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

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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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Most cited references 25

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Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China

Chaolin Huang, Yeming Wang, Xingwang Li … (2020)

Summary Background A recent cluster of pneumonia cases in Wuhan, China, was caused by a novel betacoronavirus, the 2019 novel coronavirus (2019-nCoV). We report the epidemiological, clinical, laboratory, and radiological characteristics and treatment and clinical outcomes of these patients. Methods All patients with suspected 2019-nCoV were admitted to a designated hospital in Wuhan. We prospectively collected and analysed data on patients with laboratory-confirmed 2019-nCoV infection by real-time RT-PCR and next-generation sequencing. Data were obtained with standardised data collection forms shared by WHO and the International Severe Acute Respiratory and Emerging Infection Consortium from electronic medical records. Researchers also directly communicated with patients or their families to ascertain epidemiological and symptom data. Outcomes were also compared between patients who had been admitted to the intensive care unit (ICU) and those who had not. Findings By Jan 2, 2020, 41 admitted hospital patients had been identified as having laboratory-confirmed 2019-nCoV infection. Most of the infected patients were men (30 [73%] of 41); less than half had underlying diseases (13 [32%]), including diabetes (eight [20%]), hypertension (six [15%]), and cardiovascular disease (six [15%]). Median age was 49·0 years (IQR 41·0–58·0). 27 (66%) of 41 patients had been exposed to Huanan seafood market. One family cluster was found. Common symptoms at onset of illness were fever (40 [98%] of 41 patients), cough (31 [76%]), and myalgia or fatigue (18 [44%]); less common symptoms were sputum production (11 [28%] of 39), headache (three [8%] of 38), haemoptysis (two [5%] of 39), and diarrhoea (one [3%] of 38). Dyspnoea developed in 22 (55%) of 40 patients (median time from illness onset to dyspnoea 8·0 days [IQR 5·0–13·0]). 26 (63%) of 41 patients had lymphopenia. All 41 patients had pneumonia with abnormal findings on chest CT. Complications included acute respiratory distress syndrome (12 [29%]), RNAaemia (six [15%]), acute cardiac injury (five [12%]) and secondary infection (four [10%]). 13 (32%) patients were admitted to an ICU and six (15%) died. Compared with non-ICU patients, ICU patients had higher plasma levels of IL2, IL7, IL10, GSCF, IP10, MCP1, MIP1A, and TNFα. Interpretation The 2019-nCoV infection caused clusters of severe respiratory illness similar to severe acute respiratory syndrome coronavirus and was associated with ICU admission and high mortality. Major gaps in our knowledge of the origin, epidemiology, duration of human transmission, and clinical spectrum of disease need fulfilment by future studies. Funding Ministry of Science and Technology, Chinese Academy of Medical Sciences, National Natural Science Foundation of China, and Beijing Municipal Science and Technology Commission.

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Temporal dynamics in viral shedding and transmissibility of COVID-19

Xi. He, Eric Lau, Peng Wu … (2020)

We report temporal patterns of viral shedding in 94 patients with laboratory-confirmed COVID-19 and modeled COVID-19 infectiousness profiles from a separate sample of 77 infector-infectee transmission pairs. We observed the highest viral load in throat swabs at the time of symptom onset, and inferred that infectiousness peaked on or before symptom onset. We estimated that 44% (95% confidence interval, 25-69%) of secondary cases were infected during the index cases' presymptomatic stage, in settings with substantial household clustering, active case finding and quarantine outside the home. Disease control measures should be adjusted to account for probable substantial presymptomatic transmission.

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Is Open Access

Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts

Joel Hellewell, Sam Abbott, Amy Gimma … (2020)

Summary Background Isolation of cases and contact tracing is used to control outbreaks of infectious diseases, and has been used for coronavirus disease 2019 (COVID-19). Whether this strategy will achieve control depends on characteristics of both the pathogen and the response. Here we use a mathematical model to assess if isolation and contact tracing are able to control onwards transmission from imported cases of COVID-19. Methods We developed a stochastic transmission model, parameterised to the COVID-19 outbreak. We used the model to quantify the potential effectiveness of contact tracing and isolation of cases at controlling a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-like pathogen. We considered scenarios that varied in the number of initial cases, the basic reproduction number (R 0), the delay from symptom onset to isolation, the probability that contacts were traced, the proportion of transmission that occurred before symptom onset, and the proportion of subclinical infections. We assumed isolation prevented all further transmission in the model. Outbreaks were deemed controlled if transmission ended within 12 weeks or before 5000 cases in total. We measured the success of controlling outbreaks using isolation and contact tracing, and quantified the weekly maximum number of cases traced to measure feasibility of public health effort. Findings Simulated outbreaks starting with five initial cases, an R 0 of 1·5, and 0% transmission before symptom onset could be controlled even with low contact tracing probability; however, the probability of controlling an outbreak decreased with the number of initial cases, when R 0 was 2·5 or 3·5 and with more transmission before symptom onset. Across different initial numbers of cases, the majority of scenarios with an R 0 of 1·5 were controllable with less than 50% of contacts successfully traced. To control the majority of outbreaks, for R 0 of 2·5 more than 70% of contacts had to be traced, and for an R 0 of 3·5 more than 90% of contacts had to be traced. The delay between symptom onset and isolation had the largest role in determining whether an outbreak was controllable when R 0 was 1·5. For R 0 values of 2·5 or 3·5, if there were 40 initial cases, contact tracing and isolation were only potentially feasible when less than 1% of transmission occurred before symptom onset. Interpretation In most scenarios, highly effective contact tracing and case isolation is enough to control a new outbreak of COVID-19 within 3 months. The probability of control decreases with long delays from symptom onset to isolation, fewer cases ascertained by contact tracing, and increasing transmission before symptoms. This model can be modified to reflect updated transmission characteristics and more specific definitions of outbreak control to assess the potential success of local response efforts. Funding Wellcome Trust, Global Challenges Research Fund, and Health Data Research UK.

