Studying within-person variability in children's behavior is frequently hindered by challenges collecting repeated observations. This study used wearable accelerometers to collect an intensive time series (2.7 million observations) of young children's movement at school (N = 62, M_age = 4.5 years, 54% male, 74% Non-Hispanic White) in 2021. Machine learning analyses indicated that children's typical forward acceleration was strongly correlated with lower teacher-reported inhibitory control and attention (r = −.69). Using forward movement intensity as a proxy for impulsivity, we partitioned the intensive time series and found that (1) children modulated their behavior across periods of the school day, (2) children's impulsivity increased across the school week, and (3) children with greater impulsivity showed greater variability in behavior across days.

中文翻译：

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利用幼儿运动的密集时间序列来捕捉学龄前环境中的冲动和注意力不集中的行为


研究儿童行为的个体内部变异性经常受到收集重复观察的挑战的阻碍。这项研究使用可穿戴加速度计收集了 2021 年幼儿在学校运动的密集时间序列（270 万次观察）（N = 62，M _年龄 = 4.5 岁，54% 男性，74% 非西班牙裔白人）。机器学习分析表明，儿童的典型向前加速度与教师报告的抑制控制和注意力较低 （r = −.69） 密切相关。使用向前运动强度作为冲动的代理，我们对密集时间序列进行了分区，发现 （1） 儿童在上学期间调节了他们的行为，（2） 儿童的冲动性在整个上学周都增加了，以及 （3） 冲动性较强的儿童在一天中表现出更大的行为可变性。