Division of Integrated Sciences, J. F. Oberlin University, Tokyo 194-0294, Japan.
Faculty of Creative Engineering, Chiba Institute of Technology, Chiba 275-0023, Japan.
Int J Environ Res Public Health. 2019 Mar 15;16(6):931. doi: 10.3390/ijerph16060931.
: An algorithm for the classification of ambulatory and non-ambulatory activities using the ratio of unfiltered to filtered synthetic acceleration measured with a triaxial accelerometer and predictive models for physical activity intensity (METs) in adults and in elementary school children has been developed. The purpose of the present study was to derive predictive equations for METs with a similar algorithm in young children. : Thirty-seven healthy Japanese children (four- to six-years old) participated in this study. The five non-ambulatory activities including low-intensity activities, and five ambulatory activities were selected. The raw accelerations using a triaxial accelerometer and energy expenditure by indirect calorimetry using the Douglas bag method during each activity were collected. : For non-ambulatory activities, especially light-intensity non-ambulatory activities, linear regression equations with a predetermined intercept (0.9) or quadratic equations were a better fit than the linear regression. The equations were different from those for adults and elementary school children. On the other hand, the ratios of unfiltered to filtered synthetic acceleration in non-ambulatory activities were different from those in ambulatory activities, as in adults and elementary school children. : Our calibration model for young children could accurately predict intensity of physical activity including low-intensity non-ambulatory activities.
利用三轴加速度计测量的未过滤与过滤合成加速度之比以及针对成年人和小学生的体力活动强度(代谢当量)的预测模型,开发了一种用于分类非运动和运动活动的算法。本研究的目的是在幼儿中用类似的算法推导出代谢当量的预测方程。
37 名健康的日本儿童(4 至 6 岁)参与了这项研究。选择了包括低强度活动在内的 5 种非运动活动和 5 种运动活动。在每项活动中,使用三轴加速度计收集原始加速度,并使用袋法间接测热法收集能量消耗。
对于非运动活动,特别是低强度非运动活动,具有预定截距(0.9)的线性回归方程或二次方程比线性回归更适合。这些方程与成年人和小学生的方程不同。另一方面,非运动活动中未过滤与过滤合成加速度的比值与成年人和小学生的运动活动不同。
我们为幼儿校准的模型可以准确预测包括低强度非运动活动在内的体力活动强度。