A group led by Dr. Daniel Forger showed that decomposing heart rate from low-cost wearable technologies into signals from different systems can give a multidimensional description of physiological changes due to COVID-19 infection.
Their findings indicate that multiple separate physiological features and autocorrelation of heart rate are significantly altered in COVID disease and can classify symptomatic versus healthy periods. Increased heart rate and autocorrelation begin at symptom onset, while the heart rate response to activity increases soon after symptom onset and increases more in individuals experiencing coughing symptoms. Symptom onset is associated with a blunting of circadian variation in heart rate, as measured by the uncertainty in the phase estimate. This work establishes an innovative data analytic approach to monitor disease progression remotely using consumer-grade wearable devices.
Read the full report, "Consumer-grade wearables identify changes in multiple physiological systems during COVID-19 disease progression" in Cell Reports Medicine.