Biomechanics and Medicine in Swimming XI

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TABLE 1. Performances during competition events (5000-m, 1500-m, 800-m) expressed in real time and as percent of world record. S LP T5000-m (h:min:s) T1500-m (min:s) 1500-m (% wr) T-800-m (min:s) 800-m (% wr) 1 N 55:31 16:25 88.7 8 :31 89.7 2 N 53:57 16:12 89.9 8 :17 92.2 3 N 55:07 16:12 89.9 8 :32 89.6 4 I 53:46 15:51 91.9 8 :12 93.1 5 I 54:37 16:05 90.6 8 :28 90.3 6 I 54:46 16:01 91.1 8 :16 92.4 7 N 55:44 16:08 90.3 8 :22 91.3 M 55:18 16:03 90:3 8 :37 91.2 SE 00 :44 00 :15 1.1 0 :07 1.3 All swimmers completed an incremental test performed to exhaustion. This test was composed of five consecutive 300-m swims separated by 30s of rest. The speed of each increment was determined from the best time performance of each swimmer in a 400-m freestyle event achieved during the month preceding the test. The speed for the first 300m was 30s slower than the best 400-m time: this time was reduced by increments of 5s for each consecutive 300m until the final 300m performed maximally. Swimming speeds were monitored with Aquapacer ‘Solo’ (Challenge and Response, Inverurie, UK) so that each swimmer could match auditory signals with visual markers positioned every 12.5m along the border of the pool. Blood lactate level (expressed in mM/L) was determined from a fingertip blood sample which was analyzed immediately using a portable lactate analyzer (Lactate Pro, Arkray, Japan). Breath-by-breath respiratory data were collected with a portable gas analyzer (Cosmed K4b2, Rome, Italy) connected to an Aquatrainer snorkel (Cosmed, Rome, Italy). Heart rate (HR) was recorded during the K 4 b 2 exercise using a belt system (Polar Electro, Finland). The lactate threshold (LT) was mathematically determined using the DMax method (Cheng et al., 1992). HRpeak (expressed in beats·min -1 ) was defined as the highest 15-s averaged HR value. The interval training session consisted in a 500-m interval set. The swimmers were informed of their velocity using the protocol described above. Because they were wearing a snorkel, the swimmers used open turns between 50-m bouts. During a pilot run, the time needed to execute a turn with the snorkel was estimated individually at 1±0.37 s compared with a conventional kick-off turn. Swimmers were instructed to perform an open turn, always performed to the same lateral wall side, without underwater gliding and breathing continually. The velocity (m·s-1) was calculated by dividing the length of the 500-m by the time with individual correction for number of turns. Expired gases collected breath-by-breath was monitored continuously and recorded every 5-s. Serum lactate level was measured at the end of the IT session. The parameters of · VO 2 kinetics were calculated for the first 500m of the IT6x500 session. Since the intensity of the exercise was equal to LT, breath-by-breath data were fitted to two distinct models (Bell et al., 2001): a single exponential model (equation 1) and a double-exponential model (equation 2). · VO 2 (t) = A 0 + A 1 × [1 – e – (t – TD 1 )/τ 1 )] (1) Where A 0 is the baseline value (mL.min -1 ), A 1 is the asymptotic amplitude for the exponential term (mL.min -1 ), τ 1 is the time constant (sec), TD 1 is the time delay from the onset of exercise (sec) · VO 2 (t) = A 0 + A 1 × [1 – e – (t – TD 1 )/τ 1 )]*u 1 + A 2 × [1- e -(t – TD 2 )/ τ 2 (2) · where A 1 is the baseline value, TD 1 and τ 1 respectively the time delay and the time constant for the first exponential term which is the rapid phase of oxygen uptake adjustment and TD 2 (about 120 sec) and τ 2 are respectively the delay and the time constant for the second exponential chaPter3.PhysioLogyandBioenergetics term which is the slow phase of oxygen uptake adjustment. Since the first phase is unaffected by cardiodynamic factors (Bell et al., 2001), it was not modeled in this study. As a consequence, the first 20sec of data recording was removed from the analysis to ensure that the early initial component would not influence the result (Bell et al., 2001). The parameters of the model were determined by interaction minimization of the sum of the mean squares of the differences between modeled VO · 2 and measured · VO2 (Bell et al., 2001). The measured · VO2 values differing by more than three standard deviations from the modeled · VO2 were excluded from the analysis. This exclusion was performed before determining the final parameters for the model and accounted for only 2.5% of collected data. If the amplitude of the slow component was 0, the second exponential term was removed and · VO2 kinetics was modeled with the first exponential term (Bell et al., 2001). A Fisher test was used to determine the significance of the exponential model. Linear and non-linear regressions were