Validation of accelerometer wear and nonwear time classification algorithm.
Validation of accelerometer wear and nonwear time classification algorithm.
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DOI：
10.1249/mss.0b013e3181ed61a3
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发表时间：
2011-02
影响因子：
4.1
通讯作者：
Buchowski MS
中科院分区：
文献类型：
--
作者：
Choi L;Liu Z;Matthews CE;Buchowski MS
在干预和基于人群的研究中使用运动监测器（加速度计）测量身体活动（PA）正在成为客观测量久坐和活动行为以及验证主观PA自我报告的标准方法。PA测量中的一个重要步骤是使用其记录（计数）和加速度计特定算法将每日时间分类为加速度计磨损和非磨损间隔。验证和改进一种常用的算法，用于使用在全房间间接热量计中获得的客观运动数据对加速度计磨损和非磨损时间间隔进行分类。我们进行了一个磨损/非磨损自动算法的验证研究，使用从49名成年人和76名青年佩戴加速度计在严格监控的24小时停留在一个房间的热量计的数据。将该算法分类的加速度计磨损时间和非磨损时间与实际磨损时间进行了比较。潜在的改进算法进行了检查，使用最小的分类错误作为优化目标。新算法中推荐的元素是：1）非磨损时间间隔内的零计数阈值，2）连续零/非零计数的90分钟时间窗口，以及3）允许2分钟非零计数间隔与上/下游30分钟连续零计数窗口用于检测人为移动。与真实佩戴状态相比，对算法的改进减少了清醒和24小时期间的非佩戴时间错误分类（均P < 0.001）。加速度计磨损/非磨损时间算法的改进可能会导致更准确地估计久坐和活动行为所花费的时间。
The use of movement monitors (accelerometers) for measuring physical activity (PA) in intervention and population-based studies is becoming a standard methodology for the objective measurement of sedentary and active behaviors and for validation of subjective PA self-reports. A vital step in PA measurements is classification of daily time into accelerometer wear and nonwear intervals using its recordings (counts) and an accelerometer-specific algorithm. To validate and improve a commonly used algorithm for classifying accelerometer wear and nonwear time intervals using objective movement data obtained in the whole-room indirect calorimeter. We conducted a validation study of a wear/nonwear automatic algorithm using data obtained from 49 adults and 76 youth wearing accelerometers during a strictly monitored 24-h stay in a room calorimeter. The accelerometer wear and nonwear time classified by the algorithm was compared with actual wearing time. Potential improvements to the algorithm were examined using the minimum classification error as an optimization target. The recommended elements in the new algorithm are: 1) zero-count threshold during a nonwear time interval, 2) 90-min time window for consecutive zero/nonzero counts, and 3) allowance of 2-min interval of nonzero counts with the up/downstream 30-min consecutive zero counts window for detection of artifactual movements. Compared to the true wearing status, improvements to the algorithm decreased nonwear time misclassification during the waking and the 24-h periods (all P < 0.001). The accelerometer wear/nonwear time algorithm improvements may lead to more accurate estimation of time spent in sedentary and active behaviors.