Automated, multiparametric monitoring of respiratory biomarkers and vital signs in clinical and home settings for COVID-19 patients

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Significance

Continuous measurements of health status can be used to guide the care of patients and to manage the spread of infectious diseases. Conventional monitoring systems cannot be deployed outside of hospital settings, and existing wearables cannot capture key respiratory biomarkers. This paper describes an automated wireless device and a data analysis approach that overcome these limitations, tailored for COVID-19 patients, frontline health care workers, and others at high risk. Vital signs and respiratory activity such as cough can reveal early signs of infection and quantitate responses to therapeutics. Long-term trials on COVID-19 patients in clinical and home settings demonstrate the translational value of this technology.

Abstract

Capabilities in continuous monitoring of key physiological parameters of disease have never been more important than in the context of the global COVID-19 pandemic. Soft, skin-mounted electronics that incorporate high-bandwidth, miniaturized motion sensors enable digital, wireless measurements of mechanoacoustic (MA) signatures of both core vital signs (heart rate, respiratory rate, and temperature) and underexplored biomarkers (coughing count) with high fidelity and immunity to ambient noises. This paper summarizes an effort that integrates such MA sensors with a cloud data infrastructure and a set of analytics approaches based on digital filtering and convolutional neural networks for monitoring of COVID-19 infections in sick and healthy individuals in the hospital and the home. Unique features are in quantitative measurements of coughing and other vocal events, as indicators of both disease and infectiousness. Systematic imaging studies demonstrate correlations between the time and intensity of coughing, speaking, and laughing and the total droplet production, as an approximate indicator of the probability for disease spread. The sensors, deployed on COVID-19 patients along with healthy controls in both inpatient and home settings, record coughing frequency and intensity continuously, along with a collection of other biometrics. The results indicate a decaying trend of coughing frequency and intensity through the course of disease recovery, but with wide variations across patient populations. The methodology creates opportunities to study patterns in biometrics across individuals and among different demographic groups.

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Most cited references 39

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Real-time tracking of self-reported symptoms to predict potential COVID-19

Cristina Menni, Ana M. Valdes, Maxim Freidin … (2020)

A total of 2,618,862 participants reported their potential symptoms of COVID-19 on a smartphone-based app. Among the 18,401 who had undergone a SARS-CoV-2 test, the proportion of participants who reported loss of smell and taste was higher in those with a positive test result (4,668 of 7,178 individuals; 65.03%) than in those with a negative test result (2,436 of 11,223 participants; 21.71%) (odds ratio = 6.74; 95% confidence interval = 6.31–7.21). A model combining symptoms to predict probable infection was applied to the data from all app users who reported symptoms (805,753) and predicted that 140,312 (17.42%) participants are likely to have COVID-19.

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Covid-19: a remote assessment in primary care

Trisha Greenhalgh, Gerald Koh, Josip Car (2020)

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Is Open Access

On coughing and airborne droplet transmission to humans

Talib DBOUK, Dimitris Drikakis (2020)

Our understanding of the mechanisms of airborne transmission of viruses is incomplete. This paper employs computational multiphase fluid dynamics and heat transfer to investigate transport, dispersion, and evaporation of saliva particles arising from a human cough. An ejection process of saliva droplets in air was applied to mimic the real event of a human cough. We employ an advanced three-dimensional model based on fully coupled Eulerian–Lagrangian techniques that take into account the relative humidity, turbulent dispersion forces, droplet phase-change, evaporation, and breakup in addition to the droplet–droplet and droplet–air interactions. We computationally investigate the effect of wind speed on social distancing. For a mild human cough in air at 20 °C and 50% relative humidity, we found that human saliva-disease-carrier droplets may travel up to unexpected considerable distances depending on the wind speed. When the wind speed was approximately zero, the saliva droplets did not travel 2 m, which is within the social distancing recommendations. However, at wind speeds varying from 4 km/h to 15 km/h, we found that the saliva droplets can travel up to 6 m with a decrease in the concentration and liquid droplet size in the wind direction. Our findings imply that considering the environmental conditions, the 2 m social distance may not be sufficient. Further research is required to quantify the influence of parameters such as the environment’s relative humidity and temperature among others.

