Investigation of the use of a sensor bracelet for the presymptomatic detection of changes in physiological parameters related to COVID-19: an interim analysis of a prospective cohort study (COVI-GAPP)
Martin Risch, Kirsten Grossmann, Stefanie Aeschbacher, Ornella C. Weideli, Marc Kovac, Fiona Pereira, Nadia Wohlwend, Corina Risch, Dorothea Hillmann, Thomas Lung, Harald Renz, Raphael Twerenbold, Martina Rothenbühler, Daniel Leibovitz, Vladimir Kovačević, Andjela Markovic, Paul Klaver, Timo B. Brakenhoff, Billy Franks, Marianna Mitratza, George S. Downward, Ariel V. Dowling, Santiago Montes, Diederick E. Grobbee, Maureen Cronin, David Conen, Brianna M. Goodale, Lorenz Risch
Abstract
OBJECTIVES: We investigated machinelearningbased identification of presymptomatic COVID-19 and detection of infection-related changes in physiology using a wearable device. DESIGN: Interim analysis of a prospective cohort study. SETTING, PARTICIPANTS AND INTERVENTIONS: Participants from a national cohort study in Liechtenstein were included. Nightly they wore the Ava-bracelet that measured respiratory rate (RR), heart rate (HR), HR variability (HRV), wrist-skin temperature (WST) and skin perfusion. SARS-CoV-2 infection was diagnosed by molecular and/or serological assays. RESULTS: A total of 1.5 million hours of physiological data were recorded from 1163 participants (mean age 44±5.5 years). COVID-19 was confirmed in 127 participants of which, 66 (52%) had worn their device from baseline to symptom onset (SO) and were included in this analysis. Multi-level modelling revealed significant changes in five (RR, HR, HRV, HRV ratio and WST) device-measured physiological parameters during the incubation, presymptomatic, symptomatic and recovery periods of COVID-19 compared with baseline. The training set represented an 8-day long instance extracted from day 10 to day 2 before SO. The training set consisted of 40 days measurements from 66 participants. Based on a random split, the test set included 30% of participants and 70% were selected for the training set. The developed long short-term memory (LSTM) based recurrent neural network (RNN) algorithm had a recall (sensitivity) of 0.73 in the training set and 0.68 in the testing set when detecting COVID-19 up to 2 days prior to SO. CONCLUSION: ISRCTN51255782; Pre-results.