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)

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)

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...

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Main Authors: Stefanie Aeschbacher, David Conen, Diederick E Grobbee, Raphael Twerenbold, Thomas Lung, Theo Rispens, Jakob Kjellberg, Lorenz Risch, Martin Risch, Marianna Mitratza, Harald Renz, Spiros Denaxas, Billy Franks, Diederick Grobbee, Martina Rothenbühler, Janneke Wijgert, Santiago Montes, Richard Dobson, Hans Reitsma, Christian Simon, Titia Leurink, Charisma Hehakaya, Patricia Bruijning, Kirsten Grossmann, Ornella C Weideli, Marc Kovac, Fiona Pereira, Nadia Wohlwend, Corina Risch, Dorothea Hillmann, Daniel Leibovitz, Vladimir Kovacevic, Andjela Markovic, Paul Klaver, Timo B Brakenhoff, George S Downward, Ariel Dowling, Maureen Cronin, Brianna M Goodale, Brianna Goodale, Ornella Weideli, Regien Stokman, Hans Van Dijk, Eric Houtman, Jon Bouwman, Kay Hage, Lotte Smets, Marcel van Willigen, Maui Chodura, Niki de Vink, Tessa Heikamp, Timo Brakenhoff, Wendy van Scherpenzeel, Wout Aarts, Alison Kuchta, Antonella Chiucchiuini, Steve Emby, Annemarijn Douwes, George Downward, Nathalie Vigot, Pieter Stolk, Duco Veen, Daniel Oberski, Amos Folarin, Pablo Fernandez Medina, Eskild Fredslund
Format: Article
Language:English
Published: BMJ Publishing Group 2022-06-01
Series:BMJ Open
Online Access:https://bmjopen.bmj.com/content/12/6/e058274.full
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Summary: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.Trial registration numberISRCTN51255782; Pre-results.
ISSN:2044-6055

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