Postdoctoral researcher in the Snyder Lab. My research focuses on the human gut microbiome, and I am involved in multiple multiomic projects investigating how physiological systems through the human body interact across different lifestyles and health states. I perform both wet and dry lab aspects of multiomics analyses, and am involved in two coronavirus research projects including handling of positive SARS-COV-2 samples.
Michael Snyder, Postdoctoral Faculty Sponsor
Pre-symptomatic detection of COVID-19 from smartwatch data.
Nature biomedical engineering
Consumer wearable devices that continuously measure vital signs have been used to monitor the onset of infectious disease. Here, we show that data from consumer smartwatches can be used for the pre-symptomatic detection of coronavirus disease 2019 (COVID-19). We analysed physiological and activity data from 32 individuals infected with COVID-19, identified from a cohort of nearly 5,300 participants, and found that 26 of them (81%) had alterations in their heart rate, number of daily steps or time asleep. Of the 25 cases of COVID-19 with detected physiological alterations for which we had symptom information, 22 were detected before (or at) symptom onset, with four cases detected at least nine days earlier. Using retrospective smartwatch data, we show that 63% of the COVID-19 cases could have been detected before symptom onset in real time via a two-tiered warning system based on the occurrence of extreme elevations in resting heart rate relative to the individual baseline. Our findings suggest that activity tracking and health monitoring via consumer wearable devices may be used for the large-scale, real-time detection of respiratory infections, often pre-symptomatically.
View details for DOI 10.1038/s41551-020-00640-6
View details for PubMedID 33208926