Pfledderer Christopher D, Burkart Sarah, Dugger Roddrick, Parker Hannah, von Klinggraeff Lauren, Okely Anthony D, Weaver R Glenn, Beets Michael W
Department of Health Promotion and Behavioral Sciences, University of Texas Health Science Center Houston, School of Public Health in Austin, Austin, TX, 78701, USA.
Arnold School of Public Health, University of South Carolina, 921 Assembly Street, Columbia, SC, 29208, USA.
J Act Sedentary Sleep Behav. 2024 Jan 2;3(1):1. doi: 10.1186/s44167-023-00041-5.
Despite the widespread endorsement of 24-h movement guidelines (physical activity, sleep, screentime) for youth, no standardized processes for categorizing guideline achievement exists. The purpose of this study was to illustrate the impact of different data handling strategies on the proportion of children meeting 24-h movement guidelines (24hrG) and associations with overweight and obesity.
A subset of 524 children (ages 5-12 years) with complete 24-h behavior measures on at least 10 days was used to compare the impact of data handling strategies on estimates of meeting 24hrG. Physical activity and sleep were measured via accelerometry. Screentime was measured via parent self-report. Comparison of meeting 24hrG were made using (1) average of behaviors across all days (AVG-24 h), (2) classifying each day and evaluating the percentage meeting 24hrG from 10 to 100% of their measured days (DAYS-24 h), and (3) the average of a random sample of 4 days across 10 iterations (RAND-24 h). A second subset of children (N = 475) with height and weight data was used to explore the influence of each data handling strategy on children meeting guidelines and the odds of overweight/obesity via logistic regression.
Classification for AVG-24 h resulted in 14.7% of participants meeting 24hrG. Classification for DAYS-24 h resulted in 63.5% meeting 24hrG on 10% of measured days with < 1% meeting 24hrG on 100% of days. Classification for RAND-24 h resulted in 15.9% of participants meeting 24hrG. Across 10 iterations, 63.6% of participants never met 24hrG regardless of the days sampled, 3.4% always met 24hrG, with the remaining 33.0% classified as meeting 24hrG for at least one of the 10 random iterations of days. Using AVG-24 h as a strategy, meeting all three guidelines associated with lower odds of having overweight obesity (OR = 0.38, 95%CI: 0.21-0.70, p < 0.05). The RAND-24 h strategy produced a range of odds from 0.27 to 0.56. Using the criteria of needing to meet 24hrG on 100% of days, meeting all three guidelines associated with the lowest odds of having overweight and obesity as well (OR = 0.04, 95%CI: 0.01-0.18, p < 0.05).
Varying estimates of meeting the 24hrG and the odds of overweight and obesity results from different data handling strategies and days sampled.
尽管青少年24小时运动指南（身体活动、睡眠、屏幕使用时间）已得到广泛认可，但目前尚无对指南达成情况进行分类的标准化流程。本研究旨在阐明不同数据处理策略对符合24小时运动指南（24hrG）儿童比例的影响以及与超重和肥胖的关联。
选取524名5至12岁儿童的子集，这些儿童至少有10天的完整24小时行为测量数据，用于比较数据处理策略对24hrG评估的影响。身体活动和睡眠通过加速度计测量。屏幕使用时间通过家长自我报告测量。使用以下方法比较符合24hrG的情况：（1）所有天数行为的平均值（AVG - 24小时）；（2）对每一天进行分类，并评估从测量天数的10%到100%符合24hrG的百分比（DAYS - 24小时）；（3）在10次迭代中对4天的随机样本求平均值（RAND - 24小时）。另一组有身高和体重数据的儿童子集（N = 475）用于通过逻辑回归探索每种数据处理策略对符合指南儿童的影响以及超重/肥胖的几率。
AVG - 24小时分类法导致14.7%的参与者符合24hrG。DAYS - 24小时分类法导致63.5%的参与者在10%的测量天数符合24hrG，而在100%的天数中符合24hrG的比例小于1%。RAND - 24小时分类法导致15.9%的参与者符合24hrG。在10次迭代中，63.6%的参与者无论抽取哪几天都从未符合24hrG，3.4%的参与者总是符合24hrG，其余33.0%的参与者在10次随机抽取天数的迭代中至少有一次被分类为符合24hrG。以AVG - 24小时作为策略，符合所有三项指南与超重肥胖几率较低相关（OR = 0.38，95%CI：0.21 - 0.70，p < 0.05）。RAND - 24小时策略产生的几率范围为0.27至0.56。使用在100%的天数都需要符合24hrG的标准，符合所有三项指南也与超重和肥胖的最低几率相关（OR = 0.04，95%CI：0.01 - 0.18，p < 0.05）。
不同的数据处理策略和抽取的天数导致对24hrG达成情况以及超重和肥胖几率的估计存在差异。
