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Assessment of the Feasibility of Using Noninvasive Wearable Biometric Monitoring Sensors to Detect Influenza and the Common Cold Before Symptom Onset.

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Date
2021-09
Authors
Grzesiak, Emilia
Bent, Brinnae
McClain, Micah T
Woods, Christopher W
Tsalik, Ephraim L
Nicholson, Bradly P
Veldman, Timothy
Burke, Thomas W
Gardener, Zoe
Bergstrom, Emma
Turner, Ronald B
Chiu, Christopher
Doraiswamy, P Murali
Hero, Alfred
Henao, Ricardo
Ginsburg, Geoffrey S
Dunn, Jessilyn
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(17 total)
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Abstract
<h4>Importance</h4>Currently, there are no presymptomatic screening methods to identify individuals infected with a respiratory virus to prevent disease spread and to predict their trajectory for resource allocation.<h4>Objective</h4>To evaluate the feasibility of using noninvasive, wrist-worn wearable biometric monitoring sensors to detect presymptomatic viral infection after exposure and predict infection severity in patients exposed to H1N1 influenza or human rhinovirus.<h4>Design, setting, and participants</h4>The cohort H1N1 viral challenge study was conducted during 2018; data were collected from September 11, 2017, to May 4, 2018. The cohort rhinovirus challenge study was conducted during 2015; data were collected from September 14 to 21, 2015. A total of 39 adult participants were recruited for the H1N1 challenge study, and 24 adult participants were recruited for the rhinovirus challenge study. Exclusion criteria for both challenges included chronic respiratory illness and high levels of serum antibodies. Participants in the H1N1 challenge study were isolated in a clinic for a minimum of 8 days after inoculation. The rhinovirus challenge took place on a college campus, and participants were not isolated.<h4>Exposures</h4>Participants in the H1N1 challenge study were inoculated via intranasal drops of diluted influenza A/California/03/09 (H1N1) virus with a mean count of 106 using the median tissue culture infectious dose (TCID50) assay. Participants in the rhinovirus challenge study were inoculated via intranasal drops of diluted human rhinovirus strain type 16 with a count of 100 using the TCID50 assay.<h4>Main outcomes and measures</h4>The primary outcome measures included cross-validated performance metrics of random forest models to screen for presymptomatic infection and predict infection severity, including accuracy, precision, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC).<h4>Results</h4>A total of 31 participants with H1N1 (24 men [77.4%]; mean [SD] age, 34.7 [12.3] years) and 18 participants with rhinovirus (11 men [61.1%]; mean [SD] age, 21.7 [3.1] years) were included in the analysis after data preprocessing. Separate H1N1 and rhinovirus detection models, using only data on wearble devices as input, were able to distinguish between infection and noninfection with accuracies of up to 92% for H1N1 (90% precision, 90% sensitivity, 93% specificity, and 90% F1 score, 0.85 [95% CI, 0.70-1.00] AUC) and 88% for rhinovirus (100% precision, 78% sensitivity, 100% specificity, 88% F1 score, and 0.96 [95% CI, 0.85-1.00] AUC). The infection severity prediction model was able to distinguish between mild and moderate infection 24 hours prior to symptom onset with an accuracy of 90% for H1N1 (88% precision, 88% sensitivity, 92% specificity, 88% F1 score, and 0.88 [95% CI, 0.72-1.00] AUC) and 89% for rhinovirus (100% precision, 75% sensitivity, 100% specificity, 86% F1 score, and 0.95 [95% CI, 0.79-1.00] AUC).<h4>Conclusions and relevance</h4>This cohort study suggests that the use of a noninvasive, wrist-worn wearable device to predict an individual's response to viral exposure prior to symptoms is feasible. Harnessing this technology would support early interventions to limit presymptomatic spread of viral respiratory infections, which is timely in the era of COVID-19.
Type
Journal article
Subject
Humans
Rhinovirus
Common Cold
Mass Screening
Early Diagnosis
Biological Assay
Severity of Illness Index
Area Under Curve
Sensitivity and Specificity
Cohort Studies
Feasibility Studies
Biometry
Virus Shedding
Models, Biological
Adult
Female
Male
Influenza, Human
Influenza A Virus, H1N1 Subtype
Young Adult
Wearable Electronic Devices
Permalink
https://hdl.handle.net/10161/23938
Published Version (Please cite this version)
10.1001/jamanetworkopen.2021.28534
Publication Info
Grzesiak, Emilia; Bent, Brinnae; McClain, Micah T; Woods, Christopher W; Tsalik, Ephraim L; Nicholson, Bradly P; ... Dunn, Jessilyn (2021). Assessment of the Feasibility of Using Noninvasive Wearable Biometric Monitoring Sensors to Detect Influenza and the Common Cold Before Symptom Onset. JAMA network open, 4(9). pp. e2128534. 10.1001/jamanetworkopen.2021.28534. Retrieved from https://hdl.handle.net/10161/23938.
This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.
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Scholars@Duke

Doraiswamy

P. Murali Doraiswamy

Professor of Psychiatry and Behavioral Sciences
Murali Doraiswamy MBBS FRCP is Professor of Psychiatry and Professor in Medicine at Duke University School of Medicine where he is a highly cited physician scientist at the Duke Institute for Brain Sciences.  He is also a Senior Fellow at the Duke Center for the Study of Aging and an Affiliate Faculty at the Duke Center for Precision Medicine and Applied Genomics as well as the Duke Microbiome Center.  He directs a clinical trials unit that has been involved in the development of ma
Dunn

Jessilyn Dunn

Assistant Professor of Biomedical Engineering
Developing new tools and infrastructure for multi-modal biomedical data integration to drive precision/personalized methods for early detection, intervention, and prevention of disease.
Ginsburg

Geoffrey Steven Ginsburg

Adjunct Professor in the Department of Medicine
Dr. Geoffrey S. Ginsburg's research interests are in the development of novel paradigms for developing and translating genomic information into medical practice and the integration of personalized medicine into health care.
Henao

Ricardo Henao

Associate Professor in Biostatistics & Bioinformatics
McClain

Micah Thomas McClain

Associate Professor of Medicine
Tsalik

Ephraim Tsalik

Adjunct Associate Professor in the Department of Medicine
My research at Duke has focused on understanding the dynamic between host and pathogen so as to discover and develop host-response markers that can diagnose and predict health and disease.  This new and evolving approach to diagnosing illness has the potential to significantly impact individual as well as public health considering the rise of antibiotic resistance. With any potential infectious disease diagnosis, it is difficult, if not impossible, to determine at the time of pre
Woods

Christopher Wildrick Woods

Wolfgang Joklik Distinguished Professor of Global Health
1. Emerging Infections 2. Global Health 3. Epidemiology of infectious diseases 4. Clinical microbiology and diagnostics 5. Bioterrorism Preparedness 6. Surveillance for communicable diseases 7. Antimicrobial resistance
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