Biomechanics and Medicine in Swimming XI

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MLSSv (m.s -1 ) 1,50 1,45 1,40 1,35 1,30 1,25 y = 0.78x + 0.25 r 2 = 0.89 1,20 1,25 1,30 1,35 1,40 1,45 1,50 1,55 1,60 CV (m.s -1 ) Figure 1. Linear regression of MLSSv on CV (SSE = 0.02 m·s -1 ). MLSSv and CV were highly correlated (r = 0.94; p < 0.01). Both were associated with V400 (r = 0.97; p < 0.01 and r = 0.95; p < 0.01, respectively). Regression analysis of CV over MLSSv (SSE = 0.02 m·s -1 ) revealed that the latter can be predicted with reasonable accuracy from the 200 m / 400 m swim trials CV. Stroke cycle parameters were unrelated to performance, MLSSv or CV. 10 0 9 +1.96 SD 8 7 6 8.8 5 Mean 4 3 2 4.8 1 -1.96 SD 0 0.8 1.25 1.30 1.35 1.40 1.45 1.50 1.55 Figure 2. Bland-Altman plot showing the bias and limits of agreement between CV and MLSSv. X axis: velocity of CV and MLSS (m/s) and y-axis: CV-MLSS (%). The random scatter of points between the upper and lower confidence limits is indicative of a good fit. dIscussIon MLSS was determined employing only two to three attempts of 30-min swims at constant velocity. In a study conducted with ten inter-regional and national swimmers, Baron et al. (2005) indicated that MLSSv corresponded to 86.5 ± 5.5% of V400. Our results (extreme values of 2.6 and 7.8 mmol.L -1 ) are consistent with previous studies conducted with well-trained athletes (i.e. Baron et al., 2003) and indicate that it is impossible to link the true MLSS to a fixed lactate concentration. Dekerle et al. (2005a) found a decrease in stroke length at intensities above MLSS. It has been suggested that this decrease in SL is related to local muscular fatigue and rising of blood lactate concentration. In the present study, SL was maintained constant by all swimmers during the 30-min velocity swims at MLSS, evidencing that athletes were not under increasing metabolic imbalance. Wakayoshi et al. (1993) found that CV determined by 200 m and 400m trials almost agreed with the exercise intensity at MLSS. Our results confirm the finding of Dekerle et al. (2005b) that CV over estimates MLSSv in swimmers. The use of a 2-parameter model may explain part of the difference between CV and MLSSv, since this model provides higher estimates than the 3-paramater one, thought to be more accurate and closer to the MLSS velocity or power output (Billat et al., 2003; Morton, 2006). Another point is the variation of the swimming energy cost with velocity, tending to produce a faster CV when shorter distances are used (di Prampero et al., 2008). Bishop et al. (1998) showed that the shorter the predictive trials, the greater the slope of the distance-time regression, even in tasks where mechanical efficiency is rather constant throughout the range of intensities used, attributing this chaPter3.PhysioLogyandBioenergetics effect to the aerobic inertia dependent on the primary phase of the VO2 on-kinetics. Several studies have confirmed this shift to faster estimated swimming velocities when using shorter distances (Martin and Whyte, 2000; Toubekis et al., 2006; Reis and Alves, 2006; Costa et al., 2009). In this study, 200 m and 400 m swimming distances in the estimation of CV is recommended. This is mainly due to the usefulness of a simple enough test to be used frequently in training conditions, in spite of being aware that using more trials would improve the accuracy of the calculation (Dekerle et al., 2005b), but also because these distances comply with the physiological requirements of the model (di Prampero, 1999), corresponding to effort durations that lie between limits within which the hyperbolic relationship appears to provide a good characterization of the physiological response. Exercise durations of less than 1-min would fail, with strong probability, two criteria: the reaching of maximal oxygen consumption (VO- 2max) and the exhaustion of the available anaerobic energy. So, distances of 50 m should not be used for the estimation of CV, and it would be cautioned not to use 100 m trials as well, for the same reasons. The inclusion of a longer distance, of 600 to 800 m, would certainly lessen the tendency to overestimate a true steady metabolic rate power output (Bishop et al., 1998) but this would make the testing much more timeconsuming and less practical in a day-to-day basis. It is not