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Author and article information

Journal

Journal ID (nlm-ta): Proc Natl Acad Sci U S A

Journal ID (iso-abbrev): Proc Natl Acad Sci U S A

Journal ID (hwp): pnas

Journal ID (pmc): pnas

Journal ID (publisher-id): PNAS

Title: Proceedings of the National Academy of Sciences of the United States of America

Publisher: National Academy of Sciences

ISSN (Print): 0027-8424

ISSN (Electronic): 1091-6490

Publication date (Print): 11 May 2021

Publication date (Electronic): 23 April 2021

Publication date PMC-release: 23 April 2021

Volume: 118

Issue: 19

Electronic Location Identifier: e2026610118

Affiliations

[1] ^aQuerrey Simpson Institute for Bioelectronics, Northwestern University , Evanston, IL 60208;

[2] ^bDepartment of Mechanical Engineering and Materials Science, Duke University , Durham, NC 27708;

[3] ^cDepartment of Biomedical Engineering, Northwestern University , Evanston, IL 60208;

[4] ^dMedical Scientist Training Program, Feinberg School of Medicine, Northwestern University , Chicago, IL 60611;

[5] ^eCollege of Computing, Georgia Institute of Technology , Atlanta, GA 30332;

[6] ^fSibel Inc. , Niles, IL 60714;

[7] ^gSonica Health , Niles, IL 60714;

[8] ^hMax Nader Lab for Rehabilitation Technologies and Outcomes Research, Center for Bionic Medicine, Shirley Ryan AbilityLab , Chicago, IL 60611;

[9] ⁱCollege of Medicine, University of Illinois at Chicago , Chicago, IL 60612;

[10] ^jFeinberg School of Medicine, Northwestern University , Chicago, IL 60611;

[11] ^kDivision of Thoracic Surgery, Feinberg School of Medicine, Northwestern University , Chicago, IL 60611;

[12] ^lWearifi Inc. , Evanston, IL 60201;

[13] ^mDepartment of Materials Science and Engineering, Northwestern University , Evanston, IL 60208;

[14] ⁿSchool of Chemical Engineering, Sungkyunkwan University , Suwon, 16419, Republic of Korea;

[15] ^oDepartment of Mechanical Science and Engineering, University of Illinois at Urbana –Champaign , Champaign, IL 61801;

[16] ^pDepartment of Surgery, Feinberg School of Medicine, Northwestern University , Chicago, IL 60611;

[17] ^qDepartment of Dermatology, Feinberg School of Medicine, Northwestern University , Chicago, IL 60611;

[18] ^rDepartment of Mechanical Engineering, Northwestern University , Evanston, IL 60208;

[19] ^sDepartment of Chemistry, Northwestern University , Evanston, IL 60208;

[20] ^tDepartment of Electrical Engineering and Computer Science, Northwestern University , Evanston, IL 60208;

[21] ^uDepartment of Neurological Surgery, Northwestern University , Evanston, IL 60208

Author notes

²To whom correspondence may be addressed. Email: stevexu@ 123456northwestern.edu or jrogers@ 123456northwestern.edu .

Contributed by John A. Rogers, March 22, 2021 (sent for review January 11, 2021; reviewed by Metin Akay and Jun Chen)

Author contributions: X.N., W.O., H.J., J.-T.K., L.P.C., A.R.B., A.B., A.J., S.X., and J.A.R. designed research; X.N., W.O., H.J., J.-T.K., A.T., A.M., C.W., J.Y.L., M.K., C.K.M., M.P., N.S., J.-K.C., K.L., J.U.K., A.R.B, A.B., A.J., S.X., and J.A.R. performed research; X.N., W.O., J.-T.K., A.T., A.M., C.W., J.H., H.C., S.R., Y.W., F.L., Y.J.K., and J.A.R. analyzed data; and X.N., W.O., H.J., J.-T.K., S.X., and J.A.R. wrote the paper.

Reviewers: M.A., University of Houston; and J.C., University of California, Los Angeles.

¹X.N., W.O., and H.J. contributed equally to this work.