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Review 1: "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 1: "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:

  • Strong. The main study claims are very well-justified by the data and analytic methods used. There is little room for doubt that the study produced has very similar results and conclusions as compared with the hypothetical ideal study. The study’s main claims should be considered conclusive and actionable without reservation.

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Review:

I am submitting this review for the manuscript titled “Assessment of physiological signs associated with COVID-19 measured using wearable devices.” The authors have utilized a wearable health monitoring device, Fitbit, to monitor respiration rate, heart rate, and heart rate variability and have collected surveys from participants to assess COVID-19 cases. Two approaches were considered in this study, a symptom-based approach and physiological signs-based method. The first utilized logistic regression classifier to predict the need for hospitalization of COVID-19 cases based on the self-recorded symptoms that the patients exhibited. The second utilized a neural network classifier to predict whether a person was sick from four days prior until the day patients began showing symptoms. Based on the results presented in this manuscript, I recommend this manuscript for the publication in this journal. To strength the manuscript, the following questions and comments are considered to be addressed:

1. On page 4, Can the authors elaborate how the Fitbit system distinguishes light, deep, and REM sleep?

2. On page 4, when RMSSD and entropy were only computed between midnight and 7am, were the authors assuming the subjects were sleeping? Is it an important parameter that the subjects are sleeping? Please elaborate.

3. On page 6, reference to Figure 5 is missing.

4. It is unclear how the parameters for the need for hospitalization were determined and what they mean, since there is variation in the symptoms people experience compared to the progression of the disease.

5. How were the death cases considered in the study, if any?

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