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)

TY - JOUR

T1 - 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)

AU - Risch, Martin

AU - Grossmann, Kirsten

AU - Aeschbacher, Stefanie

AU - Weideli, Ornella C

AU - Kovac, Marc

AU - Pereira, Fiona

AU - Wohlwend, Nadia

AU - Risch, Corina

AU - Hillmann, Dorothea

AU - Lung, Thomas

AU - Renz, Harald

AU - Twerenbold, Raphael

AU - Rothenbühler, Martina

AU - Leibovitz, Daniel

AU - Kovacevic, Vladimir

AU - Markovic, Andjela

AU - Klaver, Paul

AU - Brakenhoff, Timo B

AU - Franks, Billy

AU - Mitratza, Marianna

AU - Downward, George S

AU - Dowling, Ariel

AU - Montes, Santiago

AU - Grobbee, Diederick E

AU - Cronin, Maureen

AU - Conen, David

AU - Goodale, Brianna M

AU - Risch, Lorenz

AU - COVID-19 remote early detection (COVID-RED) consortium

N1 - © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

PY - 2022/6/21

Y1 - 2022/6/21

N2 - 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 number ISRCTN51255782; Pre-results.

AB - 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 number ISRCTN51255782; Pre-results.

KW - Adult

KW - COVID-19/diagnosis

KW - Cohort Studies

KW - Humans

KW - Middle Aged

KW - Prospective Studies

KW - SARS-CoV-2

U2 - 10.1136/bmjopen-2021-058274

DO - 10.1136/bmjopen-2021-058274

M3 - SCORING: Journal article

C2 - 35728900

VL - 12

JO - BMJ OPEN

JF - BMJ OPEN

SN - 2044-6055

IS - 6

M1 - e058274

ER -