Review
Abstract
Background: eHealth literacy has increasingly emerged as a critical determinant of health, highlighting the importance of identifying its influencing factors; however, these factors remain unclear. Numerous studies have explored this concept across various populations, presenting an opportunity for a systematic review and synthesis of the existing evidence to better understand eHealth literacy and its key determinants.
Objective: This study aimed to provide a systematic review of factors influencing eHealth literacy and to examine their impact across different populations.
Methods: We conducted a comprehensive search of papers from PubMed, CNKI, Embase, Web of Science, Cochrane Library, CINAHL, and MEDLINE databases from inception to April 11, 2023. We included all those studies that reported the eHealth literacy status measured with the eHealth Literacy Scale (eHEALS). Methodological validity was assessed with the standardized Joanna Briggs Institute (JBI) critical appraisal tool prepared for cross-sectional studies. Meta-analytic techniques were used to calculate the pooled standardized β coefficient with 95% CIs, while heterogeneity was assessed using I2, the Q test, and τ2. Meta-regressions were used to explore the effect of potential moderators, including participants’ characteristics, internet use measured by time or frequency, and country development status. Predictors of eHealth literacy were integrated according to the Literacy and Health Conceptual Framework and the Technology Acceptance Model (TAM).
Results: In total, 17 studies met the inclusion criteria for the meta-analysis. Key factors influencing higher eHealth literacy were identified and classified into 3 themes: (1) actions (internet usage: β=0.14, 95% CI 0.102-0.182, I2=80.4%), (2) determinants (age: β=–0.042, 95% CI –0.071 to –0.020, I2=80.3%; ethnicity: β=–2.613, 95% CI –4.114 to –1.112, I2=80.2%; income: β=0.206, 95% CI 0.059-0.354, I2=64.6%; employment status: β=–1.629, 95% CI –2.323 to –0.953, I2=99.7%; education: β=0.154, 95% CI 0.101-0.208, I2=58.2%; perceived usefulness: β=0.832, 95% CI 0.131-1.522, I2=68.3%; and self-efficacy: β=0.239, 95% CI 0.129-0.349, I2=0.0%), and (3) health status factor (disease: β=–0.177, 95% CI –0.298 to –0.055, I2=26.9%).
Conclusions: This systematic review, guided by the Literacy and Health Conceptual Framework model, identified key factors influencing eHealth literacy across 3 dimensions: actions (internet usage), determinants (age, ethnicity, income, employment status, education, perceived usefulness, and self-efficacy), and health status (disease). These findings provide valuable guidance for designing interventions to enhance eHealth literacy.
Trial Registration: PROSPERO CRD42022383384; https://www.crd.york.ac.uk/PROSPERO/view/CRD42022383384
doi:10.2196/50313
Keywords
Introduction
The links between literacy and health have attracted increasing international attention over the past decade []. Numerous studies have demonstrated that health outcomes, such as health behaviors and disease risks, are influenced by various forms of literacy, including health literacy, eHealth literacy, and other related literacy []. Especially in an era characterized by advanced information technology, the importance of eHealth literacy cannot be more pronounced due to the widespread adoption of digital health applications. eHealth literacy is defined as the capacity to search for, access, and analyze health information from electronic resources to solve health problems []. According to the Lily model of eHealth literacy, the 6 core literacies of eHealth literacy encompass essential aspects of health literacy and digital information literacy, including traditional literacy, health literacy, information literacy, scientific literacy, media literacy, and computer literacy []. Therefore, eHealth literacy not only directly influences the adoption of eHealth by users [] but also is founded as a new determinant of health [].
Studies have also found that eHealth literacy is positively associated with different aspects of health promotion and care. It influences an individual’s acquisition, judgment, and application of health knowledge []. It also shapes their perception and attitude toward health and disease [,], especially in an era of advanced social media. eHealth literacy also plays a facilitating role in promoting the positive adoption of health-promoting behaviors [-]. Ultimately, this has a positive impact on health-related physical, behavioral, psychosocial, and cognitive outcomes [,,]. Thus, understanding the determinants of eHealth literacy to identify intervention targets is of significant research value. Such efforts can enhance patients’ eHealth literacy levels, ultimately contributing to improved health outcomes.
It is crucial for health care professionals to assess an individual’s eHealth literacy before developing and conducting target interventions. Some tools have been developed to measure eHealth literacy. These include the eHealth Literacy Scale (eHEALS), the eHealth Literacy Questionnaire (eHLQ) [], and the Digital Health Literacy Instrument (DHLI) []. eHEALS, developed by Norman and Skinner [], is the most widely used scale to assess eHealth literacy. It comprises 8 items and encompasses 3 dimensions (application, evaluation, and decision-making capabilities related to online health information and services). eHEALS has been translated into different languages and validated as exhibiting robust reliability and validity. Additionally, numerous studies have evaluated individuals’ eHealth literacy. However, the findings have not been systematically summarized. Therefore, this review aimed to synthesize the findings of studies that have used eHEALS to assess eHealth literacy. Although many studies have explored eHealth literacy among various populations, such as students, older adults, and the general population, the results have been inconsistent. Additionally, the influencing factors of eHealth literacy are diverse, including sociodemographic factors (ie, age, gender, and educational level), health-related behaviors, and internet usage. Despite the large number of cross-sectional surveys conducted to explore these factors, complex influences from both micro and macro levels have been identified, with results varying significantly []. The Literacy and Health Conceptual Framework, developed by the Literacy and Health Research Program in Canada, outlines 4 key themes: actions, determinants, literacy, and effects of literacy. Actions (eg, policy, training) and determinants (eg, sociodemographic status) can influence literacy (eg, general, health, and eHealth literacy). The Technology Acceptance Model (TAM) is a widely used theoretical framework. TAM posits that perceived usefulness and perceived ease of use directly influence an individual’s attitude toward using a technology, in turn, affecting their intention to use the technology. TAM has been widely used to explore the user acceptance of various information systems and technologies. Thus, the Literacy and Health Conceptual Framework [] and TAM can serve as theoretical frameworks to summarize and integrate the influencing factors of eHealth literacy [,].
Consequently, we systematically reviewed studies on eHealth literacy among different populations and synthesized their findings through a meta-analysis to understand the eHealth literacy status and examine its influencing factors.
Methods
Overview
This systematic literature review was conducted in accordance with the Cochrane Collaboration [] and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines [] (). The study was registered in PROSPERO (International Prospective Register of Systematic Reviews; ID CRD42022383384).
