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During a global pandemic, it is critical that the public is able to rapidly acquire new and accurate health information. The internet is a major source of health information. eHealth literacy is the ability of individuals to find, assess, and use health information available on the internet.
The goals of this study were to assess coronavirus-related eHealth literacy and examine the relationship between eHealth literacy and COVID-19−related knowledge, attitudes, and practices (KAPs).
We conducted a web-based survey of a representative sample of 1074 US adults. We adapted the 8-item eHealth Literacy Scale to develop the Coronavirus-Related eHealth Literacy Scale (CoV-eHEALS) to measure COVID-19−related knowledge, conspiracy beliefs, and adherence to protective behaviors (eg, wearing facial masks and social distancing). Our analyses identified sociodemographic associations with the participants’ CoV-eHEALS scores and an association between the CoV-eHEALS measure and COVID-19 KAPs.
The internal consistency of the adapted CoV-eHEALS measure was high (Cronbach α=.92). The mean score for the CoV-eHEALS was 29.0 (SD 6.1). A total of 29% (306/1074) of the survey participants were classified as having low coronavirus-related eHealth literacy (CoV-eHEALS score <26). Independent associations were found between CoV-eHEALS scores and ethnicity (standardized β=–.083,
This study provides an estimate of coronavirus-related eHealth literacy among US adults. Our findings suggest that a substantial proportion of US adults have low coronavirus-related eHealth literacy and are thus at a greater risk of lower and less-protective COVID-19 KAPs. These findings highlight the need to assess and address eHealth literacy as part of COVID-19 control efforts. Potential strategies include improving the quality of health information about COVID-19 available on the internet, assisting or simplifying web-based search for information about COVID-19, and training to improve general or coronavirus-specific search skills.
During a global pandemic, it is critical that members of the public are able to rapidly acquire new and accurate health information [
The internet has become a major source of health information for the public [
The goals of this study were to: (1) understand the ability of individuals to identify, assess, and effectively utilize health information about the coronavirus available on the internet and (2) determine how this ability might be associated with COVID-19–related knowledge, attitudes, and practices (KAPs). We performed a web-based survey of a nationally representative sample of US adults to achieve these goals.
For each of the following measures, specific survey items were presented to the survey respondents in random order.
We slightly modified items from the well-established eHEALS [
I know what health resources about coronavirus are available on the internet.
I know where to find helpful health resources about coronavirus on the internet.
I know how to find helpful health resources about coronavirus on the internet.
I know how to use the internet to answer my questions about my health and coronavirus.
I know how to use the health information about coronavirus I find on the internet to help me.
I have the skills I need to evaluate the health resources about coronavirus I find on the internet.
I can tell high-quality health resources from low-quality health resources about coronavirus on the internet.
I feel confident in using information about coronavirus from the internet to make health decisions.
An overall CoV-eHEALS score was computed based on the sum of the scores for each item (range 8-40). The internal consistency of the CoV-eHEALS measure was 0.92, and this was not improved by the deletion of any specific item. Some prior studies have also defined cut-off points to characterize respondents as having low versus high eHealth literacy [
We assessed the survey respondents’ COVID-19 KAPs by using the following measures.
We created a 7-item scale based on common key facts related to COVID-19, recognized as of May 2019 [
Coronavirus can be easily spread from one person to another.
Many thousands of people have died from coronavirus.
A vaccine is not yet available for the coronavirus.
Most people already have immunity to coronavirus.
Symptoms of coronavirus are always visible.
There are effective treatments for coronavirus that can cure most people.
Having coronavirus is about as dangerous as having the flu.
After reverse-coding of items 4-7, we created an overall knowledge score based on a mean of the scores for each item (range 1-5). The internal consistency of this knowledge measure was 0.78, and this was not improved by the deletion of any specific item.
We developed a brief, 3-item scale based on prior studies on COVID-19 and other health issues [
The real truth about coronavirus is being kept from the public.
