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Figure.  Mean Change in Wearable Data From Individual Baseline Before and After COVID-19 Symptom Onset
Mean Change in Wearable Data From Individual Baseline Before and After COVID-19 Symptom Onset

Graphs show mean deviation from baseline (lines) with 95% CIs (shaded areas) for daily resting heart rate (RHR), sleep quantity, and step count during −7 to 133 days after symptom onset for COVID-19–positive vs COVID-19–negative participants (panels A, C, and E) and for COVID-19–positive participants grouped by mean change in RHR during days 28 to 56 after symptom onset (panels B, D, and F).

Table.  Population Characteristics and Frequencies of Self-reported Symptoms Among COVID-19–Positive Participants Characterized by Mean RHR Change From Baseline During 28 to 56 Days After Symptom Onset
Population Characteristics and Frequencies of Self-reported Symptoms Among COVID-19–Positive Participants Characterized by Mean RHR Change From Baseline During 28 to 56 Days After Symptom Onset
1.
Dani  M, Dirksen  A, Taraborrelli  P,  et al.  Autonomic dysfunction in ‘long COVID’: rationale, physiology and management strategies.   Clin Med (Lond). 2021;21(1):e63-e67. doi:10.7861/clinmed.2020-0896PubMedGoogle ScholarCrossref
2.
Puntmann  VO, Carerj  ML, Wieters  I,  et al.  Outcomes of cardiovascular magnetic resonance imaging in patients recently recovered from coronavirus disease 2019 (COVID-19).   JAMA Cardiol. 2020;5(11):1265-1273. doi:10.1001/jamacardio.2020.3557PubMedGoogle ScholarCrossref
3.
Logue  JK, Franko  NM, McCulloch  DJ,  et al.  Sequelae in adults at 6 months after COVID-19 infection.   JAMA Netw Open. 2021;4(2):e210830-e210830. doi:10.1001/jamanetworkopen.2021.0830PubMedGoogle ScholarCrossref
4.
Radin  JM, Wineinger  NE, Topol  EJ, Steinhubl  SR.  Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study.   Lancet Digit Health. 2020;2(2):e85-e93. doi:10.1016/S2589-7500(19)30222-5PubMedGoogle ScholarCrossref
5.
Quer  G, Radin  JM, Gadaleta  M,  et al.  Wearable sensor data and self-reported symptoms for COVID-19 detection.   Nature Medicine. 2021;27(1):73-77. doi:10.1038/s41591-020-1123-xPubMedGoogle ScholarCrossref
6.
Amaratunga  EA, Corwin  DS, Moran  L, Snyder  R.  Bradycardia in patients with COVID-19: a calm before the storm?   Cureus. 2020;12(6):e8599. doi:10.7759/cureus.8599PubMedGoogle Scholar
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    Research Letter
    Infectious Diseases
    July 7, 2021

    Assessment of Prolonged Physiological and Behavioral Changes Associated With COVID-19 Infection

    Author Affiliations
    • 1Scripps Research Translational Institute, San Diego, California
    • 2Care Evolution, Ann Arbor, Michigan
    JAMA Netw Open. 2021;4(7):e2115959. doi:10.1001/jamanetworkopen.2021.15959
    Introduction

    Long-term COVID symptoms marked by autonomic dysfunction1 and cardiac damage2 following COVID-19 infection have been noted for up to 6 months after symptom onset,3 but to date have not been quantified, to our knowledge. Previous studies have found that wearable data can improve real-time detection of viral illness4 or discrimination of individuals with COVID-19 vs other viral infections.5 Wearable devices provide a way to continuously track an individual’s physiological and behavioral metrics beginning when healthy (ie, before infection), during the course of infection, and recovery back to baseline. In this cohort study, we aimed to examine the duration and variation of recovery among COVID-19–positive vs COVID-19–negative participants.

    Methods

    DETECT (Digital Engagement and Tracking for Early Control and Treatment) is a remote, app-based, longitudinal research study enrolling adult participants from all over the US and collecting their wearable data to better understand individual changes associated with viral illness, including COVID-19. All participants provided informed consent electronically. The protocol for this study was reviewed and approved by the Scripps Office for the Protection of Research Subjects. This study follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

    From March 25, 2020, through January 24, 2021, 37 146 participants were enrolled. This analysis focuses on 875 individuals who reported symptoms of an acute respiratory illness and underwent swab testing for COVID-19 and were found to be either positive (234 individuals) or negative (641 individuals) (eFigure in the Supplement).

    The following calculation was used for resting heart rate (RHR): deviation from baseline = daily RHR − baseline RHR mean. Individuals with COVID-19 were also grouped by their mean RHR deviation from baseline 28 to 56 days after symptom onset (<1, 1-5, or >5 beats per minute).

    Data analysis was conducted in SAS statistical software version 9.4 (SAS Institute). Significance was set at P < .05. P values were calculated with 1-way ANOVA (for mean age) or χ2 tests. Additional details about our methods can be found in the eAppendix in the Supplement.

