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; ... (2022). 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). BMJ open, 12(6), e058274. BMJ Publishing Group 10.1136/bmjopen-2021-058274

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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.

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5 Citations in Web of Science ®
6 Citations in Scopus

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