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
Year: 2025
Type: article
Study designs: Randomized Controlled Trial, Clinical Trial
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.
Source: PLoS ONE
Authors: Laura C. Zwiers, Timo B. Brakenhoff, Brianna M. Goodale, Duco Veen, George S. Downward, Vladimir Kovacevic, Andjela Markovic, Marianna Mitratza, Marcel van Willigen, Billy Franks, Janneke van de Wijgert, Santiago Montes, Serkan Korkmaz, Jakob Kjellberg, Lorenz Risch, David Conen, Martin Risch, Kirsten Grossman, Ornella C. Weideli, Theo Rispens, Jon Bouwman, Amos A. Folarin, Xi Bai, Richard Dobson, Maureen Cronin, Diederick E. Grobbee
Institutions: University Medical Center Utrecht, Julius Clinical Research BV (Netherlands), Utrecht University, North-West University, Istraživačko-razvojni institut za veštačku inteligenciju Srbije, University of Fribourg, Roche (Netherlands), VIVE - The Danish Center for Social Science Research, University of Bern, University of Liechtenstein, Private University in the Principality of Liechtenstein, Population Health Research Institute, McMaster University, Kantonsspital Graubünden, Sanquin, Amsterdam University Medical Centers, University College London Hospitals NHS Foundation Trust, King's College London, UCL Biomedical Research Centre, University College London
Language: English
Subfield: Infectious Diseases
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,382
OpenAlex ID: W4411054895