Objective: Controlling the spread of the COVID-19 pandemic largely depends on scaling up the testing infrastructure for identifying infected individuals. Consumer-grade wearables may present a solution to detect the presence of infections in the population, but the current paradigm requires collecting physiological data continuously and for long periods of time on each individual, which poses limitations in the context of rapid screening. Technology: Here, we propose a novel paradigm based on recording the physiological responses elicited by a short (~2 minutes) sequence of activities (i.e. “snapshot”), to detect symptoms associated with COVID-19. We employed a novel body-conforming soft wearable sensor placed on the suprasternal notch to capture data on physical activity, cardio-respiratory function, and cough sounds. Results: We performed a pilot study in a cohort of individuals (n=14) who tested positive for COVID-19 and detected altered heart rate, respiration rate and heart rate variability, relative to a group of healthy individuals (n=14) with no known exposure. Logistic regression classifiers were trained on individual and combined sets of physiological features (heartbeat and respiration dynamics, walking cadence, and cough frequency spectrum) at discriminating COVID-positive participants from the healthy group. Combining features yielded an AUC of 0.94 (95% CI=[0.92, 0.96]) using a leave-one-subject-out cross validation scheme. Conclusions and Clinical Impact: These results, although preliminary, suggest that a sensor-based snapshot paradigm may be a promising approach for non-invasive and repeatable testing to alert individuals that need further screening.

中文翻译：

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使用可穿戴设备和结构化活动快速筛查与COVID-19相关的生理变化：一项初步研究

目的：控制COVID-19大流行的蔓延很大程度上取决于扩大测试基础设施以识别感染者的能力。消费级可穿戴设备可能会提供一种解决方案，以检测人群中是否存在感染，但是当前的范例要求持续不断地收集每个人的生理数据，并且需要很长一段时间，这在快速筛选的背景下存在局限性。技术：在这里，我们基于记录短时间（约2分钟）的一系列活动（即“快照”）引起的生理反应，提出一种新颖的范例，以检测与COVID-19相关的症状。我们采用了一种新颖的，符合人体的柔软可穿戴传感器，该传感器置于胸骨上切迹上，以捕获有关体育活动，心脏呼吸功能和咳嗽声的数据。结果：我们在一组队列中进行了一项先导研究（n = 14），这些人群相对于一组未知的健康个体（n = 14），其COVID-19测试呈阳性，并且检测到心率，呼吸频率和心率变异性发生了变化接触。在区分健康组的COVID阳性参与者时，对Logistic回归分类器进行了个别和组合的生理特征（心跳和呼吸动力学，步行节奏和咳嗽频谱）的训练。使用留一法则交叉验证方案，组合特征得出的AUC为0.94（95％CI = [0.92，0.96]）。结论和临床影响：这些结果尽管是初步的，但表明基于传感器的快照范例对于无创且可重复的测试可能是一种有前途的方法，以提醒需要进一步筛查的个体。