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The COVID-19 pandemic has imposed a heavy burden on health care systems and governments. Health literacy (HL) and eHealth literacy (as measured by the eHealth Literacy Scale [eHEALS]) are recognized as strategic public health elements but they have been underestimated during the pandemic. HL, eHEALS score, practices, lifestyles, and the health status of health care workers (HCWs) play crucial roles in containing the COVID-19 pandemic.
The aim of this study is to evaluate the psychometric properties of the eHEALS and examine associations of HL and eHEALS scores with adherence to infection prevention and control (IPC) procedures, lifestyle changes, and suspected COVID-19 symptoms among HCWs during lockdown.
We conducted an online survey of 5209 HCWs from 15 hospitals and health centers across Vietnam from April 6 to April 19, 2020. Participants answered questions related to sociodemographics, HL, eHEALS, adherence to IPC procedures, behavior changes in eating, smoking, drinking, and physical activity, and suspected COVID-19 symptoms. Principal component analysis, correlation analysis, and bivariate and multivariate linear and logistic regression models were used to validate the eHEALS and examine associations.
The eHEALS had a satisfactory construct validity with 8 items highly loaded on one component, with factor loadings ranked from 0.78 to 0.92 explaining 76.34% of variance; satisfactory criterion validity as correlated with HL (ρ=0.42); satisfactory convergent validity with high item-scale correlations (ρ=0.80-0.84); and high internal consistency (Cronbach α=.95). HL and eHEALS scores were significantly higher in men (unstandardized coefficient [B]=1.01, 95% CI 0.57-1.45,
The eHEALS is a valid and reliable survey tool. Gender, ability to pay for medication, profession, and epidemic containment experience were independent predictors of HL and eHEALS scores. HCWs with higher HL or eHEALS scores had better adherence to IPC procedures, healthier lifestyles, and a lower likelihood of suspected COVID-19 symptoms. Efforts to improve HCWs’ HL and eHEALS scores can help to contain the COVID-19 pandemic and minimize its consequences.
COVID-19, the disease caused by SARS-CoV-2, has created unprecedented challenges worldwide [
Lockdown measures were applied in many countries, including Vietnam [
Online consultations from hospitals and health care centers were found to be a safe and effective way to reduce the negative effects of the pandemic [
We evaluated the psychometric properties of the eHEALS and examined the predictors of HL and eHEALS scores. We also examined the associations between HL and eHEALS scores with adherence to infection prevention and control (IPC) measures, lifestyle changes, and suspected COVID-19 symptoms among HCWs during the lockdown period in Vietnam.
A cross-sectional study was conducted with HCWs April 6-19, 2020, using online-based questionnaires (Text 1 in
No HCWs (doctors and nurses) in our study had provided any direct care or had contact with patients with COVID-19. A total sample of 5209 HCWs (out of 11,517 possible participants) completed an online survey. The studied and possible participants from public hospitals and health centers are presented in
Participants from the studied hospitals and health centers by geographic location.
