Biomechanics and Medicine in Swimming XI

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Arm Coordination, Active Drag and Propelling Efficiency in Front Crawl seifert, l. 1 , schnitzler, c. 1,2 , Alberty, M. 3 , chollet, d. 1 , toussaint, h.M. 4 1 CETAPS EA 3832, Faculty of Sports Sciences, University of Rouen, France 2 Faculty of Sports Sciences, Strasbourg Marc Bloch University, France 3 LEMH EA 3608, Faculty of Sports Sciences, University of Lille, France 4 Move Institute, Vrije University, Amsterdam, The Netherlands Active drag, regularity and Index of Coordination (IdC) all increase with speed in front crawl swimming, but the link between those parameters remains unclear. The aim of this study was thus to examine the relationships between the index of coordination (IdC) and propelling efficiency (e p ) and the active drag (D). Thirteen national level male swimmers completed two incremental speed tests swimming front crawl with arms only in free condition and using a Measurement of Active Drag (MAD) system. The results showed that inter-arm coordination was linked to active drag and not propelling efficiency. Key words: Biomechanics, motor control, efficiency IntroductIon Swimming speed results from the interaction of propulsive and resistive forces. However, as the swimmer’s hand lacks a fixed push of point to propel the body forward, mechanical power applied by the hand in the water is wasted in kinetic energy imparted to the water (P k ). Thus the total mechanical power out-put (P o ) is the sum of the kinetic power (P k ) and power delivered to overcome drag force (P d ). Toussaint et al. (2006) defined the propelling efficiency (e p ) as the ratio between P d and P o . The question remains how the inter-arm coordination in front crawl can be organised to have the highest e p ? For example, does superposition mode of coordination, in comparison to catch-up mode, relates to a higher e p ? Indeed, in superposition mode the total propelling force is shared by the two hands (for a brief moment in time) and it looks like force is generated with a double hand-surface. Previously, Toussaint et al. (1991) demonstrated that using paddles e p increases by 7.8%. Chollet et al. (2000) proposed the Index of Coordination (IdC) to quantify the lag time, continuity or superposition between the propulsive actions of the two arms. These authors observed that IdC changed from catch-up (IdC0%) mode when the swimmers increased their speed from the 800-m race pace to 100-m race pace. Examining eight race paces (from 1500-m to maximal speed), Seifert et al. (2007b) noted that above a critical value of speed (1.8 m·s -1 ) and stroke rate (50 stroke·minute -1 ), only the superposition coordination mode occurred in elite sprinters. Thus, it seemed that swimming at higher speeds requires an increase in IdC. In fact, Alberty et al. (2009), Seifert et al. (2007a) respectively showed that through a 400-m and a 100-m, some swimmers increased their IdC from the first to the last lap but at the same time, decreased their swimming speed and their stroke length due to fatigue. In this case, the increase in propulsive continuity between the two arms (i.e. higher IdC) seemed an effective way to deal with fatigue, i.e. a reduction in ability to deliver work per stroke. Because of the above, the first aim of this study was to explore whether interarm coordination was linked to propelling efficiency when swimming at maximal speed on 25-m. Secondly, considering that active drag (D) increases with speed square, and that IdC also increases with speed following a quadratic regression (Seifert & Chollet, 2009), the relationship between active drag and inter-arm coordination was investigated, also. This was carried out to examine to what extent overcoming the aquatic resistances is related to the increase of the propulsive forces and to the minimization of the chaPter2.Biomechanics kinetic power or to the increase of the propulsive continuity between the two arms. In other words, there exists a challenge to explore inter-arm coordination changes as a function of active drag, and to the question wether these changes relate to propelling efficiency. Methods Thirteen national male front crawl swimmers (mean age: 21.5±3.9yr, mean height: 185.5±5.2cm, mean weight: 80.5±7.8kg, time on 100-m front crawl: 53.4±3.2, years of practice: 11.8±3.5) performed two intermittent graded speed tests in randomised order, using an arms-only front crawl stroke (using a pull-buoy): one on the MAD-system (10 bouts of 25-m) and one in the free swimming condition (8 bouts of 25-m), from slow (~60%) to 100% of maximal speed (with an absolute