Slawinski Js

Billat Vl

Facsm

Slawinski Jean
email: jeanslawinski@yahoo.fr

Difference in mechanical and energy cost between highly, well and non trained runners

Keywords: oxygen uptake, biomechanics, training, running

Introduction:

Recently it has been shown that endurance training decreases the variability in stride rate. This decrease would lead to a reduction in the mechanical and the energy cost of running. Purpose: This study therefore aims to compare the mechanical and the energy cost of running according to the training status of the runner (highly, well and non trained endurance runners). Methods: The kinetic, potential and internal mechanical costs (C ke , C pe and C int ) were measured with a 3D motion analysis system (ANIMAN3D). The energy cost of running (C), was measured from pulmonary gas exchange using a breath by breath portable gas analyser (Cosmed K4b 2 , Roma, Italy). All the parameters were measured on track, for a speed of 4.84 ± 0.36 m.s -1 . Results: Highly trained runners did not exhibit significantly lower C compared to well or non trained runners (4.46 ± 0.38; 4.33 ± 0.32; 4.46 ± 0.46 J.kg - 1 .m -1 , respectively; p = 0.75). However, C pe was significantly lower in highly and well trained runners compared to non trained runners (0.43 ± 0.07; 0.45 ± 0.05; 0.54 ± 0.08 J.kg -1 .m -1 , respectively; p < 0.05). In contrast, C int was significantly higher in highly trained runners compared to well and non trained runners (respectively 0.80 ± 0.12; 0.60 ± 0.09; 0.59 ± 0.10 J.kg -1 .m -1 ; p < 0.05). Conclusion: Although there is a significant difference in C pe and in C int between runners of various training status, there is no difference in C. Differences in C pe and C int may be associated with the same self-optimizing mechanism which contributes to a reduction in the impact loads during the initial portion of the support phase of the stride.

Introduction

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In middle and long distance events, maximal performance depends on the optimisation of aerobic and biomechanical factors. The energy cost of running, defined as the amount of energy spent per unit of distance [START_REF] Di Prampero | The energy cost of human locomotion on land and in water[END_REF], reflects the sum of both aerobic and biomechanical demands. In aerobic conditions, the metabolic power can be estimated by oxygen uptake (

2 O V &
). Among long distance runners with comparable maximal oxygen uptake (

max 2 O V &
), the energy cost of running (C) is a discriminating parameter in endurance performance [START_REF] Di Prampero | The energy cost of human locomotion on land and in water[END_REF]. While various factors have been found to affect C [START_REF] Morgan | Factors affecting running economy[END_REF], little is known about training effect on C. It seems that the use of interval training sessions over a long period (14 weeks to 5 years) brings about a decrease in C [START_REF] Billat | Interval training at 2max O V & effects on aerobic performance and overtraining markers[END_REF][START_REF] Petray | Running training instruction on running technique, and running economy in 10 year-old males[END_REF][START_REF] Smith | Effects of 4-week training using Vmax/Tmax on 2max O V & and performance in athletes[END_REF]. However, many studies did not find any link between these parameters, so the reasons for C improvement with training are not well known.

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Biomechanical parameters have been identified as factors that could influence C.

Williams and Cavanagh [START_REF] Williams | Relationship between distance running mechanics, running economy, and performance[END_REF] demonstrated that 54% of the inter-individual variability of C could be explained by kinematic variables. These authors demonstrated that the more economical runners possess a characteristic running style. They present numerous differences in kinematic variables and some of them are correlated with C.

The mechanical cost of the movements of the centre of mass has also been identified as a potential biomechanical parameter that could explain the inter-individual variability in C [START_REF] Bourdin | Effect of vertical loading on energy cost and kinematics of running in trained male subjects[END_REF][START_REF] Candau | Energy cost and running mechanics during a treadmill run to voluntary exhaustion in humans[END_REF]. Indeed, C represents the energy used by the active muscle to produce mechanical work. Thus, the inter-individual differences in C have to be related to the variation in mechanical energies associated with the movement of the different segments of the body during running. Some studies have found a significant relationship between the inter-individual differences in C and the variation in mechanical energies of the centre of mass during running [START_REF] Bourdin | Effect of vertical loading on energy cost and kinematics of running in trained male subjects[END_REF][START_REF] Candau | Energy cost and running mechanics during a treadmill run to voluntary exhaustion in humans[END_REF]. Recently, Borrani et al. [START_REF] Borrani | Is the 2 O V & slow component dependent on progressive recruitment of fast-twitch fibers in trained runners?[END_REF] suggest that the modification of C could be related to a modification of an index of the internal cost. Therefore, in order to understand why there are significant differences in C among runners of different training status, there is a need to compare kinetic, potential, and internal energies indices in addition to the overall energy cost.

