Biomechanics and Medicine in Swimming XI

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The K value, expressing the stability throughout the overall swimmer’s career, was moderate (K = 0.38 ± 0.05) with 0.33 < K < 0.43 for a 95 % confidence interval. So, based on overall values of the seven consecutive seasons, a low swimming performance stability and prediction can be considered. Table 1 presents the self-correlation values for pairwised ages throughout swimmer’s career. Self-correlations were significant in all situations (P < 0.05), except between the 16 and 17 years. Overall, throughout swimmers career, self-correlation ranged between a moderate and a high stability (0.30 < r < 0.60). Indeed, most of the pair wise self-correlations were r ≥ 0.60. Stability becomes high (r = 0.644) from 14 until 18 years old. Table 1: Pearson Correlation Coefficients from children to adult age in the 100-m Breaststroke event. Age 12 13 14 15 16 17 13 0.863* 1 14 0.610* 0.850* 1 15 0.459* 0.727* 0.867* 1 16 0.300 0.582* 0.741* 0.728* 1 17 0.300 0.505* 0.696* 0.678* 0.802* 1 18 0.398* 0.485* 0.644* 0.598* 0.730* 0.839* * P < 0.05 dIscussIon The aim of this study was to track and analyze the 100-m Breaststroke performance stability throughout the elite swimmer’s career. Main data suggests an obvious performance enhancement in the 100-m Breaststroke event, from children to adult age. Analyzing the overall tracking values, the prediction of adult swimmer’s performance level, based on childhood performance is moderate. When more strict time frames are used, swimming performance stability and prediction increases starting at the age of 14. It seems that the change from 13 to 14 years can be a milestone, were the ability to predict the swimmer’s performance level strongly increases. The trend for performance enhancement throughout swimmer’s career might be associated to some scientific improvements and innovations that along with the normal individual development process, allow swimmers to obtain better performances. As swimming performance is determined by different parameters, the individual development will affect the energy cost of swimming (Kjendlie et al., 2003). Changes in anthropometric characteristics, like body length and body mass, are identified as causes to increase energy cost (Chatard et al., 1985). Along with these morphological changes, development shows an increase in swimmers muscular strength. It appears that hormonal development that occurs during maturation has a determinant role in increasing muscle size and strength (Matos and Winsley, 2007). Furthermore, the sequence in which training loads or volumes are applied, as a series of training blocks, is critical to enhance performance. The model of training load reduction adopted before important competitions has a high effect on the athlete’s performance (Mujika et al., 2002). This type of training reduction is not common in younger ages. Another explanation for this career improvement is the technological sophistication of the swimming suits. The type of material (e.g. polyurethane), the ways to sew the fabric pieces, suit types and sizes, the effect of swim suits upon wobbling body mass, and body compression might explain the major advantages of wearing these recently developed suits leading better performances in adult ages (Marinho et al., 2009). However, despite this performance improvement during the swimmers career, a slight “breaking effect” starting at the age of 16 (Figure 1) can be observed. This fact may be associated with: (i) a maximal level of external training load reached by the swimmers, which is more difficult chaPter4.trainingandPerformance to overcome; (ii) a decrease in physiological functional capacity with age or; (iii) a slowdown or stagnation in the development of anthropometric characteristics. The self-correlation values (Table 1) ranged between moderate and high stability throughout the swimmer’s career. The initial sharp increase in stability can be observed during the change from 13 to 14 years old. This can be explained by: i) maturational process, that provides greater availability for training process, in order to obtain more ambitious performances, ii) consolidation of values and culture of swimming performance acquired throughout swimmer’s career. The K value, expressing the stability throughout the overall swimmer’s career, was low (K = 0.38 ± 0.05). Based on such long careers, it is clear that swimmers performance stability is difficult to maintain on a high level. Moreover, there are several episodes that can influence the performance stability, such as illness or an acute/chronic injury. When more strict time frames are used, performance stability and prediction increases. conclusIon The prediction of adult performance level, based on childhood performance is low, when considering the swimmer’s overall career. When more strict time frames are used, swimming performance stability and prediction increases starting at the age of 14. It seems that the change from 13 to 14 years can be a milestone, where the ability to predict the final swimmer’s performance level strongly increases. reFerences Chatard, J.C., Padilla, S., Carzorla, G. & Lacour, J.R. (1985). Influence of body height, weight, hydrostatic lift and training on the energy cost of the front crawl. NZL Sports Med, 13, 82-84 Kjendlie, P.L., Ingjer F., Madsen, O., Stallman, R.K. & Stray-Gundersen, J. (2003). Differences in the energy cost between children and adults during front crawl swimming. Eur J Appl Physiol. 91, 473-480 Landis, J.R. & Koch, G.G. (1977). The measurement of observer agreement for categorical data. Biometrics, 37, 439-446. Malina, R.M. (2001). Adherence to physical activity from childhood to adulthood: a perspective forma tracking studies. Quest, 53, 346-355. Marinho, D.A., Barbosa, T.M., Kjendlie, P.L., Vilas-Boas, J.P., Alves, F.B., Rouboa, A.I. & Silva, A.J. (2009). In: M. Peters, ed. Swimming simulation: a new tool for swimming research and practical applications. Lecture Notes in Computational Science and Engineering – Computational Fluid Dynamics for Sport Simulation. Berlin: Springer. Matos, N. & Winsley, R.L. (2007). Trainability of young athletes and overtraining. J Sport Science, 6, 353-367 Mujika, I., Padilla, S., Pyne, D. (2002). Swimming performance changes during the final 3 weeks of training leading to the Sydney 2000 Olympic Games. Int J Sports Med, 23(8), 582-587. Pyne, D., Trewin, C. & Hopkins W. (2004). Progression and variability of competitive performance of Olympic swimmers. J Sports Sci. 22(7), 613-20. AcKnoWledGMents Mário J. Costa would like to acknowledge to the Portuguese Science and Technology Foundation (FCT) for the PhD grant (SFRH/ BD/62005/2009). 273
