Real-time infection prediction with wearable physiological monitoring and AI to aid military workforce readiness during COVID-19

Infectious threats, like the COVID-19 pandemic, hinder maintenance of a productive and healthy workforce. If subtle physiological changes precede overt illness, then proactive isolation and testing can reduce labor force impacts. This study hypothesized that an early infection warning service based on wearable physiological monitoring and predictive models created with machine learning could be developed and deployed. We developed a prototype tool, first deployed June 23, 2020, that delivered continuously updated scores of infection risk for SARS-CoV-2 through April 8, 2021. Data were acquired from 9381 United States Department of Defense (US DoD) personnel wearing Garmin and Oura devices, totaling 599,174 user-days of service and 201 million hours of data. There were 491 COVID-19 positive cases. A predictive algorithm identified infection before diagnostic testing with an AUC of 0.82. Barriers to implementation included adequate data capture (at least 48% data was needed) and delays in data transmission. We observe increased risk scores as early as 6 days prior to diagnostic testing (2.3 days average). This study showed feasibility of a real-time risk prediction score to minimize workforce impacts of infection.

Automated summary

This study developed a prototype tool for an early infection warning service using wearable physiological monitoring and predictive models. The tool delivers continuously updated scores of infection risk for SARS-CoV-2 and showed the feasibility of real-time risk prediction. Data from 9381 Department of Defense personnel wearing Garmin and Oura devices were used, with 491 COVID-19 positive cases identified. The predictive algorithm had an AUC of 0.82 in identifying infection before diagnostic testing.