Wood, M. orcid.org/0000-0003-1882-2355, Waterman, A. orcid.org/0000-0001-9882-7206, Mon-Williams, M. orcid.org/0000-0001-7595-8545 et al. (1 more author) (2024) Key kinematic measures of sensorimotor control identified via data reduction techniques in a population study (Born in Bradford). Wellcome Open Research, 9. 381. ISSN 2398-502X
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 Wood M et al. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | sensorimotor, longitudinal cohort, kinematics, data reduction, principal components analysis, confirmatory factor analysis |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Psychology (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 26 Nov 2024 10:45 |
Last Modified: | 26 Nov 2024 10:45 |
Status: | Published |
Publisher: | F1000Research |
Identification Number: | 10.12688/wellcomeopenres.22486.1 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:220111 |
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