People in power are using coronavirus as an excuse to monitor and control the public.
The media is making coronavirus seem more dangerous that it really is.
We computed a mean of the response to these 3 items to create a conspiracy score (range 1-5). The internal consistency of this conspiracy measure was 0.74, and this was not improved by the deletion of any specific item.
We examined the frequency of 7 self-reported behaviors practiced by the survey respondents over the past week, all of which are recommended for reducing the risk of transmitting and/or acquiring COVID-19 [
Avoiding touching my face.
Keeping my hands clean (eg, washing longer with soap and water, using hand sanitizer).
Keeping things clean in my home (eg, phone, refrigerator, doorknobs).
Staying home as much as possible.
Wearing a mask or face covering when I go out of the house.
Staying at least six feet (about 3 steps) away from people I don’t live with.
Avoiding gatherings or groups of other people.
We computed a mean of the response to these items to create a protective behavior adherence score (range 1-5). The internal consistency of this positive protective behaviors index was 0.85, and this was not improved by the deletion of any specific item.
Information on the demographics of the survey respondents, including age, gender, race or ethnicity, level of education, income, and political party affiliation, was obtained. Gender was initially assessed using 5 categories: male, female, transgender (identify as male), transgender (identify as female), and other. The responses were then collapsed into 2 categories (“identify as male” and “identify as female”). Race or ethnicity was coded as White, Black, Hispanic, multiracial, and other (which included American Indian, Asian, and other). Education was initially assessed with 10 strata, which were collapsed into 4 categories: none through high school or general education diploma, postsecondary (eg, trade school, some college, or associates), bachelor’s, and advanced degree (eg, masters, doctoral or professional). Income was assessed with 9 strata, ranging from less than US $20,000 to more than US $150,000.
The full survey assessed a range of individual and household characteristics, attitudes, and behaviors related to the COVID-19 pandemic. Surveys were completed through the Qualtrics web-based platform using a sample provided by Dynata [
During the last week of May 2020, a total of 2272 individuals clicked on our survey invitation link, of which 187 did not complete an age screener item or consent, and 609 were ineligible for the survey or refused consent. This yielded 1476 complete survey responses from age-eligible, consenting individuals. To ensure the quality of the respondent data, we further excluded 402 survey responses based on either of two criteria. First, we excluded 375 survey responses from individuals who completed the entire survey in less than 10 minutes (the minimum time we considered needed to complete a valid survey). The mean time for survey completion for these excluded respondents was 5.4 (SD 3.3) minutes. Second, we excluded 27 survey responses from individuals who answered all items within a 16-item block of items assessing attitudes (and perceived norms) toward the pandemic with an identical response. This is the equivalent of clicking down an entire column (eg, all “Strongly Agree” or “Disagree” responses) for all items. Because some of the 16 items in this section were worded in the positive direction (eg,
We have two sets of hypotheses regarding the relationship between CoV-eHEALS scores and the participants’ demographic characteristics and COVID-19–related KAPs.
We expect to find significant associations between CoV-eHEALS scores and demographic characteristics. Specifically, we have the following expectations:
Hypothesis 1a: CoV-eHEALS score will be negatively associated with age (ie, it will be lower among older individuals). This is based on several prior studies that found lower general eHEALS scores among older individuals [
Hypothesis 1b: CoV-eHEALS score will be lower among ethnic minority groups. This is based on prior studies that have reported lower engagement with health information available on the internet or lower general eHEALS scores among minority populations [
Hypothesis 1c: CoV-eHEALS score will be positively associated with educational attainment (ie, it will be higher among those who report completing higher formal education). This is based on several prior studies that identified this relationship between educational attainment and the general eHEALS measure [
We expect to find significant associations between CoV-eHEALS scores and COVID-19 KAPs. Specifically, we have the following expectations:
Hypothesis 2a: CoV-eHEALS score will be positively associated with COVID-19–related knowledge. This is based on prior studies showing a positive association between the general eHEALS measure and disease-specific knowledge or perceived understanding and knowledge of personal health issues [
Hypothesis 2b: CoV-eHEALS score will be negatively associated with conspiracy beliefs. Mistrust of traditional information sources (eg, government, public health agencies, and mainstream media) is a core characteristic of individuals who hold conspiracy beliefs. We believe there is likely a negative association between a person’s trust in these information sources and their confidence that they can find, assess, and use health information available on the internet.
