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

doi:10.1007/s12265-021-10098-9

Sociodemographic Characteristics Predicting Digital Health Intervention Use After Acute Myocardial Infarction

J Cardiovasc Transl Res. 2021 Oct;14(5):951-961. doi: 10.1007/s12265-021-10098-9. Epub 2021 May 17.

Authors

Lochan M Shah^{1

2}, Jie Ding^{1

2}, Erin M Spaulding^{2

3

4}, William E Yang^{1

2}, Matthias A Lee⁵, Ryan Demo⁵, Francoise A Marvel^{1

3}, Seth S Martin^{6

7

8

9}

Affiliations

¹ Johns Hopkins University School of Medicine, Baltimore, MD, USA.
² Digital 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.
³ Johns Hopkins University School of Nursing, Baltimore, MD, USA.
⁴ Johns Hopkins Center for Mobile Technologies to Achieve Equity in Cardiovascular Health (mTECH), an AHA SFRN Center for Health Technology and Innovation, Baltimore, MD, USA.
⁵ Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA.
⁶ Johns Hopkins University School of Medicine, Baltimore, MD, USA. smart100@jhmi.edu.
⁷ Digital 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. smart100@jhmi.edu.
⁸ Johns Hopkins Center for Mobile Technologies to Achieve Equity in Cardiovascular Health (mTECH), an AHA SFRN Center for Health Technology and Innovation, Baltimore, MD, USA. smart100@jhmi.edu.
⁹ Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA. smart100@jhmi.edu.

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.

Keywords: Digital health; Health disparities; Hospital readmission; Myocardial infarction; Sociodemographic factors; mHealth.

Publication types

Clinical Trial
Multicenter Study
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Adult
Age Factors
Aged
Aged, 80 and over
Attitude to Computers
Blood Pressure Monitoring, Ambulatory / instrumentation
Blood Pressure*
Coronary Artery Bypass
Female
Health Knowledge, Attitudes, Practice
Humans
Male
Marital Status
Medication Adherence
Middle Aged
Myocardial Infarction / diagnosis
Myocardial Infarction / epidemiology
Myocardial Infarction / therapy*
Patient Acceptance of Health Care*
Patient Readmission
Prospective Studies
Race Factors
Secondary Prevention
Self Care* / instrumentation
Sex Factors
Smartphone
Telemedicine* / instrumentation
Time Factors
Treatment Outcome
United States

Abstract

Publication types

MeSH terms

Grants and funding