Biomechanics and Medicine in Swimming XI

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% anaerobic capacity; thus for elite swimmers mean training intensity is the determining component of enhanced performance and not volume or frequency (which might be different in age group swimmers). In addition it was shown that swimmers who improved their performace significantly in a season were primarily those who managed to swim the first races of a new season at relatively higher speed (in % of their personal best). Concerning high intensity training regimes, costill, Kovaleski, Porter, Kirwan, Fielding, King published already in 1985 that elite athletes might possess a stimulus threshold requiring HIT conditions, resulting in higher power output and faster race times. The coach also decided to consider this activity as a psycho-physical issue. An activity is a necessary condition which is modulated by personal traits resulting in an internal fatiguing impulse (Mujika, Busso, Geyssant, Chatard, Barale, Lacoste, 1996) and becomes the sufficient determining condition for biological adaptation. Ulmer (1986) pointed out that delayed stress triggers adaptation on the cellular level causing changes in biological mass, like mitochondria, enzymes, vessels, including energy delivery or power systems using existing genetical pathways. The effect of the daily training routines must not be separated from the daily induced stress of everyday life circumstances, like amount of sleep or psychological stress may individually influence internal thresholds related to effects of the fatiguing impulse. It was decided to separate training load (strain) from individually perceived effects (stress) and to monitor both aspects for training steering purpose (the saying “no pain no gain” can be understood as the importance of the individual reaction because a given load is not felt to be painful by all members of the squad). The stress level perceived by the athlete can be recorded using a defined scale. Training documentation formats, used so far, were replaced by a more intelligent format introduced by Mujika et. al. (1996), called MITS (mean intensity of a training season). MITS is an easy way to represent training components - intensity, volume and frequency - in a single value instead of data in abundance. Training loads are distinguished in five loading zones which are characterized by established intensity index. MITS is calculated by multiplying the volume data and stress index, respectively, summming up the products – including a dryland training equivalent- and dividing the sum by the total meter sum during the training period taken into consideration. The dryland equivalent was described by Mujika et al. (1996) “ … it was empirically considered that 1 h of dryland training was equivalent to 1 km swum at intensity I, 0.5 km at intensity IV and 0.5 km at intensity V.” How MITS-level effects the organism is unknown in detail. However, each coach would appreciate when the number of influencing parameters –which are likely to be related to each other in a still unknown way- is reduced. The purpose of this case study is to report the MITS-values and the perceived delayed stress-values based on being fatigued. Methods This is a case study. An elite female swimmer of 14 years of regular training experience and 1:10.57 for 100 m breaststroke gave her consent to document twice a day the perceived level of fatigue a) directly after the workouts and b) on the following morning according to an exhaustion scale. A stress scale was negotiated between coach and athlete before starting the monitoring period. The details were: totally refreshed = 1, recovered = 2 – 3, relaxed = 4 – 5, reposed = 6, flagged = 7, tired = 8 – 9, nerveless = 10 -11, exhausted = 12 – 13, sick = 14. The values of i) and ii) were substracted from each other. The difference between both perceived fatigue values was calculated and called delayed perceived fatigue. Over a period of 144 days MITS-values per day were calculated based on the recorded training components; MITS-values are placed between 1 and 3 and values near to 3 represent most straining situations (Mujika et al., 1996). Five intensity zones were defined according to blood lactate and combined with stress index: I1: < 2 mmol/l � 1, I2: 4 mmol/l � 2, I3: 6 mmol/l � 3, I4: 10 mmol/l � 5, I5: sprint � 8. The calculation of MITS as a representative strain factor was as follows. chaPter4.trainingandPerformance 1. multiply the distance