Gauci Joshua, Olds Timothy, Maher Carol, Watson Amanda, Fraysse François, Munzberg Mason, Hoepfl Isaac, Dumuid Dorothea
Alliance for Research in Exercise, Nutrition and Activity (ARENA), Allied Health & Human Performance, University of South Australia, City East Campus, Frome Rd, Adelaide, South Australia, GPO Box 2471, Australia.
Centre for Adolescent Health, Murdoch Children's Research Institute, Parkville, VIC, Australia.
J Act Sedentary Sleep Behav. 2023 Jul 3;2(1):14. doi: 10.1186/s44167-023-00023-7.
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.
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.
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 (χ = 11.9, p = 0.008), parental education (χ = 23.0, p = 0.001), aerobic fitness (χ = 10.7, p = 0.01) and academic performance (χ = 15.9, p = 0.001).
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.
儿童睡眠、久坐不动以及参与体育活动的时长会影响他们的健康和幸福。为了提供关于儿童时间利用的准确指导方针，了解设备测量的时间使用情况与自我报告的时间使用情况之间的差异以及可能影响这些差异的因素很重要。在澳大利亚小学适龄儿童中，本研究旨在描述设备测量的和自我报告的睡眠、久坐行为、轻度身体活动（LPA）和中度至剧烈身体活动（MVPA）之间的差异，并探讨社会人口统计学和个人特征如何与这些差异相关联。
参与者（n = 120，67%为女性，年龄9 - 11岁）来自“假期生活”队列研究。设备测量的时间使用情况来自于在两年内五个时间点佩戴7天的加速度计，自我报告的时间使用情况来自于在相同时间点进行的为期2天的儿童和成人多媒体活动回忆（MARCA）。对于每位参与者和测量方法，在研究期间，针对任何重叠的日子（同时有两种测量类型）得出平均每日用于睡眠、久坐时间、LPA和MVPA的时间。参与者特征要么从基线家长调查中获取（年龄、性别、父母教育程度、青春期），要么从研究时间点的直接测量平均值中得出（穿梭跑的有氧适能、人体测量的体重指数、国家标准测试的学业成绩）。使用布兰德 - 奥特曼图描述设备测量的和自我报告的时间使用情况之间的差异。使用成分结果线性回归模型来确定哪些参与者特征与按时间使用测量类型划分的差异相关联。
相对于设备测量，自我报告的每日LPA被低估了83分钟（差异35%），而睡眠（+37分钟；差异6%）、MVPA（+34分钟；差异33%）和久坐时间（+12分钟；差异3%）被高估。测量类型之间差异的基础特征包括性别（χ = 11.9，p = 0.008）、父母教育程度（χ = 23.0，p = 0.001）、有氧适能（χ = 10.7，p = 0.01）和学业成绩（χ = 15.9，p = 0.001）。
在小学适龄儿童中，设备测量的和自我报告的时间使用测量不应互换使用，因为相对于社会人口统计学特征存在系统偏差和差异。
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