Reviewed by Dr. Michael Payne, MD; London Health Sciences Centre
COVID-19 transmission is challenging to interrupt at a community and health care facility level as individuals are infectious prior to the onset of symptoms (pre-symptomatic) and a proportion of infections are asymptomatic. Large scale asymptomatic testing of patients and staff have been undertaken during the pandemic to identify cases prior to the onset of symptoms and control transmission, but these interventions are costly and require testing at high frequency intervals to detect infections in an actionable timeframe. This study challenged 20 healthy adults with H3N2 influenza A virus and prospectively monitored from 7 days before through 10 days after inoculation, using wearable electrocardiogram and physical activity sensors. 85% of participants developed infection, the algorithm correctly identified 16 of 17 (94%) positive pre-symptomatic and asymptomatic individuals, on average 58 hours post inoculation and 23 hours before the symptom onset. This study shows promise for the early detection of respiratory illness as the algorithm is compatible with data collected from smartwatches. This study involved healthy adults and may not perform as well in patient populations who are admitted to health care facilities with other illnesses/co-morbidities.
Temple, et al. Wearable sensor-based detection of influenza in pre-symptomatic and asymptomatic individuals. The Journal of Infectious Diseases, 27 June 2022, jiac262, https://doi.org/10.1093/infdis/jiac262