advisable to include longer distances as well, not only would that increase the variation in energy cost but also would introduce another source of biasing, since the effort would be performed at a lower %VO2max. Therefore, it appears that CV in swimmers does not demarcate the transition from heavy to severe exercise and may not provide a direct non-invasive measure of MLSSv. Differences in exercise intensity of this magnitude have substantial effects on performance factors such as time to exhaustion, as well as the physiological demands of continuous exercises. However, our data also indicates that MLSSv could be estimated from CV with enough accuracy to be used as a guidance to exercise prescription. conclusIon Our results confirm that the direct determination of MLSSv remains the most accurate procedure for exercise prescription aiming at aerobic loading. This study also confirms previous observations that the CV, estimated from two repeated maximal swims, using a two-parameter model approach, overestimates MLSSv, corresponding to an intensity of exercise well into the severe domain. Swimming critical velocity is still a valuable tool to assess training adaptations and could be used as an estimator of the MLSSv. reFerences Baron, B., Dekerle, J., Depretz, S., Lefevre, T., Pelayo, P. (2005). Self selected speed and maximal lactate steady state in swimming. J Sports Med Phys Fitness; 45 (1), 1-6. Baron, B., Dekerle, J., Robin, S., Neviere, R., Dupont, L., Matran, R., Vanvelcenaher, J., Robin, H., Pelayo, P. (2003). Maximal lactate steady state does not correspond to a complete physiological steady state. Int J Sports Med; 24 (8), 582-587. Beneke, R. (2003). Methodological aspects of maximal lactate steady state-implications for performance testing. Eur J Appl Physiol; 89, 95-99. Billat, V.L., Sirvent, P., Py, G., Koralsztein, J.P., Mercier, J. (2003). The concept of maximal lactate steady state: a bridge between biochemistry, physiology and sport science. Sports Med; 33 (6), 407-426. Bishop, D., Jenkins, D.G., Howard, A. (1998). The critical power function is dependent on the duration of the predictive exercise tests chosen. Int J Sports Med; 19, 125-129. Costa, A.M., Silva, A.J., Louro, H., Reis, V.M., Garrido, N.D., Marques, M.C., Marinho, D.A. (2009). Can the curriculum be used to estimate critical velocity in young competitive swimmers. J Sports Sci & Med; 8, 17-23. 195
BiomechanicsandmedicineinswimmingXi Dekerle, J., Nesi, X., Lefevre, T., Depretz, S., Sidney, M., Marchand, F.H., Pelayo, P. (2005a). Stroking parameters in front crawl swimming and maximal lactate steady state speed. Int J Sports Med; 26, 53-58. Dekerle, J., Pelayo, P., Clipet, B., Depretz, S., Lefevre, T., Sidney, M. (2005b). Critical swimming speed does not represent the speed at maximal lactate steady state. Int J Sports Med; 26 (7), 524-530. Dekerle, J., Sidney, M., Hespel, J.M., Pelayo, P. (2002). Validity and reliability of critical speed, critical stroke rate, and anaerobic capacity in relation to front crawl swimming performances. Int J Sports Med; 23, 93-98. di Prampero, P.E. (1999). The concept of critical velocity: a brief analysis. Eur J Appl Physiol Occup Physiol; 80 (2), 162-164. di Prampero, P.E., Dekerle, J., Capelli, C., Zamparo, P. (2008). The critical velocity in swimming. Eur J Appl Physiol; 102 (2),165-171. Lavoie, J.M. and Leone, M. (1988). Functional maximal aerobic power and prediction of swimming performances. J. Swimming Research; 4 (4), 17-19. Martin, L. and Whyte, G.P. (2000). Comparison of critical swimming velocity and velocity at lactate threshold in elite triathletes. Int J Sports Med; 21 (5), 366-368. Morton, R.H. (2006). The critical power and related whole-body bioenergetic models. Eur J Appl Physiol, 96 (4), 339-354. Poole, D.C., Ward, S.A., Gardner, G.W., Whipp, B.J. (1988). A metabolic and respiratory profile of the upper limit for prolonged exercise in man. Ergonomics; 31, 1265-1279. Pringle, J.S. and Jones, A.M. (2002). Maximal lactate steady state, critical power and EMG during cycling. Eur J Appl Physiol; 88, 214-226. Reis, J. and Alves, F. (2006). Training induced changes in critical velocity and V4 in age group swimmers. In: Vilas-Boas, J.P., Alves, F. and Marques, A. (eds.). Biomechanics and Medicine in Swimming X. Portuguese Journal of Sport Sciences; Porto, 