Search Strategy
We searched PubMed, CNKI, Embase, Web of Science, Cochrane Library, CINAHL, and MEDLINE databases from inception to April 11, 2023, to obtain a preliminary list of relevant studies. To develop search strategies, we checked 2 main sets of keywords: (1) eHealth literacy and corresponding synonyms (digital health literacy, electronic health literacy, e-health literacy, online health literacy, etc) and (2) influencing factors and corresponding synonyms. For both sets of keywords, we also reviewed the thesaurus and Medical Subject Headings (MeSH) terms. Next, we developed a search strategy for each database. An expert in information retrieval was consulted during this process. After formulating the initial search strategy, we conducted a preliminary search for each database. The search terms and results for each database were then sent to the expert for confirmation and refinement, ensuring the accuracy and comprehensiveness of the search strategy across all databases. The detailed strategies for the different databases are presented in . These search terms were used to search titles and abstracts in all the selected databases without applying any filters or limits.
Eligibility Criteria and Study Selection
We included all studies that were written in English or Chinese and reported the eHealth literacy status measured with eHEALS. For influencing factor analysis, we excluded (1) studies that did not examine the relationship between influencing factors and eHealth literacy or included only univariate analysis; (2) studies that did not provide effect size indicator values in the results or values that could not be converted to β, SE, and 95% CI values; and (3) reviews, case studies, poster presentations, and conference presentations. However, we examined their references to identify additional relevant papers for inclusion. We also manually searched the reference lists of studies in the final sample for additional relevant papers.
Data Extraction and Management
Two reviewers (authors HZ and QL) independently screened each identified study in duplicate for inclusion, initially reviewing the title and abstract, followed by a full-text review, as necessary. All discrepancies were resolved through consensus. We extracted the following data from each study: country, year conducted, sample size, questionnaire used to measure eHealth literacy, status of eHealth literacy, influencing factors with details, and effect size values. Where relevant data were missing or incomplete, we contacted the authors for clarification and verification.
Critical Appraisal of Methodological Quality
The included studies were critically appraised for methodological quality using the standardized Joanna Briggs Institute (JBI) critical appraisal tool prepared for cross-sectional studies and cohort studies [] (). Each study was evaluated by 2 independent assessors.
Data Analysis
Descriptive Statistics and Narrative Synthesis of the Studies in the Final Sample
Descriptive statistics were used to summarize the characteristics of the included studies. Narrative synthesis was used to synthesize the findings related to eHealth literacy in the studies, for which means (SDs) were reported. The year, country, sample size, study population, mean age (SD) of the sample, funding, mean score (SD) of eHEALS, and influencing factors were examined.
Random-Effects Meta-Analyses and Fixed-Effects Meta-Analyses to Examine Factors Affecting eHealth Literacy
Meta-analyses were conducted separately for eHealth literacy and each influencing factor using StataMP 17 software. Pooling was considered when at least 2 studies assessed eHealth literacy and its influencing factors. Standardized β coefficient estimates and their 95% CIs were calculated using random- or fixed-effects meta-analysis based on I2 and the number of studies [,]. If I2≤50%, the study was considered homogeneous and a fixed-effects model was used for meta-analysis. If I2>50%, the random-effects model was selected. If I2>85% and the source of heterogeneity could not be determined, meta-analysis was not performed and descriptive analysis was used. In addition, the fixed effects model was considered if the number of studies that reported eHealth literacy’s influencing factors was small (<5) [,]. Forest plots were created to show the results, and all comparisons were 2-tailed using a threshold of P≤.05.
The heterogeneity of effect sizes was determined using the following statistics: (1) Q test of heterogeneity, (2) τ2 estimate of true between-study variance, and (3) I2 statistic of the proportion of true variation in observed effects. The Egger test was used to assess the possibility of publication bias [].
Meta-Regression Analysis to Examine the Cofounders Affecting eHealth Literacy
The effect of moderators (participants’ characteristics, internet use measured by time or frequency, country development status) was measured via random-effects univariate meta-regressions using restricted maximum-likelihood estimation. For the analysis, participants were categorized into 3 groups: students (coded as 1), the general population (coded as 2), and older adults (coded as 3). Regarding the country variable, studies conducted in low- to middle-income countries were coded as 1, while those conducted in high-income countries were coded as 2. The development status of a country was queried on the United Nations Big Data Global Platform []. To conduct cofounder analyses and obtain robust coefficient estimates, we followed the recommendations of Fu et al [] and conducted moderator analysis only when at least 4 studies per group were available.
Assessment of the Quality of Evidence
The quality of evidence for the results of the meta-analyses was evaluated by authors QL and HZ using the GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) framework []. For each factor influencing eHealth literacy, the initial quality of evidence was considered high and was downgraded by 1 level for every significant concern identified within the domains of risk of bias, inconsistency, indirectness, imprecision, and publication bias. The risk of bias was evaluated by examining potential biases in participant selection, measurement instruments, data collection, and analysis. The inconsistency domain was assessed using I2 values, and the GRADE quality was downgraded when I2 was 50% or higher. Indirectness was evaluated based on whether the studies directly assessed the influence of various factors on eHealth literacy. Imprecision was determined by the width of the CIs of the estimates and the sample size. Publication bias was assessed using the Egger test, with GRADE quality being downgraded for statistically significant results (P<.05) in this test.
Theoretical Support for the Integration Framework of Influencing Factors
This study combined the Literacy and Health Conceptual Framework [] and TAM [,] to synthesize and present factors that influence eHealth literacy, as identified in the literature review. The Literacy and Health Conceptual Framework (
Figure 1), developed through the Literacy and Health Research Program sponsored by the Social Sciences and Humanities Research Council of Canada, comprises 4 key themes: actions, determinants, literacy, and effects of literacy. Actions and determinants can impact literacy and the effects of literacy. Each theme is further subdivided into various conceptual elements that have been delineated in greater detail. Arrows and line segments within the framework illustrate the interconnections and relationships among these elements. Actions include policy, training, community development, and communication. Determinants comprise sociodemographic status, living and working status, education, and personal ability. Literacy comprises health literacy, general literacy, and other literacy. In our study, we used the Literacy and Health Conceptual Framework as the primary structure to organize all influencing factors (
Figure 2). We identified literacy as eHealth literacy and summarized its influencing factors into actions and determinants.
