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With widespread use of the internet and mobile devices, many people have gained improved access to health-related information online for health promotion and disease management. As the health information acquired online can affect health-related behaviors, health care providers need to take into account how each individual’s online health literacy (eHealth literacy) can affect health-related behaviors.
To determine whether an individual’s level of eHealth literacy affects actual health-related behaviors, the correlation between eHealth literacy and health-related behaviors was identified in an integrated manner through a systematic literature review and meta-analysis.
The MEDLINE, Embase, Cochrane, KoreaMed, and Research Information Sharing Service databases were systematically searched for studies published up to March 19, 2021, which suggested the relationship between eHealth literacy and health-related behaviors. Studies were eligible if they were conducted with the general population, presented eHealth literacy according to validated tools, used no specific control condition, and measured health-related behaviors as the outcomes. A meta-analysis was performed on the studies that could be quantitatively synthesized using a random effect model. A pooled correlation coefficient was generated by integrating the correlation coefficients, and the risk of bias was assessed using the modified Newcastle-Ottawa Scale.
Among 1922 eHealth literacy–related papers, 29 studies suggesting an association between eHealth literacy and health-related behaviors were included. All retrieved studies were cross-sectional studies, and most of them used the eHealth Literacy Scale (eHEALS) as a measurement tool for eHealth literacy. Of the 29 studies, 22 presented positive associations between eHealth literacy and health-related behaviors. The meta-analysis was performed on 14 studies that presented the correlation coefficient for the relationship between eHealth literacy and health-related behaviors. When the meta-analysis was conducted by age, morbidity status, and type of health-related behavior, the pooled correlation coefficients were 0.37 (95% CI 0.29-0.44) for older adults (aged ≥65 years), 0.28 (95% CI 0.17-0.39) for individuals with diseases, and 0.36 (95% CI 0.27-0.41) for health-promoting behavior. The overall estimate of the correlation between eHealth literacy and health-related behaviors was 0.31 (95% CI 0.25-0.34), which indicated a moderate correlation between eHealth literacy and health-related behaviors.
Our results of a positive correlation between eHealth literacy and health-related behaviors indicate that eHealth literacy can be a mediator in the process by which health-related information leads to changes in health-related behaviors. Larger-scale studies with stronger validity are needed to evaluate the detailed relationship between the proficiency level of eHealth literacy and health-related behaviors for health promotion in the future.
The development of digital media and communication technology has increased access to information, and a growing proportion of health-related information is being gained through the internet. A survey conducted in the United States reported that 59% of survey participants had experience in retrieving health information online and 35% had experience in self-diagnosing their health status using online health information [
Furthermore, owing to the widespread penetration of the internet and mobile devices, numerous health care professionals increasingly use web-based or online materials to provide information to patients [
Therefore, moving forward from the concept of traditional health literacy, the ability to seek, find, understand, and appraise health information from an electronic source has emerged as eHealth literacy [
Therefore, we performed a systematic review and meta-analysis to describe the effect of eHealth literacy on the types of health-related behaviors and to present the pooled quantitative relationship between them.
The theoretical definition of eHealth literacy refers to the ability to seek, find, understand, and appraise health information from electronic sources and apply the knowledge gained to addressing or solving a health problem [
Health-related behaviors were defined as “behavioral patterns, actions, and habits that relate to health maintenance, to health restoration, and to health improvement” and included the use of health care services, such as vaccinations and health checkups, compliance with medical therapy, such as treatment diet or medication, and self-directed health behaviors related to diet, exercise, smoking, drinking, etc [
Operational classification and definitions of health-related behaviors.
Health-related behavior | Operational definition |
Health-promoting behavior | A holistic behavioral pattern that includes health responsibility, nutrition, physical activity, stress management, interpersonal relations, and self-realization [ |
Health-supporting behavior | Lifestyle habits and disease prevention behaviors for maintaining health [ |
Disease management behavior | All activities performed to manage a specific disease |
We performed this systematic review and meta-analysis in accordance with the PICO-SD (Population, Intervention, Comparator, Outcome, Study Design) framework and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines (
For the systematic review, searched studies were selected according to the following inclusion criteria:
Population: Study participants were from the general population and were not health care professionals or students majoring in health care. Participants were not excluded based on their age, race, or morbidity status.