Hypothesis 2c: CoV-eHEALS score will be positively associated with adherence to behaviors that protect from COVID-19. This is based on prior studies showing more positive health behaviors (eg, healthy lifestyle and engagement in cancer screening) among individuals with higher general eHEALS scores [
For hypothesis 1, we examined the relationship between demographic variables and the CoV-eHEALS score. Age and income (represented as 9 income strata) were examined as continuous variables. Gender, ethnicity, and educational attainment were examined as categorical variables. We first examined these associations separately using Pearson’s correlation to examine the association between CoV-eHEALS score and continuous variables (eg, age and income) and analysis of variance to examine the association between CoV-eHEALS score and categorical variables (eg, gender, ethnicity, and educational attainment). We then examined the independent association between demographic variables and CoV-eHEALS score by using a linear regression model with CoV-eHEALS score as the dependent variable and demographic characteristics as the independent variables (with dummy coding of gender, ethnicity, and education).
For hypothesis 2, we examined the association between CoV-eHEALS score and COVID-19 knowledge, conspiracy beliefs, and protective behaviors. We performed multivariate analysis of variance (MANOVA) to simultaneously assess the relationship between these three dependent variables (scores for knowledge, conspiracy belief, and protective behavior adherence) and our main variable of interest (ie, low vs high CoV-eHEALS scores), while controlling for demographic characteristics as covariates (with age and income as continuous variables and dummy coding for gender, ethnicity, and education). To further illustrate the relationship between CoV-eHEALS scores and COVID-19 KAPs, we created simplified composite variables to represent each KAP measures. For knowledge, we computed a sum of the total number of knowledge items answered correctly (ie, answered “Definitely true” or “Probably true” for knowledge items 1-3 and “Definitely false” or “Probably false” for knowledge items 4-7) by each respondent (range 0-7). For conspiracy beliefs, we computed a sum of the total number of conspiracy items rejected (ie, answered “Definitely false” or “Probably false”) by each respondent (range 0-3). For protective behaviors, we computed a sum of the total number of behaviors for which the respondent reported routine engagement (eg, answered “Always” or “Almost Always”). We then compared the distribution of these compositive variables for respondents classified as having low versus high CoV-eHEALS scores by using chi-square tests to assess statistical significance.
All analyses for this study were performed using SPSS software (version 25; IBM Corp).
This survey study was reviewed and judged to be exempt (survey without identifying information) by the University of Michigan’s institutional review board.
The demographic characteristics of the study participants and their CoV-eHEALS and COVID-19 KAP scores are shown in
Results of the analyses related to hypothesis 1 (ie, Associations between CoV-eHEALS score and demographic characteristics) are shown in
Characteristics of study participants (N=1074) and their mean scores for various study measures.
Variable | Value, n (%) | ||
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|||
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18-35 | 304 (29.5) | |
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36-50 | 263 (25.6) | |
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51-65 | 277 (26.9) | |
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≥65 | 185 (18) | |
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<30,000 | 291 (28.1) | |
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30,000-74,999 | 397 (38.4) | |
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≥75,000 | 346 (33.5) | |
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Male | 459 (44.4) | |
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Female | 575 (55.6) | |
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White | 723 (69.9) | |
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Black | 84 (8.1) | |
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Hispanic | 95 (9.2) | |
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Multiracial | 65 (6.3) | |
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Other | 67 (6.5) | |
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Up to high school or GEDa | 225 (21.8) | |
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Postsecondary (eg, trade school, some college, or associates) | 326 (31.6) | |
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Bachelor’s degree | 310 (30) | |
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Advanced degree (eg, Masters, Doctoral or Professional) | 172 (16.7) | |
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Coronavirus-related eHealth Literacy Scale (range 8-40) | 29.0 (6.1) | |
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Knowledge (range 1-5) | 3.8 (0.8) | |
|
Conspiracy beliefs (range 1-5) | 2.9 (1.1) | |
|
Positive behavior adherence (range 1-5) | 3.9 (0.9) |
aGED: Tests of General Educational Development.
