The role of e-health literacy and some cognitive factors in adopting protective behaviors of COVID-19 in Khalkhal residents

• Main Authors: Hamed Rezakhani Moghaddam (Author), Soheila Ranjbaran (Author), Towhid Babazadeh (Author)
• Format: Book
• Published: Frontiers Media S.A., 2022-07-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 2296-2565; 10.3389/fpubh.2022.916362
• Keywords: e-health literacy; health belief model; COVID-19; protective behaviors; Iran; Public aspects of medicine; RA1-1270
• Source: Frontiers in Public Health, Vol 10 (2022)

BackgroundSeveral vaccines have recently been generated and are being utilized to prevent COVID-19 mortality. Although the disease is causing many fatalities worldwide, preventative practices should be prioritized, even if vaccines are available. Therefore, this study aimed to identify the role of e-health literacy and some cognitive factors in adopting protective behaviors against COVID-19 in Khalkhal residents.MethodsIn the present cross-sectional study we recruited 380 people aged 18-65 according to cluster sampling from September 2021 to December 2021 in Khalkhal County, Iran. Reliable and validated tools were applied to data collection, including the eHealth Literacy Scale (eHEALS) in Persian and the Cognitive factors assessment questionnaire based on the Health Belief Model (HBM). Data were analyzed using Chi-square, one-way ANOVA, independent samples t-test, and bivariate correlation. The predictors were also determined using hierarchical linear regression analysis.ResultsThe average age of the participants was 35.26 ± 11.51 years. The regression analysis implied that gender (p-value = 0.032), education level (p-value = 0.001), occupational status (p-value = 0.002), income (p-value = 0.001), and marriage (p-value = 0.001) had statistically significant associations with e-HL. Additionally, education level (p-value = 0.001), occupational status (p-value = 0.001), income (p-value = 0.001), and marriage (p-value = 0.002) revealed statistically significant associations with COVID-19 preventive behaviors. Approximately 16.5% of the variation in the COVID-19 protective behaviors is explained by the cognitive factors and the demographic variables. Overall, demographic, cognitive, and e-HL variables were able to explain roughly 35.5% of the variation in COVID-19 protective behaviors. Furthermore, self-efficacy was the strongest predictor of protective behaviors (β = 0.214).ConclusionsHBM constructs successfully predicted the role of e-health literacy and some cognitive factors in adopting COVID-19 protective behaviors. People with high socioeconomic levels were better at e-health literacy and COVID-19 protective behaviors during the pandemic. Moreover, applying approaches to adopting COVID-19 protective behaviors is essential, especially in low socioeconomic status (SES) groups.