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.
Tiago M. Barbosa, Wan Xiu Goh, Jorge E. Morais and Mário J. Costa
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.
Jorge E. Morais, António J. Silva, Daniel A. Marinho, Ludovic Seifert and Tiago M. Barbosa
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 < P ≤ .68). Membership of clusters was moderately stable over the season (stability range 46.1–75% for the 2 clusters with most subjects).
Cluster stability is a feasible, comprehensive, and informative method to gain insight into changes in performance and its determinant factors in young swimmers. Talented swimmers were characterized by anthropometrics and kinematic features.
Jorge E. Morais, Sérgio Jesus, Vasco Lopes, Nuno Garrido, António Silva, Daniel Marinho and Tiago M. Barbosa
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).
Tiago M. Barbosa, Mário Costa, Daniel A. Marinho, Joel Coelho, Marc Moreira and António J. Silva
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.
Jorge E. Morais, António J. Silva, Daniel A. Marinho, Vítor P. Lopes and Tiago M. Barbosa
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.
Pedro G. Morouço, Tiago M. Barbosa, Raul Arellano and João P. Vilas-Boas
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.
Tiago M. Barbosa, Kelly de Jesus, J. Arturo Abraldes, João Ribeiro, Pedro Figueiredo, João Paulo Vilas-Boas and Ricardo J. Fernandes
The assessment of energetic and mechanical parameters in swimming often requires the use of an intermittent incremental protocol, whose step lengths are corner stones for the efficiency of the evaluation procedures.
To analyze changes in swimming kinematics and interlimb coordination behavior in 3 variants, with different step lengths, of an intermittent incremental protocol.
Twenty-two male swimmers performed n × d i variants of an intermittent and incremental protocol (n ≤ 7; d 1 = 200 m, d 2 = 300 m, and d 3 = 400 m). Swimmers were videotaped in the sagittal plane for 2-dimensional kinematical analysis using a dualmedia setup. Video images were digitized with a motion-capture system. Parameters that were assessed included the stroke kinematics, the segmental and anatomical landmark kinematics, and interlimb coordination. Movement efficiency was also estimated.
There were no significant variations in any of the selected variables according to the step lengths. A high to very high relationship was observed between step lengths. The bias was much reduced and the 95%CI fairly tight.
Since there were no meaningful differences between the 3 protocol variants, the 1 with shortest step length (ie, 200 m) should be adopted for logistical reasons.
Tiago M. Barbosa, Jorge E. Morais, Mário J. Costa, José Goncalves, Daniel A. Marinho and António J. Silva
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 (CD 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, CD 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), CD 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 CD 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.
Henrique P. Neiva, Mario C. Marques, Ricardo J. Fernandes, João L. Viana, Tiago M. Barbosa and Daniel A. Marinho
To investigate the effect of warm-up on 100-m swimming performance.
Twenty competitive swimmers (with a training frequency of 8.0 ± 1.0 sessions/wk) performed 2 maximal 100-m freestyle trials on separate days, with and without prior warm-up, in a counterbalanced and randomized design. The warm-up distance totaled 1000 m and replicated the swimmers’ usual precompetition warm-up strategy. Performance (time), physiological (capillary blood lactate concentrations), psychophysiological (perceived exertion), and biomechanical variables (distance per stroke, stroke frequency, and stroke index) were assessed on both trials.
Performance in the 100-m was fastest in the warm-up condition (67.15 ± 5.60 vs 68.10 ± 5.14 s; P = .01), although 3 swimmers swam faster without warm-up. Critical to this was the 1st 50-m lap (32.10 ± 2.59 vs 32.78 ± 2.33 s; P < .01), where the swimmers presented higher distance per stroke (2.06 ± 0.19 vs. 1.98 ± 0.16 m; P = .04) and swimming efficiency compared with the no-warm-up condition (stroke index 3.46 ± 0.53 vs 3.14 ± 0.44 m2 · c−1 · s−1; P < .01). Notwithstanding this better stroke-kinematic pattern, blood lactate concentrations and perceived exertion were similar between trials.
These results suggest that swimmers’ usual warm-up routines lead to faster 100-m freestyle swimming performance, a factor that appears to be related to better swimming efficiency in the 1st lap of the race. This study highlights the importance of performing swimming drills (for higher distance per stroke) before a maximal 100-m freestyle effort in similar groups of swimmers.