Evidence map›Paper›PMID 28130203›Full record

ArticleJMIR human factors2017

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

Sarah S Richtering, Karice Hyun, Lis Neubeck, Genevieve Coorey, John Chalmers, Tim Usherwood, David Peiris, Clara K Chow, Julie Redfern

Abstract read
In one paragraph

Article in JMIR human factors, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 112 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
112citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

112 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Low income and health literacy: a systematic scoping review.Archives of public health = Archives belges de sante publique · 2025
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52 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Sarah S RichteringThe George Institute for Global Health, Sydney, Australia.ORCID http://orcid.org/0000-0002-4270-0748
Karice HyunThe George Institute for Global Health, Sydney, Australia.ORCID http://orcid.org/0000-0002-0164-7725
Lis NeubeckThe George Institute for Global Health, Sydney, Australia.ORCID http://orcid.org/0000-0001-5852-1034
Genevieve CooreyThe George Institute for Global Health, Sydney, Australia.ORCID http://orcid.org/0000-0003-4624-9947
John ChalmersThe George Institute for Global Health, Sydney, Australia.ORCID http://orcid.org/0000-0002-9931-0580
Tim UsherwoodThe George Institute for Global Health, Sydney, Australia.ORCID http://orcid.org/0000-0002-7522-0345
David PeirisThe George Institute for Global Health, Sydney, Australia.ORCID http://orcid.org/0000-0002-6898-3870
Clara K ChowThe George Institute for Global Health, Sydney, Australia.ORCID http://orcid.org/0000-0003-4693-0038
Julie RedfernThe George Institute for Global Health, Sydney, Australia.ORCID http://orcid.org/0000-0001-8707-5563

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundElectronic 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.

objectiveThe 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.

methodsDemographic 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.

resultsThe 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).

conclusionsThis 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.

Indexed as

cardiovascular systemchronic diseaseeHealthhealth literacyInternetsocioeconomic factors

Identifiers

PMID28130203
PMCPMC5303199

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.