在澳大利亚小学生样本中,与24小时设备测量和自我报告的睡眠、久坐行为和体育活动差异相关的特征

Joshua Gauci, Timothy Olds, Carol Maher, Amanda Watson, François Fraysse, Mason Munzberg, Isaac Hoepfl, Dorothea Dumuid
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引用次数: 0

摘要

背景:儿童花多少时间睡觉、久坐和参加体育活动影响他们的健康和幸福。为了给儿童的时间使用提供准确的指导,重要的是要了解设备测量和自我报告的时间使用测量之间的差异,以及可能影响这些差异的因素。在澳大利亚的小学学龄儿童中,本研究旨在描述设备测量和自我报告的睡眠、久坐行为、轻强度体力活动(LPA)和中等强度体力活动(MVPA)之间的差异,并探索社会人口统计学和个人特征如何与这些差异相关联。方法:参与者(n = 120, 67%女性,年龄9-11岁)从假期生活队列研究中抽取。设备测量的时间使用情况来自于在2年期间的5个时间点上佩戴的7天加速度计,而自我报告的时间使用情况来自于在同一时间点进行的2天儿童和成人多媒体活动回忆(MARCA)。对于每个参与者和测量方法,在研究期间的任何重叠天数(有两种测量方法)中,平均每天的睡眠时间、久坐时间、LPA和MVPA都是推导出来的。参与者的特征要么来自基线父母调查(年龄、性别、父母教育程度、青春期),要么来自研究时间点的直接测量的平均值(穿梭跑步的有氧适能、人体测量的体重指数、国家标准化测试的学习成绩)。使用Bland-Altmann图描述设备测量和自我报告的时间使用之间的差异。使用组合结果线性回归模型来确定哪些参与者特征与使用时间测量类型的差异相关。结果:相对于设备测量,自我报告的每日LPA被低估了83分钟(35%的差异),而睡眠(+ 37分钟;6%差异),MVPA(+ 34分钟;33%的差异)和久坐时间(+ 12分钟;3%的差异)被高估。不同测量类型之间差异的特征为性别(χ2 = 11.9, p = 0.008)、父母教育程度(χ2 = 23.0, p = 0.001)、有氧健身(χ2 = 10.7, p = 0.01)和学习成绩(χ2 = 15.9, p = 0.001)。结论:在小学学龄儿童中,设备测量和自我报告的时间使用测量不应该互换使用,因为存在与社会人口统计学特征相关的系统性偏差和差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Characteristics associated with differences in 24-hour device-measured and self-reported sleep, sedentary behaviour and physical activity in a sample of Australian primary school children.

Characteristics associated with differences in 24-hour device-measured and self-reported sleep, sedentary behaviour and physical activity in a sample of Australian primary school children.

Characteristics associated with differences in 24-hour device-measured and self-reported sleep, sedentary behaviour and physical activity in a sample of Australian primary school children.

Characteristics associated with differences in 24-hour device-measured and self-reported sleep, sedentary behaviour and physical activity in a sample of Australian primary school children.

Background: How much time children spend sleeping, being sedentary and participating in physical activity affects their health and well-being. To provide accurate guidelines for children's time use, it is important to understand the differences between device-measured and self-reported use-of-time measures, and what may influence these differences. Among Australian primary school-aged children, this study aimed to describe the differences between device-measured and self-reported sleep, sedentary behaviour, light-intensity physical activity (LPA), and moderate-vigorous-intensity physical activity (MVPA), and to explore how sociodemographic and personal characteristics were associated with these differences.

Methods: Participants (n = 120, 67% female, age 9-11 years) were drawn from the Life on Holidays cohort study. Device measured use of time was from 7-day accelerometry worn over five timepoints in a 2-year period, and self-reported use of time was from 2-day Multimedia Activity Recall for Children and Adults (MARCA), conducted at the same timepoints. For each participant and measurement method, average daily time spent in sleep, sedentary time, LPA and MVPA was derived for any overlapping days (that had both types of measurement) across the study period. Participant characteristics were either obtained from baseline parental survey (age, sex, parental education, puberty) or derived from the average of direct measurements across the study timepoints (aerobic fitness from shuttle run, body mass index from anthropometric measurements, academic performance from national standardised tests). Differences between device-measured and self-reported use of time were described using Bland-Altmann plots. Compositional outcome linear-regression models were used to determine which participant characteristics were associated with differences by use-of-time measurement type.

Results: Relative to device-measured, self-reported daily LPA was underestimated by 83 min (35% difference), whilst sleep (+ 37 min; 6% difference), MVPA (+ 34 min; 33% difference) and sedentary time (+ 12 min; 3% difference) were overestimated. Characteristics underpinning the differences between measurement types were sex (χ2 = 11.9, p = 0.008), parental education (χ2 = 23.0, p = 0.001), aerobic fitness (χ2 = 10.7, p = 0.01) and academic performance (χ2 = 15.9, p = 0.001).

Conclusions: Among primary school-aged children, device-measured and self-reported use-of-time measurements should not be used interchangeably as there are systematic biases and differences relative to socio-demographic characteristics.

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