Time-to-indication and corresponding p-values.

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.