PDF(Pubmed)
Abstract：
BACKGROUND: Measuring the amount of physical activity and its patterns using wearable sensor technology in real-world settings can provide critical insights into health status.
OBJECTIVE: This study\'s aim was to develop and evaluate the analytical validity and transdemographic generalizability of an algorithm that classifies binary ambulatory status (yes or no) on the accelerometer signal from wrist-worn biometric monitoring technology.
METHODS: Biometric monitoring technology algorithm validation traditionally relies on large numbers of self-reported labels or on periods of high-resolution monitoring with reference devices. We used both methods on data collected from 2 distinct studies for algorithm training and testing, one with precise ground-truth labels from a reference device (n=75) and the second with participant-reported ground-truth labels from a more diverse, larger sample (n=1691); in total, we collected data from 16.7 million 10-second epochs. We trained a neural network on a combined data set and measured performance in multiple held-out testing data sets, overall and in demographically stratified subgroups.
RESULTS: The algorithm was accurate at classifying ambulatory status in 10-second epochs (area under the curve 0.938; 95% CI 0.921-0.958) and on daily aggregate metrics (daily mean absolute percentage error 18%; 95% CI 15%-20%) without significant performance differences across subgroups.
CONCLUSIONS: Our algorithm can accurately classify ambulatory status with a wrist-worn device in real-world settings with generalizability across demographic subgroups. The validated algorithm can effectively quantify users\' walking activity and help researchers gain insights on users\' health status.
OBJECTIVE: This study\'s aim was to develop and evaluate the analytical validity and transdemographic generalizability of an algorithm that classifies binary ambulatory status (yes or no) on the accelerometer signal from wrist-worn biometric monitoring technology.
METHODS: Biometric monitoring technology algorithm validation traditionally relies on large numbers of self-reported labels or on periods of high-resolution monitoring with reference devices. We used both methods on data collected from 2 distinct studies for algorithm training and testing, one with precise ground-truth labels from a reference device (n=75) and the second with participant-reported ground-truth labels from a more diverse, larger sample (n=1691); in total, we collected data from 16.7 million 10-second epochs. We trained a neural network on a combined data set and measured performance in multiple held-out testing data sets, overall and in demographically stratified subgroups.
RESULTS: The algorithm was accurate at classifying ambulatory status in 10-second epochs (area under the curve 0.938; 95% CI 0.921-0.958) and on daily aggregate metrics (daily mean absolute percentage error 18%; 95% CI 15%-20%) without significant performance differences across subgroups.
CONCLUSIONS: Our algorithm can accurately classify ambulatory status with a wrist-worn device in real-world settings with generalizability across demographic subgroups. The validated algorithm can effectively quantify users\' walking activity and help researchers gain insights on users\' health status.
摘要：
背景：在现实世界中使用可穿戴传感器技术测量身体活动量及其模式可以提供对健康状况的关键见解。
目的：本研究的目的是开发和评估一种算法的分析有效性和跨人口统计学的概括性，该算法对腕上佩戴的生物特征监测技术的加速度计信号进行二进制动态状态分类（是或否）。
方法：生物识别监测技术算法验证传统上依赖于大量自我报告的标签或使用参考设备进行高分辨率监测的周期。我们使用两种方法对从两个不同的研究中收集的数据进行算法训练和测试，一个具有来自参考设备的精确地面实况标签（n=75），第二个具有来自更多样化的参与者报告的地面实况标签，更大的样本(n=1691)；总共，我们收集了1670万10秒的数据。我们在组合数据集上训练了神经网络，并在多个保留的测试数据集中测量了性能，总体和人口分层亚组。
结果：该算法在10秒时期（曲线下面积0.938;95％CI0.921-0.958）和每日汇总指标（每日平均绝对百分比误差18％;95％CI15％-20％）的动态状态分类方面是准确的，在各个亚组之间没有显着性能差异。
结论：我们的算法可以在现实世界中使用腕部穿戴设备对动态状态进行准确分类，并具有跨人口亚组的普适性。经过验证的算法可以有效地量化用户的步行活动，并帮助研究人员了解用户的健康状况。
目的：本研究的目的是开发和评估一种算法的分析有效性和跨人口统计学的概括性，该算法对腕上佩戴的生物特征监测技术的加速度计信号进行二进制动态状态分类（是或否）。
方法：生物识别监测技术算法验证传统上依赖于大量自我报告的标签或使用参考设备进行高分辨率监测的周期。我们使用两种方法对从两个不同的研究中收集的数据进行算法训练和测试，一个具有来自参考设备的精确地面实况标签（n=75），第二个具有来自更多样化的参与者报告的地面实况标签，更大的样本(n=1691)；总共，我们收集了1670万10秒的数据。我们在组合数据集上训练了神经网络，并在多个保留的测试数据集中测量了性能，总体和人口分层亚组。
结果：该算法在10秒时期（曲线下面积0.938;95％CI0.921-0.958）和每日汇总指标（每日平均绝对百分比误差18％;95％CI15％-20％）的动态状态分类方面是准确的，在各个亚组之间没有显着性能差异。
结论：我们的算法可以在现实世界中使用腕部穿戴设备对动态状态进行准确分类，并具有跨人口亚组的普适性。经过验证的算法可以有效地量化用户的步行活动，并帮助研究人员了解用户的健康状况。