used to analyze the distribution of residual errors between measured and modeled · VO2 over time. The determination coefficient was calculated and the Cp score computed as comparison criteria to confirm accuracy and sensitivity of the modeling. results Swimming velocity (vLT), oxygen uptake ( · VO 2 LT), and blood lactate concentration ([La]b) at LT were 1.46 ± 0.01 m·sec -1 , 352 ± 17-s, 60.1 ± 6.4 mL·kg -1 .min -1 and 3.1 ± 1.2 mmol·L -1 , respectively. · VO 2 LT was 86.8 ± 1.7% of · VO 2 max and v LT was 96.7 ± 0.5% of v · VO 2 max . v · VO 2 max was 91.1 ± 1.4 m·sec -1 of the 400-m best performance (with diving start, without snorkel and with kick-off turns). · VO 2 max , HR peak , RPE and [La] b obtained at v · VO 2 max during the increment test were 68.7 ± 5.2 mL·kg -1 . min -1 , 196.3 ± 7.3 b·min -1 , 18.4 ± 3.2 a.u., 6.9 ± 1.4 mmol·L -1 respectively. The fit was statistically superior for the two-term exponential model compared with the one-term model (r²= 0.62 ± 0.18 vs. 0.52 ± 0.21, P < 0.01). Furthermore, the Cp score was lower for the bi-exponential model indicating a lesser prediction error (Cp=33.5±20.8 vs. 35.4±20.5, p
BiomechanicsandmedicineinswimmingXi interval performed at lactate threshold, all seven swimmers exhibited a slow component of · VO2 leading to an attainment of 99% of · VO2 max. The present study demonstrated that the two-term exponential model fit performance better than the single-term exponential model when applied to a population of seven elite swimmers swimming at a velocity corresponding to LT (higher determination coefficient and lower CP score). Bell et al. (2001) demonstrated that this method of modeling the · VO2 response (two-components model from 20 sec after exercise onset to the end) is more appropriate and accurate than previous methods, even if the description of phase 3 is still unsatisfying and if the physiological basis for fitting an exponential to this phase is still unclear. The determination coefficient, r² = 0.62 ± 0.17 was slightly lower and the coefficients of variation equivalent to those observed in the ecological running setting (Borrani et al., 2001). Using the same estimation method, these authors reported that the coefficient of variation for the amplitude of the slow component A2 was 47.8% with the same · VO2 kinetics model during a limited time of running at 100% v · VO2 max. The greater imprecision compared with laboratory studies such as reported by Borrani et al. (2001) who found coefficients of variation below 15% for all parameters of the model except TD1 could be explained by mathematical and methodological differences. The number of data points for breath-by-breath analysis (240 ± 36) would have to be greater to allow for more accurate adjustment of the two-component model with six parameters. Since the bi-exponential model is non-linear, inference is based on asymptotic theory which, as recommended (Wetherill et al., 1986), would require 180 to 300 data points for our study. Another source of variability would be greater variations in exertion in the setting of ecological exercises (running, swimming) compared with laboratory conditions. The fact that our swimmers controlled their velocity using the visual or auditory landmarks probably imposed a certain degree of variability on muscle workload which was expressed by greater · VO2 variability. In addition, the turning phases with the imposed hand-turn created a pause in workload and certainly induced a variable VO · 2 response which would compromise the accuracy of the model using a complex set of parameters (Wetherill et al., 1986). The slow component recorded in this study (401.7 ± 129.9 mlO2·min-1 and 5.69 ± 1.96 ml·min-1 ·kg-1 ) was equal if not greater than those reported in swimming at equivalent if not higher intensity of exercise, but in less trained swimmers (Bentley et al., 2005; Demarie et al., 2001; Fernandez et al., 2003). Demarie et al., (2001) reported values of 239 ± 194 mlO2·min-1 when measured in six elite pentathletes exercising at above critical velocity but below v · VO2 peak (v = 1.22 ± 0.06 m·sec-1 ; tlim =375 ± 38-s ). Fernandez et al., (2008) reported values of 274.11 ± 152.83 mlO2·min-1 for a group of 15 elite swimmers performing a test to exhaustion at v · VO2 max (v = 1.46 ± 0.06 m·s-1 ; tlim =260.20 ± 60.73-s ) while Bentley et al., (2005) found a slow component (7.7±3.1 mlO2·min-1 ·kg-1 ) only in 5 of the 9 elite swimmers tested, when swimming at a velocity representing 25% of the difference between ventilatory threshold and · VO2 max (1.35±0.03 m·s-1 ) for 400-m. One study (Lucia et al., 1998) reports a slow component of 130 mlO2·min-1 in 9 professional road cyclists performing a 20-min exercise above ventilatory threshold (~80% · VO2 max; [La]b < 3 mmol·l-1 ). In agreement with the work