Prediction of activity energy expenditure using accelerometers in children
Prediction of activity energy expenditure using accelerometers in children
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DOI：
10.1249/01.mss.0000139898.30804.60
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发表时间：
2004-09-01
影响因子：
4.1
通讯作者：
Butte, NF
中科院分区：
文献类型：
--
作者：
Puyau, MR;Adolph, AL;Butte, NF
目的：验证两个基于加速度计的活动监测器作为儿童身体活动的措施，使用能量消耗作为标准措施。研究方法：Actiwatch（AW）和Actical（AC）活动监测器进行了验证，对连续4小时的测量能量消耗（EE）在呼吸室热量计和1小时的测量在运动实验室使用便携式热量计和跑步机的32名儿童，年龄7-18岁。孩子们进行结构化的活动，包括基础代谢率（BMR），玩任天堂，使用电脑，清洁，有氧运动，投球，跑步机行走和跑步。根据AW或AC与年龄、性别、体重和身高的幂函数，建立了预测活动能量消耗（AEE = EE-BMR）和体力活动比率（PAR-EE/BMR）的方程。研究人员决定将久坐、轻度、中度和剧烈的体力活动进行分类。结果：活动计数占AEE和PAR变异性的大部分，年龄、性别、体重和身高的贡献较小。总体而言，AW方程解释了AEE和PAR的76-79%的变异，AC方程解释了81%的变异。相对较宽的95%预测区间表明，加速度计最好应用于群体而不是个人。活力阈值的灵敏度（97%）高于其他阈值（86-92%）。特异性为66- 73%。对于AW，久坐、轻度、中度和剧烈类别的阳性预测值分别为80%、66%、69%和74%，而对于AC，阳性预测值分别为81%、68%、72%和74%。结论：这两种基于加速度计的活动监测器提供了有效的措施，儿童的AEE和PAR，并可用于区分久坐，轻，中度和剧烈的体力活动水平，但需要进一步发展，以准确预测AEE和PAR的个人。
Purpose: To validate two accelerometer-based activity monitors as measures of children's physical activity using energy expenditure as the criterion measure. Methods: Actiwatch (AW) and Actical (AC) activity monitors were validated against continuous 4-h measurements of energy expenditure (EE) in a respiratory room calorimeter and 1-h measurements in an exercise laboratory using a portable calorimeter and treadmill in 32 children, ages 7-18 yr. The children performed structured activities including basal metabolic rate (BMR), playing Nintendo, using a computer, cleaning, aerobic exercise, ball toss, treadmill walking, and running. Equations were developed to predict activity energy expenditure (AEE = EE - BMR), and physical activity ratio (PAR - EE/BMR) from a power function of AW or AC, and age, sex, weight, and height. Thresholds were determined to categorize sedentary, light, moderate, and vigorous levels of physical activity. Results: Activity counts accounted for the majority of the variability in AEE and PAR, with small contributions of age, sex, weight, and height. Overall, AW equations accounted for 76-79% and AC equations accounted for 81% of the variability in AEE and PAR. Relatively wide 95% prediction intervals suggest the accelerometers are best applied to groups rather than individuals. Sensitivities were higher for the vigorous threshold (97%) than the other thresholds (86-92%). Specificities were on the order of 66-73%. The positive predictive values for sedentary, light, moderate, and vigorous categories were 80, 66, 69, and 74% for AW, respectively, and 81, 68, 72, 74% for AC, respectively. Conclusion: Both accelerometer-based activity monitors provided valid measures of children's AEE and PAR, and can be used to discriminate sedentary, light, moderate, and vigorous levels of physical activity but require further development to accurately predict AEE and PAR of individuals.