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SenseWear迷你臂带在儿童半结构化活动环境中的验证。

Validation of the SenseWear mini armband in children during semi-structure activity settings.

作者信息

Lee Jung-Min, Kim Youngwon, Bai Yang, Gaesser Glenn A, Welk Gregory J

机构信息

School of Health, Physical Education and Recreation, University of Nebraska-Omaha, USA.

Department of Kinesiology, College of Human Sciences, Iowa State University, USA.

出版信息

J Sci Med Sport. 2016 Jan;19(1):41-5. doi: 10.1016/j.jsams.2014.10.004. Epub 2014 Oct 14.

Abstract

OBJECTIVES

The purpose of the study is to evaluate the validity of different SenseWear software (algorithms v5.2 vs. algorithm v2.2) for estimating energy expenditure (EE) in children.

DESIGN

Original research.

METHODS

Forty-five children aged 7-13 years performed 12 randomly assigned activities (out of a set of 24) while wearing a SWA with simultaneous monitoring via portable calorimetry (IC). Each activity lasted 5min, with a 1min break between activities. The estimated EE values from the SWA were compared to the measured EE values from the IC using 3-way (Method×Algorithm×Activity) mixed model ANOVA.

RESULTS

The analyses revealed a significant method (IC vs. SWA)×Algorithm (v5.2 vs. v2.2) interaction, with significantly smaller errors (IC-SWA) for the newer v5.2 algorithms (0.25±0.09kcalmin(-1)) than the older v2.2 algorithms (1.04±0.09kcalmin(-1)). The mean absolute percent error (MAPE) was 17.0±12.1% for SWA5.2 algorithm and 31.4±11.1% for SWA2.2 algorithm. The v5.2 algorithms yielded non-significant (p>0.5) differences in EE estimates for most of the walking related activities as well as for stationary cycling at moderate intensity (MAPE=14.5%).

CONCLUSIONS

The smaller errors in estimated EE with the SenseWear v5.2 algorithms (compared to v2.2) demonstrate continued incremental improvements in estimates of EE for monitoring free-living activities in children.

摘要

目的

本研究旨在评估不同的SenseWear软件(算法v5.2与算法v2.2)在估计儿童能量消耗(EE)方面的有效性。

设计

原创研究。

方法

45名7至13岁的儿童在佩戴SenseWear臂带(SWA)的同时,通过便携式量热法(IC)进行同步监测,完成了从24项活动中随机分配的12项活动。每项活动持续5分钟,活动之间休息1分钟。使用三因素(方法×算法×活动)混合模型方差分析,将SWA估计的EE值与IC测量的EE值进行比较。

结果

分析显示,方法(IC与SWA)×算法(v5.2与v2.2)存在显著交互作用,较新的v5.2算法(0.25±0.09千卡/分钟(-1))的误差(IC - SWA)明显小于较旧的v2.2算法(1.04±0.09千卡/分钟(-1))。SWA5.2算法的平均绝对百分比误差(MAPE)为17.0±12.1%,SWA2.2算法为31.4±11.1%。对于大多数与步行相关的活动以及中等强度的固定自行车运动,v5.2算法在EE估计上产生的差异不显著(p>0.5)(MAPE = 14.5%)。

结论

与v2.2算法相比,SenseWear v5.2算法在估计EE时误差更小,这表明在监测儿童自由活动的EE估计方面持续有渐进式改进。

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