屏幕时间、体育活动和睡眠时间与美国青少年体重指数的纵向关系

IF 5.6 1区 医学 Q1 NUTRITION & DIETETICS
Jennifer Zink, Robert Booker, Dana L. Wolff-Hughes, Norrina B. Allen, Mercedes R. Carnethon, Shaina J. Alexandria, David Berrigan
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引用次数: 0

摘要

青少年使用不同形式的屏幕时间(如流媒体、游戏)可能与体重指数(BMI)有关。屏幕时间与其他行为(包括体育活动和睡眠时间)无关。统计方法(如等时替代或组成数据分析(CoDA))可以模拟这些非独立行为与健康结果之间的关联。很少有研究同时研究不同类型的屏幕时间、体育活动和睡眠时间与体重指数的关系。数据来自青少年脑认知发展研究(Adolescent Brain Cognitive Development Study)的基线(2017-2018 年)和一年随访(2018-2019 年),该研究是一项对美国青少年(N = 10,544 人,平均 [SE] 基线年龄 = 9.9 [0.03] 岁,48.9% 为女性,45.4% 为非白人)进行的具有全国代表性的多站点研究。参与者报告了每天的屏幕时间(流媒体、游戏、社交)、体育活动和睡眠时间。性别分层模型估算了基线行为与后续体重指数 Z 值之间的关系,并控制了人口统计学特征、内化症状和基线体重指数 Z 值。根据等时替代模型估计,在女性中,用 30 分钟体育活动替代 30 分钟社交活动 (β [95% CI] = -0.03 [-0.05, -0.002])、流媒体 (-0.03 [-0.05, -0.01])或游戏 (-0.03 [-0.06, -0.01])与较低的后续 BMI z 分数相关。在男性中,用 30 分钟睡眠取代 30 分钟社交(-0.03 [-0.05, -0.01])、流媒体(-0.02 [-0.03, -0.01])或游戏(-0.02 [-0.03, -0.01])与较低的后续 BMI z 值相关。在男性中,用 30 分钟游戏代替 30 分钟社交与较低的后续 BMI zcore(-0.01 [-0.03, -0.0001])相关。CoDA 估计,在男性中,相对于其他行为,基线社交时间比例越高,其后续体重指数 z 值越高 (0.05 [0.02, 0.08])。在女性中,使用 CoDA 没有观察到屏幕时间与体重指数之间的关系。屏幕时间与体重指数之间的一年纵向关系可能取决于屏幕时间的形式、屏幕时间所替代的行为(体育活动或睡眠)以及参与者的性别。其他统计方法得出的结果略有不同。通过对屏幕时间的实验操作和对观察到的性别差异背后的生物心理社会机制的调查,可以进行因果推断,并为干预措施提供依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Longitudinal associations of screen time, physical activity, and sleep duration with body mass index in U.S. youth
Youth use different forms of screen time (e.g., streaming, gaming) that may be related to body mass index (BMI). Screen time is non-independent from other behaviors, including physical activity and sleep duration. Statistical approaches such as isotemporal substitution or compositional data analysis (CoDA) can model associations between these non-independent behaviors and health outcomes. Few studies have examined different types of screen time, physical activity, and sleep duration simultaneously in relation to BMI. Data were baseline (2017–2018) and one-year follow-up (2018–2019) from the Adolescent Brain Cognitive Development Study, a multi-site study of a nationally representative sample of U.S. youth (N = 10,544, mean [SE] baseline age = 9.9 [0.03] years, 48.9% female, 45.4% non-White). Participants reported daily minutes of screen time (streaming, gaming, socializing), physical activity, and sleep. Sex-stratified models estimated the association between baseline behaviors and follow-up BMI z-score, controlling for demographic characteristics, internalizing symptoms, and BMI z-score at baseline. In females, isotemporal substitution models estimated that replacing 30 min of socializing (β [95% CI] = -0.03 [-0.05, -0.002]), streaming (-0.03 [-0.05, -0.01]), or gaming (-0.03 [-0.06, -0.01]) with 30 min of physical activity was associated with a lower follow-up BMI z-score. In males, replacing 30 min of socializing (-0.03 [-0.05, -0.01]), streaming (-0.02 [-0.03, -0.01]), or gaming (-0.02 [-0.03, -0.01]) with 30 min of sleep was associated with a lower follow-up BMI z-score. In males, replacing 30 min of socializing with 30 min of gaming was associated with a lower follow-up BMI z-score (-0.01 [-0.03, -0.0001]). CoDA estimated that in males, a greater proportion of time spent in baseline socializing, relative to the remaining behaviors, was associated with a higher follow-up BMI z-score (0.05 [0.02, 0.08]). In females, no associations between screen time and BMI were observed using CoDA. One-year longitudinal associations between screen time and BMI may depend on form of screen time, what behavior it replaces (physical activity or sleep), and participant sex. The alternative statistical approaches yielded somewhat different results. Experimental manipulation of screen time and investigation of biopsychosocial mechanisms underlying the observed sex differences will allow for causal inference and can inform interventions.
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来源期刊
CiteScore
13.80
自引率
3.40%
发文量
138
审稿时长
4-8 weeks
期刊介绍: International Journal of Behavioral Nutrition and Physical Activity (IJBNPA) is an open access, peer-reviewed journal offering high quality articles, rapid publication and wide diffusion in the public domain. IJBNPA is devoted to furthering the understanding of the behavioral aspects of diet and physical activity and is unique in its inclusion of multiple levels of analysis, including populations, groups and individuals and its inclusion of epidemiology, and behavioral, theoretical and measurement research areas.
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