A novel method for using accelerometer data to predict energy expenditure
A novel method for using accelerometer data to predict energy expenditure
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DOI：
10.1152/japplphysiol.00818.2005
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发表时间：
2006-04-01
影响因子：
3.3
通讯作者：
Bassett, DR
中科院分区：
文献类型：
--
作者：
Crouter, SE;Clowers, KG;Bassett, DR
本研究的目的是开发一种新的双回归模型，将Activograph活动计数与各种体力活动的能量消耗相关联。48名参与者[年龄35岁（11.4）]进行了各种活动，选择代表久坐，轻，中度和剧烈的强度。18项活动被分成三个程序，每个程序由20个人执行，总共60次测试。随机选择了45个测试来开发新方程，并使用15个测试来交叉验证新方程，并将其与现有方程进行比较。在每次例行活动中，参与者在臀部佩戴Activograph加速计，同时通过便携式代谢系统测量氧气消耗量。对于每项活动，计算每10 s计数的变异系数（CV），以确定活动是步行/跑步还是其他活动。如果CV为10，则使用生活方式/休闲时间体力活动回归。在交叉验证组中，使用新算法（具有不活动阈值的2-回归模型）的平均估计值在所进行的每项活动的实测代谢当量（MET）的0.75代谢当量（MET）范围内（P >= 0.05），这比单回归模型有实质性改进。新的算法是更准确的预测能量消耗比目前公布的回归方程使用Activograph加速度计。
The purpose of this study was to develop a new two-regression model relating Actigraph activity counts to energy expenditure over a wide range of physical activities. Forty-eight participants [age 35 yr (11.4)] performed various activities chosen to represent sedentary, light, moderate, and vigorous intensities. Eighteen activities were split into three routines with each routine being performed by 20 individuals, for a total of 60 tests. Forty-five tests were randomly selected for the development of the new equation, and 15 tests were used to cross-validate the new equation and compare it against already existing equations. During each routine, the participant wore an Actigraph accelerometer on the hip, and oxygen consumption was simultaneously measured by a portable metabolic system. For each activity, the coefficient of variation ( CV) for the counts per 10 s was calculated to determine whether the activity was walking/running or some other activity. If the CV was 10, a lifestyle/leisure time physical activity regression was used. In the cross-validation group, the mean estimates using the new algorithm (2-regression model with an inactivity threshold) were within 0.75 metabolic equivalents (METs) of measured METs for each of the activities performed (P >= 0.05), which was a substantial improvement over the single-regression models. The new algorithm is more accurate for the prediction of energy expenditure than currently published regression equations using the Actigraph accelerometer.