Impact of automated data flow and reminders on adherence and resource utilization for remotely monitoring physical activity in individuals with stroke or chronic obstructive pulmonary disease.

Autor:	French MA; Department of Physical Therapy and Athletic Training, University of Utah, Salt Lake City, Utah, United States of America., Balasubramanian A; Department of Pulmonary and Critical Care Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America., Hansel NN; Department of Pulmonary and Critical Care Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America., Penttinen SK; inHealth Precision Medicine Program, Technology Innovation Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America., Wise R; inHealth Precision Medicine Program, Technology Innovation Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America., Raghavan P; Department of Physical Medicine and Rehabilitation, Johns Hopkins University, Baltimore, Maryland, United States of America., Wegener ST; Department of Physical Medicine and Rehabilitation, Johns Hopkins University, Baltimore, Maryland, United States of America., Roemmich RT; Department of Physical Medicine and Rehabilitation, Johns Hopkins University, Baltimore, Maryland, United States of America.; Center for Movement Studies, Kennedy Krieger Institute, Baltimore, Maryland, United States of America., Celnik PA; Department of Physical Medicine and Rehabilitation, Johns Hopkins University, Baltimore, Maryland, United States of America.
Jazyk:	angličtina
Zdroj:	MedRxiv : the preprint server for health sciences [medRxiv] 2024 Apr 18. Date of Electronic Publication: 2024 Apr 18.
DOI:	10.1101/2024.04.15.24305852
Abstrakt:	As rehabilitation advances into the era of digital health, remote monitoring of physical activity via wearable devices has the potential to change how we provide care. However, uncertainties about patient adherence and the significant resource requirements needed create challenges to adoption of remote monitoring into clinical care. Here we aim to determine the impact of a novel digital application to overcome these barriers. The Rehabilitation Remote Monitoring Application (RRMA) automatically extracts data about physical activity collected via a Fitbit device, screens the data for adherence, and contacts the participant if adherence is low. We compare adherence and estimate the resources required (i.e., time and financial) to perform remote monitoring of physical activity with and without the RRMA in two patient groups. Seventy-three individuals with stroke or chronic obstructive pulmonary disease completed 28 days of monitoring physical activity with the RRMA, while 62 individuals completed 28 days with the data flow processes being completed manually. Adherence (i.e., the average percentage of the day that the device was worn) was similar between groups (p=0.85). However, the RRMA saved an estimated 123.8 minutes or $50.24 per participant month when compared to manual processes. These results demonstrate that automated technologies like the RRMA can maintain patient adherence to remote monitoring of physical activity while reducing the time and financial resources needed. Applications like the RRMA can facilitate the adoption of remote monitoring in rehabilitation by reducing barriers related to adherence and resource requirements. Competing Interests: Conflicting interests: The Authors declare that there is no conflict of interest.
Databáze:	MEDLINE
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