Intervention: Studies that reported levels of eHealth literacy measured by validated quantification tools, such as the eHEALS, were included.
Comparator: There was no specific comparator.
Outcomes: The outcomes of the included studies had to suggest objectively measured health-related behaviors. The behaviors could be evaluated individually or could be integrated.
Study design: Studies were selected regardless of their study design, except for qualitative studies.
Studies were excluded if they were (1) not measuring eHealth literacy or not using validated eHealth literacy measurement tools; (2) qualitative studies or not original research papers; (3) not written in either English or Korean; or (4) not available in full text.
The literature search and selection process was performed independently by 2 reviewers (KK and SS). Any discordance among the reviewers during the process of literature selection was resolved through mutual agreement or by involving a third researcher (SK) in a discussion. If two 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 (KK and SS): 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 health-related behaviors whose correlations with eHealth literacy were verified; methods of measuring health-related behaviors; statistical analysis methods; types of outcome indicators; and values of outcome indicators. Any inconsistency or ambiguity was resolved by discussion with other reviewers (SK and EL).
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 [
For a qualitative analysis of the results, study country, study population, eHealth literacy measurement tools, types of health-related behaviors and measurement tools, and the relationship between eHealth literacy and health-related behaviors were presented descriptively. The characteristics of the study population were described by age and morbidity status. The specific contents of health-related behaviors were summarized, and they were also classified into the following 3 categories:
For evaluating the association between eHealth literacy and health-related behaviors by a quantitative method, the pooled correlation coefficient was estimated by Fisher z-transformation and construction of the inverse transformation [
The total effect size (ie, pooled correlation coefficient) was derived from each group of studies divided by the participants’ mean age, morbidity status, and types of health-related behaviors, and from all studies that could be quantitatively synthesized. Through this, we tried to evaluate changes in the effect size according to detailed characteristics. All statistical analyses were performed using Comprehensive Meta-analysis, version 2 software (Biostat).
Of 1922 identified nonduplicate studies, 1481 studies were excluded after a review of the studies’ titles and abstracts. The remaining 441 studies were assessed for eligibility through full-text review. Finally, 29 studies, which presented the association between eHealth literacy and health-related behaviors, were selected for qualitative analysis. Out of these, only 14 studies that were quantitatively synthesizable for analysis were included in the meta-analysis. The detailed study selection process with the reasons for exclusion during screening steps is shown in
The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flowchart of the study selection process.
The overall characteristics of the included studies are summarized in
In 25 out of the 29 studies, the original eHEALS (comprising an 8-item questionnaire with a 5-point Likert scale) or its language or culturally adapted versions for respective countries were used. In the studies analyzed, versions of the eHEALS adapted into Korean, Chinese, Turkish, Japanese, and Persian that were undergoing a reliability test were used. Among the 4 studies that used measurement tools other than the eHEALS, 3 studies [
Health-related behaviors considered to be correlated with eHealth literacy included
Characteristics of the included studies.
