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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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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