Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study

  1. Robert P Hirten
  2. Matteo Danieletto
  3. Lewis Tomalin
  4. Katie Hyewon Choi
  5. Micol Zweig
  6. Eddye Golden
  7. Sparshdeep Kaur
  8. Drew Helmus
  9. Anthony Biello
  10. Renata Pyzik
  11. Alexander Charney
  12. Riccardo Miotto
  13. Benjamin S Glicksberg
  14. Matthew Levin
  15. Ismail Nabeel
  16. Judith Aberg
  17. David Reich
  18. Dennis Charney
  19. Erwin P Bottinger
  20. Laurie Keefer
  21. Mayte Suarez-Farinas
  22. Girish N Nadkarni
  23. Zahi A Fayad

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Abstract

Changes in autonomic nervous system function, characterized by heart rate variability (HRV), have been associated with infection and observed prior to its clinical identification.

Objective

We performed an evaluation of HRV collected by a wearable device to identify and predict COVID-19 and its related symptoms.

Methods

Health care workers in the Mount Sinai Health System were prospectively followed in an ongoing observational study using the custom Warrior Watch Study app, which was downloaded to their smartphones. Participants wore an Apple Watch for the duration of the study, measuring HRV throughout the follow-up period. Surveys assessing infection and symptom-related questions were obtained daily.

Results

Using a mixed-effect cosinor model, the mean amplitude of the circadian pattern of the standard deviation of the interbeat interval of normal sinus beats (SDNN), an HRV metric, differed between subjects with and without COVID-19 (P=.006). The mean amplitude of this circadian pattern differed between individuals during the 7 days before and the 7 days after a COVID-19 diagnosis compared to this metric during uninfected time periods (P=.01). Significant changes in the mean and amplitude of the circadian pattern of the SDNN was observed between the first day of reporting a COVID-19–related symptom compared to all other symptom-free days (P=.01).

Conclusions

Longitudinally collected HRV metrics from a commonly worn commercial wearable device (Apple Watch) can predict the diagnosis of COVID-19 and identify COVID-19–related symptoms. Prior to the diagnosis of COVID-19 by nasal swab polymerase chain reaction testing, significant changes in HRV were observed, demonstrating the predictive ability of this metric to identify COVID-19 infection.

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  1. Version published to 10.2196/26107
  2. Version published to 10.2196/preprints.26107
  3. ScreenIT

    SciScore for 10.1101/2020.11.06.20226803: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementIRB: This study was approved by the Institutional Review Board at The Icahn School of Medicine at Mount Sinai.
    Consent: Study Procedures: Participants downloaded the custom Warrior Watch App to complete eligibility questionnaires and sign an electronic consent form.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    There are several limitations to our study. First, there was a small number of participants who were diagnosed with COVID-19 in our cohort limiting our ability to determine how predictive HRV can be of infection. However, these preliminary findings support the further evaluation of HRV as a metric to identify and predict COVID-19 and warrant further study. An additional limitation is the sporadic collection of HRV by the Apple Watch. While our statistical modelling was able to account for this a denser dataset would allow for expanded evaluation of the relationship between this metric and infections/symptoms. The Apple Watch also only provides HRV in one time-domain (SDNN), limiting assessment of the relationship between other HRV parameters with COVID-19 outcomes. Lastly, an additional limitation is that we relied on self-reported data in this study, precluding independent verification of COVID-19 diagnosis. In summary, we demonstrated a relationship between longitudinally collected HRV acquired from a commonly used wearable device and SARS-CoV-2 infection. These preliminary results support the further evaluation of HRV as a biomarker of SARS-CoV-2 infection by remote sensing means. While further study is needed, this may allow for the identification of SARS-CoV-2 infection during the pre-symptomatic period, in asymptomatic carriers and prior to diagnosis by a SARS-CoV-2 nasal PCR tests. These findings warrant further evaluation of this approach to track and identify COVID-1...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

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  4. Version published to 10.1101/2020.11.06.20226803v1 on medRxiv