However, effects of endurance training on these biomechanical factors have been poorly investigated. Indeed, most of the current studies have focused on the effect of training on simple mechanical stride parameters such as stride rate or stride length [START_REF] Bailey | Variation in stride length and running economy in male novice runners subsequent to a seven-week training program[END_REF][START_REF] Williams | Relationship between distance running mechanics, running economy, and performance[END_REF]. More recently, a study performed in our laboratory [START_REF] Petray | Running training instruction on running technique, and running economy in 10 year-old males[END_REF], showed that endurance training leads to a decrease in the stride rate variability. It has been hypothesised that the decrease of the stride rate variability would induce a decrease in the mechanical cost of running by reducing the amplitude of movement of the centre of mass.

However, direct measurements of the effect of the training on kinetics, potential and internal mechanical cost (C ke , C pe and C int ) are not available.

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Therefore, this study aims to examine the differences of mechanical and energy cost for groups of runners with different levels of training. The hypothesis being that the lower energy cost in highly trained runners would be associated with a lower mechanical cost. In order to test this hypothesis, C, C ke , C pe and C int were measured in three populations with different training status.

METHODS

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Subjects

Three groups took part of the experimental protocol:

-Seven highly trained (3 men and 4 women) runners (170 ± 7 cm; 55.

O V & ).
All subjects were informed of risks and stress associated with the experimental protocol and gave a written voluntary informed consent in accordance with the guidelines of the hospital of Paris S t Louis.

Paragraph Number 5 Experimental design

The energy cost of running (C), the kinetic and potential mechanical cost associated with the movements of the centre of mass (C ke and C pe ), and the internal mechanical cost associated with the movements of the body's segments around the centre of mass (C int ) were measured during a constant load running exercise (V = 4.84 ± 0.36 m.s -1 ).

The energy cost of running (C), the kinetic and potential mechanical cost associated with the movements of the centre of mass (C ke and C pe ), and the internal mechanical cost associated with the movements of the body's segments around the centre of mass (C int ) were measured during a constant load running exercise (V = 4.84 ± 0.36 m.s -1 ).

Highly trained runners carried out a 30 minute run on a level road at a constant velocity (table 1). The velocity was set at the average velocity sustained during their best marathon race. Runners followed a pacing cyclist traveling at the required velocity. The cyclist was equipped with a speedometer with a speed precision of ± 0.

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The well trained and the non trained subjects performed two exhaustive exercises. The first exercise was an incremental test allowing

Paragraph Number 7 Energy cost measurement (C).

Throughout the constant velocity exercises, the respiratory and pulmonary gas exchange variables were measured using a breath-by-breath portable gas analyzer (Cosmed K4b 2 , Roma, Italy ( 26)). Before each test, O 2 and CO 2 analyser were calibrated using ambient air and sample gas references. The flowmeter was calibrated with a 3-l syringe (Quinton instruments, Seattle, USA). Blood lactate concentration was measured for the three groups of runners, before and after the constant velocity exercises.

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The breath-by-breath oxygen uptake data were reduced to 5-s stationary averages.

These data were then smoothed, using a 3-step average filter, to reduce the noise, so as to enhance the underlying characteristics. These data were finally fitted to mathematical exponential models (2) using an iterative nonlinear regression on Sigma Plot software (SPSS, Chicago, IL, USA). A single-exponential model (eq. 1) and a double-exponential models were used, comprising two exponential terms which start at two distinct time delays from the onset of exercise (eq. 2). The Fisher test, which was performed by the Sigma Plot software, was used to choose the model for which the fit was associated with the highest F value (2).