TAM, a theoretical framework developed by Venkatesh and coworkers [,], predicts individuals’ adoption of new information technologies and has been extensively applied in eHealth domains. TAM posits that perceived usefulness and perceived ease of use mediate individuals’ intention to use technology by shaping their attitudes toward it. The third iteration of this model, TAM3, expands on the original by integrating additional components, such as computer self-efficacy, which reflects an individual’s confidence in their ability to perform tasks using a computer. Studies have found that perceived importance is strongly associated with both perceived usefulness and eHealth literacy [-]. In our study, we categorized key aspects of TAM3, including perceived usefulness, perceived importance, and self-efficacy, under personal capabilities. This emphasized that technology acceptance abilities are part of an individual’s overall capabilities (Figure 1).
Results
Literature Selection Process and Characteristics of the Final Studies
The process of literature search and selection is shown in 
Figure 3. In total, 1295 potential papers were identified, but only 17 (1.3%) were identified as eligible and included in the final review to analyze the relationship between eHealth literacy and influencing factors (Figure 2). All the studies were cross-sectional in design (
Table 1 and Table S1 in ). They were conducted in Turkey (n=1, 6%), China (n=1, 6%), Italy (n=1, 6%), South Korea (n=2, 12%), Serbia (n=1, 6%), Sweden (n=1, 6%), Australia (n=1, 6%), Vietnam (n=1, 6%), Kuwait (n=1, 6%), Saudi Arabia (n=1, 6%), Thailand (n=2, 12%), the United States (n=1, 6%), Malaysia (n=1, 6%), the United Kingdom (n=1, 6%), and Portugal (n=1, 6%). The study samples were the general population (n=3, 17.6%), older adults (n=3, 17.6%), nursing students (n=2, 12%), adult residents of urban and rural communities (n=1, 6%), undergraduate and postgraduate students (n=1, 6%), only undergraduate students (n=1, 6%), middle school students (n=1, 6%), adolescents (n=1, 6%), adults (n=1, 6%), medical students (n=2, 12%), and army personnel (n=1, 6%). Only 10 (59%) studies reported the age of participants. The mean age of students ranged from 20.13 (SD 2.16) to 24 (SD 1.48) years, and the mean age of adults ranged from 47 (SD not reported) to 78 (SD 2.4) years. All the studies were published in 2015 and later, and the majority (n=14, 82%) were published between 2018 and 2023. In addition, 15 (88%) studies had sample sizes greater than 100.
| Study | Country | Study population | Age (years), mean (SD) | eHealth literacy statusa, mean (SD) |
| Tennant et al [] | United States | Older adults | 67.46 (9.98) | 29.05 (5.75) |
| Alhuwail and Abdulsalam [] | Kuwait | General population | NRb | 28.63 (5.6) |
| Sinan et al [] | Turkey | Nursing students at nursing departments of 2 state universities in Ankara, Turkey | 21.14 (1.62) | 29.28 (4.73) |
| Zuo et al [] | China | Adult residents of urban and rural communities | NR | 32 (24.40)c, 28 (21.34)d |
| Del Giudice et al [] | Italy | Undergraduate and postgraduate students | NR | 28.2 (6.2) |
| Park [] | South Korea | Middle school students | NR | 28.72 (5.68) |
| Gazibara et al [] | Serbia | Adolescents (14-19 years) | NR | 26.0 (10.0) |
| Ghazi et al [] | Sweden | Older adult participants of the Swedish National Study on Aging and Care, Blekinge (SNAC-B) | 78 (2.4) | 27.5 (10.8) |
| Richtering et al [] | Australia | Adults with moderate-to-high risk for a cardiovascular disease event | 67 (8.0) | 27.2 (6.59) |
| Hoang Nguyen and Bich Thi Le [] | Vietnam | Medical students | NR | 27.03 (3.54) |
| Almoajel et al [] | Saudi Arabia | General population | 24 (1.48) | 28.79 (6.75) |
| Wongjinda and Taneepanichsakul [] | Thailand | Royal Thai Army (RTA) personnel | NR | 31.6 (NR) |
| Lee et al [] | Malaysia | General population | 47 (NR) | 27.38 (6.59) |
| Kim and Jeon [] | South Korea | Nursing students | 21.69 (2.77) | 29.68 (NR) |
| Holch and Marwood [] | United Kingdom | Undergraduate students | 20.13 (2.16) | 29.46 (4.91) |
| Tanasombatkul et al [] | Thailand | Medical students | 22 (NR) | 33.45 (3.28) |
| Martins et al [] | Portugal | Older adults | 67.36 (7.23) | NR |
aMeasured with the eHealth Literacy Scale (eHEALS).
bNR: not reported.
cMedian (IQR) of eHEALS for rural community residents.
dMedian (IQR) of eHEALS for urban community residents.
Quality of Included Studies
Based on the JBI critical appraisal tool, the overall quality of the included studies was high. Of the 17 studies, 6 (35%) achieved a perfect score of 100%, 7 (41%) scored 87.5%, 3 (18%) scored 75%, and only 1 (5%) scored 62.5% (). This result demonstrated that the majority of the included studies (n=13, 76%) were rated as high-quality studies, with scores of 75% or higher [].
Influencing Factors
According to the Literacy and Health Conceptual Framework [], the factors influencing eHealth literacy were categorized into 3 themes: actions, determinants, and health status (Figure 2). Perceived importance, perceived usefulness, and self-efficacy, as defined by TAM3 [], were integrated under the broader concept of personal capabilities. All the outcomes of the meta-analyses are presented in Table 2.
| Theme and influencing factors | Studies (N=17), n (%); sample size, N | β (95% CI) | I2 (%) | Egger test P value | GRADEa quality of evidence | ||||||
| Actions | |||||||||||
| Number of devices | 3 (18); 781 | 0.172 (–0.460 to 0.805) | 0 | .35 | Moderate | ||||||
| Internet usage | 10 (59); 4243 | 0.141 (0.102 to 0.182)b | 80.4 | .56 | Low | ||||||
| Determinants:social determinants of health | |||||||||||
| Age | 13 (76); 4330 | –0.042 (–0.071 to –0.020)b | 80.3 | .18 | Low | ||||||
| Ethnicity | 2 (12); 559 | –2.613 (–4.114 to –1.112)b | 80.2 | N/Ac | Very low | ||||||
| Marital status | 4 (24); 1535 | –0.143 (–0.354 to 0.069) | 27.4 | .84 | Moderate | ||||||
| Sex | 8 (47); 2812 | –0.134(–0.561 to 0.304) | 64.6 | .30 | Moderate | ||||||
| Determinants:economic and working conditions | |||||||||||
| Income | 6 (35); 1997 | 0.206 (0.059 to 0.354)b | 64.6 | .30 | Moderate | ||||||
| Insurance | 2 (12); 789 | –0.007 (–0.199 to 0.184) | 0 | N/A | Moderate | ||||||
| Employment status | 2 (12); 612 | –1.629 (–2.323 to –0.953)b | 99.7 | N/A | Very low | ||||||
| Occupation type | 2 (12); 916 | –0.242 (–0.644 to 0.160) | 42.7 | N/A | Moderate | ||||||
| Determinants:personal capabilities | |||||||||||
| Perceived importance | 3 (18); 1745 | 0.563 (–0.198 to 1.325) | 58.1 | .14 | Moderate | ||||||
| Perceived usefulness | 2 (12); 686 | 0.832 (0.131 to 1.522)b | 68.3 | N/A | Low | ||||||
| Self-efficacy | 2 (12); 393 | 0.239 (0.129 to 0.349)b | 0 | N/A | Moderate | ||||||
| Determinants: education | 16 (94); 6331 | 0.154 (0.101 to 0.208)b | 58.2 | .86 | Moderate | ||||||
| Health status | |||||||||||
| Self-rated health status | 3 (18); 1451 | 0.564 (–0.198 to 1.323) | 17.3 | .47 | Moderate | ||||||
| Disease | 7 (41); 2817 | –0.177 (–0.298 to –0.055)b | 26.9 | .29 | High | ||||||
aGRADE: Grading of Recommendations, Assessment, Development, and Evaluation.
