Real-time alerting system for COVID-19 and other stress events using wearable data
Level 3 - non-randomized controlled study
Prospective cohort study evaluating an alerting algorithm without randomized controls
OpenAlex W3215572702 · doi:10.1038/s41591-021-01593-2
What was done
Researchers developed an online smartwatch-based detection system tracking continuous physiological and activity signals (heart rate and step counts) to detect early aberrant patterns. The system was prospectively tested in a cohort of 3,318 participants to evaluate its ability to trigger real-time alerts for pre-symptomatic and asymptomatic SARS-CoV-2 infections, with participant survey data used to evaluate other potential non-infection triggers.
What was found
Among 84 participants infected with SARS-CoV-2, the alerting system successfully flagged pre-symptomatic or asymptomatic infection in 67 individuals (80%). Signals occurred at a median of 3 days before symptom onset. While other events (such as other respiratory infections, psychological stress, alcohol intake, and travel) also generated alerts, they did so at a lower mean frequency of 1.15 alert days per person compared to 3.42 alert days per person in COVID-19 cases.
Why it matters
This demonstrates the feasibility of using continuous, consumer-grade wearable data to provide early advance warnings of viral respiratory infections several days before clinical symptom onset.
Limits
The study is observational and relies on self-reported surveys for symptom timing and non-infection triggers. The algorithm lacks specificity for SARS-CoV-2, as baseline physiological stressors (alcohol, travel, stress) also produced false-positive alerts, and 20% of confirmed infections were missed.
Cited by
- supports A study conducted by Oura and Stanford showed that biometric data from the Oura Ring could detect infection onset days before clinical symptoms appeared.