What is the clinical evidence from this study?

Study design: Observational. Population: COVID-19 (n=297). Intervention: Apple Watch (wearable device) vs. Subjects without COVID-19. Primary outcome: Mean amplitude of the circadian pattern of SDNN (milliseconds) (Difference -4.07 ms, 95% CI -7.29 to -2.07, p=0.006).

February 2, 2021Open Access

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

Q: What does this research mean for the field?

An Apple Watch measuring heart rate variability can detect COVID-19 infection up to 7 days prior to clinical diagnosis by identifying a significantly lower mean amplitude of the circadian SDNN pattern in infected subjects compared to uninfected subjects (1.23 vs 5.30 ms). Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

RHRobert HirtenDartmouth–Hitchcock Medical Center MDMatteo DanielettoIcahn School of Medicine at Mount Sinai LTLewis E. TomalinIcahn School of Medicine at Mount Sinai

Key Result

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).

Study Design

Type

Observational (n=297)

Multicenter

Structured PICO

Does continuous HRV monitoring via a wearable device identify and predict SARS-CoV-2 infection and related symptoms in health care workers?

Population

297 health care workers in the Mount Sinai Health System (aged ≥18 years), median age 36, 69.4% female. Key inclusion: current employees, had an iPhone Series 6 or higher, and willing to wear an Apple Watch Series 4 or higher. Key exclusion: underlying autoimmune disease or taking medications known to interfere with autonomic nervous system function.

Intervention

Continuous heart rate variability (HRV) monitoring via Apple Watch Series 4 or 5, specifically measuring the standard deviation of the interbeat interval of normal sinus beats (SDNN) using a custom Warrior Watch app.

Comparator

Subjects without COVID-19, or uninfected time periods within the same individuals.

Outcome

Differentiation of participants infected and not infected with SARS-CoV-2 based on changes in HRV.surrogate

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.

Main Result

Effect estimate: Difference -4.07 ms (95% CI -7.29 to -2.07)

Absolute Event Rate: 1.23% vs 5.3%

p-value: p=0.006

Limitations

Small number of participants diagnosed with COVID-19
Sporadic collection of HRV by the Apple Watch
Apple Watch only provides HRV in one time domain (SDNN)
Did not capture times of day participants were awake or sleeping
Relied on self-reported data, precluding independent verification of COVID-19 diagnosis

Abstract

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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