bThe factor was significant in the meta-analysis.
cN/A: not applicable (as <3 experiments were analyzed).
Influencing Factors in the Actions Theme
The influencing factors in actions included the number of devices and internet usage. Three studies [,,] identified an association between eHealth literacy and the number of devices (
Figure 4). These studies examined whether the number of electronic devices an individual owns is correlated with their eHealth literacy. However, the results of our analysis did not yield any statistically significant findings. Eight studies [,,,,,,,] demonstrated a positive correlation between eHealth literacy and internet usage (β=0.14, 95% CI 0.10-0.18).
Furthermore, we conducted subgroup analyses to examine internet use measured by duration or frequency (
Figure 5). The results from both subgroups showed a positive correlation between internet usage and eHealth literacy, indicating that higher levels of internet use are associated with increased eHealth literacy.
Influencing Factors in the Determinants Theme
Social determinants of health, economic and working conditions, education, and personal capabilities were included under the determinants theme.
Social Determinants of Health
The following social determinants of health were analyzed to assess their relationship with eHealth literacy: age, ethnicity, marital status, and sex. The age of participants was significantly associated with eHealth literacy (
Figure 6). Only 1 (13%) [] of the 8 (47%) studies assessing eHealth literacy found a positive correlation with age, with the other 9 (53%) studies [,,-,] reporting a negative correlation. Subgroup analyses based on age revealed that studies conducted among students showed a positive correlation between eHealth literacy and age (β=0.10, 95% CI 0.04 to –0.17). Conversely, in the general population (β=–0.05, 95% CI –0.08 to –0.02) and among older adults (β=–0.21, 95% CI –0.29 to –0.14), eHealth literacy was found to be negatively correlated with age.
Ethnicity was also found to affect eHealth literacy (β=–2.61, 95% CI –4.11 to –1.11) [,]. Lee et al [] found that lower eHealth literacy is associated with patients of minority ethnicity (Malaysian Chinese). However, Tennant et al [] found that ethnicity is not significantly associated with eHealth literacy (
Figure 7). Marital status was also included in the analysis (
Figure 8). However, no statistically significant association was found, consistent with the results of the 3 (18%) studies [,,].
Seven studies [,,,,,,] assessed the relationship between sex and eHealth literacy using multivariate analysis (
Figure 9). Of these, 6 (15%) studies [,,,,,] found that women are at a higher risk of low eHealth literacy than men. However, the final meta-analysis results showed no statistically significant association.
Economic and Working Conditions
Regarding economic and working conditions, we analyzed eHealth literacy in relation to economic aspects (eg, income and insurance coverage), as well as working conditions (eg, employment status and occupation type). All 5 (29%) included studies [,,,,] found a positive correlation between income and eHealth literacy (
Figure 10), which was also confirmed by the final meta-analysis (β=0.21, 95% CI 0.06-0.35). However, in subgroup analyses, the correlation between income and eHealth literacy was only confirmed in the general population (β=0.63, 95% CI 0.36-0.91). Private insurance is also considered an economic factor. Neither of the 2 (12%) included studies [,] found a correlation between the presence or absence of private insurance and the level of eHealth literacy, which aligns with the findings of our meta-analysis (
Figure 11). Unemployment status was identified as a risk factor for eHealth literacy levels by Almoajel et al [] and Lee et al []. However, our meta-analysis revealed substantial heterogeneity, which prevented reliable statistical inferences (
Figure 12). Zuo et al [] observed variations in eHealth literacy according to occupation type, but these findings were not supported by the final results of our meta-analysis (
Figure 13).
Education
As shown in 
Figure 14, 10 (59%) studies [,,-,,,,,] identified lower educational status or a lack of formal education as being associated with lower eHealth literacy (β=0.15, 95% CI 0.10-0.21).
Personal Capabilities
The personal capabilities of perceived importance, perceived usefulness, and self-efficacy were found to significantly influence eHealth literacy (
Figures 15-). Perceived importance [,,], perceived usefulness (β=0.83, 95% CI 0.13-1.52) [,], and self-efficacy (β=0.24, 95% CI 0.13-0.35) [,] were positively correlated with eHealth literacy levels in all included studies.
Influencing Factors in the Health Status Theme
Health status, specifically self-rated health and disease status, was found to significantly influence eHealth literacy (
Figures 18 and ). According to Del Giudice et al [], Tennant et al [], and Wongjinda and Taneepanichsakul [], individuals with better self-rated health exhibit higher levels of eHealth literacy. The 6 (35%) included studies [,,,,,] found that participants with existing disease or at a higher risk for disease have lower levels of eHealth literacy (β=–0.15, 95% CI –0.8 to –0.16).
