Evidence map›Paper›PMID 25048379›Full record

ArticleJournal of medical Internet research2014

Predictors of eHealth usage: insights on the digital divide from the Health Information National Trends Survey 2012.

Emily Kontos, Kelly D Blake, Wen-Ying Sylvia Chou, Abby Prestin

Registry-linked trialAbstract read
In one paragraph

Article in Journal of medical Internet research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06867081 (Effectiveness of a Customized Digital Platform to Increase Coordination of Care and Uptake of Evidence-based Practices), which is not on this map. Cited by 485 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
485citing papers in PubMed, 6 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.

NCT06867081 nanot yet recruitingnot on this mapstarted 2025, after this paper: background citation

Effectiveness of a Customized Digital Platform to Increase Coordination of Care and Uptake of Evidence-based Practices: a Protocol for a Stepped-wedge Cluster Randomized Trial in Quebec

TypeinterventionalSponsorCentre for Interdisciplinary Research in Rehabilitation of Greater MontrealRan2025 to 2029Enrolled962ConditionsAquired Brain Injury, Traumatic Brain Injuries, Stroke, Cardiovascular DiseasesArmsThe intervention will be the BRILLIANT Platform, an online platform which provides five Modules aimed at increasing coordination of care and direct rehabilitation time
3 · Its place in the literature

Who cites it

485 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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425 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

4 authors.

Emily KontosHarvard School of Public Health, Department of Social and Behavioral Sciences, Boston, MA, United States. emily_kontos@dfci.harvard.edu.ORCID http://orcid.org/0000-0003-3002-1382

Funding

TRAINING AND CAREER DEVELOPMENTP50CA148596 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI SORENSEN, GLORIAN C · 2010 to 2014
$10.7M
NCI NIH HHS P50 CA148596NCI NIH HHS P50CA148596
6 · The paper itself

Abstract

backgroundRecent eHealth developments have elevated the importance of assessing the extent to which technology has empowered patients and improved health, particularly among the most vulnerable populations. With noted disparities across racial and social groups in chronic health outcomes, such as cancer, obesity, and diabetes, it is essential that researchers examine any differences in the implementation, uptake, and impact of eHealth strategies across groups that bear a disproportionate burden of disease.

objectiveThe goal was to examine eHealth use by sociodemographic factors, such as race/ethnicity, socioeconomic status (SES), age, and sex.

methodsWe drew data from National Cancer Institute's 2012 Health Information National Trends Survey (HINTS) (N=3959) which is publicly available online. We estimated multivariable logistic regression models to assess sociodemographic predictors of eHealth use among adult Internet users (N=2358) across 3 health communication domains (health care, health information-seeking, and user-generated content/sharing).

resultsAmong online adults, we saw no evidence of a digital use divide by race/ethnicity. However, there were significant differences in use by SES, particularly for health care and health information-seeking items. Patients with lower levels of education had significantly lower odds of going online to look for a health care provider (high school or less: OR 0.50, 95% CI 0.33-0.76) using email or the Internet to communicate with a doctor (high school or less: OR 0.46, 95% CI 0.29-0.72), tracking their personal health information online (high school or less: OR 0.53, 95% CI 0.32-0.84), using a website to help track diet, weight, and physical activity (high school or less: OR 0.64, 95% CI 0.42-0.98; some college: OR 0.67, 95% CI 0.49-0.93), or downloading health information to a mobile device (some college: OR 0.54, 95% CI 0.33-0.89). Being female was a consistent predictor of eHealth use across health care and user-generated content/sharing domains, whereas age was primarily influential for health information-seeking.

conclusionsThis study illustrates that lower SES, older, and male online US adults were less likely to engage in a number of eHealth activities compared to their counterparts. Future studies should assess issues of health literacy and eHealth literacy and their influence on eHealth engagement across social groups. Clinical care and public health communication efforts attempting to leverage Web 2.0 and 3.0 platforms should acknowledge differential eHealth usage to better address communication inequalities and persistent disparities in health.

Indexed as

Socioeconomic FactorsAdolescentAdultAgedAged, 80 and overAge FactorsEthnicityFemaleHealth LiteracyHumansInternetLogistic ModelsMaleMiddle AgedSex FactorsTelemedicinecommunication barriersconsumer health informationhealth communicationInternet

Identifiers

PMID25048379
PMCPMC4129114

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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.