Time-to-indication and corresponding p-values.

BackgroundRapid 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 designProspective, single-blinded, two-period, two-sequence, randomised controlled crossover trial.MethodsSubjects 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.ResultsA 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%).ConclusionsOur 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 registrationNetherlands Trial Register number NL9320.

研究背景：快速且早期检出严重急性呼吸综合征冠状病毒2型（SARS-CoV-2）感染，尤其是在潜伏期或无症状阶段，可助力降低病毒传播风险。通过可穿戴设备（wearable device）采集的生理参数可被高效分析，以实现感染的早期预警。本新型冠状病毒肺炎远程早期检测（COVID-RED）试验探究了可穿戴设备（Ava bracelet）在实时优化SARS-CoV-2感染早期检测中的应用。

试验设计：前瞻性、单盲、两周期、两序列随机对照交叉试验。

研究方法：受试者佩戴该医疗设备，并将其与移动应用程序同步，同时通过该应用上报自身症状。试验组受试者基于融合可穿戴设备数据与自我上报症状数据的算法，获取实时感染提示；而对照组受试者仅通过每日症状上报获取提示。受试者收到应用程序生成的警报后，需接受SARS-CoV-2检测，同时还需定期进行SARS-CoV-2血清学检测。采用灵敏度、特异度等指标，对两种算法的整体及早期检测性能进行评估与对比。

研究结果：本研究共纳入17825名随机受试者。试验组受试者收到警报的时间显著早于对照组（中位数分别为SARS-CoV-2检测阳性前0天与7天）。在特定时段内检测感染时，试验组算法灵敏度较高（93.8%~99.2%）但特异度较低（0.8%~4.2%）；而对照组算法的灵敏度与特异度则处于中等水平，分别为43.3%~46.4%与65.0%~66.4%。在单日感染检测中，试验组算法的灵敏度仍高于对照组（45%~52% vs 28%~33%），但特异度显著更低（38%~50% vs 93%~97%）。

研究结论：本研究结果证实了可穿戴设备在SARS-CoV-2感染早期检测中的应用潜力。试验组算法存在感染过判的问题，但未来的算法迭代可通过微调提升其特异度，并实现对呼吸道疾病的鉴别区分。

试验注册：荷兰试验注册库编号NL9320。