Consumer-grade wearables identify changes in multiple physiological systems during COVID-19 disease progression

Caleb Mayer; Jonathan Tyler; Yu Fang; Christopher Flora; Elena Frank; Muneesh Tewari; Sung Won Choi; Srijan Sen; Daniel B Forger

doi:10.1016/j.xcrm.2022.100601

Consumer-grade wearables identify changes in multiple physiological systems during COVID-19 disease progression

Cell Rep Med. 2022 Apr 19;3(4):100601. doi: 10.1016/j.xcrm.2022.100601.

Authors

Caleb Mayer¹, Jonathan Tyler², Yu Fang³, Christopher Flora⁴, Elena Frank³, Muneesh Tewari⁵, Sung Won Choi⁶, Srijan Sen³, Daniel B Forger⁷

Affiliations

¹ Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA.
² Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA; Division of Pediatric Hematology/Oncology, Department of Pediatrics, University of Michigan, Ann Arbor, MI 48109, USA.
³ Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA.
⁴ Division of Hematology and Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA.
⁵ Division of Hematology and Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA; Rogel Comprehensive Cancer Center, University of Michigan, Ann Arbor, MI 48109, USA; Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
⁶ Division of Pediatric Hematology/Oncology, Department of Pediatrics, University of Michigan, Ann Arbor, MI 48109, USA; Rogel Comprehensive Cancer Center, University of Michigan, Ann Arbor, MI 48109, USA.
⁷ Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA. Electronic address: forger@umich.edu.

Abstract

Consumer-grade wearables are needed to track disease, especially in the ongoing pandemic, as they can monitor patients in real time. We show that decomposing heart rate from low-cost wearable technologies into signals from different systems can give a multidimensional description of physiological changes due to COVID-19 infection. We find that the separate physiological features of basal heart rate, heart rate response to physical activity, circadian variation in heart rate, and autocorrelation of heart rate are significantly altered and can classify symptomatic versus healthy periods. Increased heart rate and autocorrelation begin at symptom onset, while the heart rate response to activity increases soon after symptom onset and increases more in individuals exhibiting cough. Symptom onset is associated with a blunting of circadian variation in heart rate, as measured by the uncertainty in the phase estimate. This work establishes an innovative data analytic approach to monitor disease progression remotely using consumer-grade wearables.

Keywords: COVID-19; circadian rhythms; disease monitoring; heart rate; mathematical modeling; wearables.

Publication types

Research Support, U.S. Gov't, Non-P.H.S.
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

COVID-19*
Disease Progression
Heart Rate
Humans
Monitoring, Physiologic
Wearable Electronic Devices*

Abstract

Publication types

MeSH terms

Grants and funding