Research Group Lifestyle and Health, Research Centre Healthy and Sustainable Living, University of Applied Sciences, Utrecht, the Netherlands.
Faculty of Social and Behavioral Sciences, Department of Pedagogical and Educational Sciences, Utrecht University, Utrecht, the Netherlands.
Early Hum Dev. 2021 Jun;157:105366. doi: 10.1016/j.earlhumdev.2021.105366. Epub 2021 Apr 1.
Interindividual variability in gross motor development of infants is substantial and challenges the interpretation of motor assessments. Longitudinal research can provide insight into variability in individual gross motor trajectories.
To model a gross motor growth curve of healthy term-born infants from 3.5 to 15.5 months with the Alberta Infant Motor Scale (AIMS) and to explore groups of infants with different patterns of development.
A prospective longitudinal study including six assessments with the AIMS. A Linear Mixed Model analysis (LMM) was applied to model motor growth, controlled for covariates. Cluster analysis was used to explore groups with different pathways. Growth curves for the subgroups were modelled and differences in the covariates between the groups were described and tested.
In total, data of 103 infants was included in the LMM which showed that a cubic function (F(1,571) = 89.68, p < 0.001) fitted the data best. None of the covariates remained in the model. Cluster analysis delineated three clinically relevant groups: 1) Early developers (32%), 2) Gradual developers (46%), and 3) Late bloomers (22%). Significant differences in covariates between the groups were found for birth order, maternal education and maternal employment.
The current study contributes to knowledge about gross motor trajectories of healthy term born infants. Cluster analysis identified three groups with different gross motor trajectories. The motor growth curve provides a starting point for future research on motor trajectories of infants at risk and can contribute to accurate screening.
婴儿粗大运动发育的个体间差异很大，这对运动评估的解释提出了挑战。纵向研究可以深入了解个体粗大运动轨迹的变异性。
使用 Alberta 婴儿运动量表（AIMS）为 3.5 至 15.5 个月的健康足月婴儿建立粗大运动生长曲线，并探索具有不同发育模式的婴儿群体。
一项前瞻性纵向研究，包括六次 AIMS 评估。应用线性混合模型分析（LMM）来对运动发育进行建模，同时控制协变量。聚类分析用于探索不同发展路径的群体。为亚组建立生长曲线，并描述和测试组间协变量的差异。
共有 103 名婴儿的数据纳入 LMM，结果表明三次函数（F（1,571）= 89.68，p < 0.001）最适合拟合数据。协变量中没有一个保留在模型中。聚类分析划定了三个具有临床意义的群体：1）早期发育者（32%），2）渐进发育者（46%），3）后期发育者（22%）。在出生顺序、母亲教育程度和母亲就业方面，组间存在显著的协变量差异。
本研究为健康足月婴儿粗大运动轨迹的知识做出了贡献。聚类分析确定了具有不同粗大运动轨迹的三个群体。该运动生长曲线为处于风险中的婴儿的运动轨迹的未来研究提供了起点，并有助于进行准确的筛查。
