TY - JOUR
T1 - Investigation of the use of a sensor bracelet for the presymptomatic detection of changes in physiological parameters related to COVID-19
T2 - an interim analysis of a prospective cohort study (COVI-GAPP)
AU - COVID-19 remote early detection (COVID-RED) consortium
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
N1 - Funding Information:
The COVI-GAPP study received grants from the Innovative Medicines Initiative (IMI grant agreement number 101005177), the Princely House of Liechtenstein, the government of the Principality of Liechtenstein, and the Hanela Foundation in Aarau (Switzerland). None of the funders played a role in the study design, data collection, data analysis, data interpretation, writing of the report, or decision to publish.
Publisher Copyright:
© 2022 BMJ Publishing Group. All rights reserved.
PY - 2022/6/1
Y1 - 2022/6/1
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 - COVID-19
KW - Health & safety
KW - Health informatics
KW - Infection control
KW - Public health
KW - VIROLOGY
UR - http://www.scopus.com/inward/record.url?scp=85132271399&partnerID=8YFLogxK
U2 - 10.1136/bmjopen-2021-058274
DO - 10.1136/bmjopen-2021-058274
M3 - Article
C2 - 35728900
SN - 2044-6055
VL - 12
SP - 1
EP - 12
JO - BMJ Open
JF - BMJ Open
IS - 6
M1 - e058274
ER -