Search Results

Purpose: To evaluate the effect of strength training on Olympic time-based sports (OTBS) time-trial performance and provide an estimate of the impact of type of strength training, age, training status, and training duration on OTBS time-trial performance. Methods: A search on 3 electronic databases was conducted. The analysis comprised 32 effects in 28 studies. Posttest time-trial performance of intervention and control group from each study was used to estimate the standardized magnitude of impact of strength training on OTBS time-trial performance. Results: Strength training had a moderate positive effect on OTBS time-trial performance (effect size = 0.59, P < .01). Subgroup meta-analysis showed that heavy weight training (effect size = 0.30, P = .01) produced a significant effect, whereas other modes did not induce significant effects. Training status as factorial covariate was significant for well-trained athletes (effect size = 0.62, P = .04), but not for other training levels. Meta-regression analysis yielded nonsignificant relationship with age of the participants recruited (β = −0.04; 95% confidence interval, −0.08 to 0.004; P = .07) and training duration (β = −0.05; 95% confidence interval, −0.11 to 0.02; P = .15) as continuous covariates. Conclusion: Heavy weight training is an effective method for improving OTBS time-trial performance. Strength training has greatest impact on well-trained athletes regardless of age and training duration.

Context: In front-crawl swimming, the upper limbs perform alternating movements with the aim of achieving a continuous application of force in the water, leading to lower intracyclic velocity variation (dv). This parameter has been identified as a crucial criterion for swimmers’ evaluation. Purpose: To examine the assessment of intracyclic force variation (dF) and to analyze its relationship with dv and swimming performance. Methods: A total of 22 high-level male swimmers performed a maximal-effort 50-m front-crawl time trial and a 30-s maximal-effort fully tethered swimming test, which were randomly assigned. Instantaneous velocity was obtained by a speedometer and force by a strain-gauge system. Results: Similarity was observed between the tests, with dF attaining much higher magnitudes than dv (P < .001; d = 8.89). There were no differences in stroke rate or in physiological responses between tethered and free swimming, with a high level of agreement for the stroke rate and blood lactate increase. Swimming velocity presented a strong negative linear relationship with dF (r = −.826, P < .001) and a moderate negative nonlinear relationship with dv (r = .734, P < .01). With the addition of the maximum impulse to dF, multiple-regression analysis explained 83% of the free-swimming performance. Conclusions: Assessing dF is a promising approach for evaluating a swimmer’s performance. From the experiments, this new parameter showed that swimmers with higher dF also present higher dv, leading to a decrease in performance.

The aim was to develop a path-flow analysis model for young swimmers’ performance based on biomechanical and energetic parameters, using structural equation modeling. Thirty-eight male young swimmers served as subjects. Performance was assessed by the 200-m freestyle event. For biomechanical assessment the stroke length, the stroke frequency and the swimming velocity were analyzed. Energetics assessment included the critical velocity, the stroke index and the propulsive efficiency. The confirmatory model explained 79% of swimming performance after deleting the stroke index-performance path, which was nonsignificant (SRMR = 0.06). As a conclusion, the model is appropriate to explain performance in young swimmers.

The aim of this study was to develop a structural equation model (i.e., a confirmatory technique that analyzes relationships among observed variables) for young swimmer performance based on selected kinematic, anthropometric and hydrodynamic variables. A total of 114 subjects (73 boys and 41 girls of mean age of 12.31 ± 1.09 years; 47.91 ± 10.81 kg body mass; 156.57 ± 10.90 cm height and Tanner stages 1–2) were evaluated. The variables assessed were the: (i) 100 [m] freestyle performance; (ii) stroke index; (iii) speed fluctuation; (iv) stroke distance; (v) active drag; (vi) arm span and; (vii) hand surface area. All paths were significant (p < .05). However, in deleting the path between the hand surface area and the stroke index, the model goodness-of-fit significantly improved. Swimming performance in young swimmers appeared to be dependent on swimming efficiency (i.e., stroke index), which is determined by the remaining variables assessed, except for the hand surface area. Therefore, young swimmer coaches and practitioners should design training programs with a focus on technical training enhancement (i.e., improving swimming efficiency).

The aim of this study was to classify and identify young swimmers’ performance, and biomechanical determinant factors, and understand if both sexes can be clustered together. Thirty-eight swimmers of national level (22 boys: 15.92 ± 0.75 years and 16 girls: 14.99 ± 1.06 years) were assessed. Performance (swim speed at front crawl stroke) and a set of kinematic, efficiency, kinetic, and hydrodynamic variables were measured. Variables related to kinetics and efficiency (p < .001) were the ones that better discriminated the clusters. All three clusters included girls. Based on the interaction of these determinant factors, there are girls who can train together with boys. These findings indicate that not understanding the importance of the interplay between such determinants may lead to performance suppression in girls.

The aim was to examine the variation of linear and nonlinear proprieties of the behavior in participants with different levels of swimming expertise among the four swim strokes. Seventy-five swimmers were split into three groups (highly qualified experts, experts and nonexperts) and performed a maximal 25m trial for each of the four competitive swim strokes. A speed-meter cable was attached to the swimmer’s hip to measure hip speed; from which speed fluctuation (dv), approximate entropy (ApEn) and fractal dimension (D) variables were derived. Although simple main effects of expertise and swim stroke were obtained for dv and D, no significant interaction of expertise and stroke were found except in ApEn. The ApEn and D were prone to decrease with increasing expertise. As a conclusion, swimming does exhibit nonlinear properties but its magnitude differs according to the swim stroke and level of expertise of the performer.

The aim of this article has been to classify swimmers based on kinematics, hydrodynamics, and anthropometrics. Sixty-seven young swimmers made a maximal 25 m front-crawl to measure with a speedometer the swimming velocity (v), speed-fluctuation (dv) and dv normalized to v (dv/v). Another two 25 m bouts with and without carrying a perturbation device were made to estimate active drag coefficient (C_{D a}). Trunk transverse surface area (S) was measured with photogrammetric technique on land and in the hydrodynamic position. Cluster 1 was related to swimmers with a high speed fluctuation (ie, dv and dv/v), cluster 2 with anthropometrics (ie, S) and cluster 3 with a high hydrodynamic profile (ie, C_{D a}). The variable that seems to discriminate better the clusters was the dv/v (F = 53.680; P < .001), followed by the dv (F = 28.506; P < .001), C_{D a} (F = 21.025; P < .001), S (F = 6.297; P < .01) and v (F = 5.375; P = .01). Stepwise discriminant analysis extracted 2 functions: Function 1 was mainly defined by dv/v and S (74.3% of variance), whereas function 2 was mainly defined by C_{D a} (25.7% of variance). It can be concluded that kinematics, hydrodynamics and anthropometrics are determinant domains in which to classify and characterize young swimmers’ profiles.