitative analysis. Thirteen studies lacking sufficient data to compute correlations were excluded from the quantitative synthesis; among these, 2 studies also had a high risk of bias (NOS ≤3), resulting in 19 studies [33-51] included in the meta-analysis. A total of 19 eligible studies contributed 20 independent effect sizes, as one study reported separate subgroups analyzed independently. The detailed study selection process with the reasons for exclusion during screening steps is shown in Figure 2.

The overall characteristics of the included studies are summarized in Multimedia Appendix 2 [33-64]. Among the 32 studies, most were conducted in the United States [41,43,45,53,55,60,62] and China (n=7) [34,36,38,39,42,46,54], followed by Iran [56,61], Korea [47,58], and Serbia (n=2) [37,51]. The remaining studies were from Japan (n=1) [33], Austria (n=1) [52], Thailand (n=1) [57], Australia (n=1) [59], Bangladesh (n=1) [40], the United Kingdom (n=1) [44], Indonesia (n=1) [63], Norway (n=1) [64], Poland (n=1) [48], Denmark (n=1) [49], and Brazil (n=1) [50]. Additionally, one [35] study was an international collaboration involving multiple countries. Most studies were conducted in Asia. The majority of studies (n=28) were cross-sectional investigations using questionnaires, while 2 studies implemented mixed methods frameworks [33,52]. The age groups of the study participants varied: adolescents [37,46,50-52], college or university students [36,38,40,44,56,63], and older adults [41,47,62]. Nine [39,42,45,49,55,59-61,64] studies focused on patients or clinical groups, including those with chronic obstructive pulmonary disease [60], stroke [39], various chronic conditions [59], physical disabilities [55], type 2 diabetes [61], and patients in primary care [42], otolaryngology clinics [45], or after coronary interventions [49,64].

Among the 32 included studies, 31 used the original eHEALS or a country-specific language or culturally adapted version. Versions of the eHEALS were adapted into Japanese, Korean, Chinese, Serbian, Indonesian, Persian, and Brazilian Portuguese. One study used the Polish version of the Transactional e-Health Literacy Instrument [48]. Characteristics of the OHIS observed across the included studies are summarized in Multimedia Appendix 2. Preventive information seeking was most often conducted through high-reach and low-threshold sources such as search engines, public or government health portals, news aggregators, and widely used social or video platforms, indicating that content related to lifestyle guidance, nutrition, physical activity, and vaccination is typically consumed within mainstream and multimedia environments. Diagnosis- and treatment-related information seeking was concentrated in more authoritative sources, including hospital and professional society websites, national health portals, encyclopedic health resources, and online medical service platforms, reflecting a stronger demand for credibility and clinical specificity at the point of illness or decision-making. Self-management information seeking gravitated toward interactive and longitudinal platforms such as online support groups, patient forums, disease-specific apps, subscription or call-center services, and—emerging in the most recent studies—conversational agents and large language model–based chatbots, underscoring a shift from one-time retrieval to ongoing, peer-supported engagement in chronic care. Across all 3 dimensions, marked heterogeneity in source preference was evident by age group, health status, and digital experience. Adolescent and student samples concentrated their preventive and diagnostic searches within short video, microblogging, and mobile-first environments, whereas older adults and patient cohorts emphasized official portals, structured apps, and desktop-based access for self-management and treatment information. This generational layering of digital health practices, visible across multiple countries, suggests that the adoption of social and AI-enabled platforms does not occur uniformly but reflects differing needs and affordances.

The risk of bias assessment revealed that 11 studies [37,39,41,42,45-48,51,60,61] had a low risk of bias, and 19 studies [33-36,38,40,43,44,49,50,52-57,59,62,64] had an unclear risk of bias. Two studies [56,58] were found to have a high risk of bias with a NOS score of 3, exhibiting significant selection biases in population representativeness and sample size calculation, alongside failure to control confounding variables and use validated tools for measuring OHIS (Multimedia Appendix 3).