311-313. Toubekis, A.G., Tsami, A.P., Tokmakidis, S.P. (2006). Critical velocity and lactate threshold in young swimmers. Int J Sports Med; 27 (2),117-123. Wakayoshi, K., Yoshida, T., Udo, M., Kasai, T., Moritani, T., Mutoh, Y., Miyashita, M. (1992). A simple method for determining critical speed as swimming fatigue threshold in competitive swimming. Int J Sports Med; 13, 367-371. Wakayoshi, K., Yoshida, T., Udo, M., Harada, T., Moritani, T., Mutoh, Y., Miyashita, M. (1993). Does critical swimming velocity represent exercise intensity at maximal lactate steady state? Eur J Appl Physiol Occup Physiol; 66, 90-95. AcKnoWledGeMents The first author gratefully acknowledges the ‘Fundação para a Ciência e Tecnologia, Portugal’ (‘The Foundation for Science and Technology’) for their doctoral fellowship award (SFRH/BD/41417/2007). 196 Modelling the VO 2 Slow Component in Elite Long Distance Swimmers at the Velocity Associated with Lactate Threshold hellard, P. 1, 4 , dekerle, J. 2 , nesi, X. 2 , toussaint, J.F. 4 , houel, n. 1 , hausswirth, c. 3 1Département recherche, Fédération Française de Natation, Paris France 2Chelsea School, University of Brighton, England 3Département des Sciences du Sport, INSEP, Paris, France 4Institut de recherche en médecine et en épidémiologie du sport, IRMES, Paris, France Seven elite male long-distance swimmers (Mean ± SD, age 21.4 ± 3.5 yrs; weight 71 ± 5 kg ; height 180 ± 5 cm) participated in two experimental tests performed during a one-week period. The first test consisted in a 6 x 300-m incremental swimming exercise performed to exhaustion in order to determine lactate threshold (LT = 3.1 ± 1.2 mmol·L -1 ), and the velocity associated with LT (v LT = 1.45 ± 0.01 m·sec-1 = 97.3 ± 5.6% of v · VO2 max ). The parameters of the · VO2 kinetics were calculated during a 500-m interval swum at v LT using a two- and one-term exponential model. The fit was statistically superior for the two-term model (r²= 0.62 ± 0.18 vs. 0.52 ± 0.21, P < 0.01). A slow component (A2 = 401.7 ± 129.9 ml·min·kg -1 , Td 2 = 189.7 ± 63.3 s, 2 τ = 64.5 ± 114.4 s) can be observed in elite male long-distance swimmers swimming at submaximal intensities (vLT; 96.7 ± 0.5% of v · VO 2 max ). Key words: lactate threshold, Vo 2 kinetics, long distance swimmers, modeling IntroductIon The lactate threshold has been shown to represent an essential factor of high-level performance in endurance sports (marathon, Nordic skiing, triathlon, cycling; Billat et al., 2001; Mahood et al., 2001). With training, a reduction in blood lactate accumulation for a given intensity would be the result of lower lactate production and/or increased lactate clearance (Billat et al., 2001, Mahood et al., 2001) associated with lower energy cost (Billat et al., 2001). The oxygen response to constant load exercise can be characterized by three transient phases. Phase I is a rapid early rise in · VO 2 (lasting 15- 30-s) and represents the circulatory transit delay between the exercising muscle and lungs (Whipp, 1996). Following the phase I response, the phase II rise in · VO 2 is best characterized as an exponential function that attains a steady state within 2-3 min. If the intensity of the exercise is performed above the lactate threshold a slowly developing component of increasing · VO 2 (termed the slow component of O2 uptake kinetics) can be observed (Gaesser et Poole, 1996). Continuous exercise performed at the lactate threshold is also thought to reduce the amplitude of the slow component after a period of training (Carter et al., 2000; Gaesser et Poole, 1996; Whipp, 1996). The · VO 2 slow component appears to be more frequent among athletes having a high fractional · VO 2 at the lactate threshold (Billat, 2001) which is typical of ultra-endurance athletes (Billat et al., 2001; Lucia et al., 1998; Mahood et al., 2001). In high-level long-distance swimmers, it is hypothesized that a long swimming interval induces a greater slow component of · VO 2 . Methods Seven elite long-distance male swimmers participated in this study. Their performances over a 800-, 1500-, and 5000-meter front crawl test (V800, V1500, V5000) were recorded. They are expressed in percentage of the world record in Table 1.
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Pahlen Norge AS Pahlen Norge AS Cha
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