O V & (t) = A 0 + A1×(1-e -(t-TD1)/ τ 1 ) ×U 1 (linear and fast component) [1] + A2×(1-e -(t-TD2)/ τ 2 ) ×U 2 (slow component) [2]
With:

A 0 : the basal metabolic rate (ml.min -1 ).

A1 and A2 : the asymptotic amplitudes for the exponential terms (ml.min -1 ).

τ 1 and τ 2 : the time constants (s).

TD1 and TD2: the time delay from the onset of exercise (s).

C was expressed in J. kg -1 .m -1 and computed from the following equation:

C (J.kg -1 .m -1 ) = ) t ( O V 2 & × 2 O E × V -1 × M -1
To calculate the energy expenditure, an energy equivalent of oxygen (

F = 0.94 -10 -3 × T [3]
Where "T" is the best performance time (min) achieved in an endurance race. The intensity of the 30 min test was set at the average velocity sustained during the best marathon race of each runner. Therefore, F can be deduced from the best time achieved during the best marathon. From F and the oxygen uptake

O V & and v 2max O V & : 2max O V & (ml.kg -1 .min -1 ) = F ) t ( O V 2 & × M -1 [4] v 2max O V & (m.min -1 ) = 1 - 1 - 2 2max M V (t) O V O V × × & & [5]
Where "V" is the average velocity of the 30 min test, (expressed in m.min -1 ) and "M" is the subject's mass.

Paragraph Number 10

Mechanical cost measurement

During the constant load exercise, runners were filmed by a digital video camera (Sony, Japan, TRV 900) using a sampling frequency of 25 Hz. Two consecutive steps were analysed at the end of the exercise (figure 1). The video sequences were digitised on a computer using a video card (Perception Video Recorder, Digital System Inc.), and then transformed into a set of bitmap pictures. The parametric trajectories of the anatomical points of the runner were determined using a motion analysis system, ANIMAN3D [START_REF] Tavernier | A 3D motion analysis system using a numerical human model[END_REF]. This system uses a numerical manikin "MAN3D" (figure 2). The morphology of this numerical manikin integrates inertial properties of the different segments. The morphology of this numerical manikin could be adjusted to each runner from their height and weight. This model measures the 3D positions of the body, during running. In the present work, the lateral displacements of the different centres of masses have not been directly measured because only one camera was used. All the trajectories of the different centres of mass (segments and body centre of mass) were smoothed using a polynomial method in order to obtain by derivation the speed displacement of the different centres of mass. Variations of potential and kinetic energies (ΔE pe and ΔE ke respectively) were then calculated (in joules).

ΔE pe = ( ) min max H H g M - × × [6] ΔE ke = ( ) 2 min 2 max V V M 2 1 - × [7]
"M" is the body mass (kg), "g" is the gravitational acceleration (9.81 m.s -2 ), "H max and H min " are the maximal and minimal heights of the body centre of mass (CM) during one step (m). "V max and V min " are the maximal and minimal horizontal velocities of the CM during one step (m.s -1 ).

Paragraph Number 11

The variation of the internal energy (ΔE int ) represents the variations in mechanical energies associated with the movement of the different segments of the body around the CM. The present model used in this work (figure 2) is composed of 17 different segments. The variation of internal energy (Δ int E ) is calculated,

( ) ∑ = + = 17 1 i 2 s s 2 s s int ω I V m 2 1 Δ ΔE [8]
Where "s" represents a segment, "m s " is the mass of the segment (kg) considered and "I s " is the moment of inertia of the segment (kg.m 2 ). " s V " is the velocity of the centre of mass of the segment (m.s -1 ) with respect to the referential linked to the CM. " s ω "

is the angular velocities of the centre of mass of the segment (rad.s -1 ) with respect to the referential linked to the CM of the segment. Kinetic, potential and internal mechanical cost expressed in J.kg -1 .m -1 (C ke , C pe , C int ) are equal to the positive variation of ΔE ke , ΔE pe and ΔE int divided by the mass of the subject and his step amplitude. These parameters were measured during the steady state of

RESULTS

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Characteristics of the highly, well and non trained runners

2max O V & and v 2max O V &
were significantly greater in trained subjects. Indeed, both parameters were greater for highly trained runners compared to well trained runners or compared to non-trained runners (table 1).

