Biomechanics and Medicine in Swimming XI

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tude and time delay of the slow component. In our study, low adjusted coefficients for the second exponential (fitting VO2 slow component kinetics) were observed to both trials above and below RCP. Despite high variability, this variable was in an acceptable range for the goodness of fit. There was observed a substantial variability for the most of the amplitude and time parameters of VO2 kinetics above and below RCP. Although, the accuracy of the estimation was not compromised and the on-kinetic response was described comprehensively across heavy and severe domains shifting exercise intensity slight above and below RCP. The main VO2 response shown in the two swim velocities were (a) patterns in the time constant for the primary and slow components remained unchanged; (b) time delay was significantly shorter during exercising at severe domain only for primary component; and (c) although slow component was unchanged between heavy and severe domains, it lead to the attainment of VO2max when above RCP. Thus, some of trends reported to Carter et al. (2002) for changes in the amplitudes and kinetics of the VO2 response profiles on treadmill across heavy and severe domains were also present in this study. The major finding of this study is that VO2 kinetics in swimming around RCP gathers the pulmonary VO2 responses reflecting heavy and severe domains of exercise. Therefore, VO2 kinetics may be applied rather than CP to either diminish the tests used to access domain boundaries or the protocol variability associated with the determination of CP. Furthermore, if a challenge for coaches is to induce a required profile of physiological responses from a given training load, the study of VO2 kinetics during different swimming intensities could provide a valuable insight for training prescription. reFerences Beaver W.L., Wasserman K., & Whipp B.J. (1986). A new method for detecting threshold by gas exchange. J Appl Physiol, 60(6), 2020-2027. Beneke R. (2003). Maximal lactate steady state concentration (MLSS): experimental and modelling approaches. Eur J Appl Physiol, 88, 361– 369. Carter H., Pringle J.S.M., Jones A.M., & Doust J.H. (2002). Oxygen uptake kinetics during treadmill running across exercise intensity domains. Eur J Appl Physiol, 86, 347–354. Dekerle J., Baron B., Dupont L., Vanvelcenaher J., & Pelayo P. (2003). Maximal lactate steady state, respiratory compensation threshold and critical power. Eur J Appl Physiol, 89, 281–288. Dekerle J., Brickley G., Alberty M., & Pelayo P. (2009). Characterinzing the slope of the distance-time relationship in swimming. J Sci Med Sport, doi:10.1016/j.jsams.2009.05.007 Demarie S., Sardella F., Billat V., Magini W., & Faina M. (2001). The VO 2 slow component in swimming. Eur J Appl Physiol, 84, 95-99. Fernandes R.J., Cardoso C.S., Soares S.M., Ascensão A., Colaço P.J.; & Vilas-Boas J.P. (2003) Time limit and VO 2 slow component at intensities corresponding to VO 2max in swimmers. Int J Sport Med, 24, 576-581. Jones A.M., & Poole D.C. (2005). Introduction to oxygen uptake kinetics and historical development of the discipline. In Jones A.M., & Poole D.C. (eds) Oxygen uptake kinetics in sports, exercise and medicine (pp: 03-36). Routledge: Abingdon. Keskinen K.L., Rodríguez F.A., & Keskinen O.P. (2003). Respiratory snorkel and valve system for breath-by-breath gas analysis in swimming. Scand J Med Sci Sports, 13, 322–329. Morton R.H. (2006). The critical power and related whole-body bioenergetic models. Eur J Appl Physiol, 96, 339-354. Pringle J.S., & Jones A.M. (2002). Maximal lactate steady state, critical power and EMG during cycling. Eur J Appl Physiol, 88, 214–226. chaPter3.PhysioLogyandBioenergetics Hormonal, Immune, Autonomic and Mood State Variation in the Initial Preparation Phase of a Winter Season, in Portuguese Male Swimmers rama, l. 1 , Alves, F. 2 , teixeira, A. 1 1Research Centre for Sport and Physical Activity, University of Coimbra, Portugal 2Faculty of Human Kinetics, Technical University of Lisbon, Portugal At the initial phase of seasonal preparation, swimmers are submitted to a sudden increment of training load. Because the amount of training is normally less than the maximal that will be imposed later on in the season, coaches do not pay much attention to how athletes accommodate this increment. This study aims to analyse at rest, the variation of serum and salivary cortisol and testosterone, salivary IgA, HRV and the Profile of Mood States (POMS) after the first meso-cycle of a winter swimming season in a sample of 13 male Portuguese swimmers of national level. In this study, the impact of the initial phase of seasonal training on the athlete’s adaptation in a multidimensional approach was investigated. Significant variation in hormonal, mood and autonomic variables were found, which justifies paying attention to athletes work tolerance, especially in the initial phase. Key words: salivary IgA, heart rate variability, hormonal response, PoMs IntroductIon Swimming as an endurance sport is recognized as a sport where athletes are normally submitted to heavy training loads. In seasonal planning the option at the beginning of the season of a marked load in the training volume is common, aiming to hit as soon as possible, high volumes focusing on aerobic adaptation (Pyne & Goldsmith, 2005). Because this does not correspond yet to the maximal requirements of the training