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Randomized Controlled Trial
. 2025 Jun 5;20(6):e0325116.
doi: 10.1371/journal.pone.0325116. eCollection 2025.

Remote early detection of SARS-CoV-2 infections using a wearable-based algorithm: Results from the COVID-RED study, a prospective randomised single-blinded crossover trial

Laura C Zwiers  1   2 , Timo B Brakenhoff  1 , Brianna M Goodale  1   3 , Duco Veen  4   5 , George S Downward  2   6 , Vladimir Kovacevic  3   7 , Andjela Markovic  3   8 , Marianna Mitratza  2 , Marcel van Willigen  1 , Billy Franks  1 , Janneke van de Wijgert  9 , Santiago Montes  10 , Serkan Korkmaz  11 , Jakob Kjellberg  11 , Lorenz Risch  12   13   14 , David Conen  15 , Martin Risch  12   16 , Kirsten Grossman  12   13 , Ornella C Weideli  12   13 , Theo Rispens  17   18 , Jon Bouwman  1 , Amos A Folarin  19   20   21 , Xi Bai  19 , Richard Dobson  19 , Maureen Cronin  3 , Diederick E Grobbee  1   2 ; COVID-RED consortium
Affiliations
Randomized Controlled Trial

Remote early detection of SARS-CoV-2 infections using a wearable-based algorithm: Results from the COVID-RED study, a prospective randomised single-blinded crossover trial

Laura C Zwiers et al. PLoS One. .

Abstract

Background: Rapid and early detection of SARS-CoV-2 infections, especially during the pre- or asymptomatic phase, could aid in reducing virus spread. Physiological parameters measured by wearable devices can be efficiently analysed to provide early detection of infections. The COVID-19 Remote Early Detection (COVID-RED) trial investigated the use of a wearable device (Ava bracelet) for improved early detection of SARS-CoV-2 infections in real-time.

Trial design: Prospective, single-blinded, two-period, two-sequence, randomised controlled crossover trial.

Methods: Subjects wore a medical device and synced it with a mobile application in which they also reported symptoms. Subjects in the experimental condition received real-time infection indications based on an algorithm using both wearable device and self-reported symptom data, while subjects in the control arm received indications based on daily symptom-reporting only. Subjects were asked to get tested for SARS-CoV-2 when receiving an app-generated alert, and additionally underwent periodic SARS-CoV-2 serology testing. The overall and early detection performance of both algorithms was evaluated and compared using metrics such as sensitivity and specificity.

Results: A total of 17,825 subjects were randomised within the study. Subjects in the experimental condition received an alert significantly earlier than those in the control condition (median of 0 versus 7 days before a positive SARS-CoV-2 test). The experimental algorithm achieved high sensitivity (93.8-99.2%) but low specificity (0.8-4.2%) when detecting infections during a specified period, while the control algorithm achieved more moderate sensitivity (43.3-46.4%) and specificity (66.4-65.0%). When detecting infection on a given day, the experimental algorithm also achieved higher sensitivity compared to the control algorithm (45-52% versus 28-33%), but much lower specificity (38-50% versus 93-97%).

Conclusions: Our findings highlight the potential role of wearable devices in early detection of SARS-CoV-2. The experimental algorithm overestimated infections, but future iterations could finetune the algorithm to improve specificity and enable it to differentiate between respiratory illnesses.

Trial registration: Netherlands Trial Register number NL9320.

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Conflict of interest statement

The authors have read the journal’s policy and have the following competing interests: Laura Zwiers, Marcel van Willigen, Jon Bouwman and Diederick Grobbee are current employees of Julius Clinical BV. Timo Brakenhoff, Brianna Goodale and Duco Veen are former employees of Julius Clinical BV. Billy Franks is a former employee of Julius Clinical BV and now an employee of Haleon. Brianna Goodale, Vladimir Kovacevic, Andjela Markovic and Maureen Cronin are past employees of Ava AG. Marianna Mitratza is a current employee of P95 CVBA. Lorenz Risch and Martin Risch are current employees and key shareholders of Dr Risch Medical Laboratory. Kirsten Grossman and Ornella Weideli are current or former employees of Dr Risch Medical Laboratory. David Conen has received consulting fees from Roche Diagnostics, outside of the current work. There are no patents, products in development or marketed products associated with this research to declare. These competing interests do not alter our adherence to PLOS ONE policies on sharing data and materials.

Figures

Fig 1
Fig 1. Illustration of the in-app messages given in case of unlikely indication for infection (left) and in case of a “red alert” (right).
Fig 2
Fig 2. Schematic illustration of the study periods and algorithms applied during the COVID-RED study.
Fig 3
Fig 3. CONSORT diagram of the COVID-RED trial.
See this image and copyright information in PMC

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