Evidence map›Paper›PMID 33999374›Full record

Trial reportJournal of cardiovascular translational research2021

Sociodemographic Characteristics Predicting Digital Health Intervention Use After Acute Myocardial Infarction.

Lochan M Shah, Jie Ding, Erin M Spaulding, William E Yang, Matthias A Lee, Ryan Demo, Francoise A Marvel, Seth S Martin

Registry-linked trialAbstract readClinical TrialMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Journal of cardiovascular translational research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03760796 (The Johns Hopkins Myocardial Infarction, COmbined-device, Recovery Enhancement), which is not on this map. Cited by 13 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 2 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.

NCT03760796 nacompletednot on this map

The Johns Hopkins Myocardial Infarction, COmbined-device, Recovery Enhancement (MiCORE) Study

TypeinterventionalSponsorJohns Hopkins UniversityRan2016 to 2019Enrolled200ConditionsAcute Myocardial Infarction, Coronary Artery Disease, Acute Coronary Syndrome, Myocardial InfarctionArmsCorrie Health Digital Platform
3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Review
  4. Article
  5. Article
  6. Review
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. The Recent Advances of Mobile Healthcare in Cardiology Practice.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2022
    Review
  13. Article
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

8 authors.

Lochan M ShahJohns Hopkins University School of Medicine, Baltimore, MD, USA.
Jie DingJohns 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.
William E YangJohns Hopkins University School of Medicine, Baltimore, MD, USA.
Matthias A LeeJohns Hopkins University Whiting School of Engineering, Baltimore, MD, USA.
Ryan DemoJohns Hopkins University Whiting School of Engineering, Baltimore, MD, USA.
Francoise A MarvelJohns Hopkins University School of Medicine, Baltimore, MD, USA.
Seth S MartinJohns Hopkins University School of Medicine, Baltimore, MD, USA. smart100@jhmi.edu.ORCID 0000-0002-7021-7622

Funding

Sex Hormones in Pulmonary Arterial HypertensionP01HL108800 · NHLBI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI LOYD, JAMES E · 2012 to 2021
$22.0M
CARDIOVASCULAR EPIDEMIOLOGY INSTITUTIONAL TRAININGT32HL007024 · NHLBI · JOHNS HOPKINS UNIVERSITY · PI Elizabeth Selvin · 1985 to 2026
$17.5M
Interdisciplinary Training in Cardiovascular Health ResearchT32NR012704 · NINR · JOHNS HOPKINS UNIVERSITY · PI ALLEN, JERILYN K · 2011 to 2020
$1.8M
The Process of User Engagement with mHealthF31NR017328 · NINR · JOHNS HOPKINS UNIVERSITY · PI SPAULDING, ERIN MURDOCK · 2017 to 2019
$129k
NHLBI NIH HHS P01 HL108800NHLBI NIH HHS T32 HL007024NINR NIH HHS F31 NR017328NINR NIH HHS NR012704NINR NIH HHS NR017328NINR NIH HHS T32 NR012704
6 · The paper itself

Abstract

Increasing evidence suggests that digital health interventions (DHIs) are an effective tool to reduce hospital readmissions by improving adherence to guideline-directed therapy. We investigated whether sociodemographic characteristics influence use of a DHI targeting 30-day readmission reduction after acute myocardial infarction (AMI). Covariates included age, sex, race, native versus loaner iPhone, access to a Bluetooth-enabled blood pressure monitor, and disease severity as marked by treatment with CABG. Age, sex, and race were not significantly associated with DHI use before or after covariate adjustment (fully adjusted OR 0.98 (95%CI: 0.95-1.01), 0.6 (95%CI: 0.29-1.25), and 1.22 (95% CI: 0.60-2.48), respectively). Being married was associated with high DHI use (OR 2.12; 95% CI 1.02-4.39). Our findings suggest that DHIs may have a role in achieving equity in cardiovascular health given similar use by age, sex, and race. The presence of a spouse, perhaps a proxy for enhanced caregiver support, may encourage DHI use.

Indexed as

Blood PressurePatient Acceptance of Health CareSelf CareTelemedicineAdultAgedAged, 80 and overAge FactorsAttitude to ComputersBlood Pressure Monitoring, AmbulatoryCoronary Artery BypassFemaleHealth Knowledge, Attitudes, PracticeHumansMaleMarital StatusDigital healthHealth disparitiesHospital readmissionmHealthMyocardial infarctionSociodemographic factors

Identifiers

PMID33999374
PMCPMC8127845

What OpenQuestion holds

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Read underepoch 390

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.