Biomechanics and Medicine in Swimming XI

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Part (ii): Table 3 presents the results for the highly trained population that performed 100-m, 200-m and 300-m trials at the speed of the previous 400-m. Table 3. Distance effect on physiological and NASA-TLX (highlytrained population) Distance (m) HR (Bpm) [La-] (mmol.l -1 ) TWL (/100) Mental Physical Temporal demand demand demand Own Frustraperfortionmance Effort 100 168.0±10.7 3.5±0.6c, d 32±28c, d 24±28 21±22c,d 31±20 55±37 27±28 32±16c,d 200 176.0±7.8 6.4±0.9 39±24 26±21 33±27 37±13 60±27 32±24 47±20 300 180.4±8.3 7.8±1.6 a 49±24 a 40±21 56±28 a 53±25 61±25 31±16 52±21 a 400 186.1±7.2 10.4±1.8 a 50±28 a 38±21 65±29 a 46±28 53±32 34±28 63±19 a * * * * * * Significant change with distance swum, with p
BiomechanicsandmedicineinswimmingXi these subjects since testing occurred at the beginning of re-training. After three months of intensive training, however, these athletes were probably able to accurately estimate the gap between their current best competitive performance and that of the present experiment. More surprising, then, is the lower “frustration” level found in the second test. This is only speculative, but it may have been because these athletes knew what they were capable of in competitive situations at that time and did not worry that their performance in the experimental conditions was far from what they did in competition. Finally, it is interesting to note that in the different experimental conditions, a difference in TWL was accompanied by an increase in [La-] level in the comparisons of gender, age and distance swum. But whether these changes were concomitant or the lactate increase was the consequence of the change in TWL goes beyond the scope of this article. conclusIon The multi-dimensionality of the NASA-TLX questionnaire provides an insight into sport performance. It might be a useful tool to determine the characteristics of specific populations or to monitor change due to training with more precision. reFerences Borg, G. (1998). Borg’s perceived exertion and pain scales. Champaign, IL: Human Kinetics. Craig, A. b., Skehan, P. L., Pawelczyk, J. A., & Boomer, W. L. (1985). Velocity, stroke rate and distance per stroke during elite swimming competition. Med Sci Sports Exerc, 17(6), 625-634. Garcin, M. & Billat, V. (2001). Perceived exertion scales attest to both intensity and exercise duration. Percept Mot Skills, 93(3), 661-671. Hart, S. & Staveland, L. (1988). Development of the NASA-TLX: Results of empirical and theoretical research. In P. A. Hancock & N. Meshkati (Eds.), Human mental workload (pp. 139-195). Amsterdam: North Holland. Koltyn, K. F., O’Connor, P. J. & Morgan, W. P. (1991). Perception of effort in female and male competitive swimmers. Int J Sports Med, 12(4), 427-429. Medbo, J., Mohn, A., Tabata, I., Bahr, R., Vaage, O. & Sejersted, O. (1988). Anaerobic capacity determined by maximal accumulated O2 deficit. J Appl Physiol, 64(1), 50-60. Naughton, G., Farpour-Lambert, N. J., Carlson, J., Bradney, M. & Van Praagh, E. (2000). Physiological issues surrounding the performance of adolescent athletes. Sports Med, 30(5), 309-325. Newell, K. M. (1986). Constraints on the development of coordination. In M. G. Wade & H. T. A. Whiting (Eds.), Motor development in children: aspect of coordination and control (pp. 341-360). Nijhoff: Dordrecht. Reid, G. & Nygren, T. (1988). The subjective workload assessment technique: a scaling procedure for measuring mental workload. In P. A. Hancock & N. Meshkati (Eds.), Human Mental Workload. Amsterdam: North Holland: 185-218. Schnitzler, C. Ernwein, V., Seifert, L., & Chollet, D. (2007). Comparison of spatio-temporal, metabolic, and psychometric responses in recreational and highly trained swimmers during and after a 400-m freestyle swim. Int J Sports Med, 28(2), 164-171. Toussaint, H. M. & Truijens, M. (2005). Biomechanical aspects of peak performance in human swimming. Animal Biology, 55(1), 17-40. 288 Does the Y-Intercept of a Regression Line in the Critical Velocity Concept Represent the Index for Evaluating Anaerobic Capacity? shimoyama, Y. 1 , okita, K. 1 , Baba,Y. 1 , sato, d. 1 1 Niigata University of Health and Welfare, Niigata, JAPAN The purpose of the present study was to investigate whether the yintercept of a regression line in the critical velocity concept could be utilized as an index for evaluating anaerobic capacity. Twenty-one welltrained college swimmers performed a maximum effort swim over 50, 100, 200 and 400m for modeling the distance-time (D-T) relationship and measuring peak blood lactate concentration ([La]), and a 30-s allout Wingate Anaerobic Test (WAnT) performed with arms and legs. The y-intercept was significantly related to highest [La], the mean and the peak power of the WAnT performed with both arms and legs. These results suggest that the y-intercept is a valuable index for evaluating anaerobic capacity using a non-invasive method in competitive swimming. Key words: critical velocity concept, anaerobic capacity, y-intercept IntroductIon The accurate monitoring of both aerobic and anaerobic capacities is required to make a training program effective (Maglischo 2003). Therefore, several testing procedures have been presented in the literature to measure [La] at sub-maximum intensities and VO 2 max (Bosquet et al. 2002) for evaluating the aerobic capacity, and to measure the highest [La] (Lacour et al. 1990) and the maximal oxygen deficit (Ogita et al. 1996) for the anaerobic capacity. However, it is difficult to measure these indices in the field, because they rely on the use of expensive equipment and their method are invasive. A linear relationship is observed between the total work performed during an exhaustive maximal test and the time to exhaustion. In the 2-parameter model, the slope of the linear regression line has been referred to as critical power (Monod and Scherrer 1965), which was defined as the theoretical maximum power that could be maintained without exhaustion for a long time. This critical power concept was applied by Wakayoshi (1992) in swimming (the critical swimming velocity concept). Wakayoshi et al.(1992) suggested that the slope of the linear regression line between distance (D) and time required to cover it (T) at maximal speed could be a critical velocity (CV). Further, there was a positive correlation between the CV and 30-minute maximum swimming velocity (Dekerle et al. 2002), LT (Martin et al. 2000), and the velocity at [La] of 4 mmol.L -1 (V@OBLA; Wakayoshi et al. 1993). It was therefore suggested that CV was a valuable index for evaluating the aerobic capacity of swimmers using a non-invasive method. On the other hand, the y-intercept of the regression line in the critical power concept (Monod and Scherrer 1965) was called the anaerobic work capacity (AWC) (Vandewalle et al. 1997). In previous studies, AWC has been shown to be related to the highest [La] measured at the end of exhausting running exercise (Vandewalle et al. 1997), the total amount of work during the WAnT (Vandewalle et al. 1989) and the maximal oxygen deficit (Miura et al. 2002). Therefore, it was suggested that the y-intercept of the regression line in the critical power concept would represent an index of anaerobic capacity. In the critical swimming velocity concept, Oshita et al. (2009) found a highly positive correlation between the y-intercept and the residual error obtained from the relationship between 1500-m performance and CV in Fin-swimming. Besides, when modeling the energetic contribution in swimming, di Prampero et al. (2008) assumed that the y-
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average VO2 measured during resting
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Pahlen Norge AS Pahlen Norge AS Cha
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