Evidence map›Paper›PMID 39206726›Full record

ArticleJournal of the American Heart Association2024

Patterns of Telehealth Visits After the COVID-19 Pandemic Among Individuals With or at Risk for Cardiovascular Disease in the United States.

Anjali Bhatla, Jie Ding, Omar Mhaimeed, Erin M Spaulding, Yvonne Commodore-Mensah, Timothy B Plante, Rongzi Shan, Francoise A Marvel, Seth S Martin

Abstract read
In one paragraph

Article in Journal of the American Heart Association, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–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

9 citing papers in PubMed.

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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

9 authors.

Anjali BhatlaDigital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine Johns Hopkins University School of Medicine Baltimore MD USA.
Jie DingDigital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine Johns Hopkins University School of Medicine Baltimore MD USA.ORCID 0000-0002-7071-7424
Omar MhaimeedDigital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine Johns Hopkins University School of Medicine Baltimore MD USA.
Erin M SpauldingDigital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine Johns Hopkins University School of Medicine Baltimore MD USA.ORCID 0000-0001-8390-2277
Yvonne Commodore-MensahJohns Hopkins University School of Nursing Baltimore MD USA.ORCID 0000-0002-5054-3025
Timothy B PlanteDepartment of Medicine Larner College of Medicine at the University of Vermont Burlington VT USA.ORCID 0000-0001-9992-9597
Rongzi ShanDepartment of Cardiology Smidt Heart Institute, Cedars-Sinai Medical Center Los Angeles CA USA.ORCID 0000-0002-0445-5702
Francoise A MarvelDigital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine Johns Hopkins University School of Medicine Baltimore MD USA.ORCID 0000-0002-6422-1772
Seth S MartinDigital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine Johns Hopkins University School of Medicine Baltimore MD USA.ORCID 0000-0002-7021-7622

Funding

Sex Hormones in Pulmonary Arterial HypertensionP01HL108800 · NHLBI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI HEMNES, ANNA R · 2012 to 2021
$22.0M
NHLBI NIH HHS P01 HL108800
6 · The paper itself

Abstract

backgroundPrior studies have shown that cardiovascular disease (CVD) can be effectively managed through telehealth. However, there are little national data on the use of telehealth in people with CVD or CVD risk factors. We aimed to determine the prevalence of telehealth visits and visit modality (video versus audio-only) in people with CVD and CVD risk factors. We also assessed their rationale and satisfaction with telehealth visits. METHODS AND

resultsA nationally representative sample of 6252 participants from the 2022 Health Information National Trends Survey 6 was used. We defined the CVD risk categories as having no self-reported CVD (coronary heart disease or heart failure) or CVD risk factors (hypertension, diabetes, obesity, or current smoking), CVD risk factors alone, and CVD. Multivariable logistic regression, adjusting for major sociodemographic factors, assessed the relationship between CVD risk and telehealth uptake. The weighted prevalence of using telehealth was 50% (95% CI, 44%-56%) for individuals with CVD and 40% (95% CI, 37%-43%) for those with CVD risk factors alone. Individuals with CVD had the highest odds of using any telehealth (audio-only or video) (adjusted odds ratio [OR], 2.02 [95% CI, 1.39-2.93]) when compared with those without CVD or CVD risk factors. Notably, 21% (95% CI, 16.3%-25.6%) of patients with CVD used audio-only visits (adjusted OR, 2.38 [95% CI, 1.55-3.64]) compared with patients without CVD or CVD risk factors.

conclusionsIn a nationally representative survey, there was high prevalence of any (video or audio-only) telehealth visits in people with CVD, and audio-only visits comprised a significant proportion of telehealth visits in this population.

Indexed as

Cardiovascular DiseasesCOVID-19TelemedicineAdultAgedFemaleHeart Disease Risk FactorsHumansMaleMiddle AgedPrevalenceRisk FactorsSARS-CoV-2United StatesYoung Adultdigital healthmHealthpolicytelehealth

Identifiers

PMID39206726
PMCPMC11646522

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.