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

Abstract

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 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.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 10 6 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 (continued) 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 18participants wi...

A Method for Intelligent Allocation of Diagnostic Testing by Leveraging Data from Commercial Wearable Devices: A Case Study on COVID-19

“…For the subset of participants in our dataset with available symptom onset dates, daily RHR and step count that differed beyond two standard deviations from the baseline mean occurred as early as ve days before the symptom onset date (Supplementary Fig 1). Timelines for this and other real-world infection studies should be considered as rough estimates because exact dates of exposure and symptom onset are unknown, unlike in controlled infection studies [22], [27]. Our ndings, however, are consistent with the 2-14 day COVID-19 incubation period reported by the CDC [28].…”

“…The basis of the ITA method is the detection of physiological changes associated with infection onset, which are well-established to be detectable by biometric sensors [18], [22], [24]- [26], [32]- [35], [43], [44].…”

“…The rapid adoption of commercial wearable devices such as smartwatches and activity trackers brings forth opportunities to develop novel tools to support an intelligent disease detection infrastructure. Multiple studies suggest the utility of digital biomarkers, objective and quanti able digitally-collected physiological and behavioral data (e.g., resting heart rate, step count, sleep duration, and respiratory rate), collected by consumer devices along with patient-reported symptoms to monitor the progression of respiratory and in uenza-like illnesses [16]- [22].…”

Consumer-grade wearables identify changes in multiple physiological systems during COVID-19 disease progression

“… 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 Many of these approaches use a physiological signal from wearables, such as heart rate (HR), along with black box or machine learning methods to classify case status or disease progression. 22 , 23 , 24 …”

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“…Wearable sensors have already been established as tools to detect deviations from users’ physiological baselines 7 . Recent findings suggest that wearable sensors may also be able to detect infections caused by respiratory pathogens, including SARS- CoV-2, before or absent symptoms 8–10 . Alavi et al , for example, developed an algorithm that analyzes patterns in smartwatch- captured overnight resting heart rate and provides real-time alerts of potential presymptomatic and asymptomatic SARS- CoV-2 infection 10 .…”