BiomechanicsandmedicineinswimmingXi Effect of Subjective Effort on Stroke Timing in Breaststroke Swimming ohba, M. 1 , sato, s. 1 , shimoyama, Y. 2 , sato, d. 2 1 Niigata University, Niigata, Japan 2 Niigata University of Health and Welfare, Niigata, Japan This study examined the relationship between stroke timing and subjective effort during breaststroke swimming (BR) in comparison with front crawl swimming (FC). Eight 25-m swim trials were conducted, consisting of two styles (FC and BR) and four levels of subjective effort. The levels were four steps from 70–100% effort with the same clearance for one’s maximal effort. A significant positive correlation was found between subjective effort and SV. Increasing and decreasing the swimming velocity depends remarkably upon SR, not only for FC but also for BR. However, a significant interaction was found for SV. No significant interaction was found for SR. Both strokes have the same ratio of SR increase as stepping up the subjective effort, but not the same ratio of SV increase. Results show that the degree of SV increase by SR increase in BR is less than in FC, which might be attributed to technical characteristics. Key words: subjective effort, breaststroke, grading, output intensity IntroductIon It is important for competitive swimmers to subjectively control their output. To do so, they must have the capacity of changing swimming speed. It is difficult for athletes to control their own motion because they attempt to control their own motion depending on subjective sensations. They might be surprised by the difference between actual motion and imaging when they confirm their motion displayed on a monitor. Such a difference might engender poor results in competitive swimming. The relation between subjective sensations and the actual output intensity is strongly associated with the ability to grade. Some reports of studies of grading for swimming (Goya et al. 2005, 2008) have described that the subjective effort was correlated significantly with objective intensity at given conditions during 50 m crawl swimming in male and female subjects. However, no reports in the literature describe the effects of subjective effort in the timing of breaststroke swimming. This study was undertaken to examine the relation between stroke timing and subjective effort during BR in comparison with FC. Methods Subjects In this study, 22 well-trained college swimmers (11 male, 11 female) participated after giving their consent. Their main characteristics were the following: age, 19.5±0.7 y; height, 176.5±5.0 cm; mass, 69.4±4.0 kg; and age, 20.2±0.5y; height, 163.6±5.3 cm; mass, 59.3±5.2 kg. Experimental schema Figure 1 presents the experimental design. After 30 min free warm-up, each was asked to swim at an imposed subjective effort of “maximal”. Eight 25-m swim trials were conducted in all, which consisted of two swimming styles (FC and BR) and four levels of subjective effort. The levels were four steps––from 70% to 100% effort. The maximal effort trial was conducted on the fourth trial with each style. Other levels of effort were selected arbitrarily. Subjects were given 5–6 min rest between trials. Subjects were instructed as follows: 1) Swim with your subjective feeling only. 2) Do not speak about your own or another subject’s performance. 3) Swim with your highest concentration. Subjects were not informed of their swimming time after each trial. 274 Figure 1. Experimental design of this study Measurement methods Two experienced timers holding the swimming coach license, recorded the trial times using a stopwatch, enabling calculation of the swimming speed (SV, m/s). A digital camcorder (Sony Corp.) placed on the pool deck videotaped and timed the swimmers over all distances from a side view, enabling calculation of the stroke rates (SR, strokes/min) for three strokes (from 3 rd to 5 th ). The stroke length (SL, m/stroke) was computed from SV and SR values. A second camcorder (Sony Corp.) in a waterproof case (Sony Corp.) was placed underwater to record the swimmers for at least one complete stroke cycle, enabling analyses of the stroke phases. After being downloaded on a PC, the pictures were analyzed using motion analysis software (Fram Dias 4.0; DKH Corp.). Figure 2 portrays definitions of stroke phases during respective swimming styles as follows. Phase 1, from the 1 st point to the 2 nd point, included the glide phase. Phase 2, from the 2 nd point to the 3 rd point, included the propulsion phase. Phase 3, from the 3 rd point to the next 1 st point, included the recovery phase. Figure 2. Definitions of stroke phases of each swimming style. Statistical analysis Data, as the mean ± standard deviation (SD), were analyzed using computer software (SPSS v.15; SPSS Inc.). Stroke × grading level (2 × 4) repeated measures ANOVA was used to analyze the significance of changes with grading level between strokes. Tukey’s post hoc test was used. The level of significance was set to p
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average VO2 measured during resting
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Pahlen Norge AS Pahlen Norge AS Cha
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