Wearable Devices Could Predict SARS-CoV-2

Researchers from the Icahn School of Medicine at Mount Sinai in the USA have developed a digital platform to identify SARS-CoV-2 infection in healthcare personnel. The heart rate variability was measured using a novel smartphone app installed in a commercial wearable device. About 30-45% of individuals infected with SARS-CoV-2 remain asymptomatic, which increases the viral spread. Physiological parameters like heart rate, sleep pattern, etc. can be measured with wearable devices.

Heart rate variability is one such parameter that provides important information about crosstalk between parasympathetic and sympathetic nervous systems. This crosstalk plays a vital role in modulating cardiac contractility and beat-to-beat interval variability. A low HRV, which indicates enhanced sympathetic balance, is known to be a good predictor of infection state. The participants in the study were asked to wear a device that measures the heart rate variability for at least 8 hours a day and data relating to their symptoms was collected daily.

The circadian pattern of heart rate variability, especially the standard deviation of N-N interval also known as R-R interval was found to differ significantly between participants with and without COVID-19. A significant variation in this parameter was also observed in participants from 7 days before to 7 days after the COVID-19 diagnosis. After 7-14 days of COVID-19 diagnosis, this circadian pattern of HRV tended to normalize. Significant changes in the circadian HRV patterns were noted on the first day of symptom onset. 

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Ref link: https://www.medrxiv.org/content/10.1101/2020.11.06.20226803v1