Estimating physical activity in youth using a wrist accelerometer.
Estimating physical activity in youth using a wrist accelerometer.
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DOI：
10.1249/mss.0000000000000502
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发表时间：
2015-05
影响因子：
4.1
通讯作者：
Bassett DR Jr
中科院分区：
文献类型：
--
作者：
Crouter SE;Flynn JI;Bassett DR Jr
本研究的目的是开发和验证方法，分析手腕加速度计数据的青年。181名青少年（平均值±标准差;年龄，12.0±1.5岁）完成了30分钟的仰卧休息和2至7项结构性活动（从25项列表中选出）各8分钟。受试者操作特征（ROC）曲线和回归分析用于开发能量消耗（儿童MET;测量的活动VO 2除以测量的静息VO 2）的预测方程和计算久坐行为（SB）、轻度（LPA）、中度（MPA）和剧烈（VPA）体力活动所花费时间的临界点。每5秒的垂直轴（VA）和矢量幅度（VM）计数均用于此目的。验证研究包括42名完成约2小时非结构性PA的青年（年龄，12.6±0.8岁）。在所有测量过程中，使用ActiGraph GT 3X或GT 3X+（位于优势手腕上）收集活动数据。使用Cosmed K4 b2测量氧消耗。重复测量ANOVA用于比较测量与预测的儿童MET（仅回归），以及SB、LPA、MPA和VPA花费的时间。ROC曲线下面积（≥0.825）、敏感性（≥0.756）和特异性（≥0.634）的临界点相似，显著低估LPA和高估VPA（P<0.05）。VA和VM回归模型分别在平均测量的child-MET的±0.21 child-MET和SB、LPA、MPA和VPA的测量时间的±2.5分钟内（P>0.05）。与测量值相比，基于腕部加速度计数据开发的VA和VM回归模型对于儿童MET和SB、LPA、MPA和VPA花费的时间具有不显著的平均偏倚;然而，它们具有较大的个体误差。
The purpose of this study was to develop and validate methods for analyzing wrist accelerometer data in youth. 181 youth (mean±SD; age, 12.0±1.5 yrs) completed 30-min of supine rest and 8-min each of 2 to 7 structured activities (selected from a list of 25). Receiver Operator Characteristic (ROC) curves and regression analyses were used to develop prediction equations for energy expenditure (child-METs; measured activity VO2 divided by measured resting VO2) and cut-points for computing time spent in sedentary behaviors (SB), light (LPA), moderate (MPA), and vigorous (VPA) physical activity. Both vertical axis (VA) and vector magnitude (VM) counts per 5 seconds were used for this purpose. The validation study included 42 youth (age, 12.6±0.8 yrs) who completed approximately 2-hrs of unstructured PA. During all measurements, activity data were collected using an ActiGraph GT3X or GT3X+, positioned on the dominant wrist. Oxygen consumption was measured using a Cosmed K4b2. Repeated measures ANOVAs were used to compare measured vs predicted child-METs (regression only), and time spent in SB, LPA, MPA, and VPA. All ROC cut-points were similar for area under the curve (≥0.825), sensitivity (≥0.756), and specificity (≥0.634) and they significantly underestimated LPA and overestimated VPA (P<0.05). The VA and VM regression models were within ±0.21 child-METs of mean measured child-METs and ±2.5 minutes of measured time spent in SB, LPA, MPA, and VPA, respectively (P>0.05). Compared to measured values, the VA and VM regression models developed on wrist accelerometer data had insignificant mean bias for child-METs and time spent in SB, LPA, MPA, and VPA; however they had large individual errors.