    Results

    For this analysis, our study population consisted of 234 COVID-19–positive individuals (mean [range] age, 45.3 [18-76] years; 164 women [70.9%]) and 641 COVID-19–negative symptomatic individuals (mean [range] age, 44.7 [19-75] years; 455 women [71.1%]). Individuals with COVID-19 took longer to return to their RHR (Figure, A and B), sleep (Figure, C and D), and activity (Figure, E and F) baselines compared with symptomatic individuals who were COVID-19 negative. This difference was most marked for RHR, with COVID-19–positive individuals initially experiencing a transient bradycardia followed by a prolonged relative tachycardia that did not return to baseline, on average, until 79 days after symptom onset. Step count and sleep quantity returned to baseline sooner than RHR at 32 and 24 days, respectively. During recovery, individuals with COVID-19 experienced different trajectories in the return of their RHR to their normal compared with COVID-19–negative individuals (Figure, B). A small subset of COVID-19–positive participants (32 participants [13.7%]) maintained an RHR more than 5 beats per minute greater than their baseline RHR that did not return to their normal for more than 133 days. During the acute phase of COVID-19, individuals in this group reported higher frequencies of cough (27 participants [84.4%] vs 57 participants [55.3%] in the <1 beat per minute group and 57 participants [57.6%] in the 1-5 beats per minute group), body ache (20 participants [62.5%] vs 42 participants [40.8%] in the <1 beat per minute group and 35 participants [35.4%] in the 1-5 beats per minute group), and shortness of breath (9 participants [28.1%] vs 9 participants [8.7%] in the <1 beat per minute group and 6 participants [6.1%] in the 1-5 beats per minute group) compared with the other groups (Table).

    Discussion

    To our knowledge, this is the first study to examine longer duration wearable sensor data. We found a prolonged physiological impact of COVID-19 infection, lasting approximately 2 to 3 months, on average, but with substantial intraindividual variability, which may reflect various levels of autonomic nervous system dysfunction or potentially ongoing inflammation. Transient bradycardia has been noted in a case study6 approximately 9 to 15 days after symptom onset, which was also seen in our population. Our data suggest that early symptoms and larger initial RHR response to COVID-19 infection may be associated with the physiological length of recovery from this virus.

    Symptom data were collected only during the acute phase of infection, which limited our ability to compare long-term physiological and behavioral changes with long-term symptoms. In the future, with larger sample sizes and more comprehensive participant-reported outcomes, it will be possible to better understand factors associated with interindividualized variability in COVID-19 recovery.

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    Article Information

    Accepted for Publication: May 3, 2021.

    Published: July 7, 2021. doi:10.1001/jamanetworkopen.2021.15959

    Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Radin JM et al. JAMA Network Open.

    Corresponding Author: Jennifer M. Radin, PhD, MPH, Scripps Research Translational Institute, 3344 N Torrey Pines Ct, Plaza Level, La Jolla, CA 92037 (jmradin@scripps.edu).

    Author Contributions: Dr Radin had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Radin, Ramos, Baca-Motes, Steinhubl.

    Acquisition, analysis, or interpretation of data: All authors.

    Drafting of the manuscript: Radin, Steinhubl.

    Critical revision of the manuscript for important intellectual content: All authors.

    Statistical analysis: Radin, Quer, Gadaleta.

    Obtained funding: Baca-Motes, Topol.

    Administrative, technical, or material support: Quer, Baca-Motes, Gadaleta, Topol.

    Supervision: Ramos, Steinhubl.

    Conflict of Interest Disclosures: Dr Steinhubl reports being employed part time with PhysIQ, which is not involved in this study. No other disclosures were reported.

    Funding/Support: This work was funded by grant UL1TR002550 from the National Center for Advancing Translational Sciences at the National Institutes of Health (support to Drs Topol, Radin, and Steinhubl).

    Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    Additional Contributions: Lauren Ariniello, BS, Erin Coughlin, RN, Royan Kamyar, MD, MBA, Danny Oran, MS, Sasri Dedigama, BS, and Katie Quartuccio, BA (Scripps Research Translational Institute); Tyler Peters, BA (Wondros); and Vik Kheterpol, MD, and Chris Nowak, BA (Care Evolution), helped make the DETECT study (http://www.detectstudy.org) a success. They were not compensated for their contributions beyond their normal salaries.

    References
    1.
    Dani  M, Dirksen  A, Taraborrelli  P,  et al.  Autonomic dysfunction in ‘long COVID’: rationale, physiology and management strategies.   Clin Med (Lond). 2021;21(1):e63-e67. doi:10.7861/clinmed.2020-0896PubMedGoogle ScholarCrossref
    2.
    Puntmann  VO, Carerj  ML, Wieters  I,  et al.  Outcomes of cardiovascular magnetic resonance imaging in patients recently recovered from coronavirus disease 2019 (COVID-19).   JAMA Cardiol. 2020;5(11):1265-1273. doi:10.1001/jamacardio.2020.3557PubMedGoogle ScholarCrossref
    3.
    Logue  JK, Franko  NM, McCulloch  DJ,  et al.  Sequelae in adults at 6 months after COVID-19 infection.   JAMA Netw Open. 2021;4(2):e210830-e210830. doi:10.1001/jamanetworkopen.2021.0830PubMedGoogle ScholarCrossref
    4.
    Radin  JM, Wineinger  NE, Topol  EJ, Steinhubl  SR.  Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study.   Lancet Digit Health. 2020;2(2):e85-e93. doi:10.1016/S2589-7500(19)30222-5PubMedGoogle ScholarCrossref
    5.
    Quer  G, Radin  JM, Gadaleta  M,  et al.  Wearable sensor data and self-reported symptoms for COVID-19 detection.   Nature Medicine. 2021;27(1):73-77. doi:10.1038/s41591-020-1123-xPubMedGoogle ScholarCrossref
    6.
    Amaratunga  EA, Corwin  DS, Moran  L, Snyder  R.  Bradycardia in patients with COVID-19: a calm before the storm?   Cureus. 2020;12(6):e8599. doi:10.7759/cureus.8599PubMedGoogle Scholar
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