Among the 32 included studies, 24 [33-43,45-47,50,52,54-56,58,61-64] reported significant positive associations between eHealth literacy and OHIS, 4 [44,49,53,57] reported no significant associations, 2 [48,60] reported mixed or dimension-specific findings, and 2 [51,59] reported inverse associations.

The null results tended to arise in studies with narrow outcomes or small samples and in analyses treating eHealth literacy as a mediator or moderator rather than a direct predictor [44,49,53,57]. Mixed and dimension-specific patterns highlighted substantial heterogeneity. For example, one study using a multidimensional instrument demonstrated that functional, communicative, and critical eHealth literacy could be associated with different health services in opposite directions [48]. Another study documented that eHealth literacy was significantly and positively correlated with most types of OHIS except for nutrition information seeking and online health product purchasing [60]. The 2 inverse associations were observed in a chronic-condition sample, suggesting possible reverse causation or compensatory information-seeking behavior [59], and in an adolescent sample, where higher eHealth literacy was associated with lower likelihood of seeking mental health information online [51].

Across 24 studies [33-43,45-47,50,52,54-56,58,61-64] reporting positive associations, higher eHealth literacy was most consistently linked to greater search frequency, use of a wider range of sources, more active cross-checking, increased adoption of health portals and mobile apps, participation in online support communities, and targeted searches related to prevention, diagnosis, and self-management. Objective measurement studies using screen recordings or browser logs corroborated these self-reported patterns and documented richer and more diverse search behaviors among participants with higher literacy levels [44,58]. One study explicitly examined large language model–based chatbots and conversational assistants as information sources and found that higher eHealth literacy predicted both greater use of these AI-mediated tools and more frequent follow-up cross-checking after chatbot interactions [35]. However, studies explicitly involving AI-mediated information sources remained limited, and generalization of these findings should be cautious. The relationship between eHealth literacy and OHIS in the included studies is summarized in Multimedia Appendix 4.

OHIS is a central component of contemporary health self-management [11,12], and eHealth literacy is often considered a key determinant of how individuals engage with online health resources [16,50]. However, whether eHEALS meaningfully predicts OHIS across contemporary cohorts and platforms remains unclear. Previous studies have shown that higher eHealth literacy tends to correlate with more frequent and effective health information–seeking behavior, particularly in health promotion and disease prevention [14,15]. However, the relationship between eHealth literacy and health behavior is not always straightforward [41]. Studies suggest that individuals with higher eHealth literacy are generally more engaged in OHIS, such as physical activity and disease prevention, but these behaviors may vary based on factors like age and health status [51].

Moreover, while frequent engagement with health information online is common, it is not always linked to positive health outcomes [11]. For example, despite a higher frequency of online health information searching, individuals with lower eHealth literacy may struggle to critically assess the quality of the information they access, which can result in misinformed decisions or health behavior reluctance [52,53]. Older adults and those with chronic conditions may face additional barriers when navigating online health resources, even if they engage with health information more often [14]. Our subgroup results indicate that age and morbidity status are not merely covariates but boundary conditions that reshape the literacy-behavior link.

Our analysis showed an overall positive relationship between eHealth literacy and various types of OHIS. Across most of the included studies, higher eHEALS scores were significantly associated with more frequent or more diverse OHIS, including preventive information, diagnosis- and treatment-related topics, and self-management behavior. This positive pattern was consistent across different cultural and linguistic adaptations of the eHEALS [18]. However, a few studies reported mixed or even negative findings. For example, Lee et al [59] reported nonsignificant or weak associations between eHealth literacy and the perceived ease of searching, suggesting contextual variations, and Quinn et al [44] reported that eHEALS was weakly negatively correlated with the search difficulty score. Therefore, the effect of eHealth literacy on OHIS should be carefully interpreted, and the possibility of other factors mediating the relationship should be considered. As eHealth literacy alone cannot explain the correlation between online health information acquisition and OHIS, additional research is needed to identify other factors [14,54]. The GRADE assessment rated the certainty of this evidence as very low, primarily because all included studies were cross-sectional in design and heterogeneity remained extreme across all analyses. This rating is typical of correlational meta-analyses in the eHealth literacy field, where observational designs predominate, and it highlights the importance of interpreting the pooled estimates as indicative trends rather than precise quantifications [14,16]. Accordingly, the subgroup patterns identified in this review should be regarded as hypothesis-generating findings that require confirmation through longitudinal and performance-based research.

