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

  • Type:

    Article

  • Date:

    27 January 2017

  • Publication Status:

    Published

  • Publisher

    JMIR Publications Inc.

  • DOI:

    10.2196/humanfactors.6217

  • ISSN:

    2292-9495

  • Library of Congress:

    RA Public aspects of medicine

Citation

Richtering, S. S., Hyun, K., Neubeck, L., Coorey, G., Chalmers, J., Usherwood, T., …Redfern, J. (2017). eHealth Literacy: Predictors in a Population With Moderate-to-High Cardiovascular Risk. JMIR Human Factors, 4(1), e4. https://doi.org/10.2196/humanfactors.6217

Authors

Keywords

eHealth; socioeconomic factors; health literacy; cardiovascular system; chronic disease; Internet

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

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    ©Sarah S Richtering, Karice Hyun, Lis Neubeck, Genevieve Coorey, John Chalmers, Tim Usherwood, David Peiris, Clara K Chow, Julie Redfern. Originally published in JMIR Human Factors (http://humanfactors.jmir.org), 27.01.2017.
    This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Human Factors, is properly cited. The complete bibliographic information, a link to the original publication on http://humanfactors.jmir.org, as well as this copyright and license information must be included

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