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  3. 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).

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BORIS DOI
10.48350/170795
Date of Publication
June 21, 2022
Publication Type
Article
Division/Institute

Universitätsinstitut ...

Contributor
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, Lorenzorcid-logo
Universitätsinstitut für Klinische Chemie (UKC)
Subject(s)

600 - Technology::610...

Series
BMJ open
ISSN or ISBN (if monograph)
2044-6055
Publisher
BMJ Publishing Group
Language
English
Publisher DOI
10.1136/bmjopen-2021-058274
PubMed ID
35728900
Uncontrolled Keywords

COVID-19 Health & saf...

Description
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.
Handle
https://boris-portal.unibe.ch/handle/20.500.12422/85727
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e058274.full.pdftextAdobe PDF1.2 MBAttribution-NonCommercial (CC BY-NC 4.0)publishedOpen
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