Patient Demographics and Clinic Type Are Associated With Patient Engagement Within a Remote Monitoring Program
Publication: Telemedicine and e-Health
Volume 27, Issue Number 8
Abstract
Background: Remote physiological monitoring (RPM) is accessible, convenient, relatively inexpensive, and can improve clinical outcomes. Yet, it is unclear in which clinical setting or target population RPM is maximally effective.
Objective: To determine whether patients' demographic characteristics or clinical settings are associated with data transmission and engagement.
Methods: This is a prospective cohort study of adults enrolled in a diabetes RPM program for a minimum of 12 months as of April 2020. We developed a multivariable logistic regression model for engagement with age, gender, race, income, and primary care clinic type as variables and a second model to include first-order interactions for all demographic variables by time. The participants included 549 adults (mean age 53 years, 63% female, 54% Black, and 75% very low income) with baseline hemoglobin A1c ≥8.0% and enrolled in a statewide diabetes RPM program. The main measure was the transmission engagement over time, where engagement is defined as a minimum of three distinct days per week in which remote data are transmitted.
Results: Significant predictors of transmission engagement included increasing age, academic clinic type, higher annual household income, and shorter time-in-program (p < 0.001 for each). Self-identified race and gender were not significantly associated with transmission engagement (p = 0.729 and 0.237, respectively).
Conclusions: RPM appears to be an accessible tool for minority racial groups and for the aging population, yet engagement is impacted by primary care location setting and socioeconomic status. These results should inform implementation of future RPM studies, guide advocacy efforts, and highlight the need to focus efforts on maintaining engagement over time.
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The content is solely the responsibility of the authors and does not necessarily represent the official views nor an endorsement by NIH, HRSA, HHS, or the U.S. Government.
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Copyright 2021, Mary Ann Liebert, Inc., publishers.
History
Published online: 6 August 2021
Published in print: August 2021
Published ahead of print: 11 June 2021
Accepted: 22 February 2021
Revision received: 22 February 2021
Received: 22 January 2021
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Disclosure Statement
E.K., W.P.M., P.M., A.S., S.O.S, M.H., J.M., and J.Z. report grants from National Center for Advancing Translational Sciences of the National Institutes of Health and grants from Health Resources and Services Administration, during the conduct of the study.
Funding Information
This publication was supported, in part, by the National Center for Advancing Translational Sciences of the National Institutes of Health (NIH) under Grant Number UL1 TR001450 and by the Health Resources and Services Administration (HRSA) of the U.S. Department of Health and Human Services (HHS) as part of the National Telehealth Center of Excellence Award (U66 RH31458).
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