评估自由活动身体活动的 ActiGraph 2-回归模型、马修斯切点和 NHANES 切点的有效性。
Validity of ActiGraph 2-regression model, Matthews cut-points, and NHANES cut-points for assessing free-living physical activity.
机构信息
Dept of Exercise and Health Sciences, University of Massachusetts, Boston, USA.
出版信息
J Phys Act Health. 2013 May;10(4):504-14. doi: 10.1123/jpah.10.4.504. Epub 2012 Sep 11.
BACKGROUND
The purpose of this study was to compare the 2006 and 2010 Crouter algorithms for the ActiGraph accelerometer and the NHANES and Matthews cut-points, to indirect calorimetry during a 6-hr free-living measurement period.
METHODS
Twenty-nine participants (mean ± SD; age, 38 ± 11.7 yrs; BMI, 25.0 ± 4.6 kg·m-2) were monitored for 6 hours while at work or during their leisure time. Physical activity (PA) data were collected using an ActiGraph GT1M and energy expenditure (METs) was measured using a Cosmed K4b2. ActiGraph prediction equations were compared with the Cosmed for METs and time spent in sedentary behaviors, light PA (LPA), moderate PA (MPA), and vigorous PA (VPA).
RESULTS
The 2010 Crouter algorithm overestimated time spent in LPA, MPA, and VPA by 9.0%-44.5% and underestimated sedentary time by 20.8%. The NHANES cut-points overestimated sedentary time and LPA by 8.3%-9.9% and underestimated MPA and VPA by 50.4%-56.7%. The Matthews cut-points overestimated sedentary time (9.9%) and MPA (33.4%) and underestimated LPA (25.7%) and VPA (50.1%). The 2006 Crouter algorithm was within 1.8% of measured sedentary time; however, mean errors ranged from 34.4%-163.1% for LPA, MPA, and VPA.
CONCLUSION
Of the ActiGraph prediction methods examined, none of them was clearly superior for estimating free-living PA compared with indirect calorimetry.
背景
本研究旨在比较 2006 年和 2010 年 Crouter 算法与 NHANES 和 Matthews 切点，用于间接测热法在 6 小时自由生活测量期间。
方法
29 名参与者（平均±SD；年龄，38±11.7 岁；BMI，25.0±4.6kg·m-2）在工作或闲暇时间监测 6 小时。使用 ActiGraph GT1M 收集身体活动（PA）数据，使用 Cosmed K4b2 测量能量消耗（METs）。比较 ActiGraph 预测方程与 Cosmed 方程，用于测量 METs 和久坐行为、轻度 PA（LPA）、中度 PA（MPA）和剧烈 PA（VPA）的时间。
结果
2010 年 Crouter 算法高估了 LPA、MPA 和 VPA 的时间，高估了 9.0%-44.5%，低估了久坐时间 20.8%。NHANES 切点高估了久坐时间和 LPA，高估了 8.3%-9.9%，低估了 MPA 和 VPA，低估了 50.4%-56.7%。Matthews 切点高估了久坐时间（9.9%）和 MPA（33.4%），低估了 LPA（25.7%）和 VPA（50.1%）。2006 年 Crouter 算法与测量的久坐时间相差 1.8%；然而，LPA、MPA 和 VPA 的平均误差范围为 34.4%-163.1%。
结论
在所检查的 ActiGraph 预测方法中，没有一种方法明显优于间接测热法，用于估计自由生活中的 PA。
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