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 - 367
Main Authors: Morais, Jorge E., Marinho, Daniel A., Cobley, Stephen, Barbosa, Tiago M.
Format: research tables/charts Journal Article
Published: Hakan Gur, Journal of Sports Science & Medicine Jun2023
Online Access:View this record in EBSCOhost
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      dt: Jun2023
      vid: 22
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      pid: 26030
      pub: Hakan Gur, Journal of Sports Science & Medicine
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        165629984
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        10.52082/jssm.2023.358
        165629984
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        atl: Identifying Differences in Swimming Speed Fluctuation in Age-Group Swimmers by Statistical Parametric Mapping: A Biomechanical Assessment for Performance Development.
      aug:
        au:
          Morais, Jorge E.
          Marinho, Daniel A.
          Cobley, Stephen
          Barbosa, Tiago M.
        affil: Department of Sport Sciences, Instituto Politécnico de Bragança, Bragança, Portugal
      sug:
        subj:
          Biomechanics
          Athletic Performance Evaluation
          Swimming In Adolescence
          Human
          Adolescence
          Male
          Female
          Descriptive Statistics
          Sex Factors
          Parametric Statistics
          Anthropometry
          Athletes, Elite
          Athletic Training
          Motion Analysis Systems
          Time Series
          Confidence Intervals
          Two-Way Analysis of Variance
          Data Analysis Software
          Comparative Studies
          Funding Source
          Adolescent: 13-18 years
          Male
          Female
      ab: 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.
      pubtype: Academic Journal
      doctype:
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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