Analysis of Children’s Physical Characteristics Based on Clustering Analysis

This study assessed the physical development, physical fitness (muscular endurance, muscular strength, flexibility, agility, power, balance), and basal metabolic rate (BMR) in a total of 4410 children aged six (73–84 months) residing in Korea. Their physical fitness was visually classified according...

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Main Authors: Eunjung Kim, Yumi Won, Jieun Shin
Format: Article
Language:English
Published: MDPI AG 2021-06-01
Series:Children
Subjects:
Online Access:https://www.mdpi.com/2227-9067/8/6/485
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spelling doaj-6e5e115c2234404ba5bd7a1f308020a12021-06-30T23:32:23ZengMDPI AGChildren2227-90672021-06-01848548510.3390/children8060485Analysis of Children’s Physical Characteristics Based on Clustering AnalysisEunjung Kim0Yumi Won1Jieun Shin2Division of Sports Science, Myongji University, Yongin-si 17058, KoreaDepartment of Exercise Rehabilitation, Daewon University College, Jecheon-si 27135, KoreaDepartment of Biomedical Informatics, College of Medicine, Konyang University, Daejeon-si 35365, KoreaThis study assessed the physical development, physical fitness (muscular endurance, muscular strength, flexibility, agility, power, balance), and basal metabolic rate (BMR) in a total of 4410 children aged six (73–84 months) residing in Korea. Their physical fitness was visually classified according to the physical fitness factor and—considering that children showed great variations in the physical fitness criteria depending on their physique and body composition—the study aimed to assess characteristics such as physique and BMR, the precursor for fat-free mass, based on the physical health clusters selected through a multivariate approach. As a result, the physical health clusters could be subdivided into four clusters: balance (1), muscular strength (2), low agility (3), and low physical fitness (3) cluster. Cluster 1 showed a high ratio of slim and slightly slim children, while cluster 2 had a high proportion of children that were obese, tall, or heavy, and had the highest BMR. We consider such results as important primary data for constituting physical fitness management programs customized to each cluster. It seems that it is necessary to have a multidirectional approach toward physical fitness evaluation and analysis methodologies that involve various physical fitness factors of children.https://www.mdpi.com/2227-9067/8/6/485childrenphysical fitnesscluster
collection DOAJ
language English
format Article
sources DOAJ
author Eunjung Kim
Yumi Won
Jieun Shin
spellingShingle Eunjung Kim
Yumi Won
Jieun Shin
Analysis of Children’s Physical Characteristics Based on Clustering Analysis
Children
children
physical fitness
cluster
author_facet Eunjung Kim
Yumi Won
Jieun Shin
author_sort Eunjung Kim
title Analysis of Children’s Physical Characteristics Based on Clustering Analysis
title_short Analysis of Children’s Physical Characteristics Based on Clustering Analysis
title_full Analysis of Children’s Physical Characteristics Based on Clustering Analysis
title_fullStr Analysis of Children’s Physical Characteristics Based on Clustering Analysis
title_full_unstemmed Analysis of Children’s Physical Characteristics Based on Clustering Analysis
title_sort analysis of children’s physical characteristics based on clustering analysis
publisher MDPI AG
series Children
issn 2227-9067
publishDate 2021-06-01
description This study assessed the physical development, physical fitness (muscular endurance, muscular strength, flexibility, agility, power, balance), and basal metabolic rate (BMR) in a total of 4410 children aged six (73–84 months) residing in Korea. Their physical fitness was visually classified according to the physical fitness factor and—considering that children showed great variations in the physical fitness criteria depending on their physique and body composition—the study aimed to assess characteristics such as physique and BMR, the precursor for fat-free mass, based on the physical health clusters selected through a multivariate approach. As a result, the physical health clusters could be subdivided into four clusters: balance (1), muscular strength (2), low agility (3), and low physical fitness (3) cluster. Cluster 1 showed a high ratio of slim and slightly slim children, while cluster 2 had a high proportion of children that were obese, tall, or heavy, and had the highest BMR. We consider such results as important primary data for constituting physical fitness management programs customized to each cluster. It seems that it is necessary to have a multidirectional approach toward physical fitness evaluation and analysis methodologies that involve various physical fitness factors of children.
topic children
physical fitness
cluster
url https://www.mdpi.com/2227-9067/8/6/485
work_keys_str_mv AT eunjungkim analysisofchildrensphysicalcharacteristicsbasedonclusteringanalysis
AT yumiwon analysisofchildrensphysicalcharacteristicsbasedonclusteringanalysis
AT jieunshin analysisofchildrensphysicalcharacteristicsbasedonclusteringanalysis
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