Evidence map›Paper›PMID 38198196›Full record

ArticleJMIR mHealth and uHealth2024

The Association of eHealth Literacy Skills and mHealth Application Use Among US Adults With Obesity: Analysis of Health Information National Trends Survey Data.

George Shaw, Bianca A Castro, Laura H Gunn, Keith Norris, Roland J Thorpe

Open access · goldAbstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 2 pooled it
14.4field-weighted citation impact, top 1% of its field
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

12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 17 citations in OpenAlex.

  1. Inequalities in Exclusively Mobile Interventions Targeting Weight-Related Behaviors: Systematic Review of Observational Studies.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2026
    Pooled it
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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

5 authors at 3 institutions in 1 country.

George ShawDepartment of Public Health Sciences, School of Data Science, University of North Carolina at Charlotte, Charlotte, NC, United States.ORCID 0000-0001-5508-3139
Bianca A CastroDepartment of Public Health Sciences, University of North Carolina at Charlotte, Charlotte, NC, United States.ORCID 0009-0008-8223-8640
Laura H GunnDepartment of Public Health Sciences, School of Data Science, University of North Carolina at Charlotte, Charlotte, NC, United States.ORCID 0000-0003-3962-4526
Keith NorrisThe University of California Los Angeles Division of General Internal Medicine and Health Services Research, University of California, Los Angeles, Los Angeles, CA, United States.ORCID 0000-0003-3071-0700
Roland J ThorpeDepartment of Health, Behavior and Society, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0002-4448-4997
University of North Carolina at Charlotte · USJohns Hopkins University · USUniversity of California, Los Angeles · US

Funding

Why Place MattersU54MD000214 · NIMHD · JOHNS HOPKINS UNIVERSITY · PI THORPE, ROLAND J. · 2017 to 2022
$8.8M
Obesity Health Disparities Research PRIDE Program (OHD PRIDE)R25HL126145 · NHLBI · UNIVERSITY OF MISSISSIPPI MED CTR · PI BEECH, BETTINA M., BRUCE, MARINO A · 2014 to 2025
$3.9M
NHLBI NIH HHS R25 HL126145NIMHD NIH HHS U54 MD000214
6 · The paper itself

Abstract

backgroundPhysical inactivity and a poor diet are modifiable behaviors that contribute to obesity. Obesity is a well-recognized risk factor for chronic diseases, including diabetes. Mobile health (mHealth) apps can play an important adjuvant role in preventing and treating chronic diseases and promoting positive health behavior change among people with obesity, and eHealth literacy skills have the potential to impact mHealth app use.

objectiveThe purpose of this study was to explore the associations between the 2 dimensions, access and application, of eHealth literacy skills and mHealth app use among US adults (≥18 years of age) with obesity (BMI ≥30 kg/m

methodsData were obtained from February to June 2020 using the Health Information National Trends Survey 5. A total of 1079 respondents met the inclusion criteria of adults with obesity and owners of smartphones. Individual associations between mHealth app use and sociodemographic variables were explored using weighted chi-square and 2-tailed t tests. A multivariable weighted logistic regression model was fitted, and adjusted odds ratios (ORs) of using mHealth apps with corresponding 95% CIs were reported across multiple sociodemographic variables. An Ising model-weighted network visualization was produced. A receiver operating characteristic curve was calculated, and the area under the curve was reported with the corresponding Delong 95% CI.

resultsA majority of respondents were female (550/923, 59.6%) or non-Hispanic White (543/923, 58.8%). Individuals in households earning less than US $50,000 comprised 41.4% (382/923) of the sample. All sociodemographic variables were found to be univariately significant at the 5% level, except employment and region. Results from the multivariable weighted logistic regression model showed that the adjusted odds of using an mHealth app are 3.13 (95% CI 1.69-5.80) and 2.99 (95% CI 1.67-5.37) times higher among those with an access eHealth literacy skill of using an electronic device to look for health or medical information for themselves and an application eHealth literacy skill of using electronic communications with a doctor or doctor's office, respectively. Several sociodemographic variables were found to be significant, such as education, where adjusted ORs comparing subgroups to the lowest educational attainment were substantial (ORs ≥7.77). The network visualization demonstrated that all eHealth literacy skills and the mHealth app use variable were positively associated to varying degrees.

conclusionsThis work provides an initial understanding of mHealth app use and eHealth literacy skills among people with obesity, identifying people with obesity subpopulations who are at risk of a digital health divide. Future studies should identify equitable solutions for people with obesity (as well as other groups) and their use of mHealth apps.

Indexed as

Health LiteracyMobile ApplicationsTelemedicineAdultChronic DiseaseFemaleHumansMaleObesitySurveys and QuestionnairesUnited StatesaccessibilityeHealth literacymHealthmultivariable logistic regressionobesitysmartphones

Identifiers

PMID38198196
PMCPMC10809169
OpenAlexW4390704714

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.