Author (year) | Country | Population (sample n) | eHLa measurement tool | Health-related behaviors measurement tool | Types of health-related behaviors | Risk of bias |
An et al (2021) [ |
United States | Adults aged over 18 years (n=1074) | Coronavirus-related eHEALSb | 7 self-reported items | Infection prevention behaviors | 6 |
Blackstock et al (2016) [ |
United States | HIV-infected adult women (n=63) | eHEALS | HIV Risk-Taking Behaviour Scale and Addiction Severity Index | High-risk sexual and drug use behaviors | 6 |
Britt et al (2017) [ |
United States | College students (n=420) | eHEALS | Questions for the 8 health areas identified from the American College Health Association | Diet, exercise, sleep, harmful substances, vaccination, safe sex practices, social relationship, and overall health | 3 |
Cho and Ha (2019) [ |
Korea | Adult outpatients with hypertension (n=156) | Korean version of eHEALS | Self-care behaviors measurement tool | Diet, weight control, stress management, alcohol and tobacco use, physical activity, and medication | 7 |
Choi (2020) [ |
Korea | Older adults aged over 65 years (n=198) | Korean version of eHEALS | Adapted HPLPc Ⅱ | Health responsibility, physical activity, nutrition, spiritual development, interpersonal support, and stress management | 6 |
Chuang et al (2019) [ |
Taiwan | Adults with heart failure (n=141) | Chinese version of eHEALS | 22-item instrument Self-Care of Heart Failure Index version 6.2 | Self-care maintenance, management, and confidence in heart failure | 6 |
Cui et al (2021) [ |
China | Older adults aged over 60 years (n=1201) | Chinese version of eHEALS | HPLP | Self-actualization, health responsibility, exercise, nutrition, interpersonal support, and stress management | 8 |
Guo et al (2021) [ |
Taiwan | Diabetes mellitus outpatients aged 20 to 65 years (n=249) | eHEALS | 36-item Diabetes Self-care Behavior questionnaire | Self-care activities related to diabetes mellitus | 4 |
Gürkan and Ayar (2020) [ |
Turkey | High school students (n=219) | Turkish version of eHEALS | Adolescent Health Promotion Scale | Diet, life appreciation, social support, exercise, stress management, and health responsibility | 6 |
Hsu et al (2014) [ |
Taiwan | College students (n=525) | eHLSd | Self-developed 12-item Health Behavior Scale | Diet, exercise, and sleep behaviors | 8 |
Hwang and Kang (2019) [ |
Korea | College students (n=242) | eHL scale composed of functional, communicative, and critical eHL | Adapted HPLP Ⅱ | Health responsibility, physical activity, nutrition, spiritual development, interpersonal support, and stress management | 8 |
Kim and Kim (2020) [ |
Korea | Cancer patients aged 19 to 64 years (n=76) | Adapted eHEALS | Adapted HPLP Ⅱ | Health responsibility, physical activity, nutrition, spiritual development, interpersonal support, and stress management | 6 |
Kim and Son (2017) [ |
Korea | Young adults aged 18 to 39 years (n=230) | Korean version of eHEALS | 5-item validated Health-Related Behaviors Scale | Behaviors to prevent disease and promote health | 9 |
Korkmaz Aslan et al (2021) [ |
Turkey | Students aged 14 to 19 years (n=409) | Turkish version of eHEALS | Adolescent Health Promotion Scale | Diet, life appreciation, social support, exercise, stress management, and health responsibility | 8 |
Lee et al (2017) [ |
Korea | Adults aged 20 to 59 years (n=195) | Adapted eHEALS | Adapted HPLP Ⅱ | Health responsibility, physical activity, nutrition, spiritual development, interpersonal support, and stress management | 7 |
Li et al (2021) [ |
China | Older adults aged over 60 years (n=2300) | Chinese version of eHEALS | HPLP | Self-actualization, health responsibility, exercise, nutrition, interpersonal support, and stress management | 8 |
Li and Liu (2020) [ |
China | Internet users aged 20 to 60 years (n=802) | Chinese version of eHEALS | Self-developed 10-item protective behaviors measurement scale | COVID-19 prevention behaviors | 7 |
Lin et al (2020) [ |
Iran | Older adults aged over 65 years with heart failure (n=468) | Persian version of eHEALS | 5-item self-reported Medication Adherence Report Scale | Medication adherence | 7 |
Mitsutake et al (2012) [ |
