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)
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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). / Risch, Martin; Grossmann, Kirsten; Aeschbacher, Stefanie; Weideli, Ornella C; Kovac, Marc; Pereira, Fiona; Wohlwend, Nadia; Risch, Corina; Hillmann, Dorothea; Lung, Thomas; Renz, Harald; Twerenbold, Raphael; Rothenbühler, Martina; Leibovitz, Daniel; Kovacevic, Vladimir; Markovic, Andjela; Klaver, Paul; Brakenhoff, Timo B; Franks, Billy; Mitratza, Marianna; Downward, George S; Dowling, Ariel; Montes, Santiago; Grobbee, Diederick E; Cronin, Maureen; Conen, David; Goodale, Brianna M; Risch, Lorenz; COVID-19 remote early detection (COVID-RED) consortium.
in: BMJ OPEN, Jahrgang 12, Nr. 6, e058274, 21.06.2022.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
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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 -