Geographic location and hospital/health center | Possible participants | Studied participants | ||
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Military Hospital 103 | 1660 | 177 |
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E hospital | 1125 | 335 |
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General Hospital of Agricultural | 555 | 424 |
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Thai Nguyen National Hospital | 1186 | 988 |
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Bac Ninh Obstetrics and Pediatrics Hospital | 391 | 364 |
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Quang Ninh General Hospital | 922 | 675 |
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Bai Chay Hospital | 819 | 476 |
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Quang Ninh Obstetrics and Pediatrics Hospital | 478 | 290 |
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Trieu Phong District Health Center | 271 | 203 |
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Da Nang Oncology Hospital | 555 | 134 |
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Tan Phu District Hospital | 530 | 241 |
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Hospital District 2 | 812 | 318 |
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District 9 Health Center | 170 | 102 |
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Thu Duc District Health Center | 302 | 291 |
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Can Tho University of Medicine and Pharmacy Hospital | 424 | 191 |
Total | 11,517 | 5209 |
Vietnam applied a nationwide lockdown measure April 1-15, 2020 [
HCWs reported their age (21-40 years versus 41-60 years), gender (woman versus man), marital status (never married versus ever married), ability to pay for medication (very or fairly difficult versus very or fairly easy), social status (low versus middle to high), profession (doctor, nurse, or other, the last of which included medical technicians, midwives, pharmacists, pharmacy technicians, administrative staff, catering staff, and cleaners), type of health care facility (second-line versus frontline, the latter of which includes the outpatient department, emergency department, quarantine and isolation areas, medical imaging and laboratory diagnosis department, and patient administration areas), and previous epidemic (eg, SARS, tuberculosis, influenza A) containment experience (no versus yes). Additionally, comorbidity was assessed using the Charlson comorbidity index items [
A 12-item short-form health literacy questionnaire (HLS-SF12) was used. The questionnaire was validated and used in Asian countries [
The widely used eHealth literacy scale (eHEALS) with 8 items was used to assess HCWs’ eHealth literacy skills [
Participants reported the practices and activities performed related to COVID-19 IPC during health care interactions. The questionnaire was adapted from the interim guidance of the World Health Organization [
HCWs reported their current smoking (never/stopped/less versus unchanged or more), drinking (never/stopped/less versus unchanged or more), physical activity (never/stopped/less versus unchanged or more), and eating (less healthy versus unchanged or healthier) behaviors as compared with that before the pandemic [
HCWs were screened for suspected COVID-19 symptoms [
The study was reviewed and approved by the Institutional Ethical Review Committee of Hanoi University of Public Health, Vietnam (Institutional Review Board number 133/2020/YTCC-HD3). The HCWs voluntarily took the survey.
The construct validity of the eHEALS was examined using principal component analysis (PCA). An Kaiser-Meyer Olkin (KMO) value ≥0.6 was set to measure sampling adequacy and a Bartlett Test of Sphericity value <0.05 was set to determine the suitability of the data for PCA [
The Spearman correlation was used to check the correlations between the eHEALS and its eight items.
The Pearson correlation between eHEALS and HLS-SF12 was estimated to provide evidence of criterion validity [
The percentages of the lowest and highest score among HCWs were calculated. Minimal percentages (<15%) were recommended to eliminate floor and ceiling effects [
The internal consistency of the eHEALS was estimated using the Cronbach α test. A Cronbach α value ≥.70 was designated as satisfactory reliability [
The distributions of HL and eHEALS scores in different categories of studied variables were explored using a one-way analysis of variance (ANOVA) test. In addition, bivariate and multivariate linear regression models were used to examine predictors of HL and eHEALS scores and to investigate the associations of HL and eHEALS scores with adherence to IPC measures. Next, bivariate and multivariate logistic regression models were used to examine the associations of HL and eHEALS scores with lifestyle changes and suspected COVID-19 symptoms. The factors that demonstrated associations with outcome variables at
Data were analyzed using IBM SPSS (Version 20.0; IBM Corp). The significance level was set at a
Characteristics, health literacy, and eHealth literacy among health care workers.