swum per training load zone times the appropriate stress factor 2. convert dry land training duration into stress factor (here a constant factor: 4.5) 3. add (a) 1 + 2 and (b) total training volume (km per day) 4. divide (a) by (b) to get MITS-Values MITS = (n kmI1)*1 + (n kmI2)*2 + (n kmI3)*3 + (n kmI4)*5 + (n kmI5)*8 + 4.5) total km results The elite female swimmer improved her best time by 2.8 % after this macro cycle over a period of 144 days or 288 half days, respectively. In this case study data of the training components were: training volume = 605 km, training frequency = 188 half days, dryland training = 552 min and 100 half days of rest. The mean distance / day was 3.2 km. The percentages of the total distance covered at each intensity level were: I1 = 13.07 %, I2 = 75.52 %, I3 = 6.38 %, I4 = 1.24 % and I5 = 3.80 %. The calculated MITS-value (mean intensity of a training season) was 2.01 ± 0.12 arbitrary units. For a macro cycle of 16 weeks the distribution of MITS-values per week is presented (Fig 1). MITS 2,30 2,10 1,90 1,70 1,50 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Weeks Fig 1 MITS (mean intensity of a training season) in arbitrary units over a period of 16 weeks; the stars indicate competitions. The representation of weekly MITS- values show a trend towards somewhat more intensive training loads. The data of the perceived status of fatigue recorded directly after all workout sessions were: mean = 6.38 units, SD = ± 1.57 and variance = 2.58 followed the next morning by mean = 5.02, SD = ± 1.97 and variance = 3.90. The differences between the two situations are presented in Figure 2 over 144 days. The level of perceived fatigue after the training sessions is generally higher than the level after sleeping, the morning of the next day, indicating recovery due to rest at night. Fig. 2 Perceived delayed fatigue data in arbitrary units over a period of 144 days 0.00 means: no difference between perceived fatigue due to sleeping, minus means: the perceived value after sleeping is lower than after training the day before 303
BiomechanicsandmedicineinswimmingXi Fig. 3 Representation of MITS-values (black lines) per day and delayed perceived fatigue level data (grey lines) over a period of 144 days; stars indicate competitions The mutual representation of daily MITS-values and delayed perceived fatigue show two independant patterns. Delayed perceived fatigue data seem to more influenced by everyday life circumstances. dIscussIon The last improvement of the personal best happened some years before, while after this macro cycle it was 2.8 %. The caculated MITS of 2.01 ± 0.12 units seems to be fairly high when compared to results of 1.53 ± 0.06 units published by Mujika et al. (1996). The MITS-value of this case study indicates a trend towards higher training intensity which was achieved predominantly due to some new methods of dryland training. The work with the dryland-training equivalent approach led to new reflections about the influence of all activities of an athlete on the mental and physical fitness. Treating a dryland training equivalent as a summand of daily training components seems nothing particular; trying to relate this component to training volume is something else. A bridge could be that the organism is not “counting” meters to check the external strain but is internally reacting to intensity over time and the conversion of intersity to volume is like (not similar) converting intensity to power demand. Expecially in swimming, the training of fitness as such is incomplete without shaping the propelling efficiency of each stroke cycle. Consequently the swimmer’s competence to adapt the breaststroke technique to the needs to swim faster was also introduced into the training concept using the approach described by Ungerechts and Schack (1996). Elite swimmers can increase their distance per stroke due to unsteady aspects of the flow around the cruising body (Ungerechts & Klauck, 1996), among others by decreasing a swimmer’s active drag. The selection of the scale for perceived fatigue was installed after the co-workers discussed this issue thoroughly. The daily calculated individual delayed fatigue values were of great value to lower the risk of over training. Especially the athlete appreciated the individualisation of the training regime as a means to trigger adaptation. Recognizing that the recovery from fatigue while sleeping can be expressed in terms of decreasing perceived stress-factors was a