Bivariate association between demographic characteristics and coronavirus-related eHealth literacy.
Variable | eHealth literacy score, mean (SD) | |||
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.47 | |||
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Male | 29.2 (6.3) |
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Female | 28.9 (5.9) |
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.21 | |||
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White | 29.1 (6.0) |
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Black | 27.6 (5.7) |
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Multiracial | 28.9 (6.3) |
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Hispanic | 29.4 (5.9) |
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Other | 29.3 (7.1) |
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<.001 | |||
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Up to high school or GEDa | 27.6 (6.6) |
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Postsecondary (eg, trade school, some college, or associates) | 28.8 (6.0) |
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Bachelor’s degree | 29.9 (5.7) |
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Advanced degree (eg, Masters, Doctoral or Professional) | 30.0 (5.5) |
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aGED: Tests of General Educational Development.
Independent association between demographic characteristics and coronavirus-related eHealth literacy.
Variable | Standardized β coefficient | ||
Age (continuous) | –.038 | .29 | |
Income (continuous, 9 strata) | .023 | .21 | |
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Male | Refa |
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Female | –.003 | .92 |
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White | Ref |
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Black | –.083 |
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Multiracial | –.018 | .60 |
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Hispanic | –.006 | .86 |
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Other | –.014 | .68 |
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Up to high school or GEDc | –.151 |
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Postsecondary (eg, trade school, some college, or associates) | –.079 | .09 |
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Bachelor’s | –.007 | .87 |
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Advanced degree (eg, Masters, Doctoral or Professional) | Ref |
|
aRef: reference value.
bItalicized values indicate statistical significance.
cGED: Tests of General Educational Development.
Results of the analyses related to hypothesis 2 (ie, association between CoV-eHEALS and COVID-19 KAP scores) are shown in
The nature of the relationship between CoV-eHEALS and COVID-19 KAP scores is further illustrated in
COVID-19 knowledge, conspiracy beliefs, and protective behaviors for respondents with low and high coronavirus-related eHealth literacy.
CoV-eHEALSa score | Estimated mean scoreb (SE) | ||
|
Knowledge | Conspiracy beliefs | Protective behaviors |
Low score (n=298) | 3.6 (0.040) | 3.0 (0.064) | 3.6 (0.049) |
High score (n=729) | 3.9 (0.025) | 2.8 (0.040) | 4.0 (0.031) |
<.001 | .03 | <.001 |
aCoV-eHEALS: coronavirus-related eHealth literacy scale.
bEstimated means adjusted for age, income, gender, ethnicity, and education level. Overall multivariate analysis of variance model; Box M=53.35; F=8.86;
Number of correct knowledge items by coronavirus-related eHealth literacy.
Number of rejected conspiracy items by coronavirus-related eHealth literacy.
Number of routine protective behaviors by coronavirus-related eHealth literacy.
