tetano
Editor, Senior Moderator
PLoS One
. 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[SUP] 1 2 [/SUP], Timo B Brakenhoff[SUP] 1 [/SUP], Brianna M Goodale[SUP] 1 3 [/SUP], Duco Veen[SUP] 4 5 [/SUP], George S Downward[SUP] 2 6 [/SUP], Vladimir Kovacevic[SUP] 3 7 [/SUP], Andjela Markovic[SUP] 3 8 [/SUP], Marianna Mitratza[SUP] 2 [/SUP], Marcel van Willigen[SUP] 1 [/SUP], Billy Franks[SUP] 1 [/SUP], Janneke van de Wijgert[SUP] 9 [/SUP], Santiago Montes[SUP] 10 [/SUP], Serkan Korkmaz[SUP] 11 [/SUP], Jakob Kjellberg[SUP] 11 [/SUP], Lorenz Risch[SUP] 12 13 14 [/SUP], David Conen[SUP] 15 [/SUP], Martin Risch[SUP] 12 16 [/SUP], Kirsten Grossman[SUP] 12 13 [/SUP], Ornella C Weideli[SUP] 12 13 [/SUP], Theo Rispens[SUP] 17 18 [/SUP], Jon Bouwman[SUP] 1 [/SUP], Amos A Folarin[SUP] 19 20 21 [/SUP], Xi Bai[SUP] 19 [/SUP], Richard Dobson[SUP] 19 [/SUP], Maureen Cronin[SUP] 3 [/SUP], Diederick E Grobbee[SUP] 1 2 [/SUP]; COVID-RED consortium
Affiliations
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.
. 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[SUP] 1 2 [/SUP], Timo B Brakenhoff[SUP] 1 [/SUP], Brianna M Goodale[SUP] 1 3 [/SUP], Duco Veen[SUP] 4 5 [/SUP], George S Downward[SUP] 2 6 [/SUP], Vladimir Kovacevic[SUP] 3 7 [/SUP], Andjela Markovic[SUP] 3 8 [/SUP], Marianna Mitratza[SUP] 2 [/SUP], Marcel van Willigen[SUP] 1 [/SUP], Billy Franks[SUP] 1 [/SUP], Janneke van de Wijgert[SUP] 9 [/SUP], Santiago Montes[SUP] 10 [/SUP], Serkan Korkmaz[SUP] 11 [/SUP], Jakob Kjellberg[SUP] 11 [/SUP], Lorenz Risch[SUP] 12 13 14 [/SUP], David Conen[SUP] 15 [/SUP], Martin Risch[SUP] 12 16 [/SUP], Kirsten Grossman[SUP] 12 13 [/SUP], Ornella C Weideli[SUP] 12 13 [/SUP], Theo Rispens[SUP] 17 18 [/SUP], Jon Bouwman[SUP] 1 [/SUP], Amos A Folarin[SUP] 19 20 21 [/SUP], Xi Bai[SUP] 19 [/SUP], Richard Dobson[SUP] 19 [/SUP], Maureen Cronin[SUP] 3 [/SUP], Diederick E Grobbee[SUP] 1 2 [/SUP]; COVID-RED consortium
Affiliations
- PMID: 40471995
- DOI: 10.1371/journal.pone.0325116
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.