Meta-Regression Analyses to Examine Cofounders Affecting eHealth Literacy
In the meta-regression analysis of confounding factors, only age grouping significantly moderated the relationship between income and eHealth literacy (Table 3). For other influencing factors, neither age nor the developmental level of the country where the study was conducted was identified as a significant confounding factor.
| Confounder and influencing factors | β coefficient (SE) | t test (df) | P value | I2 (%) | τ2 | |||||
| Age group | ||||||||||
| Internet usagea | 0.06 (0.13) | 0.49 (7) | .64 | 67.0 | 0.072 | |||||
| Sex | –0.323 (–0.78) | 0.42 (6) | .69 | 70.2 | 0.397 | |||||
| Incomea | 0.60 (0.17) | 3.63 (4) | .04b | 100.0 | 0 | |||||
| Marital status | 0.09 (0.51) | 0.19 (2) | .87 | 50.8 | 0.0481 | |||||
| Educationa | –0.018 (0.019) | –0.98 (14) | .35 | 0 | 0.001 | |||||
| Diseasea | –0.91 (0.37) | –2.48 (5) | .07 | 0 | 0 | |||||
| Country | ||||||||||
| Internet usagea | 0.08 (0.22) | 0.37 (7) | .73 | 67.0 | 0.072 | |||||
| Sex | 0.18 (1.53) | 0.12 (6) | .91 | 70.2 | 0.397 | |||||
| Incomea | –1.20 (–0.59) | 2.05 (4) | .13 | 100.0 | 0 | |||||
| Marital status | 0.09 (0.51) | 0.19 (2) | .87 | 50.8 | 0.04814 | |||||
| Educationa | –0.07 (0.03) | –2.10 (14) | .06 | 0 | 0.001 | |||||
| Diseasea | –0.89 (0.37) | –2.35 (5) | .08 | 0 | 0 | |||||
aSignificant in meta-analyses.
bSignificant P value.
Discussion
Principal Findings
We summarized and analyzed the findings from relevant scientific studies, highlighting significant variability in the reported factors influencing eHealth literacy across different study populations. Our meta-analysis indicated that the majority of influencing factors are demographic variables (eg, age, ethnicity) and social determinants of health (eg, income, education). Additionally, we identified factors amenable to clinical intervention, such as internet usage, self-efficacy, and the perceived usefulness of eHealth. However, most of these factors did not achieve a high-level grade according to the GRADE framework.
Influencing Factors of eHealth Literacy
Numerous cross-sectional studies have identified various influencing factors of eHealth literacy. This study categorized the influencing factors into 3 main themes based on the Literacy and Health Conceptual Framework and TAM. In the actions theme, this study revealed that internet usage is positively associated with eHealth literacy. A possible explanation is that eHealth literacy encompasses an individual’s comprehensive ability to seek, discover, understand, evaluate, and apply health information obtained through electronic channels to address health-related issues. According to the eHealth literacy Lily model, individuals who use the internet more frequently tend to exhibit higher levels of information literacy, media literacy, and computer literacy []. This enables them to find and access health information through electronic channels more easily, thereby enhancing their eHealth literacy. Furthermore, some studies have identified a significant negative correlation between computer use stress and eHealth literacy among users []. Additionally, users with higher internet usage frequency tend to exhibit stronger self-efficacy in using eHealth technologies, which may reduce their computer use stress and, consequently, enhance their ability to access and use internet health information and services []. Recent studies have also confirmed that eHealth literacy can be improved by internet use interventions []. Therefore, it is beneficial for health care professionals to provide health education that enhances internet skills, such as training in searching for and evaluating health information.
In the determinants theme, various social determinants of health and other complex demographic factors are included. Factors such as age, ethnicity, economic status, and education are closely associated with eHealth literacy [,]. The effect of age varies across different population subgroups. Among student groups, eHealth literacy is positively correlated with age, whereas in the general population and among older adults, the correlation is negative. According to socioemotional selectivity theory [], individual needs change with age. Young people tend to engage in growth-related activities, such as accumulating knowledge and advancing their careers []. Consequently, students are typically more active in digital environments, due to both their educational settings and their personal interests, which increasingly leverage information technology for learning and communication []. This constant interaction likely enhances their eHealth literacy as they progress through their academic careers. As people grow older, their values shift toward meaning and social needs []. When users are not yet aware of their health-related needs, they are less likely to proactively seek health information, hindering actions that could enhance their eHealth literacy. Additionally, research has shown that with aging, especially in the older adult phase, cognitive and physiological decline makes it more challenging to perceive and manage eHealth-related technologies []. This reduced ease of use can result in lower adoption of eHealth technologies, making it challenging to improve eHealth literacy levels. Other social determinants of health, such as ethnicity, economic status, and education, contribute to this issue in complex and often unmodifiable ways, closely intertwined with the digital divide []. A survey conducted by Choi and Dinitto [] revealed that low-income and older populations exhibit significantly lower levels of eHealth literacy and internet usage compared to the general population in the United States. This disparity can be attributed to factors such as limited access to computers and internet technologies, financial constraints that prevent the acquisition of personal devices, and limitations caused by medical conditions and disabilities []. Modifiable factors that can be targeted for intervention include self-efficacy [] and the perceived usefulness of eHealth. Self-efficacy reflects an individual’s confidence in the ability [], and eHealth literacy is defined as an ability []. It is reasonable to hypothesize a positive correlation between self-efficacy and eHealth literacy. Moreover, self-efficacy explains the eHealth-adopting motivation, behaviors, and awareness of appraisal and application skills []. Perceived usefulness is significantly correlated with eHealth literacy, even as a supplemental question for eHEALS [,,]. This relationship can be explained by TAM []. Perceived usefulness is one of the core beliefs that determines individuals’ intention to use IT, defined as the extent to which a person believes that using IT will enhance their performance []. Some researchers have proved that individuals with high perceived usefulness may have a more active attitude toward eHealth use [,]. In summary, the association between eHealth literacy and background attributes is difficult to modify. However, meso- and macrolevel approaches can be used. At the macro level, these include the level of government support and policy direction [], for instance, providing technology subsidies or allowances to low-income individuals. Government agencies and nongovernmental organizations (NGOs) can play a significant role in recycling and refurbishing unused or discarded computers for older adults and low-income individuals. These initiatives can be cost-effective, enabling individuals to live more independently, reducing their reliance on both formal and informal support systems, and enhancing their quality of life []. The meso level encompasses various characteristics related to the planning, design, and development of eHealth services []. Emphasizing user-friendliness is essential. For example, recommendations including simplifying operational interfaces and steps optimize workflows, create more visually prominent interfaces, provide clearer system prompts, and replace technical terms with plain language and illustrations in designs tailored for older adults []. From an individual’s micro perspective, interventions can focus on enhancing self-efficacy and perceived usefulness. Older adults who are hesitant to use the internet due to low self-efficacy in technology can be encouraged through demonstrations and educational interventions. Younger individuals with strong computer and internet skills can assist by volunteering or being employed to teach older adults how to engage with social media, browse the web, and participate in health-related activities. Particularly for low-income, homebound older adults who have limited exposure to computers and internet technology but substantial needs, it is important to emphasize the multiple benefits of internet use and to provide resources, such as equipment and training [].