season, coaches do not always pay enough attention to the inability of athletes to adapt to this sudden increment of training volume (Maglischo, 2003). Although their performance capacity does not seem to be impaired, the high requirement of body resources often stresses the athlete’s adaptation capacity. This precarious balance between positive and negative influence of the training stimulus and the related fatigue, modulate training adaptation. In the domain of control of training, researchers have put their efforts into looking for biological and psychological markers that can be used in an effective way to prevent inadequate adaptation (Norris & Smith, 2002). The hormonal response to training is a recurrent strategy in the control of training. Among the several markers used, the hormones testosterone and cortisol were among the most used to follow the response to training load (Hooper, Mackinon et al, 1999). In brief, cortisol is considered because of its response to stress and catabolic action, while testosterone is used because is considered to be a marker of anabolic function. Looking at the resting values of these hormones, the literature suggests that a decrease of testosterone and increase of cortisol in endurance training athletes is common (Duclos et al. 2001). Although the controversy persists, generally the reduction of cortisol is necessary as a pre-requisite for performance improvement (Bonifazi et al. 2000). The salivary content that corresponds to the free and biological active form of the hormones may constitute a less invasive and preferable marker (Paccotti et al. 2005). It is accepted that salivary IgA is a mucosal immune marker that shows reduced values after long periods of training which could compromise training and performance capacity (Pyne et al. 2000; Gleeson et al. 2000). Heart rate variability (HRV) has become a promissing and useful indicator that reflects the autonomic response to training. Autonomic 217
BiomechanicsandmedicineinswimmingXi imbalance associating increased sympathetic activity and reduced vagal tone has been proposed as a marker of the negative impact of poorly tolerated training load (Atlaoui et al. 2007). The interest in HRV is also reinforced because of the non-invasive approach as it is possible to assess it through the use of portable heart rate monitors able to record the R-R interval. Mood states reflect the impact of training as perceived by the swimmers, and is known to be affected by difficulties of accommodation to the requirements of the training and often constitute the first signal of problems with load adaptation. The POMS questionnaire is one of the most applied instruments in sports to access alterations of athlete mood states induced by training (Morgan et al. 1988). In this study, the aim is to control the response of these variables in resting condition just before the beginning of training season and after the first meso-cycle characterized by the sustained increased volume. The main interest is to focus on the chronic effect of training. Methods The sample in this study consisted of 13 male swimmers of national Portuguese level (17.2± 1.3 years old, 174.9 ±5.8 cm, 65.8±6.8 kg of height and weight, respectively). All subjects were informed and signed the informed consent. Training volume, intensity and time spent in dry land activities were controlled. Intensity is expressed in arbitrary units of load (Mujika et al. 1995). Blood and saliva samples were collected: 1) in the beginning of the winter season, after an off training period of 6 weeks (t0) 2) after 7 weeks of training (t1), by veno puncture, always at the same time of the day (between 15 and 17h. At (t1), a time lapse of 48 hours of rest after the last training session was respected. Serum cortisol and free testosterone were determined by Electrochemi luminescent immuno-assay with the Immulite® 2000 analyser. The salivary content of cortisol and testosterone were determined by ELISA (Salimetrics® USA). Salivary IgA was determined by ELISA as it was described elsewhere (Li & Gleeson, 2004). Salivary IgA secretion rate was calculated from salivary flow rate and the IgA concentration. For the HRV assessment, the swimmers lay 8 minutes in the supine position, wearing a heart rate monitor (Polar® S810) selected for RR interval recording. 5 minutes were used for HRV analysis, excluding the first 2min and the last minute. During heart rate recording, the athletes were required to synchronize their breath pattern at 12 cycles per min-1 with the help of a sound pacer, in order to avoid perturbation determined by breathing. The software Polar Performance SW® was used to transfer data from the monitors and to analyze and correct for ectopic and missing beats. The HRV analysis was done with the Kubios HRV Analysis Software (Kuopio, FIN). Time SDNN was used as a marker of global heart rate variability. The frequency analysis was done through fast Fourier transformation controlling the LF power, HF power and LF/HF ratio upon the R-R interval power spectra. The recommendations of the Task Force of the European Society of Radiology and North American Society Electrophysiology were respected. For the mood state assessment, the Portuguese version of the Profile of Mood States POMS short form was used. Statistical analysis using nom parametric Wilcoxon test was conducted (p
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Pahlen Norge AS Pahlen Norge AS Cha
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