Longitudinal modeling in sports: Young swimmers’ performance and biomechanics profile

• Autor(a) principal: Morais, Jorge
• Data de Publicação: 2014
• Outros Autores: Marques, MC, Marinho, Daniel, Silva, António, Barbosa, Tiago M.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.6/9303
• topic: Adolescent; Athletic Performance; Biomechanical Phenomena; Child; Female; Humans; Hydrodynamics; Male; Models Statistical; Reproducibility of Results; Research Design; Seasons; Surveys and Questionnaires; Sports; Swimming

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.