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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).

Authors:
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 Kovacevic Andjela Markovic Paul Klaver Timo B Brakenhoff Billy Franks Marianna Mitratza George S Downward Ariel Dowling Santiago Montes Diederick E Grobbee Maureen Cronin David Conen Brianna M Goodale Lorenz Risch

BMJ Open 2022 Jun 21;12(6):e058274. Epub 2022 Jun 21.

Dr Risch Medical Laboratory, Vaduz, Liechtenstein

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: Wearable sensor technology can enable COVID-19 detection during the presymptomatic period. Our proposed RNN algorithm identified 68% of COVID-19 positive participants 2 days prior to SO and will be further trained and validated in a randomised, single-blinded, two-period, two-sequence crossover trial. ISRCTN51255782; Pre-results.

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http://dx.doi.org/10.1136/bmjopen-2021-058274DOI Listing
June 2022

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