The speed of the constant load exercise was not significantly different between the groups (p=0.33). The different measures obtained from the different populations, during the constant load exercise (C pe , C ke , C int and C), were performed for similar running speeds (table 1).

Paragraph Number 13

Effect of the endurance training status on C, C pe , C ke , C int and the step rate.

The metabolic cost of running reported in table 2 is the "gross" value, including the O 2 consumption at rest. The less trained runners do not consume more energy than the highly trained runners. Indeed, in order to move forward at the same running speed (table 2) the highly trained runners present a same C as well-trained, or non-trained runners. Moreover, when the energetic equivalent of lactate (EEL) is added to C there is also no difference between the groups.

Paragraph Number 14

C pe was significantly lower in highly and well trained runners compared to non trained runners (20%). Inversely, the step rate (SR) was significantly higher in highly trained runners compared to well and non trained runners. Moreover, there was an inverse correlation between SR and C pe or the vertical displacement of the centre of mass (CM)(respectively r = -0.63; p = 0.001 and r = -0.81; p ≤ 0.0001). Figure 3 shows that runners who present the greatest vertical displacement of the CM also possess the weakest limb displacement speed around the CM. Indeed, the vertical displacement of the CM for the different group is 6.7 ± 0.9 cm for the highly trained runners compared to 7.7 ± 0.7 cm for the well trained runners, and 8.3 ± 1.3 cm for the non trained runners (p = 0.02).

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C int was also significantly higher in highly trained runners: 30% greater in comparison with well trained runners and 37% greater in comparison with non trained runners. C int was correlated with the vertical displacement of the CM (r = -0.46; p = 0.03), but not with SR (r = 0.41; p = 0.07) or with C pe (r = -0.32; p = 0.16).

Paragraph Number 16

The running speed was similar in all the runners, therefore C ke was not significantly different in the three populations of different training status (p = 0.89).

Paragraph Number 17

Relationship between C, C ke , C pe , C int , SR and

DISCUSSION

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The purpose of this work was to test the hypothesis that the lower energy cost in highly trained runners is associated with a lower mechanical cost. To test this hypothesis, C, C ke , C pe and C int were measured in runners who differ in training status. The results showed that contrary to our hypothesis, the energy cost of running was not different for runners with different training status. However, the mechanical cost is different for runners with different training status. Indeed, the mechanical cost associated to the vertical movements of the centre of mass (C pe ) was smaller in highly trained runners. Inversely, the mechanical cost associated with the movement of the segments around the centre of mass (C int ) and the step rate (SR) were greater.

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There was no difference in the energy cost of running between the three groups, whereas such a difference would be expected and has been demonstrated in previous studies [START_REF] Billat | Interval training at 2max O V & effects on aerobic performance and overtraining markers[END_REF][START_REF] Petray | Running training instruction on running technique, and running economy in 10 year-old males[END_REF][START_REF] Smith | Effects of 4-week training using Vmax/Tmax on 2max O V & and performance in athletes[END_REF]. Numerous mechanisms such as a decrease in the ventilation, an optimisation of the neuromuscular recruitment, an improvement in the transport of oxygen by the blood, the use of this oxygen by the active muscles or the increase of the muscular elasticity [START_REF] Daniels | Physiological characteristics of champion male athletes[END_REF][START_REF] Paavolainen | Explosive strength training improves 5-km running time by improving running economy and muscle power[END_REF]) could be at the origin of the improvement in the energy cost with training. However, mechanisms associated with this decrease are not well known and numerous studies do not report any change after a period of training [START_REF] Daniels | Changes in VO2 max and running performance with training[END_REF][START_REF] Lake | Six weeks of training does not change running mechanics or improve running economy[END_REF][START_REF] Petray | Running training instruction on running technique, and running economy in 10 year-old males[END_REF][START_REF] Slawinski | Effect of supra-lactate threshold training on the relationship between mechanical stride descriptors and aerobic energy cost in trained runners[END_REF][START_REF] Wilcox | Change in running economy relative to 2max O V & during a cross-country season[END_REF]. Daniels and Daniels [START_REF] Daniels | Physiological characteristics of champion male athletes[END_REF] have shown that for intensities typically used in races up through the marathon, no differences exist in C within the range of most intensities (80-100% of max 2

O V &

). In the present study, the range of intensities was comprised within 80% and 97% of

v max 2 O V &
. Therefore, the lack of difference in C among runners of different training levels may be associated with the fact that C is measured for speeds upper to speeds typically used during a marathon race.

