Key result
Apple Watch detects ~77% lower circadian SDNN amplitude in subjects with COVID-19.
Why the study?
Autonomic nervous system changes reflected in HRV occur with infection, prompting evaluation of wearable-derived HRV to identify and predict COVID-19 and related symptoms.
Does continuous HRV monitoring via a wearable device identify and predict SARS-CoV-2 infection and related symptoms in health care workers?
Observational (n=297)
No
Does continuous HRV monitoring via a wearable device identify and predict SARS-CoV-2 infection and related symptoms in health care workers?
Longitudinal HRV monitoring using a commercial wearable device can identify and predict COVID-19 infection up to 7 days prior to a positive nasal swab PCR test.
Wearable HRV differences may indicate COVID-19; hypothesis-generating and requires prospective validation before clinical use.
BACKGROUND: 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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Hirten et al. (2021) conducted an observational in COVID-19 (n=297). Apple Watch (wearable device) vs. Subjects without COVID-19 was evaluated on Mean amplitude of the circadian pattern of SDNN (milliseconds) (Difference -4.07 ms, 95% CI -7.29 to -2.07, p=0.006). An Apple Watch measuring heart rate variability detected a significantly lower mean amplitude of the circadian SDNN pattern in subjects with COVID-19 compared to uninfected subjects (1.23 vs 5.30 ms).
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