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Physical activity, motor competence and movement and gait quality: A principal component analysis / Claire, Barnes; Huw, Summers; Gareth, Stratton

Human Movement Science, Volume: 68, Start page: 102523

Swansea University Authors: Claire, Barnes, Huw, Summers, Gareth, Stratton

  • Accepted Manuscript under embargo until: 1st May 2021

Abstract

ObjectiveWhile novel analytical methods have been used to examine movement behaviours, to date, no studies have examined whether a frequency-based measure, such a spectral purity, is useful in explaining key facets of human movement. The aim of this study was to investigate movement and gait quality...

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Published in: Human Movement Science
ISSN: 0167-9457
Published: Elsevier BV 2019
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa52054
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Abstract: ObjectiveWhile novel analytical methods have been used to examine movement behaviours, to date, no studies have examined whether a frequency-based measure, such a spectral purity, is useful in explaining key facets of human movement. The aim of this study was to investigate movement and gait quality, physical activity and motor competence using principal component analysis.MethodsSixty-five children (38 boys, 4.3 ± 0.7y, 1.04 ± 0.05 m, 17.8 ± 3.2 kg, BMI; 16.2 ± 1.9 kg∙m2) took part in this study. Measures included accelerometer-derived physical activity and movement quality (spectral purity), motor competence (Movement Assessment Battery for Children 2nd edition; MABC2), height, weight and waist circumference. All data were subjected to a principal component analysis, and the internal consistency of resultant components were assessed using Cronbach's alpha.ResultsTwo principal components, with excellent internal consistency (Cronbach α >0.9) were found; the 1st principal component, termed “movement component”, contained spectral purity, traffic light MABC2 score, fine motor% and gross motor% (α = 0.93); the 2nd principal component, termed “anthropometric component”, contained weight, BMI, BMI% and body fat% (α = 0.91).ConclusionThe results of the present study demonstrate that accelerometric analyses can be used to assess motor competence in an automated manner, and that spectral purity is a meaningful, indicative, metric related to children's movement quality.
Start Page: 102523