Generating ActiGraph Counts from Raw Acceleration Recorded by an Alternative Monitor
Generating ActiGraph Counts from Raw Acceleration Recorded by an Alternative Monitor
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DOI：
10.1249/mss.0000000000001344
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发表时间：
2017-11-01
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影响因子：
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通讯作者：
Arvidsson, Daniel
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作者：
Brond, Jan Christian;Andersen, Lars Bo;Arvidsson, Daniel
目的：本研究旨在在Matlab中实现一种聚合方法，用于从使用替代加速度计设备记录的原始加速度生成ActiGraph计数，并调查该方法的有效性。研究方法：基于Matlab中生成的标准化正弦加速度信号，在ActiLife软件中进行处理，实现并优化了包括频率带通滤波器在内的聚合方法。使用机械装置和24小时自由生活记录，使用方便的样品9个科目的聚合方法的有效性进行了评估。将应用于Axivity AX3原始加速度数据的聚合方法生成的计数与ActiLife根据ActiGraph GT 3X+数据生成的计数进行比较。结果如下：一个最佳的带通滤波器进行拟合，导致在一个均方根误差为25.7计数每10秒和平均绝对误差为15.0计数每秒在整个频率范围。与原始ActiGraph方法相比，拟定聚集方法的机械评价在所有旋转频率下的绝对平均值+/- SD差异为-0.11 +/- 0.97计数/10 s。将聚合方法应用于24小时自由生活记录，导致每10秒计数-16.2至0.9的时期水平偏差，这是平均身体活动的相对差异（每分钟计数）范围为-0. 5%至4. 7%，组平均值+/- SD为2. 2%+/-1. 7%，Cohen kappa为0. 945，表明强度分类几乎完全一致。结论：所提出的带通滤波器和聚合方法对于根据使用替代器械记录的原始加速度数据生成ActiGraph计数非常有效。这将有助于使用不同设备收集原始加速度数据的研究之间的可比性。
Purpose: This study aimed to implement an aggregation method in Matlab for generating ActiGraph counts from raw acceleration recorded with an alternative accelerometer device and to investigate the validity of the method. Methods: The aggregation method, including the frequency band-pass filter, was implemented and optimized based on standardized sinusoidal acceleration signals generated in Matlab and processed in the ActiLife software. Evaluating the validity of the aggregation method was approached using a mechanical setup and with a 24-h free-living recording using a convenient sample of nine subjects. Counts generated with the aggregation method applied to Axivity AX3 raw acceleration data were compared with counts generated with ActiLife from ActiGraph GT3X+ data. Results: An optimal band-pass filter was fitted resulting in a root-mean-square error of 25.7 counts per 10 s and mean absolute error of 15.0 counts per second across the full frequency range. The mechanical evaluation of the proposed aggregation method resulted in an absolute mean +/- SD difference of -0.11 +/- 0.97 counts per 10 s across all rotational frequencies compared with the original ActiGraph method. Applying the aggregation method to the 24-h free-living recordings resulted in an epoch level bias ranging from -16.2 to 0.9 counts per 10 s, a relative difference in the averaged physical activity (counts per minute) ranging from -0.5% to 4.7% with a group mean +/- SD of 2.2% +/- 1.7%, and a Cohen' s kappa of 0.945, indicating almost a perfect agreement in the intensity classification. Conclusion: The proposed band-pass filter and aggregation method is highly valid for generating ActiGraph counts from raw acceleration data recorded with alternative devices. It would facilitate comparability between studies using different devices collecting raw acceleration data.