Assessment of the Feasibility of Using Noninvasive Wearable Biometric Monitoring Sensors to Detect Influenza and the Common Cold Before Symptom Onset.
dc.contributor.author | Grzesiak, Emilia | |
dc.contributor.author | Bent, Brinnae | |
dc.contributor.author | McClain, Micah T | |
dc.contributor.author | Woods, Christopher W | |
dc.contributor.author | Tsalik, Ephraim L | |
dc.contributor.author | Nicholson, Bradly P | |
dc.contributor.author | Veldman, Timothy | |
dc.contributor.author | Burke, Thomas W | |
dc.contributor.author | Gardener, Zoe | |
dc.contributor.author | Bergstrom, Emma | |
dc.contributor.author | Turner, Ronald B | |
dc.contributor.author | Chiu, Christopher | |
dc.contributor.author | Doraiswamy, P Murali | |
dc.contributor.author | Hero, Alfred | |
dc.contributor.author | Henao, Ricardo | |
dc.contributor.author | Ginsburg, Geoffrey S | |
dc.contributor.author | Dunn, Jessilyn | |
dc.date.accessioned | 2021-11-01T13:18:38Z | |
dc.date.available | 2021-11-01T13:18:38Z | |
dc.date.issued | 2021-09 | |
dc.date.updated | 2021-11-01T13:18:37Z | |
dc.description.abstract | ImportanceCurrently, 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.ObjectiveTo 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.Design, setting, and participantsThe 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.ExposuresParticipants 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.Main outcomes and measuresThe 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).ResultsA 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).Conclusions and relevanceThis 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.identifier | 2784555 | |
dc.identifier.issn | 2574-3805 | |
dc.identifier.issn | 2574-3805 | |
dc.identifier.uri | ||
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 | |
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 | |
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 |
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