increment of 0.05 m·s-1 , which corresponded to a relative increment of 5% of maximal speed). The bout was self-paced to avoid the speed variations that can arise when the swimmer follows a target. To be sure that the normalized v (expressed in % of maximal speed) on the MADsystem and in free swimming condition were close for each bout, two more bouts were allowed on the MAD-system as this condition was uncommon for the swimmers. Four minutes of rest were given before the next bout was swum. For the MAD-system condition, the swimmers swum by pushing off from fixed pads with each stroke. These push-off pads were attached to a 22-m rod and the distance between them was 1.35 m. The rod was mounted 0.8 m below the water surface and was connected to a force transducer, enabling direct measurement of push-off forces for each stroke. Assuming a constant mean swimming speed, the mean propelling force equals the mean drag force (D in N). Hence, swimming one bout on the system yields one data-point for the speed-drag curve. Following the equation D = K • vn , the relationship between drag force and speed was established for each swimmer and thus the individual K factor and n coefficient were determined. According to Toussaint et al. (2006), while swimmers swim on the MAD-system, propulsion is generated without wasting kinetic energy (Pk =0) and consequently all Po of can be used to overcome drag. Thus, Po equals Pd . Knowing that, Pd = D • v2 , Pd = K • v3 . If assumed that during all-out 25-m sprints, Po was maximal and, equal on the MAD-system and in the free condition, propelling efficiency (ep ) could be calculated as: ep = Pd / Po = K • v3 free / K • v3 MAD For the free swimming condition, two underwater video cameras filmed from frontal and side views at 50Hz. They were connected to a double-entry audio-visual mixer, a video timer, a video recorder and a monitoring screen to mix and genlock the frontal and lateral views on the same screen, from which the mean stroke rate was calculated. A third camera, mixed with the side view for time synchronisation, filmed all trials with a profile view from above the pool. This camera measured the time over the 12.5-m distance (from 10-m to 22.5-m) to obtain the velocity. Stroke length was calculated from the mean speed and stroke rate values. From the video device, three operators analysed the key points of each arm phase with a blind technique, i.e. without knowing the analyses of the other two operators. Each arm stroke was broken into four phases: entry and catch of the hand in the water, pull, push and recovery. The duration of the propulsive phases was the sum of pull and push phases and that of the non-propulsive phases was the sum of entry and recovery phases. Arm coordination was quantified using the index of coordination (IdC) as defined by Chollet et al. (2000). The IdC represented the lag time between the propulsive phases of each arm. The mean IdC, which was calculated from three complete strokes, was expressed as a percentage of the mean stroke duration. When there was a lag time between the propulsive phases of each arm, the stroke coordination was in “catch-up” (IdC0% corresponded to the “superposition” of the propulsive phases of both arms. According to Seifert and Chollet (2009) quadratic regression was calculated to model the relationships between IdC and speed. 115
BiomechanicsandmedicineinswimmingXi Assuming that during all-out 25-m sprints, P o was maximal and equal in the MAD- system and in the free condition, e p for each swimmer was calculated. Pearson correlation was assessed between e p and IdC for the maximal swimming condition. An ANOVA analysed the effect of bouts of 25-m on IdC and D. Quadratic regression between IdC and v in the free condition and a power regression between D and v in the MAD-system condition were established for each swimmer; then, the average equation for the whole sample of swimmers were calculated. If assumed that during all-out 25-m sprints, P o was maximal and equal both on the MAD-system and in the free condition, it was also assumed that the effort was equal when swimming bout 1 at 60% of maximal speed on the MAD-system and bout 1 at 60% of maximal speed in the free condition (and so on for bout 2 at 65% of maximal speed, then bout 3 at 70% of maximal speed). Regressions between IdC and D were established for each swimmer, after which the average equation for the whole sample of swimmers was calculated. For all tests, the level of significance was set at 95% (p
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Pahlen Norge AS Pahlen Norge AS Cha
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