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Review 2: "Assessment of physiological signs associated with COVID-19 measured using wearable devices"

This study leverages wearable device technology to track biometrics in COVID19-afflicted individuals and develop models that predict both illness and risk of hospitalization. These results should be considered reliable.

Published onNov 07, 2020
Review 2: "Assessment of physiological signs associated with COVID-19 measured using wearable devices"
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key-enterThis Pub is a Review of
Assessment of physiological signs associated with COVID-19 measured using wearable devices
Description

Respiration rate, heart rate, and heart rate variability are some health metrics that are easily measured by consumer devices and which can potentially provide early signs of illness. Furthermore, mobile applications which accompany wearable devices can be used to collect relevant self-reported symptoms and demographic data. This makes consumer devices a valuable tool in the fight against the COVID-19 pandemic. We considered two approaches to assessing COVID-19 - a symptom-based approach, and a physiological signs based technique. Firstly, we trained a Logistic Regression classifier to predict the need for hospitalization of COVID-19 patients given the symptoms experienced, age, sex, and BMI. Secondly, we trained a neural network classifier to predict whether a person is sick on any specific day given respiration rate, heart rate, and heart rate variability data for that day and and for the four preceding days. Data on 1,181 subjects diagnosed with COVID-19 (active infection, PCR test) were collected from May 21 - July 14, 2020. 11.0% of COVID-19 subjects were asymptomatic, 47.2% of subjects recovered at home by themselves, 33.2% recovered at home with the help of someone else, 8.16% of subjects required hospitalization without ventilation support, and 0.448% required ventilation. Fever was present in 54.8% of subjects. Based on self-reported symptoms alone, we obtained an AUC of 0.77 +/- 0.05 for the prediction of the need for hospitalization. Based on physiological signs, we obtained an AUC of 0.77 +/- 0.03 for the prediction of illness on a specific day with 4 previous days of history. Respiration rate and heart rate are typically elevated by illness, while heart rate variability is decreased. Measuring these metrics can help in early diagnosis, and in monitoring the progress of the disease.

RR:C19 Evidence Scale rating by reviewer:

  • Reliable. The main study claims are generally justified by its methods and data. The results and conclusions are likely to be similar to the hypothetical ideal study. There are some minor caveats or limitations, but they would/do not change the major claims of the study. The study provides sufficient strength of evidence on its own that its main claims should be considered actionable, with some room for future revision.

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Review:

General comments:

This manuscript does an excellent job in demonstrating two approaches to assessing COVID-19, a symptom-based approach and a physiological signs-based technique. 1,181 active Fitbit users in the USA and Canada were invited to participate in a survey of whether they have experienced COVID-19 or similar infections. The physiological data of each user was calculated daily using the data recorded from their Fitbit device. The authors concluded that hospitalization risk can be calculated from self-reported symptoms, and that predictive physiological signs related to COVID-19 may be detected by consumer wearable devices.

I was pleased to see that the main study claims are generally well-justified by its approaches and data analytic methods used. The manuscript cites recent related literature work and discusses several limitations which may confound some of its findings.

To sum up, I think that this paper is well written and is an important addition to the literature, particularly to enhance response to COVID-19 or similar influenza pandemics. I would recommend that the author of the manuscript should be given the opportunity to publish with the RR:C19 Journal.

Other comments:

·       I would kindly recommend more clearly and specifically mentioning the use of a Fitbit device in this study in the abstract section as well as in the last paragraph of the introduction.

·       In line 10 of the abstract, please remove one “and.”

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