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Author and article information

Contributors

Robert P Hirten:

ORCID: https://orcid.org/0000-0002-7980-9368

The Dr Henry D Janowitz Division of GastroenterologyIcahn School of Medicine at Mount Sinai1468 Madison Avenue, Annenberg Building RM 5-12New York, NY, 10029United States1 212 241 0150robert.hirten@mountsinai.org

Journal

Journal ID (nlm-ta): J Med Internet Res

Journal ID (iso-abbrev): J Med Internet Res

Journal ID (publisher-id): JMIR

Title: Journal of Medical Internet Research

Publisher: JMIR Publications (Toronto, Canada )

ISSN (Print): 1439-4456

ISSN (Electronic): 1438-8871

Publication date Collection: February 2021

Publication date (Electronic): 22 February 2021

Publication date PMC-release: 22 February 2021

Volume: 23

Issue: 2

Electronic Location Identifier: e26107

Affiliations

[1 ] The Dr Henry D Janowitz Division of Gastroenterology Icahn School of Medicine at Mount Sinai New York, NY United States

[2 ] Hasso Plattner Institute for Digital Health at Mount Sinai Icahn School of Medicine at Mount Sinai New York, NY United States

[3 ] Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New York, NY United States

[4 ] Center for Biostatistics Department of Population Health Science and Policy Icahn School of Medicine at Mount Sinai New York, NY United States

[5 ] The BioMedical Engineering and Imaging Institute Icahn School of Medicine at Mount Sinai New York, NY United States

[6 ] Department of Psychiatry Icahn School of Medicine at Mount Sinai New York, NY United States

[7 ] Pamela Sklar Division of Psychiatric Genomics Icahn School of Medicine at Mount Sinai New York, NY United States

[8 ] Department of Anesthesiology Perioperative and Pain Medicine Icahn School of Medicine at Mount Sinai New York, NY United States

[9 ] Department of Environmental Medicine and Public Health Icahn School of Medicine at Mount Sinai New York, NY United States

[10 ] Division of Infectious Diseases Icahn School of Medicine at Mount Sinai New York, NY United States

[11 ] Office of the Dean Icahn School of Medicine at Mount Sinai New York, NY United States

[12 ] Nash Family Department of Neuroscience Icahn School of Medicine at Mount Sinai New York, NY United States

[13 ] Department of Medicine Icahn School of Medicine at Mount Sinai New York, NY United States

[14 ] Charles Bronfman Institute for Personalized Medicine Icahn School of Medicine at Mount Sinai New York, NY United States

[15 ] Department of Diagnostic, Molecular and Interventional Radiology Icahn School of Medicine at Mount Sinai New York, NY United States

Author notes

Corresponding Author: Robert P Hirten robert.hirten@ 123456mountsinai.org

Author information

Robert P Hirten https://orcid.org/0000-0002-7980-9368

Matteo Danieletto https://orcid.org/0000-0002-5178-9182

Lewis Tomalin https://orcid.org/0000-0003-4545-7138

Katie Hyewon Choi https://orcid.org/0000-0001-7465-4558

Micol Zweig https://orcid.org/0000-0001-8190-7197

Eddye Golden https://orcid.org/0000-0002-9818-5693

Sparshdeep Kaur https://orcid.org/0000-0001-5386-8506

Drew Helmus https://orcid.org/0000-0001-6799-3674

Anthony Biello https://orcid.org/0000-0001-6683-5738

Renata Pyzik https://orcid.org/0000-0003-1099-1672

Alexander Charney https://orcid.org/0000-0001-8135-6858

Riccardo Miotto https://orcid.org/0000-0002-7815-6000

Benjamin S Glicksberg https://orcid.org/0000-0003-4515-8090

Matthew Levin https://orcid.org/0000-0002-6013-2684

Ismail Nabeel https://orcid.org/0000-0002-6909-1970

Judith Aberg https://orcid.org/0000-0001-8162-0284

David Reich https://orcid.org/0000-0003-0095-515X

Dennis Charney https://orcid.org/0000-0003-0610-3433

Erwin P Bottinger https://orcid.org/0000-0001-6868-6676

Laurie Keefer https://orcid.org/0000-0003-4779-8593

Mayte Suarez-Farinas https://orcid.org/0000-0001-8712-3553

Girish N Nadkarni https://orcid.org/0000-0001-6319-4314

Zahi A Fayad https://orcid.org/0000-0002-3439-7347

Article

Publisher ID: v23i2e26107

DOI: 10.2196/26107

PMC ID: 7901594

PubMed ID: 33529156

SO-VID: 8153e086-d27a-4cbf-9f3f-0a3bda032e35

Copyright © ©Robert P Hirten, Matteo Danieletto, Lewis Tomalin, Katie Hyewon Choi, Micol Zweig, Eddye Golden, Sparshdeep Kaur, Drew Helmus, Anthony Biello, Renata Pyzik, Alexander Charney, Riccardo Miotto, Benjamin S Glicksberg, Matthew Levin, Ismail Nabeel, Judith Aberg, David Reich, Dennis Charney, Erwin P Bottinger, Laurie Keefer, Mayte Suarez-Farinas, Girish N Nadkarni, Zahi A Fayad. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 22.02.2021.

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

History

Date received : 27 November 2020

Date revision requested : 18 December 2020

Date revision received : 14 January 2021

Date accepted : 29 January 2021

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