Fairclough Stuart J, Dumuid Dorothea, Taylor Sarah, Curry Whitney, McGrane Bronagh, Stratton Gareth, Maher Carol, Olds Timothy
Physical Activity and Health Research Group, Department of Sport and Physical Activity, Edge Hill University, St Helens Road, Ormskirk, Lancashire, UK.
Department of Physical Education and Sports Science, University of Limerick, Limerick, Ireland.
Int J Behav Nutr Phys Act. 2017 May 10;14(1):64. doi: 10.1186/s12966-017-0521-z.
Movement behaviours performed over a finite period such as a 24 h day are compositional data. Compositional data exist in a constrained simplex geometry that is incongruent with traditional multivariate analytical techniques. However, the expression of compositional data as log-ratio co-ordinate systems transfers them to the unconstrained real space, where standard multivariate statistics can be used. This study aimed to use a compositional data analysis approach to examine the adiposity and cardiorespiratory fitness predictions of time reallocations between children's daily movement behaviours.
This study used cross-sectional data from the Active Schools: Skelmersdale study, which involved Year 5 children from a low-income community in northwest England (n = 169). Measures included accelerometer-derived 24 h activity (sedentary time [ST], light physical activity [LPA], moderate-to-vigorous physical activity [MVPA], and sleep), cardiorespiratory fitness determined by the 20 m shuttle run test, objectively measured height, weight and waist circumference (from which zBMI and percent waist circumference-to-height ratio (%WHtR) were derived) and sociodemographic covariates. Log-ratio multiple linear regression models were used to predict adiposity and fitness for the mean movement behaviour composition, and for new compositions where fixed durations of time had been reallocated from one behaviour to another, while the remaining behaviours were unchanged. Predictions were also made for reallocations of fixed durations of time using the mean composition of three different weight status categories (underweight, normal-weight, and overweight/obese) as the starting point.
Replacing MVPA with any other movement behaviour around the mean movement composition predicted higher adiposity and lower CRF. The log-ratio model predictions were asymmetrical: when time was reallocated to MVPA from sleep, ST, or LPA, the estimated detriments to fitness and adiposity were larger in magnitude than the estimated benefits of time reallocation from MVPA to sleep, ST or LPA. The greatest differences in fitness and fatness for reallocation of fixed duration of MVPA were predicted at the mean composition of overweight/obese children.
Findings reinforce the key role of MVPA for children's health. Reallocating time from ST and LPA to MVPA in children is advocated in school, home, and community settings.
在有限时间段(如24小时)内进行的运动行为属于成分数据。成分数据存在于受限的单纯形几何结构中,这与传统多元分析技术不一致。然而,将成分数据表示为对数比坐标系可将其转换到无约束的实空间,在该空间中可使用标准多元统计方法。本研究旨在采用成分数据分析方法,研究儿童日常运动行为之间时间重新分配对肥胖和心肺适能的预测情况。
本研究使用了来自“活力学校:斯凯尔默斯代尔研究”的横断面数据,该研究涉及英格兰西北部一个低收入社区的五年级儿童(n = 169)。测量指标包括通过加速度计得出的24小时活动量(久坐时间[ST]、轻度身体活动[LPA]、中度至剧烈身体活动[MVPA]和睡眠)、通过20米穿梭跑测试确定的心肺适能、客观测量的身高、体重和腰围(由此得出zBMI和腰围身高比百分比[%WHtR])以及社会人口统计学协变量。对数比多元线性回归模型用于预测平均运动行为组成的肥胖和适能情况,以及固定时长的时间从一种行为重新分配到另一种行为,而其余行为保持不变时的新组成情况。还以三种不同体重状况类别(体重过轻、正常体重和超重/肥胖)的平均组成作为起点,对固定时长的时间重新分配情况进行了预测。
在平均运动组成周围,用任何其他运动行为替代MVPA均预测出更高的肥胖率和更低的心肺适能。对数比模型预测是不对称的:当时间从睡眠、ST或LPA重新分配到MVPA时,对适能和肥胖的估计损害幅度大于从MVPA重新分配到睡眠、ST或LPA的时间估计益处。在超重/肥胖儿童的平均组成情况下,预测出固定时长的MVPA重新分配在适能和肥胖方面的差异最大。
研究结果强化了MVPA对儿童健康的关键作用。提倡在学校、家庭和社区环境中,将儿童的时间从ST和LPA重新分配到MVPA。