Loading...
Research Project
AVALIAÇÃO LONGITUDINAL DA PERFORMANCE EM NATAÇÃO PURA DESPORTIVA
Funder
Authors
Publications
Determinant Factors of Long-Term Performance Development in Young Swimmers
Publication . Morais, Jorge; Silva, António; Marinho, Daniel; Lopes, Vítor P.; Barbosa, Tiago M.
To develop a performance predictor model based on swimmers' biomechanical profile, relate the partial contribution of the main predictors with the training program, and analyze the time effect, sex effect, and time × sex interaction. 91 swimmers (44 boys, 12.04 ± 0.81 y; 47 girls, 11.22 ± 0.98 y) evaluated during a 3-y period. The decimal age and anthropometric, kinematic, and efficiency features were collected 10 different times over 3 seasons (ie, longitudinal research). Hierarchical linear modeling was the procedure used to estimate the performance predictors. Performance improved between season 1 early and season 3 late for both sexes (boys 26.9% [20.88;32.96], girls 16.1% [10.34;22.54]). Decimal age (estimate [EST] -2.05, P < .001), arm span (EST -0.59, P < .001), stroke length (EST 3.82; P = .002), and propelling efficiency (EST -0.17, P = .001) were entered in the final model. Over 3 consecutive seasons young swimmers' performance improved. Performance is a multifactorial phenomenon where anthropometrics, kinematics, and efficiency were the main determinants. The change of these factors over time was coupled with the training plans of this talent identification and development program.
Longitudinal modeling in sports: Young swimmers’ performance and biomechanics profile
Publication . Morais, Jorge; Marques, MC; Marinho, Daniel; Silva, António; Barbosa, Tiago M.
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.
Cluster Stability as a New Method to Assess Changes in Performance and its Determinant Factors Over a Season in Young Swimmers
Publication . Morais, Jorge; Silva, António; Marinho, Daniel; Seifert, Ludovic; Barbosa, Tiago M.
To apply a new method to identify, classify, and follow up young swimmers based on their performance and its determinant factors over a season and analyze the swimmers' stability over a competitive season with that method. Fifteen boys and 18 girls (11.8±0.7 y) part of a national talent-identification scheme were evaluated at 3 different moments of a competitive season. Performance (ie, official 100-m freestyle race time), arm span, chest perimeter, stroke length, swimming velocity, speed fluctuation, coefficient of active drag, propelling efficiency, and stroke index were selected as variables. Hierarchical and k-means cluster analysis were computed. Data suggested a 3-cluster solution, splitting the swimmers according to their performance in all 3 moments. Cluster 1 was related to better performances (talented swimmers), cluster 2 to poor performances (nonproficient swimmers), and cluster 3 to average performance (proficient swimmers) in all moments. Stepwise discriminant analysis revealed that 100%, 94%, and 85% of original groups were correctly classified for the 1st, 2nd, and 3rd evaluation moments, respectively (0.11≤Λ≤0.80; 5.64≤χ2≤63.40; 0.001
A Comparison of Experimental and Analytical Procedures to Measure Passive Drag in Human Swimming
Publication . Barbosa, Tiago M.; Morais, Jorge; Forte, Pedro; Neiva, Henrique; Garrido, Nuno; Marinho, Daniel
The aim of this study was to compare the swimming hydrodynamics assessed with experimental and analytical procedures, as well as, to learn about the relative contributions of the friction drag and pressure drag to total passive drag. Sixty young talented swimmers (30 boys and 30 girls with 13.59±0.77 and 12.61±0.07 years-old, respectively) were assessed. Passive drag was assessed with inverse dynamics of the gliding decay speed. The theoretical modeling included a set of analytical procedures based on naval architecture adapted to human swimming. Linear regression models between experimental and analytical procedures showed a high correlation for both passive drag (Dp = 0.777*Df+pr; R2 = 0.90; R2a = 0.90; SEE = 8.528; P<0.001) and passive drag coefficient (CDp = 1.918*CDf+pr; R2 = 0.96; R2a = 0.96; SEE = 0.029; P<0.001). On average the difference between methods was -7.002N (95%CI: -40.480; 26.475) for the passive drag and 0.127 (95%CI: 0.007; 0.247) for the passive drag coefficient. The partial contribution of friction drag and pressure drag to total passive drag was 14.12±9.33% and 85.88±9.33%, respectively. As a conclusion, there is a strong relationship between the passive drag and passive drag coefficient assessed with experimental and analytical procedures. The analytical method is a novel, feasible and valid way to gather insight about one's passive drag during training and competition. Analytical methods can be selected not only to perform race analysis during official competitions but also to monitor the swimmer's status on regular basis during training sessions without disrupting or time-consuming procedures.
Modelling the relationship between biomechanics and performance of young sprinting swimmers
Publication . Morais, Jorge; Silva, António; Marinho, Daniel; Marques, MC; Batalha, Nuno; Barbosa, Tiago M.
The aim of this study was to compute a swimming performance confirmatory model based on biomechanical parameters. The sample included 100 young swimmers (overall: 12.3 ± 0.74 years; 49 boys: 12.5 ± 0.76 years; 51 girls: 12.2 ± 0.71 years; both genders in Tanner stages 1-2 by self-report) participating on a regular basis in regional and national-level events. The 100 m freestyle event was chosen as the performance indicator. Anthropometric (arm span), strength (throwing velocity), power output (power to overcome drag), kinematic (swimming velocity) and efficiency (propelling efficiency) parameters were measured and included in the model. The path-flow analysis procedure was used to design and compute the model. The anthropometric parameter (arm span) was excluded in the final model, increasing its goodness-of-fit. The final model included the throw velocity, power output, swimming velocity and propelling efficiency. All links were significant between the parameters included, but the throw velocity-power output. The final model was explained by 69% presenting a reasonable adjustment (model's goodness-of-fit; x(2)/df = 3.89). This model shows that strength and power output parameters do play a mediator and meaningful role in the young swimmers' performance.
Organizational Units
Description
Keywords
Contributors
Funders
Funding agency
Fundação para a Ciência e a Tecnologia
Funding programme
Funding Award Number
SFRH/BD/76287/2011