In the health status theme, disease was identified as a negative influencing factor for eHealth literacy [,,,]. Previous research indicates that individuals with higher eHealth literacy often achieve better health outcomes [,]. However, the relationship between eHealth literacy and health outcomes is complex and not strictly linear. Our study suggests a substantial overlap between populations with low eHealth literacy and those experiencing poor health conditions. Both groups are influenced by social determinants of health and are more likely to be older, less educated, and of a lower socioeconomic status. Additionally, various studies have identified mediating factors in the relationship between health outcomes and eHealth literacy. For example, a cross-sectional study by Li et al [] involving 802 Chinese residents revealed that social media use mediates the relationship between eHealth literacy and health-promoting behaviors. Similarly, Rabenbauer and Mevenkamp’s [] study of 224 Australian patients with chronic back pain identified self-efficacy as a mediator in the relationship between eHealth literacy and healthy habits. Therefore, further research is needed to clarify the relationship between eHealth literacy and health outcomes, particularly by exploring this connection after controlling for confounding factors. Future research could also apply health- and literacy-related theories to investigate whether additional factors moderate this relationship.
Limitations
This study has several limitations that should be acknowledged. First, only studies using the eHEALS measurement tool were included in this review. Insufficient data from other research instruments, such as the eHLQ and the Digital Health Literacy Questionnaire (DHLQ), prevented a comprehensive synthesis of findings across different tools. Second, papers written in languages other than English and Chinese were excluded from this review, potentially introducing language and publication bias. Third, due to the limited number of randomized controlled trials (RCTs) and the inability to combine their effect sizes, RCTs were not included in the meta-analyses. This exclusion is a significant limitation, as RCTs are considered the gold standard for establishing causal relationships. Their exclusion means that the meta-analysis relied more heavily on observational studies, which are more susceptible to confounding factors and bias. This reliance can limit the ability to draw firm conclusions about the causal relationship between eHealth literacy and health outcomes. Fourth, the standardized beta coefficient (β) was used as the effect size measure for data integration in this study, allowing for the inference of correlation relationships but not supporting causal inference. Moreover, the majority of studies included in the meta-analysis focused on student and adolescent populations, which limits the generalizability of the findings to broader populations. Additionally, some of the results reported I2 exceeding 50%, indicating substantial heterogeneity. This high level of heterogeneity is expected, as eHealth literacy is significantly influenced by social and demographic characteristics, as well as variability in the measurement of influencing factors. This heterogeneity limits the generalizability of the identified factors influencing eHealth literacy. Therefore, the results of the meta-analysis and meta-regression should be interpreted with caution. Finally, all studies included in this research categorized gender using binary terms. This approach overlooks the experiences and outcomes of nonbinary and transgender individuals, potentially leading to a less comprehensive understanding of how eHealth literacy impacts various gender identities. We encourage future research to explore whether differences exist.
Conclusion
Based on the Literacy and Health Conceptual Framework, we explored the impact of various factors on users’ eHealth literacy across 3 dimensions: actions, determinants, and health status. We found that users’ eHealth literacy is influenced by various factors, including internet usage, age, ethnicity, income, employment status, education, perceived usefulness, self-efficacy, and disease. These findings provide valuable guidance for designing interventions to enhance eHealth literacy.
Acknowledgments
This work was funded by the Science & Technology Department of the Sichuan Province Program (2023YFS0045).
Authors' Contributions
ZH was responsible for writing the manuscript and data processing, SY for writing and editing the manuscript, LQ for data selection, and CH for draft touch-ups and guidance on writing. All authors contributed to the paper and have approved the submitted version.
Conflicts of Interest
None declared.
Multimedia Appendix 1
PRISIMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist.
DOCX File , 33 KBReferences
- Rootman I, Gordon-El-Bihbety D, Frankish J, Hemming H, Kaszap M, Langille L, et al. National literacy and health research program needs assessment and environmental scan. Academia.edu. 2000. URL: https://tinyurl.com/3nu3s2hm [accessed 2025-02-18]
- Neter E, Brainin E. Association between health literacy, eHealth literacy, and health outcomes among patients with long-term conditions: a systematic review. Eur Psychol. Jan 2019;24(1):68-81. [CrossRef]
- Norman CD, Skinner HA. eHealth literacy: essential skills for consumer health in a networked world. J Med Internet Res. Jun 16, 2006;8(2):e9. [FREE Full text] [CrossRef] [Medline]
- Arias López MDP, Ong BA, Borrat Frigola X, Fernández AL, Hicklent RS, Obeles AJT, et al. Digital literacy as a new determinant of health: a scoping review. PLOS Digit Health. Oct 2023;2(10):e0000279. [FREE Full text] [CrossRef] [Medline]
- Li X, Liu Q. Social media use, eHealth literacy, disease knowledge, and preventive behaviors in the COVID-19 pandemic: cross-sectional study on Chinese netizens. J Med Internet Res. Oct 09, 2020;22(10):e19684. [FREE Full text] [CrossRef] [Medline]
- Choi NG, Dinitto DM. The digital divide among low-income homebound older adults: internet use patterns, eHealth literacy, and attitudes toward computer/Internet use. J Med Internet Res. May 02, 2013;15(5):e93. [FREE Full text] [CrossRef] [Medline]
- Jeon J, Kim S. The mediating effects of digital literacy and self-efficacy on the relationship between learning attitudes and ehealth literacy in nursing students: a cross-sectional study. Nurse Educ Today. Jun 2022;113:105378. [CrossRef] [Medline]
- Islam MM, Touray M, Yang H, Poly TN, Nguyen P, Li YJ, et al. E-health literacy and health information seeking behavior among university students in Bangladesh. Stud Health Technol Inform. 2017;245:122-125. [Medline]