Our analysis showed that the association between eHealth literacy and OHIS was notably stronger among non–Gen Z participants than among Gen Z. This finding is consistent with prior research indicating that, although younger cohorts often report higher self-rated digital health literacy and use online resources more frequently, their predictive value for actual health-related behaviors is weaker [9,10]. Jiao et al [54] found that while Chinese Gen Z respondents were active in seeking online health information, digital health literacy explained less variance in their OHIS compared with older groups. Similarly, Papp-Zipernovszky et al [10] reported that in Hungary, despite older adults scoring lower on the eHEALS, eHealth literacy was a stronger predictor of health empowerment and behavior. Together with these studies, our results suggest that in non–Gen Z populations, eHealth literacy is more effectively translated into health-related actions, whereas in Gen Z, high digital familiarity may dilute the extent to which eHealth literacy influences actual health behaviors.

A particularly notable finding is the near-null association in Gen Z (r=0.07). From the perspective of the MAO framework, this attenuation may indicate that perceived ability is no longer the primary constraint for digital natives; instead, motivation and opportunity structures may play a dominant role in determining whether and how they seek health information [65]. Moreover, eHEALS primarily captures confidence in locating and evaluating information within a Web 1.0 and Web 2.0 paradigm, such as searching and judging websites [17], while Gen Z increasingly acquires health information through algorithmically curated feeds, short-video platforms, and AI-mediated interfaces [13,35]. Under these conditions, eHEALS may serve less as a behavioral predictor and more as a general self-efficacy indicator, raising concerns about its construct validity in Web 3.0 environments [7,8]. This cognitive distinction also suggests that next-generation eHealth literacy frameworks should incorporate algorithmic literacy and heuristic resistance, particularly as search behaviors shift toward AI-mediated and conversational systems [35].

The stronger correlation between eHealth literacy and OHIS in patients likely results from the combination of higher health information needs and regular health care interactions, which make their eHealth literacy more directly applicable to seeking relevant information [11,15]. Health-related motivations, such as disease symptoms or treatment concerns, activate patients’ eHealth literacy, enabling them to effectively use online resources [11]. Health care providers also play a significant role by guiding patients to trusted online resources, enhancing the application of eHealth literacy in information seeking related to self-management [21]. In contrast, nonpatients—while still seeking preventive information or lifestyle advice—often lack the immediate health concerns that would activate their eHealth literacy in a similar way [16]. Their skills remain underused without the context of health-related urgency or professional prompts, limiting their engagement in OHIS [15]. This suggests that interventions for nonpatients should not only focus on building eHealth literacy but also incorporate cues that activate information-seeking behaviors. It should be noted, however, that the patient subgroup was itself heterogeneous with respect to disease type and severity. The included patient-population studies encompassed distinct clinical contexts: stroke inpatients in a rehabilitation setting (r=0.55, n=230) [39], otorhinolaryngology outpatients with mixed chronic and acute conditions (r=0.72, n=75) [45], and patients who underwent cardiac surgery during early recovery (r=0.35, n=33) [49]. Although all 3 yielded above-average correlations, the conditions ranged from chronic neurological disease to acute postoperative recovery, and sample sizes were small (total n=338). With only 3 studies and no within-subgroup variance in disease severity coding, formal meta-regression by disease chronicity or severity was not feasible. We note descriptively that the 2 studies involving patients with chronic or chronic-plus-acute conditions produced larger effects than the purely acute postoperative sample, consistent with the MAO framework prediction that sustained health motivation strengthens the literacy–behavior link. Future research should examine whether disease chronicity, illness severity, or treatment stage systematically moderates the eHealth literacy–OHIS association within patient populations.

