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)

Abstract

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.

Keywords: COVID-19; Health & safety; Health informatics; Infection control; Public health; VIROLOGY.

Figure 1

COVI-GAPP participants (n=1163) wore a certified medical device at night while they slept, syncing it to a complementary smartphone application on waking. The device and app were originally designed for fertility tracking in naturally menstruating women but adapted for the purposes of this study. Instead of real-time fertility indications, participants saw ‘Fertility Unknown’ on syncing (A). Additionally, the in-app daily diary asked participants about potential confounds (B) and COVID-19 symptoms (C) rather than fertility-related questions.

Figure 2

Recurrent neural network (RNN) architecture for the detection of a presymptomatic case of COVID-19. The RNN consisted of two hidden layers and one output layer. The first hidden layer contained 16 and second layer contained 64 long short-term memory (LSTM) units. The LSTM output activation was a sigmoid function, while the recurrent activation on hidden layers was the rectified linear unit function. The input of RNN was eight consecutive values of physiological signal originating from eight consecutive nights of data. The output was an indication about the potential COVID-19 infection.

Figure 3

Class depiction based on the recurrent neural network (RNN). Here, class 0 represents healthy days and class 1 represents the presymptomatic phase of COVID-19 (SO-10 to SO-2). Vectors of marked classes represent training input for the RNN. SO, symptom onset.

Figure 4

Study flow chart. From 2170 GAPP participants, 1163 participants were enrolled in the COVI-GAPP study. A total of 127 participants presented laboratory-confirmed COVID-19 disease and from these, a total of 66 positive tested participants had complete bracelet data available used for the algorithm development.

Figure 5

The wearable device can detect changes in five physiological parameters across the clinical course of COVID-19. The values of each physiological parameter (with 95% CIs) collapsed across individuals (n=66) were normalised using baseline measurements and are shown centred around participant-reported symptom onset (SO). SDNN, SD of the normal-to-normal interval.