CovIdentify Dataset

This dataset supports the study "A method for intelligent allocation of diagnostic testing by leveraging data from commercial wearable devices: a case study on COVID-19," which developed an Intelligent Testing Allocation (ITA) method. The study demonstrated the efficacy of using continuous digital biomarkers like resting heart rate and steps to enhance COVID-19 diagnostic testing positivity rates. The findings suggest significant potential for large-scale, symptom-independent surveillance testing to alleviate diagnostic test shortages. The provided data is from the CovIdentify study launched by Duke's BIG IDEAs Lab in the Biomedical Engineering Department. From April 2nd, 2020 to May 25th, 2021, 2,887 participants connected their smartwatches to the CovIdentify platform, including 1,689 Garmin, 1,091 Fitbit, and 107 Apple smartwatches

本数据集支持研究“利用商业可穿戴设备数据实现诊断检测智能分配的方法：基于COVID-19的案例研究”，该研究开发了一种智能检测分配（ITA）方法。研究证明了利用静息心率、步数等连续数字生物标志物来提高COVID-19诊断检测阳性率的有效性。研究结果暗示了大规模、症状无关的监测检测在缓解诊断检测短缺方面的巨大潜力。所提供数据源自由杜克大学生物医学工程学院BIG IDEAs实验室发起的CovIdentify研究。自2020年4月2日至2021年5月25日，共有2,887名参与者将他们的智能手表连接至CovIdentify平台，其中包括1,689款Garmin、1,091款Fitbit和107款Apple智能手表。