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Following this hypothesis, the present results show that the mechanical cost of the vertical movements of the CM (C pe ) is smaller in highly trained runners. This result is in accordance with the generally acknowledged principle which states that smaller verticals oscillations of the body centre of mass are associated with a high level of training [START_REF] Morgan | Factors affecting running economy[END_REF][START_REF] Williams | Relationship between distance running mechanics, running economy, and performance[END_REF]. However, there was no relationship observed between C pe and C (figure 4). According to the training status, the variation in C pe is not associated with the variation in C. Similarly, the kinetic mechanical cost (C ke ) or the mechanical cost of the movements of the segments around the centre of mass (C int ) were not correlated with C (figure 4). These results agree with numerous studies which have demonstrated that the relationships between C and the mechanical cost are both weak and inconsistent [START_REF] Dalleau | The spring-mass model and the energy cost of treadmill running[END_REF][START_REF] Morgan | Factors affecting running economy[END_REF][START_REF] Williams | Relationship between distance running mechanics, running economy, and performance[END_REF]. The mechanical cost reflects a global and indirect expression of the muscular effort which explains why a low correlation is observed between C and the mechanical cost. Other mechanical parameters should be correlated with C.

Indeed, it has been shown that less economical runners possess a more compliant running style during ground contact [START_REF] Dalleau | The spring-mass model and the energy cost of treadmill running[END_REF][START_REF] Heise | Leg spring" characteristics and the aerobic demand of running[END_REF].

Paragraph Number 21

Collectively, the results reported here, do not confirm our primary hypothesis and suggest that the mechanical cost (C pe and C int ) is not correlate with C but with 2max O V & . The mechanism that explains the modifications of the mechanical parameters (SR, C pe and C int ) is in relation with the level of training of the runners rather than their energy cost. However, this mechanism has to be elucidated.

Paragraph Number 22

If humans are self-optimizing machines, the minimal cost, being an optimally criterion, may be identified that governs the kinematic and kinetic detail of the performance. For example, it has been shown that adult and children tend to walk or run at frequencies that are determined by the oxygen cost [START_REF] Cavanagh | The effect of stride length variation on oxygen uptake during distance running[END_REF]. However, it is unlikely that the metabolic cost is the only optimality criteria adopted for human activity. The potential for injury may also result in the development of optimal criteria. Farley and Taylor [START_REF] Farley | A mechanical trigger for the trot-gallop transition in horse[END_REF] reported that horses naturally switch from a trot to a gallop actually increasing their metabolic cost but reducing the peak of forces on the muscles, tendons and bones. They suggest that this mechanism reduces the chance of injury for the horse. During human running, the body is subjected to high impact loads during the initial portion of the support phase of the stride. These impacts have magnitudes up to 2.3 times body weight (BW) with an impact load rate of 113 BW.s -1 [START_REF] Munro | Ground reaction forces in running: a reexamination[END_REF]. Increases in impact shock can result from an increase in running speed [START_REF] Hamill | Variation in ground reaction force parameters at different running speeds[END_REF], from running downhill [START_REF] Hamill | Effect of grade running on kinematics and impact force[END_REF], from an increase in stride length [START_REF] Derrick | Energy absorption of impacts during running at various stride lengths[END_REF][START_REF] Hamill | Shock attenuation and stride frequency during running[END_REF] or fatigue [START_REF] Verbitsky | Shock transmission and fatigue in human running[END_REF]. It would appear therefore, that impact shock attenuation may be an important factor on which individuals optimise. In a same way, Zamparo et al. [START_REF] Zamparo | The self selected speed of running in recreational long distance runners[END_REF] have recently demonstrated that the self selected speed of running depends not only on cardiovascular factors, but also on biomechanical factors.