- Tennant B, Stellefson M, Dodd V, Chaney B, Chaney D, Paige S, et al. eHealth literacy and Web 2.0 health information seeking behaviors among baby boomers and older adults. J Med Internet Res. Mar 17, 2015;17(3):e70. [FREE Full text] [CrossRef] [Medline]
- Xu RH, Zhou L, Wong EL, Wang D. The association between patients’ eHealth literacy and satisfaction with shared decision-making and well-being: multicenter cross-sectional study. J Med Internet Res. Sep 24, 2021;23(9):e26721. [FREE Full text] [CrossRef] [Medline]
- Ojelabi AO, Graham Y, Haighton C, Ling J. A systematic review of the application of Wilson and Cleary health-related quality of life model in chronic diseases. Health Qual Life Outcomes. Dec 11, 2017;15(1):241. [FREE Full text] [CrossRef] [Medline]
- Xie L, Zhang S, Xin M, Zhu M, Lu W, Mo PK. Electronic health literacy and health-related outcomes among older adults: a systematic review. Prev Med. Apr 2022;157:106997. [CrossRef] [Medline]
- Kayser L, Karnoe A, Furstrand D, Batterham R, Christensen KB, Elsworth G, et al. A multidimensional tool based on the eHealth literacy framework: development and initial validity testing of the eHealth Literacy Questionnaire (eHLQ). J Med Internet Res. Feb 12, 2018;20(2):e36. [FREE Full text] [CrossRef] [Medline]
- van der Vaart R, Drossaert C. Development of the Digital Health Literacy Instrument: measuring a broad spectrum of health 1.0 and health 2.0 skills. J Med Internet Res. Jan 24, 2017;19(1):e27. [FREE Full text] [CrossRef] [Medline]
- Norman CD, Skinner HA. eHEALS: the eHealth Literacy Scale. J Med Internet Res. Nov 14, 2006;8(4):e27. [FREE Full text] [CrossRef] [Medline]
- Norgaard O. The e-health literacy framework: a conceptual framework for characterizing e-health users and their interaction with e-health systems. Knowledge Manag E-Learn: Int J. Dec 15, 2015:522-540. [CrossRef]
- Venkatesh V, Bala H. Technology Acceptance Model 3 and a research agenda on interventions. Decis Sci. May 09, 2008;39(2):273-315. [CrossRef]
- Venkatesh V, Davis FD. A theoretical extension of the Technology Acceptance Model: four longitudinal field studies. Manag Sci. Feb 2000;46(2):186-204. [CrossRef]
- Julian H, James T. Cochrane Handbook for Systematic Reviews of Interventions, Version 63. 2022. URL: https://training.cochrane.org/handbook/current [accessed 2023-06-12]
- Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: the PRISMA statement. Int J Surg. 2010;8(5):336-341. [FREE Full text] [CrossRef] [Medline]
- Ma L, Wang Y, Yang Z, Huang D, Weng H, Zeng X. Methodological quality (risk of bias) assessment tools for primary and secondary medical studies: what are they and which is better? Mil Med Res. Feb 29, 2020;7(1):7. [FREE Full text] [CrossRef] [Medline]
- IntHout J, Ioannidis JPA, Rovers MM, Goeman JJ. Plea for routinely presenting prediction intervals in meta-analysis. BMJ Open. Jul 12, 2016;6(7):e010247. [FREE Full text] [CrossRef] [Medline]
- Tufanaru C, Munn Z, Stephenson M, Aromataris E. Fixed or random effects meta-analysis? Common methodological issues in systematic reviews of effectiveness. Int J Evid Based Healthc. Sep 2015;13(3):196-207. [CrossRef] [Medline]
- Dettori JR, Norvell DC, Chapman JR. Fixed-effect vs random-effects models for meta-analysis: 3 points to consider. Global Spine J. Sep 2022;12(7):1624-1626. [FREE Full text] [CrossRef] [Medline]
- Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. Sep 13, 1997;315(7109):629-634. [FREE Full text] [CrossRef] [Medline]
- Data query. United Nations Statistics Big Data and Global Center for Data Science. URL: https://unbdc.stats.gov.cn/zt/gjtjsj/sjcx/ [accessed 2025-02-18]
- Fu R, Gartlehner G, Grant M, Shamliyan T, Sedrakyan A, Wilt T, et al. Conducting quantitative synthesis when comparing medical interventions: AHRQ and the Effective Health Care Program. J Clin Epidemiol. Nov 2011;64(11):1187-1197. [CrossRef] [Medline]
- Guyatt G, Oxman AD, Akl EA, Kunz R, Vist G, Brozek J, et al. GRADE guidelines: 1. Introduction-GRADE evidence profiles and summary of findings tables. J Clin Epidemiol. Apr 2011;64(4):383-394. [CrossRef] [Medline]
- Alhuwail D, Abdulsalam Y. Assessing electronic health literacy in the state of Kuwait: survey of internet users from an Arab state. J Med Internet Res. May 24, 2019;21(5):e11174. [FREE Full text] [CrossRef] [Medline]
- Chung S, Park BK, Nahm E. The Korean eHealth Literacy Scale (K-eHEALS): reliability and validity testing in younger adults recruited online. J Med Internet Res. Apr 20, 2018;20(4):e138. [FREE Full text] [CrossRef] [Medline]
- Sinan O, Ayaz-Alkaya S, Akca A. Predictors of eHealth literacy levels among nursing students: a descriptive and correlational study. Nurse Educ Pract. Mar 2023;68:103592. [CrossRef] [Medline]
- Zuo Q, Cheng J, Peng W, Zhu L, Jiang X. Differences analysis of community residents' e-Health literacy level and influencing factors between urban and rural. Chin Nurs Res. Feb 2022;36(4):587-593.
- Del Giudice P, Bravo G, Poletto M, De Odorico A, Conte A, Brunelli L, et al. Correlation between eHealth literacy and health literacy using the eHealth Literacy Scale and real-life experiences in the health sector as a proxy measure of functional health literacy: cross-sectional web-based survey. J Med Internet Res. Oct 31, 2018;20(10):e281. [FREE Full text] [CrossRef] [Medline]
- Park BK. Factors influencing eHealth literacy of middle school students in Korea: a descriptive cross-sectional study. Healthc Inform Res. Jul 2019;25(3):221-229. [FREE Full text] [CrossRef] [Medline]
- Gazibara T, Cakic M, Cakic J, Grgurevic A, Pekmezovic T. Familiarity with the internet and health apps, and specific topic needs are amongst the factors that influence how online health information is used for health decisions amongst adolescents. Health Info Libr J. Sep 2024;41(3):283-297. [CrossRef] [Medline]
- Ghazi SN, Berner J, Anderberg P, Sanmartin Berglund J. The prevalence of eHealth literacy and its relationship with perceived health status and psychological distress during Covid-19: a cross-sectional study of older adults in Blekinge, Sweden. BMC Geriatr. Jan 04, 2023;23(1):5. [FREE Full text] [CrossRef] [Medline]
- Richtering SS, Hyun K, Neubeck L, Coorey G, Chalmers J, Usherwood T, et al. eHealth literacy: predictors in a population with moderate-to-high cardiovascular risk. JMIR Hum Factors. Jan 27, 2017;4(1):e4. [FREE Full text] [CrossRef] [Medline]
- Hoang Nguyen L, Bich Thi Le T. E-health literacy of medical students at a university in Central Vietnam. Indian J Public Health Res Dev. Feb 1, 2020;11(2):1299. [CrossRef]
- Almoajel A, Alshamrani S, Alyabsi M. The relationship between e-Health literacy and breast cancer literacy among Saudi women. Front Public Health. 2022;10:841102. [FREE Full text] [CrossRef] [Medline]
- Wongjinda L, Taneepanichsakul S. Determinants of eHealth literacy level among Royal Thai Army personnel: a case study. Southeast Asian J Trop Med Public Health. 2018;49(1):175-182.
- Lee WL, Lim ZJ, Tang LY, Yahya NA, Varathan KD, Ludin SM. Patients' readiness and eHealth literacy: implications for adoption and deployment of eHealth in the COVID-19 era and beyond. Comput Inform Nurs. Nov 02, 2021;40(4):244-250. [CrossRef] [Medline]
- Kim S, Jeon J. Factors influencing eHealth literacy among Korean nursing students: a cross-sectional study. Nurs Health Sci. Sep 2020;22(3):667-674. [CrossRef] [Medline]
- Holch P, Marwood JR. EHealth literacy in UK teenagers and young adults: exploration of predictors and factor structure of the eHealth Literacy Scale (eHEALS). JMIR Form Res. Sep 08, 2020;4(9):e14450. [FREE Full text] [CrossRef] [Medline]
- Tanasombatkul K, Pinyopornpanish K, Angkurawaranon C, Buawangpong N, Rojanasumapong A, Jiraporncharoen W. Is electronic health literacy associated with learning outcomes among medical students in the first clinical year?: a cross-sectional study. Eur J Investig Health Psychol Educ. Aug 19, 2021;11(3):923-932. [FREE Full text] [CrossRef] [Medline]
- Martins A, Andrade I, Pocinho R, Belo P. e-Health literacy in ageing. 2015. Presented at: TEEM '15: 3rd International Conference on Technological Ecosystems for Enhancing Multiculturality; October 7-9, 2015:53-57; Porto, Portugal. [CrossRef]
- Al-Qahtani S, Jalal Z, Paudyal V, Mahmood S, Mason J. The role of pharmacists in providing pharmaceutical care in primary and secondary prevention of stroke: a systematic review and meta-analysis. Healthcare (Basel). Nov 18, 2022;10(11):2315. [FREE Full text] [CrossRef] [Medline]
- Arcury TA, Sandberg JC, Melius KP, Quandt SA, Leng X, Latulipe C, et al. Older adult internet use and eHealth literacy. J Appl Gerontol. Feb 24, 2020;39(2):141-150. [FREE Full text] [CrossRef] [Medline]
- Shaojie L. Study on the Status and Influencing Factors of eHealth Literacy Among Older Adults. Hunan, China. Zhongnan University; 2024.
- Xie B. Experimenting on the impact of learning methods and information presentation channels on older adults' e-health literacy. J Am Soc Inf Sci. Jun 06, 2011;62(9):1797-1807. [CrossRef]
- Carstensen LL. Evidence for a life-span theory of socioemotional selectivity. Curr Dir Psychol Sci. Oct 01, 1995;4(5):151-156. [CrossRef]
- Hauk N, Hüffmeier J, Krumm S. Ready to be a silver surfer? A meta-analysis on the relationship between chronological age and technology acceptance. Comput Hum Behav. Jul 2018;84:304-319. [CrossRef]
- Gikas J, Grant MM. Internet High Educ. Oct 2013;19:18-26. [CrossRef]
- Salthouse T. Major Issues in Cognitive Aging. Oxford, UK. Oxford University Press; 2009. [CrossRef]
- Schrauben SJ, Appel L, Rivera E, Lora CM, Lash JP, Chen J, et al. CRIC Study Investigators. Mobile health (mHealth) technology: assessment of availability, acceptability, and use in CKD. Am J Kidney Dis. Jun 2021;77(6):941-950.e1. [FREE Full text] [CrossRef] [Medline]
- Bandura A. Self-Efficacy: The Exercise of Control. New York, NY. W H Freeman; 1997.
- Taba M, Allen TB, Caldwell PHY, Skinner SR, Kang M, McCaffery K, et al. Adolescents' self-efficacy and digital health literacy: a cross-sectional mixed methods study. BMC Public Health. Jun 20, 2022;22(1):1223. [FREE Full text] [CrossRef] [Medline]
- Liu S, Zhao H, Fu J, Kong D, Zhong Z, Hong Y, et al. Current status and influencing factors of digital health literacy among community-dwelling older adults in Southwest China: a cross-sectional study. BMC Public Health. May 17, 2022;22(1):996. [FREE Full text] [CrossRef] [Medline]
- Yang E, Chang SJ, Ryu H, Kim HJ, Jang SJ. Comparing factors associated with eHealth literacy between young and older adults. J Gerontol Nurs. Aug 01, 2020;46(8):46-56. [CrossRef] [Medline]
- Hsu W, Chiang C, Yang S. The effect of individual factors on health behaviors among college students: the mediating effects of eHealth literacy. J Med Internet Res. Dec 12, 2014;16(12):e287. [FREE Full text] [CrossRef] [Medline]
- Li S, Cui G, Yin Y, Wang S, Liu X, Chen L. Health-promoting behaviors mediate the relationship between eHealth literacy and health-related quality of life among Chinese older adults: a cross-sectional study. Qual Life Res. Aug 04, 2021;30(8):2235-2243. [FREE Full text] [CrossRef] [Medline]
- Rabenbauer LM, Mevenkamp N. Factors in the effectiveness of e-health interventions for chronic back pain: how self-efficacy mediates e-health literacy and healthy habits. Telemed J E Health. Feb 01, 2021;27(2):184-192. [CrossRef] [Medline]
Abbreviations
| DL: DerSimonian and Laird |
| eHEALS: eHealth Literacy Scale |
| eHLQ: eHealth Literacy Questionnaire |
| GRADE: Grading of Recommendations, Assessment, Development, and Evaluation |
| IV: inverse variance weighted |
| JBI: Joanna Briggs Institute |
| PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses |
| RCT: randomized controlled trial |
| TAM: Technology Acceptance Model |
Edited by N Cahill; submitted 26.06.23; peer-reviewed by A Sheon, HJT van Mens; comments to author 14.02.24; revised version received 30.04.24; accepted 29.01.25; published 10.03.25.
Copyright©Zhong Hua, Song Yuqing, Liu Qianwen, Chen Hong. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 10.03.2025.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.