Professional sources carry institutional credibility and structured cues that lower the effort of evaluating and verifying information, so eHealth literacy translates more directly into purposeful OHIS [6,59]. Literate users can follow clinician-recommended portals or authoritative institutional pages and reach high-quality answers quickly, which strengthens the observed association [12,59]. These sources also embed prompts and pathways that orient searches toward actionable information and increase perceived usefulness [66]. In contrast, nonprofessional venues emphasize peer narratives, algorithmic feeds, and social endorsement; even highly literate users often enter them for social or experiential reasons rather than to obtain verified solutions, which weakens the direct literacy-seeking link for authoritative content [4,54]. Interventions should therefore combine eHealth literacy training with an information environment that offers clear provenance, clinician referrals, and low-friction access to vetted portals to source skills into searches with stronger health impact [66].

This study systematically reviewed and synthesized research on the relationship between eHealth literacy and OHIS. By quantifying the pooled effect size across different populations, illness statuses, and sources, it offers a comprehensive evidence base for understanding how individual eHealth literacy shapes both the choice of information sources and the quality of retrieval [14,15]. Subgroup analyses further revealed that correlations were stronger when information was obtained from professional sources than from nonprofessional ones, highlighting the role of literacy in navigating credible online resources. These findings provide a data-driven foundation for designing targeted digital health interventions and tailoring eHealth services to different user groups. More importantly, the pronounced attenuation in Gen Z raises the hypothesis that the predictive validity of eHEALS may be cohort- and platform-dependent, although this finding should be interpreted with caution given the cross-sectional, self-report nature of the underlying data.

However, several limitations should be considered when interpreting these findings, which can be categorized across 3 distinct levels. At the measurement level, virtually all included studies relied on eHEALS as a perceived self-efficacy instrument, meaning the construct captured is “perceived ability” rather than objective or performance-based eHealth literacy; this distinction is important because the 2 may relate differently to actual information-seeking behavior [7,58]. OHIS was similarly operationalized through self-report, and the lack of behavioral or log-based measures precludes verification of actual search activity [44]. At the study design level, the predominance of cross-sectional designs prevents any assessment of temporal dynamics, directionality, or change in the eHealth literacy–OHIS relationship over time [23]; longitudinal and experimental designs are needed to establish causal ordering. At the broader conceptual level, the boundary between eHealth literacy as a predictor versus a moderator of OHIS remains undertheorized, and the role of contextual factors such as device access, internet speed, and platform affordances was rarely controlled for in primary studies, potentially confounding the observed associations [4,16]. Additionally, the GRADE certainty of evidence was very low for all outcomes, indicating that future research with stronger designs is very likely to alter the estimated associations. This underscores the need for prospective cohort studies to produce more definitive evidence. Although eHEALS was developed in the early Web era [17], the observed generational attenuation may itself be interpreted as evidence that the instrument’s behavioral predictive validity is context-dependent rather than universally stable. Future eHealth literacy measurement frameworks should consider incorporating next-generation competencies, such as algorithm awareness (the capacity to recognize and critically evaluate algorithmic content curation) and heuristic resistance (the ability to identify and counteract cognitive shortcuts exploited by platform design), alongside traditional self-efficacy constructs, to better capture health information navigation behaviors in AI-mediated and algorithmically curated digital environments [35].

The eHealth literacy–OHIS association is best understood through its boundary conditions rather than a single pooled estimate. The link was robust in non–Gen Z and professional-source contexts but near-null in Gen Z, suggesting that eHEALS, originally developed for Web 1.0/2.0 search settings, captures behaviorally relevant competence unevenly across cohorts and platforms [9,10,17]. In contrast to prior reviews that aggregated heterogeneous outcomes, focused on measurement properties, or examined single clinical populations, this synthesis isolates the eHealth literacy–OHIS link and quantifies its boundary conditions across generation, morbidity, and source credibility within one framework. For policy and practice, interventions targeting Gen Z and nonpatient groups should pair literacy training with motivational cues and access to credible, professionally curated portals, rather than relying on literacy training alone [21,66]. Given very low GRADE certainty and the cross-sectional, self-report nature of available evidence, longitudinal and performance-based studies—together with algorithm-aware extensions of eHEALS for AI-mediated and conversational platforms—are needed to clarify causal direction in the Web 3.0 era [35].