Paragraph Number 23

The running style of the highly trained runners may be associated with the same selfoptimizing mechanism which contributes to reducing the impact loads during the initial portion of the support phase of the stride. Indeed, this style is characterised by a high SR and a low C pe and a low vertical displacement of the CM. Moreover, the figure 3 shows that the vertical displacement of the CM is inversely correlated to SR.

This inverse relationship shows that a high stride rate is linked to a smaller vertical displacement of the CM. During the flight phase, the CM of highly trained runners may reach a lower height (table 2), which decreases the impact shock when the foot hits the ground. This result is in accordance with the results of Farley and Gonzales [START_REF] Farley | Leg stiffness and stride frequency in human running[END_REF] which show that at higher step rate, the initial vertical force peak is absent and that the vertical force peak is decreased. This style of running may be associated with 6)) compared to the other populations studied. For example, running 32 km.week -1 will produce over 1.3 million impacts to the body over a period of one year [START_REF] Derrick | Impacts and kinematic adjustments during an exhaustive run[END_REF]. More frequent impacts will place greater stress on the muscles, tendons and bones resulting in an increased risk of injury and degenerative disease [START_REF] Derrick | Energy absorption of impacts during running at various stride lengths[END_REF]. Therefore, to attenuate this risk, mechanical adjustments such as a decrease in the vertical oscillation of the CM may take place with training. Following this hypothesis, the modification of the mechanics of running with the training status does not have any relationship with the energy cost of running (C).

Paragraph Number 24

To conclude, highly trained runners did not display a lower C. However, C pe was 20% lower and C int was 30% greater in highly trained runners. The endurance training leads to an increase of C int and a decrease in C pe without any modification of C. These mechanical adjustments may be associated with the same self-optimizing mechanisms which contribute to reduce the impact loads during the initial portion of the support phase of the stride.

The authors gratefully acknowledge the Fédération Française de Ski and Michel

Tavernier for their assistance during the experiment.

  and the velocity at the lactate threshold (vLT) to be determined. The running speed of this test was progressively increased from 12 km.h -1 to exhaustion. The velocity increments between the stages (3 min duration) were set at 1 km.h -1 . All stages were followed by a 30-s period of rest. During this period, a fingertip capillary blood sample was collected in order to calculate the energetic equivalent of lactate (EEL). The second exercise was a test to exhaustion at the velocity corresponding to 50% of the work rate difference between vLT and v max 2 O V & (vΔ50) for the well trained runners. For the non trained runner, the velocity of the exercise was set at 95% of v 2max O V & . C, C ke , C pe and C int were determined during the last minute of the exercise. All test sessions were completed on a 400-m covered synthetic track. Throughout the tests, the subjects adopted the required velocity using an audio rhythm which gave the time to cover 25m. Visual marks were set at 25-m intervals along the track (inside the first lane).

2

 2

  during the 30 min test, it was easy to calculate 2max

Fig. 1 :

 1 Fig. 1: Representation of space calibration and of the field of the camera

Fig. 3 :

 3 Fig. 3 : Correlation between the vertical displacement of the centre of mass (VD of the CM) and the step rate (SR). The verticals bars represent the standard error of measure.

  and C int were significantly different according to the training status of the runners, there was no correlation between C and C pe or C int (figure4). The correlation coefficients were near r = 0.05 and p = 0.83 for C ke , r = -0.13 and p = 0.58 for C pe and r = 0.41, and p = 0.06 for C int . Furthermore, there was no correlation between the SR and C (r = 0.15; p = 0.53). These results showed that the interindividual variability of C can not be explained by biomechanical parameters such as SR, C pe , C ke or C int . However, SR, C pe and C int were significantly correlated with 2max O V & (respectively r = 0.58; p = 0.007; r = -0.73; p = 0.0001; r = 0.60; p = 0.004).

