ENERGY METABOLISM DURING 400 AND 100-M CRAWL SWIMMING: COMPUTER SIMULATION BASED ON FREE SWIMMING MEASUREMENTS

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1 Rodríguez F.A., Mader A.: Energy metabolism during 400 and 100-m crawl swimming: computer simulation based on free swimming measurement. In: Chatard J.C. (ed.), Biomechanics and Medicine in Swimming IX, pp Saint-Étienne: Publications de l Université de Saint-Étienne [ISBN ], ENERGY METABOLISM DURING 400 AND 100-M CRAWL SWIMMING: COMPUTER SIMULATION BASED ON FREE SWIMMING MEASUREMENTS 1) FERRAN A. RODRÍGUEZ, 2) ALOIS MADER 1) Institut Nacional d Educació Física de Catalunya, Universitat de Barcelona, Spain 2) Institute for Cardiology and Sports Medicine, Deutsche Sporthochschule Köln, Germany ABSTRACT This study aimed to approach the relative contribution of the energy delivery systems during 400 and 100-m events by simulating the dynamic behaviour of the energy metabolism during swimming on the basis of field metabolic measurements in young competitive swimmers. Ten swimmers of national level (5 M, 5 F) performed a series of five unimpeded 400-m swims (ca % V max ) and a maximal 100-m swim in a 50-m pool. Oxygen uptake was measured breath-by-breath during the immediate recovery period (Rodríguez 1999). Blood lactate was analysed in serial capillary samples. Averaged metabolic measurements for every swim were introduced into a computer simulation model (CS) based on a system of differential equations to simulate the dynamics of energy muscle metabolism (Mader et al. 1983; Mader & Heck 1994). The dynamic CS estimations showed that the aerobic energy supply ( %E tot for 400 m, and % E tot for 100 m, for males and females, respectively) clearly exceeds the values commonly found in the literature for 400 m (ranging from ca %E tot ), as well as for 100 m events (ca %E tot ). The relatively high maximal aerobic and low glycolytic power of the subjects (most of them middle-distance swimmers) partially accounts for the high contribution of the oxidative sources. However, the relevance of the aerobic contribution to total energy demands should not be underestimated, particularly in young swimmers. Key words: energy metabolism, swimming, computer simulation. INTRODUCTION The power and capacity of the immediate (ATP-PC), short-term (anaerobic glycolysis), and long-term (oxidative phosphorilation) systems of energy production are individual factors determining performance capacity in swimming, and a large part of training is devoted to the improvement of the different energy production systems (Toussaint & Hollander 1994). However, there are remarkable discrepancies in the technical and scientific literature as to how each of these systems are activated and finally contribute to the total energy output in the different events. One approach for a better insight into this problem is the modelling of experimental data using computer simulation (CS). In the last few years, a CS model of the dynamics of energy muscle metabolism has been developed and refined (Mader 1983, 1998, 2002 in press; Mader & Heck 1994). The model has been previously applied to the analysis of energy supply during sprint swimming (Ring et al. 1996). On the other hand, it is now possible to measure the main parameters needed for a better adjustment of the CS, namely speed, blood lactate and oxygen uptake, after totally unimpeded swimming (Rodríguez 1999, 2000). The aim of the present study was to approach the relative contribution of the energy delivery systems during 400 and 100-m events by simulating the dynamic behaviour of the energy metabolism during swimming on the basis of field metabolic measurements in young, endurance trained competitive swimmers.

