The Use of Principal Component Analysis for Reduction in Sleep Quality and Quantity Data in Female Professional Soccer
The main aim of the present study was to uncover multivariate relationships between sleep quantity and quality using principal component analysis (PCA) in professional female soccer players. A second aim was to examine the extent to which objective sleep quantity and quality variables can discrimina...
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| Format: | Article |
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/1/148 |
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| author | Eider Barba David Casamichana Pedro Figueiredo Fábio Yuzo Nakamura Julen Castellano |
| author_facet | Eider Barba David Casamichana Pedro Figueiredo Fábio Yuzo Nakamura Julen Castellano |
| author_sort | Eider Barba |
| collection | DOAJ |
| description | The main aim of the present study was to uncover multivariate relationships between sleep quantity and quality using principal component analysis (PCA) in professional female soccer players. A second aim was to examine the extent to which objective sleep quantity and quality variables can discriminate between perceived sleep. Ten objective sleep variables from the multisensory sleep-tracker were analyzed. PCA was conducted on the sleep variables, and meaningful principal components (PCs) were identified (eigenvalue > 2). Two sleep PCs were identified, representing the ‘quantity of sleep’ (quantity PC: eigenvalue = 4.1 and variance explained = 45.1%) and the ‘quality of sleep’ (quality PC: eigenvalue = 2.4 and variance explained = 24.1%). Cluster analysis grouped the players’ sleep into three types: long and efficient, short and efficient, and long and inefficient; however, no association was found between the perceived sleep and the sleep clusters. In conclusion, a combination of both quantity and quality sleep metrics is recommended for sleep monitoring of professional female soccer players. Players should undergo a training process to improve self-assessment of sleep quality recorded from a subjective questionnaire, contrasting the perceived information with the sleep quality recorded objectively during a defined period in order to optimize the validity of their perceptions. The aim is to optimize the validity of their perceptions of sleep quality. |
| format | Article |
| id | doaj-art-f9770f6b4a1e4f398dbaefe6916ef9ca |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-f9770f6b4a1e4f398dbaefe6916ef9ca2025-01-10T13:21:02ZengMDPI AGSensors1424-82202024-12-0125114810.3390/s25010148The Use of Principal Component Analysis for Reduction in Sleep Quality and Quantity Data in Female Professional SoccerEider Barba0David Casamichana1Pedro Figueiredo2Fábio Yuzo Nakamura3Julen Castellano4Real Sociedad Institute, Real Sociedad de Fútbol S.A.D., 20170 Donostia-San Sebastian, SpainReal Sociedad Institute, Real Sociedad de Fútbol S.A.D., 20170 Donostia-San Sebastian, SpainPhysical Education Department, College of Education, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab EmiratesResearch Center in Sports Sciences, Health Sciences and Human Development (CIDESD), University of Maia, 4475-690 Maia, PortugalGIKAFIT Research Group, University of the Basque Country (UPV/EHU), 01007 Vitoria-Gasteiz, SpainThe main aim of the present study was to uncover multivariate relationships between sleep quantity and quality using principal component analysis (PCA) in professional female soccer players. A second aim was to examine the extent to which objective sleep quantity and quality variables can discriminate between perceived sleep. Ten objective sleep variables from the multisensory sleep-tracker were analyzed. PCA was conducted on the sleep variables, and meaningful principal components (PCs) were identified (eigenvalue > 2). Two sleep PCs were identified, representing the ‘quantity of sleep’ (quantity PC: eigenvalue = 4.1 and variance explained = 45.1%) and the ‘quality of sleep’ (quality PC: eigenvalue = 2.4 and variance explained = 24.1%). Cluster analysis grouped the players’ sleep into three types: long and efficient, short and efficient, and long and inefficient; however, no association was found between the perceived sleep and the sleep clusters. In conclusion, a combination of both quantity and quality sleep metrics is recommended for sleep monitoring of professional female soccer players. Players should undergo a training process to improve self-assessment of sleep quality recorded from a subjective questionnaire, contrasting the perceived information with the sleep quality recorded objectively during a defined period in order to optimize the validity of their perceptions. The aim is to optimize the validity of their perceptions of sleep quality.https://www.mdpi.com/1424-8220/25/1/148multivariate analysissleepsmart ringsoccer |
| spellingShingle | Eider Barba David Casamichana Pedro Figueiredo Fábio Yuzo Nakamura Julen Castellano The Use of Principal Component Analysis for Reduction in Sleep Quality and Quantity Data in Female Professional Soccer Sensors multivariate analysis sleep smart ring soccer |
| title | The Use of Principal Component Analysis for Reduction in Sleep Quality and Quantity Data in Female Professional Soccer |
| title_full | The Use of Principal Component Analysis for Reduction in Sleep Quality and Quantity Data in Female Professional Soccer |
| title_fullStr | The Use of Principal Component Analysis for Reduction in Sleep Quality and Quantity Data in Female Professional Soccer |
| title_full_unstemmed | The Use of Principal Component Analysis for Reduction in Sleep Quality and Quantity Data in Female Professional Soccer |
| title_short | The Use of Principal Component Analysis for Reduction in Sleep Quality and Quantity Data in Female Professional Soccer |
| title_sort | use of principal component analysis for reduction in sleep quality and quantity data in female professional soccer |
| topic | multivariate analysis sleep smart ring soccer |
| url | https://www.mdpi.com/1424-8220/25/1/148 |
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