发现婴儿在生命的前半年内不同的运动发育轨迹
Riley C Elmer1,2, Moon Sun Kang3,4, Beth A Smith1,2,5,6
1Infant Neuromotor Control Laboratory, Division of Developmental Behavioral Pediatrics, Children's Hospital Los Angeles, Los Angeles, CA, USA.
medRxiv : the preprint server for health sciences
|February 20, 2025
概括
机器学习确定了两个不同的婴儿运动发育轨迹:低基线缓慢增长和高基线快速增长. 早产不仅仅决定了这些发育路径,突出了需要个性化婴儿评估的需要.
科学领域:
- 发育儿科 发育儿科
- 医疗保健中的机器学习
- 婴儿运动发展 婴儿运动发展
背景情况:
- 婴儿表现出不同的发育轨迹,受到诸如早产等因素的影响.
- 运动发育的个体变化需要先进的分析方法.
- 目前的方法可能无法完全捕捉婴儿发育的异质性.
研究的目的:
- 应用数据驱动的无监督机器学习来识别婴儿独特的运动发育轨迹.
- 根据运动技能的进展,将婴儿分为特定的发展路径.
- 调查早产与已识别的发育轨迹有关的作用.
主要方法:
- 在婴幼儿发育贝利尺度III版本 (BSID-III) 的运动子尺度上使用潜伏类生长分析 (LCGA).
- 分析了34名婴儿 (15名早产,19名满产) 的数据,每月评估1至6个月.
- 选择了一种线性,双类型的模型,因为它最适合粗和细动力发展.
主要成果:
- 确定了两个不同的运动发育轨迹:低基线缓慢增长 (LBSG) 和高基线快速增长 (HBFG).
- 这两种轨迹都包括了满期和早产婴儿的混合体,表明早产不是唯一的决定因素.
- 在两组之间观察到精细运动技能晚期运动复合分数的显著差异 (p = 0.04).
结论:
- 在婴儿中,LCGA有效阐明了异质的运动发育轨迹.
- 研究结果表明,早产本身并不能充分分类婴儿的发育轨迹.
- 个性化风险评估和干预措施对于优化婴儿运动发育结果至关重要.
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