2 METHODS Ten young competitive swimmers of national level gave their informed consent to take part in the study (Table 1). Six of them were middle-distance specialists ( m), and the other four were middle- and long-distance swimmers (400/1500 m). Table 1. Age and somatic characteristics of the subjects. Age (years) Height (cm) Weight (kg) Estimated fat a (% body mass) Males (n= 5) Females (n=5) 16.2 (15-17) ( ) 69.5 ( ) 6.56 ( ) 15.2 (14-17) ( ) 56.1 ( ) 14.2 ( ) Values are means (range) a From 6 skinfolds (Yuhasz 1977; Carter 1982) Subjects were tested during the specific pre-competitive preparatory period of the season. Training intensity and volume were reduced two days prior to the tests to avoid undue fatigue. All swimming tests were performed in a 50-m indoor pool in which the water temperature was 27 o C. Subjects completed a warm up and were instructed about the experimental procedures, including the target pace during the different swims. The tests consisted of five 400-m swims and one 100-m swim using the front crawl stroke. In the 400-m swims, swimming intensity was set from 75 up to 100 % of maximal speed according to the predicted best times for every subject, and they were asked to maintain an even pace throughout the swim. As lap times were measured every 50 m, subjects were asked to modify their speed accordingly if necessary. The last 400-m and the 100-m were all-out swims. Mean speed was calculated from the measured times in every swim. Rest periods were 5 min between the first three swims and 20 min before the two fastest swims. Ventilatory parameters were measured breath by breath (CPX II, Medical Graphics, USA) during 30 s in the immediate recovery period by applying the breathing mask to the face of the swimmers while they stood in the water, immersed to the shoulders (Rodríguez 1999, 2000). Care was taken to instruct the swimmers and assistants in order to reduce to a minimum the time of the first measurement (ca. 2-3 s after the cessation of exercise). Arterialized capillary blood samples were serially obtained during the recovery period after the swims. Blood lactate concentration was measured using the photo-enzymatic method (Boehringer-Mannheim, Germany). For the analysis of testing results, a computer simulation (CS) model based on a system of differential equations modelling the dynamics of energy muscle metabolism was used (Mader 1983, 1998, 2002 in press; Mader & Heck 1994). Metabolic measurements for every swim and gender were pooled and the average values were introduced into the CS in order to get the best fitting with the experimental results and to optimise the simulation parameters (metabolic factors and time constants). For the CS, parameters of the total adenine and phosphate pool, glycolysis, and oxidative phosphorilation were assumed, based on published studies using muscle biopsies or 31 PNMR. All data in the CS were based on one kg muscle weight (MW) and transformed relative to total body weight, assuming 33% active muscle space and 48% water space of body volume available for lactate distribution. A re-conversion factor of MW to body weight (BW) of 0.35 was used. From this simulation, estimated values for the relative contribution of the three energy delivery systems were calculated based on VO 2 max activation, maximal power output, and energy equivalents for the different sources.

3 RESULTS The results from each of the five 400-m and the 100-m swimming tests are summarised in Table 2. The average values were those introduced in the CS model for males and females, respectively. Table 2. Average speed, oxygen uptake, and blood lactate concentration after five 400-m swims at increasing speeds and one 100-m maximal swim in young male and females swimmers. Males 400 m 100 m v (m s -1 ) (0.026) (0.015) (0.030) (0.022) (0.014) (0.043) VO 2 (ml kg -1 min -1 ) 42.6 (3.6) 47.9 (2.4) 53.1 (3.1) 63.7 (5.4) 68.5 (5.7) 65.2 (3.8) [La] b (mmol L -1 ) 1.9 (0.6) 1.9 (0.8) 2,5 (0.9) 4.7 (2.0) 8.8 (3.3) 10.1 (3.1) Females 400 m 100 m v (m s -1 ) (0.027) (0.022) (0.031) (0.030) (0.038) (0.045) VO 2 (ml kg -1 min -1 ) (5.2) [La] b (mmol L -1 ) 2.0 (0.5) Values are mean (SD). (5.6) 1.6 (0.3) (6.0) 1.7 (0.3) (5.9) 2.9 (0.8) (6.4) 6.6 (1.7) (5.65) 6.5 (1.2) The results from the CS best fitting the experimental results (VO 2 and blood lactate) during the swimming tests are summarised in Table 3, in which the relative contribution to the total energy output (% E tot ) of the aerobic (E aer ), glycolytic (E an,lac ), and phosphates (E an,alac ) metabolism is also shown. Table 3. Summary results from the computer simulation. Distance P mean E aer E an, lac E an. alac (m) (W kgbw -1 ) (%) (%) (%) Males Females As an example of the metabolic modelling, Figure 1 shows the results of the best-fitting dynamic CS for the male subjects in the 400-m and 100-m maximal swims, during rest, exercise, and recovery. From these simulations the relative contribution of the three energy delivery sources was calculated.

4 (a) VO 2 VLa -DG ATP cyt ph [PCr] W/kg CLa(M) CLa(B) [ATP] [ADP] [PME] Rest Recovery (b) VLa VO 2 ph [PCr] -DG ATP cyt [ATP] W/kg CLa(M) [ADP] CLa(B) [PME] Rest Recovery Figure 1. Dynamic computer simulation of metabolic parameters during rest, exercise, and recovery for the male subjects in the 400-m (a), and 100-m (b) maximal swims. Units are indicated in the y-axes.

