Assessment of physiological signs associated with COVID-19 measured using wearable devices
This article has been Reviewed by the following groups
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- Evaluated articles (ScreenIT)
- Evaluated articles (Rapid Reviews Infectious Diseases)
Abstract
Respiration rate, heart rate, and heart rate variability (HRV) are some health metrics that are easily measured by consumer devices, which can potentially provide early signs of illness. Furthermore, mobile applications that 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. Data on 2745 subjects diagnosed with COVID-19 (active infection, PCR test) were collected from May 21 to September 11, 2020, consisting of PCR positive tests conducted between February 16 and September 9. Considering male (female) participants, 11.9% (11.2%) of the participants were asymptomatic, 48.3% (47.8%) recovered at home by themselves, 29.7% (33.7%) recovered at home with the help of someone else, 9.3% (6.6%) required hospitalization without ventilation, and 0.5% (0.4%) required ventilation. There were a total of 21 symptoms reported, and the prevalence of symptoms varies by sex. Fever was present in 59.4% of male subjects and in 52% of female subjects. Based on self-reported symptoms alone, we obtained an AUC of 0.82 ± 0.017 for the prediction of the need for hospitalization. Based on physiological signs, we obtained an AUC of 0.77 ± 0.018 for the prediction of illness on a specific day. Respiration rate and heart rate are typically elevated by illness, while HRV is decreased. Measuring these metrics, taken in conjunction with molecular-based diagnostics, may lead to better early detection and monitoring of COVID-19.
Article activity feed
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SciScore for 10.1101/2020.08.14.20175265: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:This study has multiple limitations which may confound some of its findings. The survey participants were all Fitbit users which may not represent the general US and Canadian population, and were all self-selecting in responding to the survey. Participants were asked to self-recall the start-date and end-date of any symptoms they …
SciScore for 10.1101/2020.08.14.20175265: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:This study has multiple limitations which may confound some of its findings. The survey participants were all Fitbit users which may not represent the general US and Canadian population, and were all self-selecting in responding to the survey. Participants were asked to self-recall the start-date and end-date of any symptoms they experienced which may be quite unreliable. Participants may also confuse active (PCR) tests with serological (antibody) tests. In order to simplify the survey, we did not ask for a breakdown of symptom presentation and severity through out the time-course of the disease. For the prediction of the need for hospitalization, our data consisted of 95 positive cases and 926 negative cases, so it is possible that our results are potentially affected by the small number of positive cases, as well as by the class imbalance. Nevertheless, we believe this survey provides an important scientific contribution by suggesting (a) hospitalization risk can be calculated from self-reported symptoms, and (b) relevant and predictive physiological signs related to COVID-19 may be detected by consumer wearable devices.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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Yehuda Weizman
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.
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Strength of evidence
Reviewers: Chi Hwan Lee (Purdue University) | 📘📘📘📘📘
Yehuda Weizman (Swinburne University of Technology) | 📗📗📗📗 ◻️ -
Chi Hwan Lee
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.
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SciScore for 10.1101/2020.08.14.20175265: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
This study has multiple limitations which may confound some of its findings. The survey participants were all Fitbit users which may not represent the general US and Canadian population, and were all self-selecting in responding to the survey. Participants were asked to selfrecall the start-date and end-date of any symptoms they …
SciScore for 10.1101/2020.08.14.20175265: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
This study has multiple limitations which may confound some of its findings. The survey participants were all Fitbit users which may not represent the general US and Canadian population, and were all self-selecting in responding to the survey. Participants were asked to selfrecall the start-date and end-date of any symptoms they experienced which may be quite unreliable. Participants may also confuse active (PCR) tests with serological (antibody) tests. In order to simplify the survey, we did not ask for a breakdown of symptom presentation and severity through out the time-course of the disease. For the prediction of the need for hospitalization, our data consisted of 95 positive cases and 926 negative cases, so it is possible that our results are potentially affected by the small number of positive cases, as well as by the class imbalance. Nevertheless, we believe this survey provides an important scientific contribution by suggesting (a) hospitalization risk can be calculated from self-reported symptoms, and (b) relevant and predictive physiological signs related to COVID-19 may be detected by consumer wearable devices. Declaration of conflicts of interest: All authors are employees of Fitbit Inc.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.
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