Biomechanics and Medicine in Swimming XI

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.
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