Assessment of the Feasibility of Using Noninvasive Wearable Biometric Monitoring Sensors to Detect Influenza and the Common Cold Before Symptom Onset | Infectious Diseases | JAMA Network Open | JAMA Network
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    Original Investigation
    Health Informatics
    September 29, 2021

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

    Author Affiliations
    • 1Biomedical Engineering Department, Duke University, Durham, North Carolina
    • 2Duke Center for Applied Genomics and Precision Medicine, Duke University Medical Center, Durham, North Carolina
    • 3Durham Veterans Affairs Medical Center, Durham, North Carolina
    • 4Department of Medicine, Duke Global Health Institute, Durham, North Carolina
    • 5Department of Infectious Disease, Imperial College London, London, United Kingdom
    • 6Department of Pediatrics, University of Virginia School of Medicine, Charlottesville
    • 7Department of Psychiatry, Duke University School of Medicine, Durham, North Carolina
    • 8Department of Medicine, Duke University School of Medicine, Durham, North Carolina
    • 9Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor
    • 10Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina
    JAMA Netw Open. 2021;4(9):e2128534. doi:10.1001/jamanetworkopen.2021.28534
    Key Points

    Question  Can noninvasive, wrist-worn wearable devices detect acute viral respiratory infection and predict infection severity before symptom onset?

    Findings  In a cohort study of 31 participants inoculated with H1N1 and 18 participants with rhinovirus, infection detection and severity prediction models trained using data on wearable devices were able to distinguish between infection and noninfection with 92% accuracy for H1N1 and 88% accuracy for rhinovirus and were able to distinguish between mild and moderate infection 24 hours prior to symptom onset with 90% accuracy for H1N1 and 89% accuracy for rhinovirus.

    Meaning  This study suggests that the use of wearable devices to identify individuals with presymptomatic acute viral respiratory infection is feasible; because wearable devices are common in the general population, using them for infection screening may help limit the spread of contagion.


    Importance  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.

    Objective  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.

    Design, Setting, and Participants  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.

    Exposures  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.

    Main Outcomes and Measures  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).

    Results  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).

    Conclusions and Relevance  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.