No Cover Image

Journal article 1002 views 246 downloads

Physical activity, motor competence and movement and gait quality: A principal component analysis

Cain C.T. Clark, Claire Barnes Orcid Logo, Michael J. Duncan, Huw Summers Orcid Logo, Gareth Stratton Orcid Logo

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

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

  • clark2019.pdf

    PDF | Accepted Manuscript

    Released under the terms of a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND).

    Download (849.53KB)

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...

Full description

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
Tags: Add Tag
No Tags, Be the first to tag this record!
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