Respiratory Health Study: Pulmonary Infection Screening Using Wearable Sensor Data

Abstract BackgroundThe proliferation of smart devices provides the possibility of the early detection of the signs of pulmonary infection. This study validates a smart device-based algorithm to monitor the risk of pulmonary infection.MethodsAn algorithm that runs on Huawei smart devices (Huawei Technologies Co., Ltd., Shenzhen, China) was developed and tested in 87 patients with pulmonary infection and 409 healthy subjects. The algorithm examines heart rate variability, respiratory rate, oxygen saturation, body temperature, and cough sound. It was embedded into the Respiratory Health Study app for a smartwatch to detect the risk of pulmonary infection and was further validated in the hospital. Pulmonary infection was diagnosed by doctors using a clinical evaluation, lab tests, and imaging examination, the gold standard for diagnosis. The accuracy, sensitivity, and specificity of the algorithm predicting pulmonary infection were evaluated.ResultsIn all, 80 patients with pulmonary infections and 85 healthy volunteers were recruited to validate the accuracy of the algorithm. The area under the curve of the algorithm for predicting pulmonary infection was 0.86 (95% confidence interval: 0.82–0.91) (P < 0.001). Compared to the gold standard, the overall accuracy of the algorithm was 85.9%, the sensitivity was 81.4%, and the specificity was 90.4%. The algorithm for heart rate, respiratory rate, oxygen saturation, and body temperature had an accuracy of 68.2%, and the accuracy of the algorithm including cough sound was 82.6%.ConclusionOur wearable system facilitated the detection of risk of pulmonary infection. Multi-source features were useful for enhancing the performance of the lung infection screening algorithm.Trial RegistrationChinese Clinical Trial Registry of the International Clinical Trials Registry Platform of the World Health Organization ChiCTR2100050843; http://www.chictr.org.cn/showprojen.aspx?proj=126556