Predicting Physical Activity Energy Expenditure Using Accelerometry in Adults From Sub-Sahara Africa
Predicting Physical Activity Energy Expenditure Using Accelerometry in Adults From Sub-Sahara Africa
复制标题
DOI：
10.1038/oby.2009.39
复制
发表时间：
2009-08-01
期刊：
影响因子：
6.9
通讯作者：
Wareham, Nicholas J.
中科院分区：
文献类型：
--
作者：
Assah, Felix K.;Ekelund, Ulf;Wareham, Nicholas J.
缺乏体育活动可能是撒哈拉以南非洲目前流行病转变的一个重要病因，其特点是肥胖症和慢性病的流行率不断增加。然而，有一个客观测量的身体活动能量消耗（PAEE）在这一地区的数据缺乏。我们试图开发回归方程，使用身体成分和加速计计数来预测PAEE。我们进行了一项横断面研究的33名成年志愿者从城市（n = 16）和农村（n = 17）在喀麦隆的住宅区。在连续7天的时间内，通过双标记水（DLW）测量能量消耗。与此同时，一个安装在臀部的活动加速计记录了身体的运动。PAEE预测方程推导使用加速度计计数，年龄，性别和身体成分变量，交叉验证的折刀法。Bland和Altman一致性限（LOAs）方法用于评估一致性。我们的结果表明，PAEE（kJ/kg/天）与加速度计的活动计数呈显著正相关（r = 0.37，P = 0.03）。导出的方程解释了PAEE中14-40%的方差。年龄、性别和加速度计计数共同解释了PAEE中34%的方差，仅加速度计计数解释了14%。DLW和推导出的方程之间的LOA很宽，预测的PAEE比测量值低或高出60 kJ/ kg/天。总之，推导出的方程在预测该人群中加速计计数的PAEE方面优于现有的已发表方程。加速度计可用于预测PAEE在这一人群中，因此，有重要的应用监测人口水平的总体力活动模式。
Lack of physical activity may be an important etiological factor in the current epidemiological transition characterized by increasing prevalence of obesity and chronic diseases in sub-Sahara Africa. However, there is a dearth of data on objectively measured physical activity energy expenditure (PAEE) in this region. We sought to develop regression equations using body composition and accelerometer counts to predict PAEE. We conducted a cross-sectional study of 33 adult volunteers from an urban (n = 16) and a rural (n = 17) residential site in Cameroon. Energy expenditure was measured by doubly labeled water (DLW) over a period of seven consecutive days. Simultaneously, a hip-mounted Actigraph accelerometer recorded body movement. PAEE prediction equations were derived using accelerometer counts, age, sex, and body composition variables, and cross-validated by the jack-knife method. The Bland and Altman limits of agreement (LOAs) approach was used to assess agreement. Our results show that PAEE (kJ/kg/day) was significantly and positively correlated with activity counts from the accelerometer (r = 0.37, P = 0.03). The derived equations explained 14-40% of the variance in PAEE. Age, sex, and accelerometer counts together explained 34% of the variance in PAEE, with accelerometer counts alone explaining 14%. The LOAs between DLW and the derived equations were wide, with predicted PAEE being up to 60 kJ/ kg/day below or above the measured value. In summary, the derived equations performed better than existing published equations in predicting PAEE from accelerometer counts in this population. Accelerometry could be used to predict PAEE in this population and, therefore, has important applications for monitoring population levels of total physical activity patterns.