The principal findings of the study show a clear and consistent association between higher coronavirus-related eHealth literacy and greater knowledge, lower conspiracy beliefs, and greater engagement in protective behaviors. The mean CoV-eHEALS score used in this study was similar to those used for the general eHEALS in several population samples [
It is important to acknowledge that we administered a modified version of the eHEALS measure that was specific to information about coronavirus (ie, CoV-eHEALS). Although we recognize that this is not typical practice for evaluation of eHealth literacy, we believe this was appropriate given the critical need to assess and understand the ability of individuals and the public to find, assess, and use information available on the internet that is specific to the coronavirus during the current COVID-19 pandemic. The findings we report for this CoV-eHEALS measure are consistent with those recently reported by other teams that administered the general eHEALS measure as part of pandemic-related studies. For instance, a study by Do et al [
The results of this study largely support our first set of hypotheses regarding the association between CoV-eHEALS scores and demographic characteristics. Multivariate analyses showed that Black participants had lower CoV-eHEALS scores than White participants (hypothesis 1b). This finding is consistent with some prior studies that have found low general eHEALS scores and a low frequency of seeking health information on the internet among ethnic minority groups [
Our study findings also consistently support our second set of hypotheses regarding the association between coronavirus-related eHealth literacy and COVID-19 KAPs. Our analyses showed a significant association in the expected directions between the CoV-eHEALS measure and COVID-19 knowledge, conspiracy beliefs, and engagement in protective behaviors. In considering these findings, it is important to recognize that self-efficacy is a central concept underlying the development of the general eHEALS and, consequently, also for this adapted CoV-eHEALS measure. Although the assessment of self-efficacy is a critical aspect of many major theories of health behavior, it is also recognized that individuals commonly overestimate their abilities to perform more complex tasks [
The relationship observed between the CoV-eHEALS measure and conspiracy beliefs also warrants further discussion. For our study participants, the mean score on the conspiracy beliefs scale was 2.9, which indicates that, on average, our sample was “unsure” about the truth or falsehood of these statements. Although differences in the wording of questions and format preclude direct comparisons, other studies have reported high rates of endorsement of COVID-19 conspiracy beliefs [
In the body of published work on the eHEALS measure, relatively few studies have reported on the relationship between eHEALS scores and specific health behaviors or health outcomes. Neter and colleagues [
There are several limitations to consider when interpreting our study findings. First, the results reported here are from a single cross-sectional survey, and thus, we cannot make claims regarding causation. For example, although we did find a negative association between CoV-eHEALS scores and conspiracy beliefs, we cannot be certain whether a higher CoV-eHEALS score led to reduced acceptance of these beliefs or whether a predisposition to conspiracy thinking led to lower CoV-eHEALS scores. Second, it is important to acknowledge that the CoV-eHEALS and COVID-19 KAP measures are based on self-report. The need for further study of the relationship between self-reported eHEALS measures and actual performance has been discussed above. Associations between trajectories of self-reported protective behaviors and COVID-19 cases supports the validity of these self-report measures; however, the precise relationship between self-reported and actual behavior (eg, difference between behavior that is reported to occur “some of the time” vs “almost all of the time”) requires additional study [
Despite these limitations, there are some potentially important implications related to our study findings. We found that although the overall level of coronavirus-related eHealth literacy in this study was relatively high, there still remains a substantial proportion of the US adult population that has low coronavirus-related eHealth literacy; this population might thus be considered at higher risk of negative COVID-19 KAPs. Recent studies assessing the quality of health information available on the internet about COVID-19 have found inconsistent coverage of key public health recommendations with a majority of websites having moderate-to-low quality scores [
Given the consistent associations between CoV-eHEALS scores and COVID-19 KAPs, there may be some benefit in teaching such search skills in general or specifically in terms of searches for COVID-19−related information. In the future, it could be important to assess and track coronavirus-related eHealth literacy at the individual and population levels. Identifying and addressing low coronavirus-related eHealth literacy could prove helpful in improving COVID-19−related knowledge, attitudes, and practices, thereby reducing future illness and deaths during this pandemic.
Coronavirus-Related eHealth Literacy Scale
eHealth Literacy Scale
knowledge, attitude, and practice
multivariate analysis of variance
This work was supported by National Cancer Institute Grant P30CA046592-29-S4 and a Google Focus award (to LA).
None declared.