Pre-symptomatic detection of COVID-19 from smartwatch data.
Level 3 - non-randomized controlled study
Prospective observational cohort with retrospective algorithmic anomaly detection
PubMed 33208926 · doi:10.1038/s41551-020-00640-6
What was done
Physiological and activity data (heart rate, daily steps, and sleep duration) were collected from consumer smartwatches within a cohort of nearly 5,300 participants. The study analyzed data from 32 individuals confirmed to have COVID-19. A two-tiered warning system based on elevations in resting heart rate relative to individual baselines was retrospectively modeled to evaluate potential real-time pre-symptomatic detection.
What was found
Of 32 individuals with COVID-19, 26 (81%) showed alterations in heart rate, steps, or sleep duration. Among 25 cases with detectable physiological changes and available symptom data, 22 were flagged before or at symptom onset, including 4 cases detected at least 9 days prior. The modeled resting heart rate alert system detected 63% of COVID-19 cases prior to symptom onset.
Why it matters
This demonstrates that continuous data from consumer smartwatches can capture physiological signals prior to COVID-19 symptom onset, suggesting potential utility for early respiratory infection surveillance.
Limits
The study is limited by a small sample of infected cases (n = 32). The real-time warning algorithm was evaluated retrospectively. The abstract does not report specificity, false-positive rates during non-infection periods, or the ability to distinguish COVID-19 from other infections.
Cited by
- supports In a study of 32 individuals wearing a Fitbit during COVID-19 infection, resting heart rate elevation was detected in 26 of 32 (81%) cases at or before symptom onset.
- supports Resting heart rate increases on average about 4 days prior to symptom onset in people infected with COVID-19.