Identifying Differences in Swimming Speed Fluctuation in Age-Group Swimmers by Statistical Parametric Mapping: A Biomechanical Assessment for Performance Development
The aim of this study was to compare the assessment of swimming speed processed as a discrete variable and as a continuous variable in young swimmers. One-hundred and twenty young swimmers (60 boys: age = 12.91 ± 0.86 years; 60 girls: age = 12.46 ± 0.94 years) were analysed. The dataset for each sex...
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| Published in | Journal of sports science & medicine Vol. 22; no. 2; pp. 358 - 366 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Turkey
Journal of Sports Science and Medicine
01.06.2023
Uludag University |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1303-2968 1303-2968 |
| DOI | 10.52082/jssm.2023.358 |
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| Abstract | The aim of this study was to compare the assessment of swimming speed processed as a discrete variable and as a continuous variable in young swimmers. One-hundred and twenty young swimmers (60 boys: age = 12.91 ± 0.86 years; 60 girls: age = 12.46 ± 0.94 years) were analysed. The dataset for each sex was divided into three tiers: (i) tier #1 - best-performing swimmers; (ii) tier #2: intermediate-performing swimmers, and; (iii) tier #3 - poorest-performing swimmers. As a discrete variable, swimming speed showed significant sex and tier effects, and a significant sex*tier interaction (p < 0.001). Speed fluctuation showed a non-significant sex effect (p > 0.05), a significant tier effect (p < 0.001), and a non-significant sex*tier interaction (p > 0.05). As a continuous variable, the swimming speed time-curve presented significant sex and tier effects (p < 0.001) throughout the stroke cycle, and a significant sex*tier interaction (p < 0.05) in some moments of the stroke cycle. Swimming speed fluctuation analysed as a discrete variable and as a continuous variable can be used in a complementary way. Nonetheless, SPM can provide deeper insight into differences within the stroke cycle. Thus, coaches and practitioners should be aware that different knowledge about the swimmers’ stroke cycle can be learned by assessing swimming speed using both methods. |
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| AbstractList | The aim of this study was to compare the assessment of swimming speed processed as a discrete variable and as a continuous variable in young swimmers. One-hundred and twenty young swimmers (60 boys: age = 12.91 ± 0.86 years; 60 girls: age = 12.46 ± 0.94 years) were analysed. The dataset for each sex was divided into three tiers: (i) tier #1 - best-performing swimmers; (ii) tier #2: intermediate-performing swimmers, and; (iii) tier #3 - poorest-performing swimmers. As a discrete variable, swimming speed showed significant sex and tier effects, and a significant sex*tier interaction (p < 0.001). Speed fluctuation showed a non-significant sex effect (p > 0.05), a significant tier effect (p < 0.001), and a non-significant sex*tier interaction (p > 0.05). As a continuous variable, the swimming speed time-curve presented significant sex and tier effects (p < 0.001) throughout the stroke cycle, and a significant sex*tier interaction (p < 0.05) in some moments of the stroke cycle. Swimming speed fluctuation analysed as a discrete variable and as a continuous variable can be used in a complementary way. Nonetheless, SPM can provide deeper insight into differences within the stroke cycle. Thus, coaches and practitioners should be aware that different knowledge about the swimmers’ stroke cycle can be learned by assessing swimming speed using both methods. The aim of this study was to compare the assessment of swimming speed processed as a discrete variable and as a continuous variable in young swimmers. One-hundred and twenty young swimmers (60 boys: age = 12.91 ± 0.86 years; 60 girls: age = 12.46 ± 0.94 years) were analysed. The dataset for each sex was divided into three tiers: (i) tier #1 - best-performing swimmers; (ii) tier #2: intermediate-performing swimmers, and; (iii) tier #3 - poorest-performing swimmers. As a discrete variable, swimming speed showed significant sex and tier effects, and a significant sex*tier interaction (p < 0.001). Speed fluctuation showed a non-significant sex effect (p > 0.05), a significant tier effect (p < 0.001), and a non-significant sex*tier interaction (p > 0.05). As a continuous variable, the swimming speed time-curve presented significant sex and tier effects (p < 0.001) throughout the stroke cycle, and a significant sex*tier interaction (p < 0.05) in some moments of the stroke cycle. Swimming speed fluctuation analysed as a discrete variable and as a continuous variable can be used in a complementary way. Nonetheless, SPM can provide deeper