Profiling movement behaviours in pre-school children: A self-organised map approach

Cain Clark, Michael Duncan, Emma Eyre, Gareth Stratton, Xavier García-Massó, Isaac Estevan

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6 Citations (Scopus)
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Application of machine learning techniques has the potential to yield unseen insights into movement and permits visualisation of complex behaviours and tangible profiles. The aim of this study was to identify profiles of relative motor competence (MC) and movement behaviours in pre-school children using novel analytics. One-hundred and twenty-five children (4.3 ± 0.5y, 1.04 ± 0.05 m, 17.8 ± 3.2 kg, BMI: 16.2 ± 1.9 kg .m 2) took part in this study. Measures included accelerometer-derived 24-h activity, MC (Movement Assessment Battery for Children second edition), height, weight and waist circumference, from which zBMI were derived. Self-Organised Map (SOM) analysis was used to classify participants’ profiles and a k-means cluster analysis was used to classify the neurons into larger groups according to the input variables. These clusters were used to describe the individuals’ characteristics according to their MC and PA compositions. The SOM analysis indicated five profiles according to MC and PA. One cluster was identified as having both the lowest MC and MVPA (profile 2), whilst profiles 4 and 5 show moderate-high values of PA and MC. We present a novel pathway to profiling complex tenets of human movement and behaviour, which has never previously been implemented in pre-school children, highlighting that the focus should change from obesity monitoring, to “moving well”. Abbreviations: MC: Motor competence; PA: Physical activity; MVPA: Moderate-to-vigorous physical activity; SOM: Self-organized map; BMI: Body mass index; MABC2: Movement assessment battery for children 2 nd edition; MANOVA: Multiple analysis of variance.

Original languageEnglish
Pages (from-to)150-158
Number of pages9
JournalJournal of Sports Sciences
Issue number2
Early online date7 Nov 2019
Publication statusPublished - 17 Jan 2020

Bibliographical note

This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Sports Sciences on 07/11/2020, available online:

Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.


  • Motor Competence
  • Machine Learning
  • Unsupervised
  • Cluster Analysis
  • Physical Activity


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