Skip to main content

ETHNOS_APP

Home • Search • Journals • List 0

Network analysis of associations between anthropometry, physical fitness, and sport-specific performance in young canoe sprint athletes

The role of age and sex

Bibliographic Data

ID5285659
AuthorsChristian Saal (0000-0002-7740-2150, Leipzig University), Helmi Chaabene (0000-0001-7812-7931, Focus (Germany)), Norman Helm (0000-0002-6742-1358), Torsten Warnke, Olaf Prieske (0000-0003-4475-4413, University of Applied Sciences Potsdam)
Year2022
Volume4
Pages1038350-1038350
Publication date2022-11-24
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Sports and Active Living (JOURNAL)
Journal identifiersISSN: 2624-9367 • E-ISSN: 2624-9367
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fspor.2022.1038350
PMID36506721
OpenAlexW4309848130
LanguageEN
Citations received1
References cited30

IntroductionAnthropometric and physical fitness data can predict sport-specific performance (e.g., canoe sprint race time) in young athletes. Of note, inter-item correlations (i.e., multicollinearity) may exist between tests assessing similar physical qualities. However, multicollinearity among tests may change across age and/or sex due to age-/sex-specific non-linear development of test performances. Therefore, the present study aimed at analyzing inter-item correlations between anthropometric, physical fitness, and sport-specific performance data as a function of age and sex in young canoe sprint athletes.MethodsAnthropometric, physical fitness, and sport-specific performance data of 618 male and 297 female young canoe sprint athletes (discipline: male/female kayak, male canoe) were recorded during a national talent identification program between 1992 and 2019. For each discipline, a correlation matrix (i.e., network analysis) was calculated for age category (U13, U14, U15, U16) and sex including anthropometrics (e.g., standing body height, body mass), physical fitness (e.g., cardiorespiratory endurance, muscle power), and sport-specific performance (i.e., 250 and 2,000-m on-water canoe sprint time). Network plots were used to explore the correlation patterns by visual inspection. Further, trimmed means (μtrimmed) of inter-item Pearson's correlations coefficients were calculated for each discipline, age category, and sex. Effects of age and sex were analyzed using one-way ANOVAs.ResultsVisual inspection revealed consistent associations among anthropometric measures across age categories, irrespective of sex. Further, associations between physical fitness and sport-specific performance were lower with increasing age, particularly in males. In this sense, statistically significant differences for μtrimmed were observed in male canoeists (p < 0.01, ξ = 0.36) and male kayakers (p < 0.01, ξ = 0.38) with lower μtrimmed in older compared with younger athletes (i.e., ≥U15). For female kayakers, no statistically significant effect of age on μtrimmed was observed (p = 0.34, ξ = 0.14).DiscussionOur study revealed that inter-item correlation patterns (i.e., multicollinearity) of anthropometric, physical fitness, and sport-specific performance measures were lower in older (U15, U16) versus younger (U13, U14) male canoe sprint athletes but not in females. Thus, age and sex should be considered to identify predictors for sport-specific performance and design effective testing batteries for talent identification programs in canoe sprint athletes

Anthropometry · Athletes · Cardiorespiratory fitness · Physical fitness · Physical therapy · Sprint · Cardiovascular and exercise physiology · Demography · Medicine · Physical Activity and Health · Psychology · Sports Performance and Training

  • Mismatches in youth sports talent development

    Open Access•Humberto Moreira Carvalho, Carlos E Gonçalves•Frontiers in Sports and Active…•2023

  • Talent identification and development in soccer

    Audrey M Williams, Thomas Reilly•Journal of Sports Sciences•2000

  • Robust statistical methods in R using the WRS2 package

    Open Access•Patrick Mair, Rand Wilcox et al.•Behavior Research Methods•2020

  • Visualizations with statistical details

    Open Access•Indrajeet Patil•Journal of Open Source Software•2021

  • Observational research methods. Research design II

    C J Mann, Clifford Mann•Emergency Medicine Journal•2003

  • Qgraph

    Open Access•Sacha Epskamp, Angélique O J Cramer et al.•Journal of Statistical Software•2012

Unique citing works1
Citations per year0,33
Citation span2023 - 2023 (1)
Citation velocityhistorical
Highly citedNo
Citation typesNeutral: 1

Tools

Open DOIOpen Access
Ethnos_APP • Open Source Project • MIT License • Frontend v2.0.0 • Privacy and Cookies • API Documentation: api.ethnos.app/docs • API Source Code: GitHub • DOI: 10.5281/zenodo.17049435 • Frontend Source Code: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae