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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. 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 2021-11-01T13:18:38Z 2021-11-01T13:18:38Z 2021-09
dc.identifier 2784555
dc.identifier.issn 2574-3805
dc.identifier.issn 2574-3805
dc.description.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.
dc.language eng
dc.publisher American Medical Association (AMA)
dc.relation.ispartof JAMA network open
dc.relation.isversionof 10.1001/jamanetworkopen.2021.28534
dc.subject Humans
dc.subject Rhinovirus
dc.subject Common Cold
dc.subject Mass Screening
dc.subject Early Diagnosis
dc.subject Biological Assay
dc.subject Severity of Illness Index
dc.subject Area Under Curve
dc.subject Sensitivity and Specificity
dc.subject Cohort Studies
dc.subject Feasibility Studies
dc.subject Biometry
dc.subject Virus Shedding
dc.subject Models, Biological
dc.subject Adult
dc.subject Female
dc.subject Male
dc.subject Influenza, Human
dc.subject Influenza A Virus, H1N1 Subtype
dc.subject Young Adult
dc.subject Wearable Electronic Devices
dc.title Assessment of the Feasibility of Using Noninvasive Wearable Biometric Monitoring Sensors to Detect Influenza and the Common Cold Before Symptom Onset.
dc.type Journal article McClain, Micah T|0327296 Woods, Christopher W|0075873 Tsalik, Ephraim L|0373391 Doraiswamy, P Murali|0114799 Henao, Ricardo|0570074 Ginsburg, Geoffrey S|0331881 Dunn, Jessilyn|0391498 2021-11-01T13:18:37Z
pubs.begin-page e2128534
pubs.issue 9
pubs.organisational-group School of Medicine
pubs.organisational-group Nursing
pubs.organisational-group Duke Cancer Institute
pubs.organisational-group Biostatistics & Bioinformatics
pubs.organisational-group Pathology
pubs.organisational-group Medicine, Cardiology
pubs.organisational-group Duke
pubs.organisational-group School of Nursing
pubs.organisational-group Institutes and Centers
pubs.organisational-group Basic Science Departments
pubs.organisational-group Clinical Science Departments
pubs.organisational-group Medicine
pubs.organisational-group Medicine, Infectious Diseases
pubs.organisational-group Duke Global Health Institute
pubs.organisational-group University Institutes and Centers
pubs.organisational-group Institutes and Provost's Academic Units
pubs.organisational-group Molecular Genetics and Microbiology
pubs.publication-status Published
pubs.volume 4
duke.contributor.orcid Woods, Christopher W|0000-0001-7240-2453
duke.contributor.orcid Tsalik, Ephraim L|0000-0002-6417-2042
duke.contributor.orcid Henao, Ricardo|0000-0003-4980-845X
duke.contributor.orcid Ginsburg, Geoffrey S|0000-0003-4739-9808
duke.contributor.orcid Dunn, Jessilyn|0000-0002-3241-8183

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