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, Mage = 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前环境中的冲动和注意力不集中行为

研究儿童行为的人内差异常常受到收集重复观察结果的挑战的阻碍。这项研究使用可穿戴加速度计收集幼儿在学校运动的密集时间序列（270 万个观察值）（氮= 62，中号年龄= 2021 年 4.5 岁，54% 男性，74% 非西班牙裔白人。机器学习分析表明，儿童典型的向前加速与教师报告的较低的抑制控制和注意力密切相关（r= −.69)。使用向前运动强度作为冲动性的代表，我们对密集时间序列进行了划分，发现（1）孩子们在整个上学期间调整了他们的行为，（2）孩子们的冲动性在整个学校周中增加了，（3）更大的冲动表明几天内的行为变化更大。