Fig. 4 :

 4 Fig. 4: Relationship between the energy cost of running (C), the kinetic mechanical cost (C ke ), the potential mechanical cost (C pe ) and the internal mechanical cost (C int ).

  the training mode of the runners. Indeed, the nature of the adaptive responses to training is specific to the training stimulus. Highly trained runners cover very long distances during training session, and perform a very high number of strides (more than 20 km.day -1 ; (

Table 2 :

 2 Effects of the endurance training status on the gross energetic cost (C), the gross energetic cost plus the energetic equivalent of lactate (C + EEL), the step rate (SR), the step length (SL), the kinetic, potential and internal mechanical costs (C ke , C pe , C int , respectively).

		Highly trained (±SD)	Well trained (±SD)	Non trained (±SD)	ANOVA
	C ke (J.kg -1 .m -1 )	1.54±0.70	1.41±0.40	1.43 ±0.33	0.89
	C pe (J.kg -1 .m -1 )	0.43±0.07	0.45±0.05	0.54 ±0.08 † §	0.02
	C int (J.kg -1 .m -1 ) 0.80±0.12	0.60±0.09 †	0.59 ±0.10 †	≤0.01
	C (J.kg -1 .m -1 )	4.46±0.38	4.33±0.32	4.46 ±0.46	0.75
	C + EEL (J.kg -1 .m -1 )	4.56±0.39	4.75±0.32	4.89 ±0.44	0.30
	R	0.94±0.08	1.00±0.03	1.06 ±0.05 †	<0.01
	SR (Hz)	3.21±0.22	3.01±0.11 †	2.96 ±0.21 †	0.05
	SL (m)	1.52±0.14	1.66±0.08 †	1.58 ±0.12	0.09
	† Significantly different from highly trained runner (p≤0.05).

§

Significantly different from well trained runner (p≤0.05).


Patrick Peretti-Watel

Valérie Guagliardo

Pierre Verger

Jacques Pruvost

Patrick Mignon

Yolande Obadia

ATHLETES Results from a Pilot Survey in South-Eastern France

Since the end of the 1980s, 'youth risky behaviours' have become a major issue for public health. The relationship between these behaviours and sporting activity is well-documented but still controversial. This article examines some sociological hypotheses related to this relationship, with data from a pilot survey conducted on a sample of 458 elite-student-athletes (ESAs) aged 16-24, gathered and trained in specialized public centres. We found a significant relationship between motives to do sport and 'risky behaviours': ESAs who considered sport as a convivial leisure were more prone to use cannabis, while ESAs who mingled sporting and extra-sporting achievements together were more likely to engage in risky behaviours on the road, possibly because they transposed values from the sporting field (speed, competition) into the 'real world'. Moreover, sporting activity may provide opportunities for drug use with peers as well as incentives to use drugs in order to cope with the anxiety induced by high-level competition. Thus 'recreational' drugs may be used as 'integrative' drugs. Key words • coping strategy • elite-student-athletes • motivations • risky behaviours Since the end of the 1980s, youth risky behaviours ('behaviours practised by young people that put their health at risk', CDC, 2002) have become a major issue for public health, and the scientific literature on this issue has quickly expanded. Many studies have attempted to assess whether or not various factors are

linked to such behaviours, including family background, socioeconomic status, psychological disorders, and sporting activity. Concerning the last topic, the literature remains controversial: according to several studies, young athletes have a healthier lifestyle than non-athletes [START_REF] Mcardle | International Variations in Youth Drug Use: The Effect of Individual Behaviours, Peer and Family Influences, and Geographical Location[END_REF][START_REF] Naylor | Drug Use Patterns among High School Athletes and Nonathletes[END_REF][START_REF] Pate | Sports Participation and Health-Related Behaviors among US Youth[END_REF], while other studies came to the opposite conclusion for drug use, risky driving or unsafe sex (Billet, 2001;[START_REF] Forman | High-Risk Behaviors in Teenage Male Athletes[END_REF][START_REF] Gonzalez | Adolescents' Perceptions of their Risk-Taking Behavior[END_REF][START_REF] Sun | The Impact of School-Organized Sport Activities on the Priority Youth Health-Risk Behaviors[END_REF]. Among possible explanations, this inconsistency may be due to differences in definitions and measures of sporting activity and its context: institutionalized practice in a competitive setting should be distinguished from recreational physical exercise [START_REF] Claeys | Evolution of the Concept of Sport and the Participation / Nonparticipation Phenomenon[END_REF][START_REF] Lorenté | Sports Involvement can be Both Formal and Informal at the Same Time: A Comment on Peretti-Watel et al[END_REF][START_REF] Thorlindsson | Sport Participation, Smoking, and Drug and Alcohol Use among Icelandic Youth[END_REF]. One way to better assess the risky behaviour-sport relationship at adolescence is to focus on athletes with a homogeneous sporting background. We present here some results from a pilot survey focusing on elitestudent-athletes (ESAs) carried out in South-Eastern France. This survey gave us the opportunity to address three hypotheses concerning the relationship between sporting activity and risky behaviours :

