Comparison of Accelerometry Methods for Estimating Physical Activity
Comparison of Accelerometry Methods for Estimating Physical Activity
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DOI：
10.1249/mss.0000000000001124
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发表时间：
2017-03-01
期刊：
影响因子：
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通讯作者：
Berrigan, David
中科院分区：
文献类型：
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作者：
Kerr, Jacqueline;Marinac, Catherine R.;Berrigan, David
目的：本研究旨在比较不同加速度计佩戴位置、佩戴时间协议和数据处理技术的身体活动估计值。方法：一个方便的样本，中年到老年妇女戴GT 3X+加速度计在手腕和臀部7天。使用三种数据处理技术计算身体活动估计值：单轴切割点，原始矢量幅度阈值和应用于三个轴的原始数据的机器学习算法。每日估计值进行了比较，为321名妇女使用广义估计方程。结果：共分析1420 d。髋关节与腕关节位置的依从率仅相差2.7%。技术、磨损位置和磨损时间方案之间的所有差异均具有统计学差异（P < 0.05）。根据地点和方法的不同，每天体力活动的平均分钟数从22到67不等。在髋关节上，1952计数临界点发现22%的参与者至少进行了150 min.wk（-1）的体力活动，原始向量幅度发现32%，机器学习算法发现74%的参与者每周步行/跑步150 min。手腕算法分别使用原始矢量幅度和机器学习技术发现59%和60%的参与者每周进行150分钟的身体活动。当手腕设备被戴了一夜，多达4%的参与者符合指南。结论：不同技术之间的估计值差异为52%，不同磨损位置之间的差异高达41%。研究结果表明，研究人员在比较不同研究的体力活动估计值时应该谨慎。需要努力将基于加速度计的身体活动估计标准化。第一步可能是报告多个程序，直到达成共识。
Purpose: This study aimed to compare physical activity estimates across different accelerometer wear locations, wear time protocols, and data processing techniques. Methods: A convenience sample of middle-age to older women wore a GT3X+ accelerometer at the wrist and hip for 7 d. Physical activity estimates were calculated using three data processing techniques: single-axis cut points, raw vector magnitude thresholds, and machine learning algorithms applied to the raw data from the three axes. Daily estimates were compared for the 321 women using generalized estimating equations. Results: A total of 1420 d were analyzed. Compliance rates for the hip versus wrist location only varied by 2.7%. All differences between techniques, wear locations, and wear time protocols were statistically different (P < 0.05). Mean minutes per day in physical activity varied from 22 to 67 depending on location and method. On the hip, the 1952-count cut point found at least 150 min.wk(-1)of physical activity in 22% of participants, raw vector magnitude found 32%, and the machine-learned algorithm found 74% of participants with 150 min of walking/running per week. The wrist algorithms found 59% and 60% of participants with 150 min of physical activity per week using the raw vector magnitude and machine-learned techniques, respectively. When the wrist device was worn overnight, up to 4% more participants met guidelines. Conclusion: Estimates varied by 52% across techniques and by as much as 41% across wear locations. Findings suggest that researchers should be cautious when comparing physical activity estimates from different studies. Efforts to standardize accelerometry-based estimates of physical activity are needed. A first step might be to report on multiple procedures until a consensus is achieved.