5 DISCUSSION The estimated mean power output (P mean = 5.5 W kgbw -1 ) for the male subjects in 400 m is in good agreement with the value calculated (5.8 W kgbw -1 ) for a mixed group of sprinters and non-sprinters using the same CS model (Ring et al. 1996). The same parameter for 100 m (8.4 W kgbw -1 ) was slightly higher than the value calculated for 50 m in a group of non-sprinters (7.8 W kgbw -1 ). The dynamic CS estimations showed that the aerobic energy supply ( %E tot for 400 m, and % E tot for 100 m, for males and females, respectively) clearly exceeds the values commonly found in the literature for 400 m (ranging from ca %E tot ), as well as for 100 m events (ca %E tot ). However, they are in agreement with the estimations by Olbrecht (2000), who did not report the method used for his calculations. Average 100 and 400 m speed, blood lactate, and oxygen uptake values denote that the group of swimmers investigated in this study were particularly well endurance-trained swimmers, most of them being middle-distance swimmers. Their relatively high maximal aerobic and low glycolytic power partially accounts for the high contribution of the oxidative sources, both in 400 and 100 m. This would explain the deviation from the estimations made by Capelli et al. (1998), who reported an average aerobic contribution of 33.3 % E tot in a 91.4 m swim at an average speed of 1.75 m s -1 by a group of male elite swimmers, substantially faster than our subjects. In addition, the CS example (Fig. 1) shows the very rapid increase of oxygen uptake. The time constant used for the mono-exponential function (τ = 20 s) is slightly lower than the average values (22.8 and 28.6 s, respectively for 100 and 400 m) found in the first study in which oxygen kinetics has been determined based on breath-by-breath measures during free swimming (Rodríguez et al. 2002). These results were in good agreement with the oxygen kinetics at the muscle level measured by nuclear magnetic resonance spectroscopy (Binzoni et al. 1992), later used by Capelli et al. (1998) for their calculations (τ = 24 s). However, the inaccuracy in the estimation of the aerobic contribution to total energy demands derived from the lower time constant used for the CS should be small. The relative energy contribution estimates for freestyle 400 m in our group of middle- and long-distance swimmers (%E aer : %E an,lac : %E an,alac = 83.2 : 10.2 : 5.8) were also in good agreement with the results reported by Ring et al. (1998) using the same CS in a group of nonsprinters (86.5 : 9.1 : 4.4). In conclusion, the common views on the relative contribution of the different energy delivery sources, frequently based on research developed on other types of exercise activities (Lavoie et Montpetit 1986), or based on linear calculations (Capelli et al. 1998), are to be revised. The modelling of the energy metabolism behaviour using computer simulation procedures, combined with free-swimming field measurements, offers new insights and allow for improved estimations on the relative contribution of the energy delivery systems. Our results clearly suggest that the relevance of the aerobic contribution to total energy demands for the distances studied (400 and 100 m) should not be underestimated, particularly in young swimmers. REFERENCES 1. Capelli, C., Pendergast, D.R., Termin, B. (1998) Energetics of swimming at maximal speeds in humans. Eur. J. Appl. Physiol. 78 : Lavoie, J.-M., Montpetit, R.R. (1986) Applied physiology of swimming. Sports Med. 3: Mader, A., H. Heck, W. Hollmann (1983) A computer simulation model of energy output in relation to metabolic rate and internal environment. In: J. Knuttgen, J., A. Vogel., J. Portmanns (eds.): Biochemistry of Exercise. International Series on Sport Sciences, Vol. 13. Campaign, III, Human Kinetics, Inc. 4. Mader, A., Heck, H. (1996) Energiestoffwechselregulation, Erweiterungen des theoretischen Konzepts und seiner Begründungen. - Nachweis der praktischen Nützlichkeit der Simulation des Energiestoffwechsels. In: Mader, A., Allmer H. (Hrsg.) (8. Jhg. 1994/2): Computersimulation - Möglichkeiten zur Theoriebildung und Ergebnisinterpretation. Brennpunkte der Sportwissenschaft. Academia Verlag, Sankt Augustin.

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