insight into differences within the stroke cycle. Thus, coaches and practitioners should be aware that different knowledge about the swimmers’ stroke cycle can be learned by assessing swimming speed using both methods. Key points dv analysis provides insightful details about the swimmer’s net balance between thrust and drag. Less speed fluctuation is related to better performances. dv and SPM of swim speed can be used complementary. The former gives an overall bird view of the stroke-cycle, of the motor behavior. The latter drills down allowing to pinpoint in which key-moment of the cycle differences can be observed. Through dv, coaches and practitioners can quickly get an overall profile of the stroke-cycle. Conversely, choosing a continuous analysis (SPM) helps to pinpoint what are the strongest and weakest key-moments of the stroke-cycle. The selection of both dv and SPM of swim speed concurrently enables coaches and support staff to customize training programs and drills to improve technique. The aim of this study was to compare the assessment of swimming speed processed as a discrete variable and as a continuous variable in young swimmers. One-hundred and twenty young swimmers (60 boys: age = 12.91 ± 0.86 years; 60 girls: age = 12.46 ± 0.94 years) were analysed. The dataset for each sex was divided into three tiers: (i) tier #1 - best-performing swimmers; (ii) tier #2: intermediate-performing swimmers, and; (iii) tier #3 - poorest-performing swimmers. As a discrete variable, swimming speed showed significant sex and tier effects, and a significant sex*tier interaction (p < 0.001). Speed fluctuation showed a non-significant sex effect (p > 0.05), a significant tier effect (p < 0.001), and a non-significant sex*tier interaction (p > 0.05). As a continuous variable, the swimming speed time-curve presented significant sex and tier effects (p < 0.001) throughout the stroke cycle, and a significant sex*tier interaction (p < 0.05) in some moments of the stroke cycle. Swimming speed fluctuation analysed as a discrete variable and as a continuous variable can be used in a complementary way. Nonetheless, SPM can provide deeper insight into differences within the stroke cycle. Thus, coaches and practitioners should be aware that different knowledge about the swimmers' stroke cycle can be learned by assessing swimming speed using both methods.The aim of this study was to compare the assessment of swimming speed processed as a discrete variable and as a continuous variable in young swimmers. One-hundred and twenty young swimmers (60 boys: age = 12.91 ± 0.86 years; 60 girls: age = 12.46 ± 0.94 years) were analysed. The dataset for each sex was divided into three tiers: (i) tier #1 - best-performing swimmers; (ii) tier #2: intermediate-performing swimmers, and; (iii) tier #3 - poorest-performing swimmers. As a discrete variable, swimming speed showed significant sex and tier effects, and a significant sex*tier interaction (p < 0.001). Speed fluctuation showed a non-significant sex effect (p > 0.05), a significant tier effect (p < 0.001), and a non-significant sex*tier interaction (p > 0.05). As a continuous variable, the swimming speed time-curve presented significant sex and tier effects (p < 0.001) throughout the stroke cycle, and a significant sex*tier interaction (p < 0.05) in some moments of the stroke cycle. Swimming speed fluctuation analysed as a discrete variable and as a continuous variable can be used in a complementary way. Nonetheless, SPM can provide deeper insight into differences within the stroke cycle. Thus, coaches and practitioners should be aware that different knowledge about the swimmers' stroke cycle can be learned by assessing swimming speed using both methods. The aim of this study was to compare the assessment of swimming speed processed as a discrete variable and as a continuous variable in young swimmers. One-hundred and twenty young swimmers (60 boys: age = 12.91 [+ or -] 0.86 years; 60 girls: age = 12.46 [+ or -] 0.94 years) were analysed. The dataset for each sex was divided into three tiers: (i) tier #1 - best-performing swimmers; (ii) tier #2: intermediate-performing swimmers, and; (iii) tier #3 -poorest-performing swimmers. As a discrete variable, swimming speed showed significant sex and tier effects, and a significant sex*tier interaction (p < 0.001). Speed fluctuation showed a nonsignificant sex effect (p > 0.05), a significant tier effect (p < 0.001), and a non-significant sex*tier interaction (p > 0.05). As a continuous variable, the swimming speed time-curve presented significant sex and tier effects (p < 0.001) throughout the stroke cycle, and a significant sex*tier interaction (p < 0.05) in some moments of the stroke cycle. Swimming speed fluctuation analysed as a discrete variable and as a continuous variable can be used in a complementary