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
Date of Publication
June 21, 2022
Publication Type
Article
Division/Institute
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 |
Subject(s)
Series
BMJ open
ISSN or ISBN (if monograph)
2044-6055
Publisher
BMJ Publishing Group
Language
English
Publisher DOI
PubMed ID
35728900
Uncontrolled Keywords
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.
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.
File(s)
| File | File Type | Format | Size | License | Publisher/Copright statement | Content | |
|---|---|---|---|---|---|---|---|
| e058274.full.pdf | text | Adobe PDF | 1.2 MB | Attribution-NonCommercial (CC BY-NC 4.0) | published |