Watson Amanda, Dumuid Dorothea, Olds Tim
University of South Australia, Adelaide, South Australia, Australia.
Health Educ Behav. 2020 Dec;47(6):905-913. doi: 10.1177/1090198120952041. Epub 2020 Aug 26.
Previous research has examined associations between individual activity behaviors and academic achievement. Yet activity behaviors should be analyzed together because they are codependent parts of the 24-hour day.
This study aims to explore the associations between all daily activity behaviors (sleep, sedentary time, light physical activity [LPA], and moderate-to-vigorous physical activity [MVPA]) and academic achievement using compositional data analysis.
Participants for this study were drawn from two cohorts: the Australian arm of the cross-sectional International Study of Childhood Obesity, Lifestyle and the Environment ( = 452; mean age 10.7 years ( = 0.4); 54% female) and CheckPoint ( = 1278; mean age 12 years [ = 0.4]; 50% female), a cross-sectional study nested between Waves 6 and 7 of the Longitudinal Study of Australian Children. Objective daily activity behavior data (sleep, sedentary time, LPA, and MVPA) were collected using 8-day, 24-hour accelerometry. Academic achievement was assessed using a nationally administered standardized test in literacy (spelling, grammar and punctuation, writing and reading) and numeracy. Compositional models (adjusted for age, sex, socioeconomic position, and pubertal development) regressed academic scores against isometric log ratios of activity behaviors. We used the models to estimate academic achievement for observed daily activity mixes. Estimated outcomes were plotted against time spent in each individual activity domain, and loess curves were fitted.
In two different cohorts using two different accelerometers, lower LPA was related to better numeracy and literacy and higher sedentary time to better literacy (relative to time spent in other domains). . LPA likely "drains time" from other movement behaviors, which are beneficial for academic achievement.
以往的研究探讨了个体活动行为与学业成绩之间的关联。然而，活动行为应综合分析，因为它们是一天24小时中相互依存的部分。
本研究旨在使用成分数据分析方法，探究所有日常活动行为（睡眠、久坐时间、轻度身体活动[LPA]和中度至剧烈身体活动[MVPA]）与学业成绩之间的关联。
本研究的参与者来自两个队列：横断面的国际儿童肥胖、生活方式与环境研究的澳大利亚分支（n = 452；平均年龄10.7岁（标准差 = 0.4）；54%为女性）和CheckPoint（n = 1278；平均年龄12岁[标准差 = 0.4]；50%为女性），后者是嵌套在澳大利亚儿童纵向研究第6波和第7波之间的横断面研究。使用为期8天的24小时加速度计收集客观的日常活动行为数据（睡眠、久坐时间、LPA和MVPA）。学业成绩通过全国范围内实施的识字（拼写、语法和标点、写作和阅读）和算术标准化测试进行评估。成分模型（根据年龄、性别、社会经济地位和青春期发育进行调整）将学业成绩分数对活动行为的等距对数比率进行回归分析。我们使用这些模型来估计观察到的日常活动组合的学业成绩。将估计结果与在每个个体活动领域所花费的时间进行绘图，并拟合局部加权散点平滑曲线。
在使用两种不同加速度计的两个不同队列中，较低的LPA与更好的算术和识字能力相关，而较长的久坐时间与更好的识字能力相关（相对于在其他领域所花费的时间）。LPA可能会从其他对学业成绩有益的运动行为中“占用时间”。
