eHealth Literacy: Predictors in a Population With Moderate-to-High Cardiovascular Risk

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Richtering, Sarah S. ; Hyun, Karice. ; Neubeck, Lis. ; Coorey, Genevieve. ; Chalmers, John. ; Usherwood, Tim. ; Peiris, David. ; Chow, Clara K. ; Redfern, Julie. (2017)
  • Publisher: JMIR Publications
  • Journal: JMIR Human Factors, volume 4, issue 1 (eissn: 2292-9495)
  • Related identifiers: pmc: PMC5303199, doi: 10.2196/humanfactors.6217
  • Subject: cardiovascular system | RA Public aspects of medicine | 610 Medicine & health | socioeconomic factors | Long-Term Conditions Research Group | eHealth | Original Paper | 362 Social welfare problems & services | health literacy | chronic disease | Wellbeing | Internet
    mesheuropmc: education

Background Electronic health (eHealth) literacy is a growing area of research parallel to the ongoing development of eHealth interventions. There is, however, little and conflicting information regarding the factors that influence eHealth literacy, notably in chronic disease. We are similarly ill-informed about the relationship between eHealth and health literacy, 2 related yet distinct health-related literacies. Objective The aim of our study was to investigate the demographic, socioeconomic, technology use, and health literacy predictors of eHealth literacy in a population with moderate-to-high cardiovascular risk. Methods Demographic and socioeconomic data were collected from 453 participants of the CONNECT (Consumer Navigation of Electronic Cardiovascular Tools) study, which included age, gender, education, income, cardiovascular-related polypharmacy, private health care, main electronic device use, and time spent on the Internet. Participants also completed an eHealth Literacy Scale (eHEALS) and a Health Literacy Questionnaire (HLQ). Univariate analyses were performed to compare patient demographic and socioeconomic characteristics between the low (eHEALS<26) and high (eHEALS≥26) eHealth literacy groups. To then determine the predictors of low eHealth literacy, multiple-adjusted generalized estimating equation logistic regression model was used. This technique was also used to examine the correlation between eHealth literacy and health literacy for 4 predefined literacy themes: navigating resources, skills to use resources, usefulness for oneself, and critical evaluation. Results The univariate analysis showed that patients with lower eHealth literacy were older (68 years vs 66 years, P=.01), had lower level of education (P=.007), and spent less time on the Internet (P<.001). However, multiple-adjusted generalized estimating equation logistic regression model demonstrated that only the time spent on the Internet (P=.01) was associated with the level of eHealth literacy. Regarding the comparison between the eHEALS items and HLQ scales, a positive linear relationship was found for the themes “usefulness for oneself” (P=.049) and “critical evaluation” (P=.01). Conclusions This study shows the importance of evaluating patients’ familiarity with the Internet as reflected, in part, by the time spent on the Internet. It also shows the importance of specifically assessing eHealth literacy in conjunction with a health literacy assessment in order to assess patients’ navigational knowledge and skills using the Internet, specific to the use of eHealth applications.