Wearable Sensor-Based Detection of Influenza in Presymptomatic and Asymptomatic Individuals

Abstract

Background: The COVID-19 pandemic highlighted the need for early detection of viral infections in symptomatic and asymptomatic individuals to allow for timely clinical management and public health interventions.

Methods: Twenty healthy adults were challenged with an influenza A (H3N2) virus and prospectively monitored from 7 days before through 10 days after inoculation, using wearable electrocardiogram and physical activity sensors. This framework allowed for responses to be accurately referenced to the infection event. For each participant, we trained a semisupervised multivariable anomaly detection model on data acquired before inoculation and used it to classify the postinoculation dataset.

Results: Inoculation with this challenge virus was well-tolerated with an infection rate of 85%. With the model classification threshold set so that no alarms were recorded in the 170 healthy days recorded, the algorithm correctly identified 16 of 17 (94%) positive presymptomatic and asymptomatic individuals, on average 58 hours postinoculation and 23 hours before the symptom onset.

Conclusions: The data processing and modeling methodology show promise for the early detection of respiratory illness. The detection algorithm is compatible with data collected from smartwatches using optical techniques but needs to be validated in large heterogeneous cohorts in normal living conditions. Clinical Trials Registration. NCT04204493.

Keywords: COVID-19; ECG; heart rate monitoring; heart rate variability; influenza; viral respiratory infection; wearable sensors.

Conflict of interest statement

Potential conflicts of interest. All authors: No reported conflicts of interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.

© The Author(s) 2022. Published by Oxford University Press on behalf of Infectious Diseases Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Figures

Figure 1.

Mean daily total symptom score for symptomatic, asymptomatic, and uninfected individuals. Day 0 is the day of inoculation. Error bars represent standard deviation.

Figure 2.

Acquired data plotted as functions of time for a symptomatic participant FC001; t = 0 marks the timing of the inoculation. (A) Interbeat interval (IBI) averaged in 5-minute epochs; (B) total symptom score (TSS); (C) activity averaged in 5-minute epochs and timing of sleep (S), acceleration due to gravity (g); and (D) z-score for IBI, matched for activity.

Figure 3.

Standardized values of IBI and selected HRV metrics: (left) positive symptomatic participant FC001; (center) positive asymptomatic participant FC007; (right) participant FC002 who tested negative for the H3N2 virus. Abbreviations: TSS, total symptom score; z-HF, z-score high frequency; z-IBI, z-score interbeat interval; z-LF, z-score low frequency.

Figure 4.

Twenty-four–hour mean values of z-scores for interbeat interval (z-IBI), low frequency (z-LF), high frequency (z-HF), and z-LF/HF ratio averaged across all H3N2-positive subjects in the study. Error bars indicate standard error of the mean.

Figure 5.

Examples of Hotelling T2 statistic plotted as a function of time for symptomatic (FC001) and asymptomatic (FC007) positive participants, and for a negative (FC002) participant. Abbreviation: CL, control limit.

Figure 6.

Timing of alerts (triangles) relative to symptom onset for all study participants using a threshold of 15.