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

• Published in: PloS one Vol. 20; no. 6; p. e0325116
• Main Authors: Zwiers, Laura C., Brakenhoff, Timo B., Goodale, Brianna M., Veen, Duco, Downward, George S., Kovacevic, Vladimir, Markovic, Andjela, Mitratza, Marianna, van Willigen, Marcel, Franks, Billy, van de Wijgert, Janneke, Montes, Santiago, Korkmaz, Serkan, Kjellberg, Jakob, Risch, Lorenz, Conen, David, Risch, Martin, Grossman, Kirsten, Weideli, Ornella C., Rispens, Theo, Bouwman, Jon, Folarin, Amos A., Bai, Xi, Dobson, Richard, Cronin, Maureen, Grobbee, Diederick E.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 05.06.2025; Public Library of Science (PLoS)
• Subjects: Adult; Aged; Algorithms; Applications programs; Asymptomatic; Biology and life sciences; Computer and Information Sciences; Control theory; COVID-19; COVID-19 - diagnosis; Cross-Over Studies; Disease transmission; Early Diagnosis; Female; Health aspects; Heart rate; Humans; Infections; Machine learning; Male; Medical devices; Medical equipment; Medicine and health sciences; Middle Aged; Mobile Applications; Mobile computing; Pandemics; Physical Sciences; Physiology; Prevention; Prospective Studies; Real time; Research and Analysis Methods; Respiratory diseases; SARS-CoV-2 - isolation & purification; Self report; Serology; Severe acute respiratory syndrome coronavirus 2; Single-Blind Method; Smartphones; Viral diseases; Wearable computers; Wearable Electronic Devices; Wearable technology; Netherlands
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0325116

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. Prospective, single-blinded, two-period, two-sequence, randomised controlled crossover trial. 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. 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%). 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. Netherlands Trial Register number NL9320.