Evidence map›Paper›PMID 33301415›Full record

ArticleJMIR mHealth and uHealth2020

Attitudes Toward Using COVID-19 mHealth Tools Among Adults With Chronic Health Conditions: Secondary Data Analysis of the COVID-19 Impact Survey.

Marlene Camacho-Rivera, Jessica Yasmine Islam, Argelis Rivera, Denise Christina Vidot

Open access · goldAbstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 2 pooled it
2.5field-weighted citation impact, top 6% 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

20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 39 citations in OpenAlex.

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  10. Factors Influencing COVID-19 Prevention Behaviors.Journal of prevention (2022) · 2023
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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

4 authors at 4 institutions in 1 country.

Marlene Camacho-RiveraDepartment of Community Health Sciences, State University of New York Downstate Health Sciences University, Brooklyn, NY, United States.ORCID 0000-0003-2343-2941
Jessica Yasmine IslamUniversity of North Carolina, Lineberger Comprehensive Cancer Center, Chapel Hill, NC, United States.ORCID 0000-0002-3690-3848
Argelis RiveraDepartment of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States.ORCID 0000-0002-9205-8861
Denise Christina VidotUniversity of Miami School of Nursing and Health Studies, Coral Gables, FL, United States.ORCID 0000-0001-8254-6928
Icahn School of Medicine at Mount Sinai · USState University of New York · USUniversity of Miami · USUniversity of North Carolina at Chapel Hill · US

Funding

TRANSPORT - the TRANSlational Program Of health disparities Research TrainingS21MD012474 · NIMHD · SUNY DOWNSTATE MEDICAL CENTER · PI Carla Boutin-Foster, William Marcus Lambert · 2017 to 2026
$18.0M
Cancer Care Quality Training ProgramT32CA116339 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI ETHAN M. BASCH, Stephanie Brooke Wheeler · 2018 to 2026
$3.0M
NCI NIH HHS T32 CA116339NIMHD NIH HHS S21 MD012474
6 · The paper itself

Abstract

backgroundAdults with chronic conditions are disproportionately burdened by COVID-19 morbidity and mortality. Although COVID-19 mobile health (mHealth) apps have emerged, research on attitudes toward using COVID-19 mHealth tools among those with chronic conditions is scarce.

objectiveThis study aimed to examine attitudes toward COVID-19, identify determinants of COVID-19 mHealth tool use across demographic and health-related characteristics, and evaluate associations between chronic health conditions and attitudes toward using COVID-19 mHealth tools (eg, mHealth or web-based methods for tracking COVID-19 exposures, symptoms, and recommendations).

methodsWe used nationally representative data from the COVID-19 Impact Survey collected from April to June 2020 (n=10,760). Primary exposure was a history of chronic conditions, which were defined as self-reported diagnoses of cardiometabolic, respiratory, immune-related, and mental health conditions and overweight/obesity. Primary outcomes were attitudes toward COVID-19 mHealth tools, including the likelihood of using (1) a mobile phone app to track COVID-19 symptoms and receive recommendations; (2) a website to track COVID-19 symptoms, track location, and receive recommendations; and (3) an app using location data to track potential COVID-19 exposure. Outcome response options for COVID-19 mHealth tool use were extremely/very likely, moderately likely, or not too likely/not likely at all. Multinomial logistic regression was used to compare the likelihood of COVID-19 mHealth tool use between people with different chronic health conditions, with not too likely/not likely at all responses used as the reference category for each outcome. We evaluated the determinants of each COVID-19 mHealth intervention using Poisson regression.

resultsOf the 10,760 respondents, 21.8% of respondents were extremely/very likely to use a mobile phone app or a website to track their COVID-19 symptoms and receive recommendations. Additionally, 24.1% of respondents were extremely/very likely to use a mobile phone app to track their location and receive push notifications about whether they have been exposed to COVID-19. After adjusting for age, race/ethnicity, sex, socioeconomic status, and residence, adults with mental health conditions were the most likely to report being extremely/very or moderately likely to use each mHealth intervention compared to those without such conditions. Adults with respiratory-related chronic diseases were extremely/very (conditional odds ratio 1.16, 95% CI 1.00-1.35) and moderately likely (conditional odds ratio 1.23, 95% CI 1.04-1.45) to use a mobile phone app to track their location and receive push notifications about whether they have been exposed to COVID-19.

conclusionsOur study demonstrates that attitudes toward using COVID-19 mHealth tools vary widely across modalities (eg, web-based method vs app) and chronic health conditions. These findings may inform the adoption of long-term engagement with COVID-19 apps, which is crucial for determining their potential in reducing disparities in COVID-19 morbidity and mortality among individuals with chronic health conditions.

Indexed as

Health Knowledge, Attitudes, PracticeAdolescentAdultChronic DiseaseCOVID-19FemaleHealth Care SurveysHumansMaleMiddle AgedMobile ApplicationsTelemedicineYoung Adultattitudechronic diseasechronic health conditionscontact tracingCOVID-19data analysisdisparityhealth disparitiesmHealthmobile appperceptionsmartphone

Identifiers

PMID33301415
PMCPMC7748389
OpenAlexW3109625320

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.