Japan | Adult internet users aged 20 to 59 years (n=2970) | Japanese version of eHEALS | A question with “Yes” or “No” answer | Colorectal cancer screening test | 7 |
Mitsutake et al (2016) [ |
Japan | Internet users aged 20 to 59 years (n=2115) | Japanese version of eHEALS | Self-developed questions | Cigarette smoking, physical exercise, alcohol consumption, sleeping hours, and dietary habits | 5 |
Nam and Jung (2020) [ |
Korea | Korean and Chinese university students (n=240) | Adapted eHEALS | 15-item Adapted Health Behavior Scale | Diet, exercise, and sleep behaviors | 4 |
Park et al (2014) [ |
United States | Adults aged over 18 years who had experience using the internet (n=108) | eHEALS | A question with “Yes” or “No” answer | Breast, cervical, colorectal, or prostate cancer screening tests | 3 |
Rabenbauer and Mevenkamp (2021) [ |
Germany and Austria | Facebook users aged over 18 years (n=224) | eHEALS | Healthy lifestyle and personal control questionnaire | Diet, daily time management, physical exercise, social support, and positive thinking | 5 |
Ryu (2019) [ |
Korea | Older adults aged over 65 years (n=99) | Korean version of eHEALS | A tool for measuring the health behavior of elderly people | Diet, exercise, restriction of cigarette smoking or alcohol use, stress management, and disease prevention | 7 |
Song and Shin (2020) [ |
Korea | Older adults aged over 65 years using the internet (n=102) | Korean version of eHEALS | Adapted HPLP Ⅱ | Health responsibility, physical activity, nutrition, spiritual development, interpersonal support, and stress management | 8 |
Tariq et al (2020) [ |
Pakistan | College students (n=505) | eHEALS | Self-developed and validated questions on health behaviors | Physical activity and use of dietary supplements | 4 |
Tsukahara S et al (2020) [ |
Japan | University students (n=3183) | Japanese version of eHEALS | Self-developed questions | Exercise, breakfast, smoking, alcohol consumption, and hours of sleep | 6 |
Yang et al (2017) [ |
Taiwan | College students (n=556) | eHLS | Health-Promoting Lifestyle Scale | Self-actualization, health responsibility, interpersonal support, exercise, nutrition, and stress management | 8 |
Yang et al (2019) [ |
Taiwan | College students (n=813) | eHLS | 14-item Dietary Behaviors Scale | Dietary habits | 7 |
aeHL: eHealth literacy.
beHEALS: eHealth Literacy Scale.
cHPLP: Health-Promoting Lifestyle Profile.
deHLS: 12-item eHealth Literacy Scale.
The risk of bias assessment revealed that 15 studies had a low risk of bias and 12 studies had an unclear risk of bias (
Among the 29 included studies, 6 showed that not all health-related behaviors were significantly associated with eHealth literacy [
The correlation coefficients between eHealth literacy and health-related behaviors in the studies [
Correlation between eHealth literacy and health-related behaviors by age, morbidity status, and type of health-related behavior.
Characteristic | Studies, n | Pooled correlation coefficient, value (95% CI) | |||
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<65 years | 9 | 0.28 (0.22-0.34) | ||
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≥65 years | 5 | 0.37 (0.29-0.44) | ||
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Patients | 4 | 0.28 (0.17-0.39) | ||
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Nonpatients | 10 | 0.32 (0.25-0.39) | ||
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Health-promoting behavior | 7 | 0.36 (0.27-0.41) | ||
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Health-supporting behavior | 3 | 0.31 (0.19-0.42) | ||
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Disease management behavior | 4 | 0.24 (0.12-0.35) |
The overall estimate of the correlation between eHealth literacy and health-related behaviors was conducted for all 14 studies available for quantitative analysis. The pooled correlation coefficient was 0.31 (95% CI 0.25-0.34;
Regarding publication bias, slight asymmetry in visual analysis using a funnel plot was observed (
Forest plot of the correlation coefficients between eHealth literacy and health-related behaviors.
Funnel plot of the correlation between eHealth literacy and health-related behaviors.
