Key kinematic measures of sensorimotor control identified via data reduction techniques in a population study (Born in Bradford)

Megan L. Wood, Amanda Waterman, Mark Mon-Williams, Liam J B Hill
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Abstract

Background Sensorimotor processes underpin skilled human behaviour and can thus act as an important marker of neurological status. Kinematic assessments offer objective measures of sensorimotor control but can generate countless output variables. This study sought to guide future analyses of such data by determining the key variables that capture children’s sensorimotor control on a standardised assessment battery deployed in cohort studies. Methods The Born in Bradford (BiB) longitudinal cohort study has collected sensorimotor data from 22,266 children aged 4–11 years via a computerised kinematic assessment battery (“CKAT”). CKAT measures three sensorimotor processing tasks (Tracking, Aiming, Steering). The BiB CKAT data were analysed using a “train then test” approach with two independent samples. Independent models were constructed for Tracking, Aiming, and Steering. The data were analysed using Principal Components Analysis followed by Confirmatory Factor Analysis. Results The kinematic data could be reduced to 4-7 principal components per task (decreased from >600 individual data points). These components reflect a wide range of core sensorimotor competencies including measures of both spatial and temporal accuracy. Further analyses using the derived variables showed these components capture the age-related differences reported in the literature (via a range of measures selected previously in a necessarily arbitrary way by study authors). Conclusions We identified the key variables of interest within the rich kinematic measures generated by a standardised tool for assessing sensorimotor control processes (CKAT). This work can guide future use of such data by providing a principled framework for the selection of the appropriate variables for analysis (where otherwise high levels of redundancy cause researchers to make arbitrary decisions). These methods could and should be applied in any form of kinematic assessment.
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在一项人口研究中,通过数据缩减技术确定传感器运动控制的关键运动学测量方法(出生于布拉德福德)
背景 感知运动过程是人类熟练行为的基础,因此可以作为神经状态的重要标志。运动学评估提供了感知运动控制的客观测量方法,但会产生无数的输出变量。本研究试图通过确定在队列研究中使用的标准化评估电池中反映儿童感觉运动控制能力的关键变量,为今后分析此类数据提供指导。方法 "出生在布拉德福德"(BiB)纵向队列研究通过计算机化运动学评估电池("CKAT")收集了 22,266 名 4-11 岁儿童的感觉运动数据。CKAT 测量三种感觉运动处理任务(跟踪、瞄准和转向)。BiB CKAT 数据采用 "先训练后测试 "的方法进行分析,有两个独立样本。为跟踪、瞄准和转向建立了独立模型。采用主成分分析法对数据进行分析,然后进行确证因子分析。结果 每项任务的运动学数据可缩减为 4-7 个主成分(从大于 600 个单独数据点缩减而来)。这些成分反映了广泛的核心感觉运动能力,包括空间和时间准确性的测量。使用衍生变量进行的进一步分析表明,这些成分捕捉到了文献中报道的与年龄有关的差异(通过研究作者之前任意选择的一系列测量方法)。结论 我们确定了用于评估感觉运动控制过程的标准化工具(CKAT)所产生的丰富运动测量中的关键变量。这项工作为选择适当的分析变量提供了一个原则性框架(否则大量的冗余会导致研究人员做出武断的决定),从而为今后此类数据的使用提供指导。这些方法可以也应该应用于任何形式的运动学评估。
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