Variables | Total (N=5209) | Health literacy |
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eHealth Literacy Scale |
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Participants, n (%) | Mean (SD) | Mean (SD) | ||||||||
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.02 | N/Ab | .88 | ||||||||
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21-40 | 4304 (82.6) | 36.1 (7.2) | N/A | 33.1 (4.7) | N/A | |||||
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41-60 | 905 (17.4) | 36.7 (7.5) | N/A | 33.1 (5.0) | N/A | |||||
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<.001 | N/A | <.001 | ||||||||
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Women | 3495 (67.1) | 35.7 (7.0) | N/A | 32.8 (4.5) | N/A | |||||
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Men | 1714 (32.9) | 37.1 (7.8) | N/A | 33.8 (5.3) | N/A | |||||
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.04 | N/A | .29 | ||||||||
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Never married | 1294 (24.8) | 35.8 (7.0) | N/A | 33.0 (4.8) | N/A | |||||
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Ever married | 3915 (75.2) | 36.3 (7.4) | N/A | 33.2 (4.7) | N/A | |||||
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<.001 | N/A | <.001 | ||||||||
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Very or fairly difficult | 2751 (52.8) | 35.3 (7.4) | N/A | 32.8 (4.9) | N/A | |||||
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Very or fairly easy | 2458 (47.2) | 37.2 (7.0) | N/A | 33.5 (4.6) | N/A | |||||
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<.001 | N/A | .27 | ||||||||
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Low | 703 (13.5) | 35.0 (7.7) | N/A | 32.9 (4.7) | N/A | |||||
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Middle or high | 4506 (86.5) | 36.3 (7.2) | N/A | 33.2 (4.8) | N/A | |||||
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<.001 | N/A | <.001 | ||||||||
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Other | 1216 (23.3) | 35.7 (7.6) | N/A | 33.1 (5.1) | N/A | |||||
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Nurse | 2540 (48.8) | 35.7 (7.2) | N/A | 32.7 (4.6) | N/A | |||||
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Doctor | 1453 (27.9) | 37.3 (7.1) | N/A | 33.9 (4.8) | N/A | |||||
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.77 | N/A | .46 | ||||||||
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Non-frontline | 2910 (55.9) | 36.2 (7.4) | N/A | 33.2 (4.7) |
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Frontline | 2299 (44.1) | 36.1 (7.1) | N/A | 33.1 (4.8) | N/A | |||||
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<.001 | N/A | <.001 | ||||||||
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No | 3210 (61.6) | 35.4 (7.2) | N/A | 32.9 (4.6) | N/A | |||||
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Yes | 1999 (38.4) | 37.4 (7.3) | N/A | 33.5 (5.0) | N/A | |||||
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.38 | N/A | .37 | ||||||||
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None | 4939 (94.8) | 36.1 (7.3) | N/A | 33.1 (4.8) | N/A | |||||
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One or more | 270 (5.2) | 36.5 (7.6) | N/A | 32.9 (5.0) | N/A | |||||
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<.001 | N/A | <.001 | ||||||||
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No | 4440 (85.2) | 36.4 (7.3) | N/A | 33.3 (4.7) | N/A | |||||
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Yes | 769 (14.8) | 34.7 (7.2) | N/A | 32.3 (5.1) | N/A | |||||
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<.001 | N/A | .002 | ||||||||
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Eat less healthy | 167 (3.2) | 34.2 (6.9) | N/A | 32.0 (5.9) | N/A | |||||
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Unchanged or healthier | 5042 (96.8) | 36.2 (7.3) | N/A | 33.2 (4.7) | N/A | |||||
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.046 | N/A | .03 | ||||||||
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Never, stopped, or less | 4981 (95.6) | 36.1 (7.2) | N/A | 33.1 (4.7) | N/A | |||||
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Unchanged or more | 228 (4.4) | 37.1 (8.6) | N/A | 33.8 (5.5) | N/A | |||||
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.18 | N/A | .53 | ||||||||
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Never, stopped, or less | 4975 (95.5) | 36.1 (7.2) | N/A | 33.1 (4.7) | N/A | |||||
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Unchanged or more | 234 (4.5) | 36.8 (8.3) | N/A | 33.3 (5.7) | N/A | |||||
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<.001 | N/A | <.001 | ||||||||
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Never, stopped, or less | 1656 (31.8) | 35.2 (7.6) | N/A | 32.5 (5.2) | N/A | |||||
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Unchanged or more | 3553 (68.2) | 36.6 (7.1) | N/A | 33.4 (4.6) | N/A | |||||
Adherence to infection prevention and control measures, mean (SD) | 30.6 (6.2) | N/A | N/A | N/A | N/A | ||||||
Health literacy index, mean (SD) | 36.2 (7.3) | N/A | N/A | N/A | N/A | ||||||
eHealth Literacy Scale score, mean (SD) | 33.1 (4.8) | N/A | N/A | N/A | N/A |
a
bN/A: not applicable.
cFrontline areas include the outpatient department, emergency department, isolation areas, imaging and laboratory diagnosis department, and patient administration areas.
dSuspected COVID-19 symptoms include common symptoms (fever, cough, dyspnea) and less common symptoms (myalgia, fatigue, sputum production, confusion, headache, sore throat, rhinorrhea, chest pain, hemoptysis, diarrhea, and nausea/vomiting).
fHealth care workers were asked whether their lifestyle behaviors got worse, better, or were unchanged during the COVID-19 pandemic as compared to before the pandemic.