The pursuit of health information can affect various health-related outcomes, such as disease prevention actions, perceived health status, and use of health care medical services [
Our meta-analysis showed a moderately positive correlation between eHealth literacy and health-related behaviors, and eHealth literacy was found to have a significant effect on health-related behaviors such as
In the results of the quantitative analysis by age, the younger population showed a relatively weak correlation between eHealth literacy and health-related behaviors, while the older population showed a moderate correlation. These results might suggest that health-related behaviors in the younger population are influenced by other factors, such as perceived health status and interest in health, going beyond the level of merely obtaining health information [
The correlation coefficient between eHealth literacy and health-related behaviors was lower in the patient group than in the nonpatient group. Since patients were reported to have more opportunities to obtain health information from various sources, including their doctors or health care providers, when compared with the general population [
In the quantitative analysis by subtypes of health-related behaviors, a correlation between eHealth literacy and
In this study, eHealth literacy–related studies were systematically reviewed and comprehensively summarized. In addition, a pooled effect size was derived for the correlation between eHealth literacy and health-related behaviors using a meta-analysis. This study is significant in that it comprehensively presented the characteristics of the research subjects to be considered for understanding eHealth literacy and provided a resource framework regarding the role of eHealth literacy in health-related behaviors and decision-making. The results of this study suggest that health care providers can manage people’s health behaviors and promote health more effectively by providing eHealth care services that consider individuals’ eHealth literacy. In addition, the moderate correlation between eHealth literacy and health-related behaviors supports the importance of eHealth literacy in the process of health care delivery.
Several limitations of the study should be considered when interpreting the findings. First, the sample size of each study included in the meta-analysis was small, and pooled estimates of the correlation coefficient showed high heterogeneity. Due to this limitation, there is a lack of generalizability, and additional research on eHealth literacy and health behaviors is required to support the results. Second, only studies that provided results in the form of correlation coefficients were included in the meta-analysis. Specifically, studies that presented the relationship between eHealth literacy and health-related behaviors in the form of regression coefficients could not be included in quantitative synthesis to estimate the pooled correlation coefficient. Therefore, the causal relationship between eHealth literacy and health behaviors could not be verified in the study, and further meta-analyses need to be performed on the data to demonstrate the effectiveness of eHealth literacy enhancement programs and the resultant changes in health-related behaviors. Third, the eHealth literacy and health-related behavior measurement items used in the included studies varied, which in turn might have led to biased analysis results. Moreover, the eHealth literacy measurement tools of the included studies, such as eHEALS, were developed in the Web 2.0 era and could not fully assess the concept of Web 3.0. Therefore, it is necessary to consider these points when interpreting the results of this study and applying them to practice. Further studies are needed to better explain the relationship between eHealth literacy and health-related behaviors by using measurement tools that are standardized and appropriate in the Web 3.0 era.
In this study, a systematic literature review was conducted on the studies investigating the association between eHealth literacy and health-related behaviors, and a meta-analysis was performed on the results of quantitatively synthesizable cross-sectional studies. Our study found that eHealth literacy has fairly significant positive correlations with health-related behaviors such as self-management behavior, medication adherence, disease management, and prevention actions. Among health-related behaviors,
PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 checklist.
Search strategy.
Risk of bias assessment for the included studies using the modified Newcastle-Ottawa Scale.
The relationship between eHealth literacy and health-related behaviors in the included studies.
eHealth Literacy Scale
Health-Promoting Lifestyle Profile
Newcastle-Ottawa Scale
Preferred Reporting Items for Systematic Reviews and Meta-Analyses
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (The Ministry of Science and ICT [Information and Communications Technology]) (number: 2020R1A2C1101560) and the fourth phase of the Brain Korea 21 Program in 2022.
KK, SS, SK, and EL conceived and designed this study. All authors participated in the selection of studies and acquisition of data. KK, SK, and EL analyzed and interpreted the data. KK, SK, and EL wrote the original draft. All authors reviewed and revised the manuscript. SK and EL equally served as corresponding authors.
None declared.