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.
Options
BORIS DOI
Publisher DOI
PubMed ID
40471995
Description
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.
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.
Date of Publication
2025
Publication Type
article
Subject(s)
500 - Science::540 - Chemistry
600 - Technology::610 - Medicine & health
Language(s)
en
Contributor(s)
Zwiers, Laura C | |
Brakenhoff, Timo B | |
Goodale, Brianna M | |
Veen, Duco | |
Downward, George S | |
Kovacevic, Vladimir | |
Mitratza, Marianna | |
van Willigen, Marcel | |
Franks, Billy | |
van de Wijgert, Janneke | |
Montes, Santiago | |
Korkmaz, Serkan | |
Kjellberg, Jakob | |
Conen, David | |
Grossman, Kirsten | |
Weideli, Ornella C | |
Rispens, Theo | |
Bouwman, Jon | |
Folarin, Amos A | |
Bai, Xi | |
Dobson, Richard | |
Cronin, Maureen | |
Grobbee, Diederick E |
Additional Credits
Institute of Clinical Chemistry
Series
PLoS ONE
Publisher
Public Library of Science
ISSN
1932-6203
Access(Rights)
open.access