challenge. The curves seem to be more influenced by everyday life circumstances. The coach and the athlete had to learn how to reason about the training of a particular person which is always combined with reasoning about individual (expected) properties, traits and personality. A unique route to improve success is not really known. The question of whether the value can be bettered by sophisticated methods, which was resolved by the fact that the adult swimmer can finally tell best what the needs will be, provided the person can rehearse, is still an open question. conclusIons The new approach intensified the motivation of everybody taking part in this cooperation. Swimming as a techno-motoric sport discipline with a strong psycho-physical endurance component is considered to be a 304 complex sports to deal with. Training frames, -methods and –means should be elements of a systematic concerted intervention which target on fitness, condition, effective strokes and stable mental processes. Monitoring training, including the individually perceived stress levels is a modern approach demanding close cooperation with the athletes because they know best their aims, intentions or how to get involved in the external steering of his/her life (by coaches). Each athlete will react differently to a given training load, based on individual biological factors and on everyday life circumstances. Successful coaches consider this when prescribing various training loads. Although a blueprint telling how much is necessary and sufficient to ensure success is still missing, a better interaction of humans based on certain data will help and is highly recommended. reFerences Chartard, J.C. & Mujika, I. (1999). Training load and performance in swimming. In: K.L. Keskinen, P.V. Komi, A.P. Hollander (eds.) Biomechanics and Medicine in Swimming VIII. Gummerus Printing, Jyväskylä, 429-434. Costill, DL, Kovaleski, J, Porter, D, Kirwan, J, Fielding, R & King, D. (1985) Energy expenditure during front crawl swimming: predicting success in middle-distance events. Int J Sports Med 6, 266-270 Costill, D.L. (1999). Training adaptation for optimal performance. In: K.L. Keskinen, P.V. Komi, A.P. Hollander (eds.) Biomechanics and Medicine in Swimming VIII. Gummerus Printing, Jyväskylä, 381-390. Holmer, I. (1983). Energetics and mencanical work in swimming. In: A.P. Hollander, P. Huijing and G. de Groot (eds.), Biomechanics and Medicine in Swimming, Human Kinetics Publishers, Champaign, IL, 154-164. Mujika, I., Chatard, J-C., Busso, T., Geyssant, A., Barale, F. & Lacoste, L. (1995). Effects of training on performance in competitive swimming. Can. J. Appl. Physiol. 20, 395-406. Mujika, I., Busso, T., Geyssant, A., Chatard, J-C., Barale, F. & Lacoste, L. (1996). training content and its effects on performance in 100 and 200 m swimmers. J.P. Troup, A.P. Hollander, D. Strass, S.W. Trappe, J.M. Cappaert, T.A. Trappe (Eds.) Biomechanics and Medicine in Swimming VII. E & FN Spon, London: 201-207. Niessen, M. Hartmann, U., Schulz, H., Heck, H., Grabow, V. & Platen, P. (2007). Selektive Blutparameter im Trainingsprozess von Langstreckenläufern. In: U. Hartmann, M Niessen, P. Spitzenpfeil (Hrsg.) Ausdauer und Ausdauertraining. Köln: Sportverlag Strauss, 195. Schultz, W.W. & Webb, P. W. (2002). Power Requirements of Swimming: Do New Methods Resolve Old Questions? Integrative and Comparative Biology. 42(5), 1018–1025. Ungerechts, B. E. & Klauck J. (2006). Consequences of non-stationary flow effects for functional attribution of swimming strokes. In: J.P. Vilas-Boas, F. Alves, A. Marques (eds.), Biomechanics and Medicine in Swimming X. Portuguese Journal of Sport Sciences, Vol. 6, Suppl. 2, 109-111. Ungerechts, B. E. & Schack, T. (2006). Mental representation of swimming strokes. In: J.P. Vilas-Boas, F. Alves, A. Marques (eds.), Biomechanics and Medicine in Swimming X. Portuguese Journal of Sport Sciences, Vol. 6,Suppl. 2, 346-348. Ulmer, H.-V. (1986). Perceived exertion as a part of a feedback system and its interaction with tactical behaviour in endurance sports. In: Borg, G., Ottoson, D. (eds.): The perception of exertion in physical work. Wenner-Gren Int. Symp. Ser., vol 46, MacMillan Press LTD, Houndmills, Basingstoke, Hampshire, London:317-326.
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Pahlen Norge AS Pahlen Norge AS Cha
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Biomechanics and Medicine in Swimming XI

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