way. Nonetheless, SPM can provide deeper insight into differences within the stroke cycle. Thus, coaches and practitioners should be aware that different knowledge about the swimmers' stroke cycle can be learned by assessing swimming speed using both methods. The aim of this study was to compare the assessment of swimming speed processed as a discrete variable and as a continuous variable in young swimmers. One-hundred and twenty young swimmers (60 boys: age = 12.91 [+ or -] 0.86 years; 60 girls: age = 12.46 [+ or -] 0.94 years) were analysed. The dataset for each sex was divided into three tiers: (i) tier #1 - best-performing swimmers; (ii) tier #2: intermediate-performing swimmers, and; (iii) tier #3 -poorest-performing swimmers. As a discrete variable, swimming speed showed significant sex and tier effects, and a significant sex*tier interaction (p < 0.001). Speed fluctuation showed a nonsignificant sex effect (p > 0.05), a significant tier effect (p < 0.001), and a non-significant sex*tier interaction (p > 0.05). As a continuous variable, the swimming speed time-curve presented significant sex and tier effects (p < 0.001) throughout the stroke cycle, and a significant sex*tier interaction (p < 0.05) in some moments of the stroke cycle. Swimming speed fluctuation analysed as a discrete variable and as a continuous variable can be used in a complementary way. Nonetheless, SPM can provide deeper insight into differences within the stroke cycle. Thus, coaches and practitioners should be aware that different knowledge about the swimmers' stroke cycle can be learned by assessing swimming speed using both methods. Key words: Assessment, biomechanics, modelling, performance, youth. The aim of this study was to compare the assessment of swimming speed processed as a discrete variable and as a continuous variable in young swimmers. One-hundred and twenty young swimmers (60 boys: age = 12.91 ± 0.86 years; 60 girls: age = 12.46 ± 0.94 years) were analysed. The dataset for each sex was divided into three tiers: (i) tier #1 - best-performing swimmers; (ii) tier #2: intermediate-performing swimmers, and; (iii) tier #3 - poorest-performing swimmers. As a discrete variable, swimming speed showed significant sex and tier effects, and a significant sex·tier interaction (p < 0.001). Speed fluctuation showed a nonsignificant sex effect (p > 0.05), a significant tier effect (p < 0.001), and a non-significant sex·tier interaction (p > 0.05). As a continuous variable, the swimming speed time-curve presented significant sex and tier effects (p < 0.001) throughout the stroke cycle, and a significant sex·tier interaction (p < 0.05) in some moments of the stroke cycle. Swimming speed fluctuation analysed as a discrete variable and as a continuous variable can be used in a complementary way. Nonetheless, SPM can provide deeper insight into differences within the stroke cycle. Thus, coaches and practitioners should be aware that different knowledge about the swimmers' stroke cycle can be learned by assessing swimming speed using both methods. |
| Audience | Academic |
| Author | Cobley, Stephen Morais, Jorge E. Barbosa, Tiago M. Marinho, Daniel A. |
| AuthorAffiliation | 1 Department of Sport Sciences, Instituto Politécnico de Bragança , Bragança, Portugal 2 Research Centre in Sports, Health and Human Development (CIDESD) , Covilhã, Portugal 3 Department of Sport Sciences, University of Beira Interior , Covilhã, Portugal 4 Discipline of Exercise & Sport Science, Faculty of Health Sciences, University of Sydney , Sydney, Australia |
| AuthorAffiliation_xml | – name: 1 Department of Sport Sciences, Instituto Politécnico de Bragança , Bragança, Portugal – name: 2 Research Centre in Sports, Health and Human Development (CIDESD) , Covilhã, Portugal – name: 3 Department of Sport Sciences, University of Beira Interior , Covilhã, Portugal – name: 4 Discipline of Exercise & Sport Science, Faculty of Health Sciences, University of Sydney , Sydney, Australia |
| Author_xml | – sequence: 1 givenname: Jorge E. surname: Morais fullname: Morais, Jorge E. – sequence: 2 givenname: Daniel A. surname: Marinho fullname: Marinho, Daniel A. – sequence: 3 givenname: Stephen surname: Cobley fullname: Cobley, Stephen – sequence: 4 givenname: Tiago M. surname: Barbosa fullname: Barbosa, Tiago M. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37293412$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Adolescent Age Analysis Biomechanical Phenomena Biomechanics Child Competitions Data collection Demographic aspects Evaluation Female Humans Kinematics Male Protozoa Sexes Swimmers Swimming |
| Title | Identifying Differences in Swimming Speed Fluctuation in Age-Group Swimmers by Statistical Parametric Mapping: A Biomechanical Assessment for Performance Development |
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