六种加速计指标评估儿童自由游戏身体活动时间模式的比较

Katherine L. McKee, K. Pfeiffer, A. Pearson, Kimberly A. Clevenger
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引用次数: 0

摘要

加速度计经常用于测量儿童的身体活动,但数据处理方法缺乏一致性,例如用于总结加速度计数据的度量,限制了研究之间的可比性。目的是比较六种加速度计指标(原始:平均振幅偏差,欧几里得范数- 1,活动指数,独立于监视器的运动汇总单位;计数:纵轴,矢量大小)用于表征一年级和二年级学生的强度和时间模式(n = 88;年龄= 7.8±0.7岁)休息体力活动。在5秒的epoch水平上,指标之间的Pearson相关性(r)在0.66到0.98之间。当每个epoch根据四分位数划分为四个强度水平之一时,加权kappa表示的指标之间的一致性范围为0.81至0.96。当分解到每个强度水平上花费的时间时,指标是强相关的(r = 0.76 - 0.99),并且在估计四分位数3或4中花费的时间时,通常在统计上是相等的。儿童从最不活跃到最活跃进行排名,指标之间的一致性很强(Spearman相关≥0.87)。使用六个指标中的每一个计算的五个碎片化指数来表征时间模式,它们是公平到强相关的(r = 0.53 - 0.99),与高强度活动次数的关联最强(r≥0.89)。大多数碎片化指数在指标之间不具有统计上的等价性。虽然指标在活动强度和时间模式上捕获了类似的趋势,但在比较来自不同指标的点估计时,需要谨慎。然而,所有指标都同样能够捕获更高强度的活动(即四分位数3或4),这是干预研究中最常见的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison of Six Accelerometer Metrics for Assessing the Temporal Patterns of Children’s Free-Play Physical Activity
Accelerometers are frequently used to measure physical activity in children, but lack of uniformity in data processing methods, such as the metric used to summarize accelerometer data, limits comparability between studies. The objective was to compare six accelerometer metrics (raw: mean amplitude deviation, Euclidean norm minus one, activity index, monitor-independent movement summary units; count: vertical axis, vector magnitude) for characterizing the intensity and temporal patterns of first and second graders’ (n = 88; age = 7.8 ± 0.7 years) recess physical activity. At a 5-s epoch level, Pearson’s correlations (r) between metrics ranged from .66 to .98. When each epoch was classified into one of four intensity levels based on quartiles, agreement between metrics as indicated by weighted kappa ranged from .81 to .96. When collapsed to time spent in each intensity level, metrics were strongly correlated (r = .76–.99) and most often statistically equivalent for estimating time spent in Quartile 3 or 4. Children were ranked from least to most active, and agreement between metrics was strong (Spearman’s correlation ≥ .87). Temporal patterns were characterized using five fragmentation indices calculated using each of the six metrics, which were fair-to-strongly correlated (r = .53–.99), with the strongest associations for number of high-intensity activity bouts (r ≥ .89). Most fragmentation indices were not statistically equivalent between metrics. While metrics captured similar trends in activity intensity and temporal patterns, caution is warranted when making comparisons of point estimates derived from different metrics. However, all metrics were able to similarly capture higher intensity activity (i.e., Quartile 3 or 4), the most common outcome of interest in intervention studies.
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