As shown in
Construct, convergent, and criterion validity, internal consistency, and floor and ceiling effects of the 8-item eHealth Literacy Scale (N=5209).
Construct validity, factor loadings | Values | |
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I know what health resources are available on the internet | 0.85 |
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I know where to find helpful health resources on the internet | 0.90 |
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I know how to find helpful health resources on the internet | 0.92 |
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I know how to use the internet to answer my questions about health | 0.90 |
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I know how to use the health information I find on the internet to help me | 0.92 |
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I have the skills I need to evaluate the health resources I find on the internet | 0.89 |
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I have the skills needed to tell high-quality health resources from low-quality health resources on the internet | 0.82 |
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I feel confident in using information from the internet to make health decisions | 0.78 |
Percentage of variance, % | 76.34 | |
Item-scale convergent validity, mean of ρa (range) | 0.83 (0.80-0.84) | |
Criterion validity, correlation with health literacy, ρb | 0.42 | |
Internal consistency, Cronbach α | .95 | |
Floor effect, % | 0.70 | |
Ceiling effect, % | 16.10 |
aρ: Spearman correlation coefficient.
bρ: Pearson correlation coefficient.
The week correlations among independent variables (ρ <0.30) suggest that there is no collider which might affect the results (Table 1 in
HCWs with higher eHEALS scores were men (B=0.72, 95% CI 0.43-1.00,
Determinants of health literacy and eHealth literacy among health care workers (N=5209).
Variables | Health literacy |
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eHealth literacy |
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Bivariate |
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Multivariate |
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Bivariate |
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Multivariate |
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Ba (95% CI) | B (95% CI) | B (95% CI) | B (95% CI) | |||||||||||||
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21-40 | Reference | N/Ab | Reference | N/A | Reference | N/A | Reference | N/A | ||||||||
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41-60 | 0.62 (0.10 to 1.14) | .02 | –0.05 (–0.58 to 0.49) | .86 | –0.03 (–0.37 to 0.31) | .88 | –0.30 (–0.65 to 0.04) | .08 | ||||||||
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Female | Reference | N/A | Reference | N/A | Reference | N/A | Reference | N/A | ||||||||
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Male | 1.36 (0.94 to 1.78) | <.001 | 1.01 (0.57 to 1.45) | <.001 | 0.99 (0.72 to 1.27) | <.001 | 0.72 (0.43 to 1.00) | <.001 | ||||||||
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Never married | Reference | N/A | Reference | N/A | Reference | N/A | N/A | N/A | ||||||||
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Ever married | 0.47 (0.01 to 0.93) | .04 | 0.34 (–0.12 to 0.81) | .15 | 0.16 (–0.14 to 0.46) | .29 | N/A | N/A | ||||||||
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Very or fairly difficult | Reference | N/A | Reference | N/A | Reference | N/A | Reference | N/A | ||||||||
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Very or fairly easy | 1.9 (1.51 to 2.29) | <.001 | 1.65 (1.25 to 2.05) | <.001 | 0.72 (0.46 to 0.98) | <.001 | 0.60 (0.34 to 0.86) | <.001 | ||||||||
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Low | Reference | N/A | Reference | N/A | Reference | N/A | N/A | N/A | ||||||||
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Middle or high | 1.29 (0.72 to 1.87) | <.001 | 0.586 (0.003 to 1.169) | .049 | 0.22 (–0.16 to 0.59) | .27 | N/A | N/A | ||||||||
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Other | Reference | N/A | Reference | N/A | Reference | N/A | Reference | N/A | ||||||||