reported by Carter et al., 2000, the hypothesis of a cause and effect relationship between the slow component and lactate accumulation or lower pH would not appear to be confirmed by our data since our athletes exhibited low serum lactate levels during the IT6*500 sessions (4.1 mmol·l-1 ) but with high slow component. This finding supports the conclusions of several researches which associate the slow component with changes in fiber type recruitment pattern (Carter et al., 2000; Gaesser et Poole, 1996; Whipp, 1996). Indeed, in the case of muscle fatigue, additional motor units or muscles (possibly less efficient) will be recruited and the · VO2 will increase in order to maintain the work rate when power output of recruited motor unit is reduced. It could be hypothesized that in swimming such progressive muscular fatigue induces a reduction in propulsive efficiency requiring a compensatory increase in stroke rate to maintain speed and consequently a delayed increase in oxygen uptake. 198 Moreover high level endurance swimmers are characterized by a strong capacity to reduce muscle lactate accumulation, so allowing them to maintain a high fraction of the · VO 2 max at speeds corresponding to the lactate threshold. Therefore high ventilatory responses led by the power of the exercise associated to the inspiratory breathing resistance increase due to the breathing in a snorkel contribute certainly to the amplitude of the O2SC. Therefore, the characteristics of the slow component observed in the present study (high amplitude A2, long delay Td2, and low time constant π2) could be attributed by both the specificity of swimming and high-level endurance of the swimmers. conclusIon Elite Long-distance swimmers attained an exceptionally high percent of · VO 2 max when swimming at the velocity corresponding to lactate threshold, with this velocity being very close to maximal speeds. All of the swimmers tested exhibited great amplitude of the slow component in their · VO 2 response. reFerences Bell C, Paterson DH, Kowalchuk JM, Padilla J, Cunningham DA (2001b). A comparison of modelling techniques used to characterise oxygen uptake kinetics during the on-transient of exercise. Exp Physiol, 86(5), 667-676, 2001(b). Bentley DJ, Roels B, Hellard P, Fauquet C, Libicz S, Millet GP (2005). Physiological responses during submaximal interval swimming training: effects of interval duration. J Sci Med Sport, 8(4), 392-402. Billat V, Demarle A, Slawinski J, Paiva M, Koralsztein JP (2001). Physical and training characteristics of top-class marathon runners. Med Sci Sports Exerc, 33(12), 2089-2097. Borrani F, Candau R, Millet GY, Perrey S, Fuchslocher J, Rouillon JD (2001). Is the ·VO2 slow component dependent on progressive recruitment of fast-twitch fibers in trained runners ? J Appl Physiol, 90(6), 2212-2220. Carter H, Jones AM, Barstow TJ, Burnley M, Williams C, Doust JH (2000). Effect of endurance training on oxygen uptake kinetics during treadmill running. J Appl Physiol, 89, 1744-1752. Cheng B., Kuipers H., Snyder A.C., Keizer H.A., Jeukendrup A., Hesselink M (1992). A new approach for the determination of ventilator and lactate thresholds. Int J Sports Med, 13, 518-522. Costill DL, Maglischo EW, Richardson AB. Swimming, Blackwell Science LTD, 1992. Demarie S, Sardella F, Billat V, Magini W, Faina M (2001). The ·VO2 slow component in swimming. Eur J Appl Physiol 84, 95-99. Fernandes RJ, Cardoso CS, Soares SM, Ascensao A, Colaco PJ, Vilas- Boas JP (2003). Time limit and VO2 slow component at intensities corresponding to VO2max in swimmers. Int J Sports Med, 24(8), 576-581. Gaesser GA, Poole DC (1996). The slow component of oxygen kinetics in human. Exerc. Sports Sc Rev, 24, 35-71. Krustrup P, Soderlund K, Mohr M, Bangsbo J (2004). The slow component of oxygen uptake during intense, sub-maximal exercise in man is associated with additional fibre recruitment. Pflugers Arch, 447(6), 855-866. Lucia A, Pardo J, Durantez A, Hoyos J, Chicharro JL (1998). Physiological differences between professional and elite road cyclists. Int J Sports Med, 19(5), 342-348. Mahood NV, Kenefick RW, Kertzer R, Quinn TJ (2001). Physiological determinants of cross-country ski racing performance. Med Sci Sports Exerc, 33(8), 1379-1384. Wetherill GB, Duncombe P, Kenward M. Regression analysis with applications. London,New York; 1986. p. 82-86; 102-104; 180-182; 226-227; 240-246. Whipp BJ. Domains of aerobic function and their limiting parameters. The Physiology and Pathophysiology of Exercise Tolerance, edited by Steinacker and Ward. Plenum Press, New York, 1996.
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Figure 2. Repeatability of the LTin
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Pahlen Norge AS Pahlen Norge AS Cha
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