Format

Send to

Choose Destination
Hum Mov Sci. 2014 Oct;37:111-22. doi: 10.1016/j.humov.2014.07.005. Epub 2014 Aug 24.

Longitudinal modeling in sports: young swimmers' performance and biomechanics profile.

Author information

1
Department of Sport Sciences, Polytechnic Institute of Bragança, Bragança, Portugal; Research Centre in Sports, Health and Human Development, Vila Real, Portugal.
2
Department of Sport Sciences, University of Beira Interior, Covilhã, Portugal; Research Centre in Sports, Health and Human Development, Vila Real, Portugal.
3
Department of Sport Sciences, Exercise and Health, University of Trás-os-Montes and Alto Douro, Vila Real, Portugal; Research Centre in Sports, Health and Human Development, Vila Real, Portugal.
4
National Institute of Education, Nanyang Technological University, Singapore, Singapore; Research Centre in Sports, Health and Human Development, Vila Real, Portugal. Electronic address: tiago.barbosa@nie.edu.sg.

Abstract

New theories about dynamical systems highlight the multi-factorial interplay between determinant factors to achieve higher sports performances, including in swimming. Longitudinal research does provide useful information on the sportsmen's changes and how training help him to excel. These questions may be addressed in one single procedure such as latent growth modeling. The aim of the study was to model a latent growth curve of young swimmers' performance and biomechanics over a season. Fourteen boys (12.33 ± 0.65 years-old) and 16 girls (11.15 ± 0.55 years-old) were evaluated. Performance, stroke frequency, speed fluctuation, arm's propelling efficiency, active drag, active drag coefficient and power to overcome drag were collected in four different moments of the season. Latent growth curve modeling was computed to understand the longitudinal variation of performance (endogenous variables) over the season according to the biomechanics (exogenous variables). Latent growth curve modeling showed a high inter- and intra-subject variability in the performance growth. Gender had a significant effect at the baseline and during the performance growth. In each evaluation moment, different variables had a meaningful effect on performance (M1: Da, β = -0.62; M2: Da, β = -0.53; M3: η(p), β = 0.59; M4: SF, β = -0.57; all P < .001). The models' goodness-of-fit was 1.40 ⩽ χ(2)/df ⩽ 3.74 (good-reasonable). Latent modeling is a comprehensive way to gather insight about young swimmers' performance over time. Different variables were the main responsible for the performance improvement. A gender gap, intra- and inter-subject variability was verified.

KEYWORDS:

Contribution; Hydrodynamics; Kinematics; Modeling; Season adaptations

PMID:
25150801
DOI:
10.1016/j.humov.2014.07.005
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center