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Nurse | 0.09 (–0.41 to 0.58) | .73 | 0.18 (–0.32 to 0.68) | .48 | –0.40 (–0.72 to –0.07) | .02 | –0.32 (–0.65 to 0.01) | .06 | ||||||||
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Doctor | 1.68 (1.13 to 2.24) | <.001 | 1.29 (0.73 to 1.84) | <.001 | 0.76 (0.39 to 1.12) | <.001 | 0.56 (0.20 to 0.93) | .003 | ||||||||
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Non-frontline | Reference | N/A | N/A | N/A | Reference | N/A | N/A | N/A | ||||||||
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Frontline | –0.06 (–0.46 to 0.34) | .77 | N/A | N/A | –0.10 (–0.36 to 0.16) | .46 | N/A | N/A | ||||||||
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No | Reference | N/A | Reference | N/A | Reference | N/A | Reference | N/A | ||||||||
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Yes | 1.95 (1.54 to 2.35) | <.001 | 1.96 (1.56 to 2.37) | <.001 | 0.55 (0.29 to 0.82) | <.001 | 0.64 (0.38 to 0.91) | <.001 | ||||||||
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None | Reference | N/A | Reference | N/A | Reference | N/A | N/A | N/A | ||||||||
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One or more | 0.40 (–0.49 to 1.29) | .378 | N/A | N/A | –0.27 (–0.85 to 0.32) | .369 | N/A | N/A |
aB: unstandardized regression coefficient.
bN/A: not applicable.
cFrontline areas are the outpatient department, emergency department, isolation areas, imaging and laboratory diagnosis department, and patient administration areas.
According to the results of a multivariate linear regression analysis (
Associations of health literacy and eHealth literacy with adherence to infection prevention and control measures, lifestyle changes, and suspected COVID-19 symptoms among health care workers (N=5209).
Variables | Adherence to IPC measuresa |
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Dietary intakeb |
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Smoking tobaccoc |
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Drinking alcohold |
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Physical activitye |
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Suspected COVID-19 symptomsf |
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Bg (95% CI) | ORh (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | |||||||
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Bivariate model | 0.13 (0.11-0.15) | <.001 | 1.04 (1.02-1.06) | <.001 | 1.02 (1.00-1.04) | .046 | 1.01 (0.99-1.03) | .18 | 1.03 (1.02-1.04) | <.001 | 0.97 (0.96-0.98) | <.001 |
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Multivariate model | 0.13 (0.10-0.15) | <.001 | 1.04 (1.01-1.06) | .001 | 1.01 (0.99-1.03) | .34 | 1.01 (0.99-1.02) | .47 | 1.03 (1.02-1.03) | <.001 | 0.97 (0.96-0.98) | <.001 |
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Bivariate model | 0.22 (0.18-0.25) | <.001 | 1.04 (1.02-1.07) | .002 | 1.03 (1-1.07) | .03 | 1.01 (0.98-1.04) | .53 | 1.04 (1.03-1.05) | <.001 | 0.96 (0.95-0.98) | <.001 |
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Multivariate model | 0.22 (0.19-0.26) | <.001 | 1.04 (1.02-1.07) | .002 | 1.01 (0.99-1.04) | .30 | 0.99 (0.97-1.02) | .69 | 1.04 (1.03-1.05) | <.001 | 0.96 (0.95-0.98) | <.001 |
aAdherence to infection prevention and control procedures; adjusted for age, gender, ability to pay for medication, social status, type of health care personnel, type of health care facility, epidemic containment experience, and comorbidity in the multivariate model.
bAdjusted for age, gender, marital status, ability to pay for medication, and social status in the multivariate model. The reference group is “less healthy diet” and the test group is “unchanged or healthier diet.”
cAdjusted for age, gender, marital status, social status, and type of health care personnel in the multivariate model. The reference group is “never, stopped, or less smoking,” and the test group is “unchanged or more smoking.”
dAdjusted for age, gender, social status, type of health care personnel, and epidemic containment experience in the multivariate model. The reference group is “never, stopped, or less drinking,” and the test group is “unchanged or more drinking.”
eAdjusted for age, gender, ability to pay for medication, social status, type of health care personnel, type of health care facility, and epidemic containment experience in the multivariate model. The reference group is “never, stopped, or less physical activity,” and the test group is “unchanged or more physical activity.”
fSuspected COVID-19 symptoms include common symptoms (fever, cough, dyspnea) and less common symptoms (myalgia, fatigue, sputum production, confusion, headache, sore throat, rhinorrhea, chest pain, hemoptysis, diarrhea, and nausea/vomiting). Adjusted for age, gender, marital status, ability to pay for medications, social status, type of health care personnel, and comorbidity in the multivariate model.
gB: unstandardized regression coefficient.
hOR: odds ratio.
The results of the multivariate logistic regression analysis shown in
As shown in
The eHEALS questionnaire was found to be valid and reliable for assessing the eHealth literacy of HCWs. The tool was found to have satisfactory construct validity, convergent validity, criterion validity, and reliability. There was no flooring effect. A marginal ceiling effect was found. A high percentage of participants with the highest possible score was also observed in a previous study that validated the digital health literacy scale [
In this study, male HCWs had higher HL and eHEALS scores. This finding is inconsistent with a previous study conducted among the general population in Europe [
Doctors had higher HL and eHEALS scores than other HCWs in this study. Similarly, HCWs with epidemic containment experience had higher HL and eHEALS scores in this study. Among HCWs, doctors receive longer professional training and they have been recognized as the group with the highest level of ability to find, understand, justify, and use health-related information. In addition, they are involved in educating and counseling other HCWs and patients [
HCWs with a better ability to pay for medication had higher HL and eHEALS scores. A positive association between the ability to pay for medication was prominently found in many previous studies [
In this study, HCWs with higher HL or eHEALS scores had better adherence to IPC procedures. This is the first study investigating these associations. In the literature, higher HL scores were found to be associated with better adherence to therapies among people with chronic diseases [
Our study found that HCWs with higher HL and eHEALS scores had a lower likelihood of suspected COVID-19 symptoms. It has been noted that individuals with higher HL scores had better health status [
This study has some limitations. First, the study was conducted online and the suspected COVID-19 symptoms were self-reported; therefore, we cannot confirm COVID-19 cases and exclude them from our study. Fortunately, there were no new confirmed cases during the data collection period [
The eHEALS questionnaire is a valid and reliable tool for assessing eHealth literacy among HCWs. HL and eHealth literacy were significantly higher in men, those with better ability to pay for medication, doctors, and those with previous epidemic containment experience. Both HL and eHealth literacy were associated with better adherence to IPC procedures, healthier lifestyles (eg, healthier eating behavior and more physical activity during the pandemic), and a lower likelihood of having suspected COVID-19 symptoms. Integrative and multidisciplinary approaches are required to improve HCWs’ HL and eHealth literacy, which could help improve adherence to IPC measures, promote healthy behaviors, and protect the health of HCWs. This would further contribute to containing the COVID-19 pandemic and minimizing its consequences.
Supplementary data.
unstandardized regression coefficient
eHealth Literacy Scale
health care workers
health literacy
12-item short-form health literacy questionnaire
infection prevention and control
Kaiser-Meyer Olkin Measure
principal component analysis
personal protective equipment
World Health Organization
We appreciate and acknowledge the participation of HCWs from the selected hospitals and health centers. This work was supported by Military Hospital 103, Vietnam, and Taipei Medical University, Taiwan (108-3805-022-400).
BND, TVT, and TVD analyzed the data and drafted the manuscript. BND, TVT, DTP, HCN, TTPN, HCN, THH, HKD, MVT, TVD, HQN, TTN, NPTN, CQT, KVT, TTD, HXP, LVN, KTN, PWC, and TVD contributed to conceptualization, investigation, methodology, validation, and manuscript revision. BND, TVT, DTP, HCN, TTPN, HCN, THH, HKD, MVT, TVD, HQN, TTN, NPTN, CQT, KVT, TTD, HXP, LVN, KTN, and TVD conducted data curation. All authors gave final approval of the manuscript.
None declared.