• Sporting activity and risky behaviours may be impelled by similar motives and values (to get thrilled, to express commitment to masculine values). Indeed, as pointed out by psychologists [START_REF] Csikszentmilhalyi | Beyond Boredom and Anxiety[END_REF][START_REF] Zuckerman | Sensation Seeking[END_REF] and sociologists [START_REF] Caillois | Assessing Health Risk Behaviors among Young People: Youth Risk Behavior Surveillance System 2002[END_REF][START_REF] Lyng | Edgework: A Social Psychological Analysis of Voluntary Risk Taking[END_REF], both activities may reveal the same sensation-seeking, the same search for vertigo, exhilaration or 'flow'. This hypothesis is especially relevant to the relationship between sliding or 'X-treme' sports and drug use in adolescence [START_REF] Beck | View of the End of Adolescence: Uses of Psychoactive Products in the ESCAPAD 2000 Survey[END_REF][START_REF] Cherpitel | Alcohol Consumption, Sensation Seeking and Ski Injury: A Case-Control Study[END_REF][START_REF] Pillard | Sport Practice and Cannabis Consumption in a Representative Sample of French High School Adolescents[END_REF]. Furthermore, in civilized societies, sport is one of the last strongholds (with some occupational spheres: army, police) for masculine values related to physical strength, aggressiveness and confrontation with opponents [START_REF] Dunning | Sport as a Male Preserve: Notes on the Social Sources of Masculinity and its Transformations[END_REF][START_REF] Sheard | The Rugby Football Club as a Type of Male Preserve: Some Sociological Notes[END_REF]. So athletes may be 'positive deviants', since they are socialized in a world that worships behaviours which are forbidden outside the sporting field [START_REF] Coakley | Deviance in Sports: Is it Out of Control?[END_REF].

• Second, like any activity that allows youngsters to go out and escape temporarily from parental control, sporting practice may provide occasions to engage in risky behaviours with peers. This hypothesis refers to the opportunity structure of deviance, which is a classic subject in the sociology of deviance [START_REF] Becker | Outsiders[END_REF][START_REF] Cloward | Delinquency and Opportunity[END_REF]. This topic has been extensively investigated for the relation between sport and juvenile delinquency, with contradictory results that highlighted the necessity for distinguishing organized and informal activities, and also for taking into account the level of competition (which could be linked to the degree of organization of sporting activity: coaches at higher level of play may exert more social control; [START_REF] Agnew | Leisure and Delinquency[END_REF][START_REF] Schafer | Some Social Sources and Consequences of Inter-Scholastic Athletics: The Case of Participation and Delinquency[END_REF][START_REF] Seagrave | An Investigation into the Relationship between Ice Hockey Participation and Delinquency[END_REF][START_REF] Yiannikis | Sport and Deviancy: A Review and Appraisal[END_REF].

• Lastly, drug use may be a means to alleviate the anxiety resulting from competition (Peretti-Watel et al., 2002a, 2002b). Indeed, competition frequently induces anxiety among young athletes [START_REF] Bray | The Relationship between Evaluative Concerns and Sport Competition State Anxiety among Youth Skiers[END_REF][START_REF] Passer | Fear of Failure, Fear of Evaluation, Perceived Competence, and Self-Esteem in Competitive-Trait-Anxious Children[END_REF][START_REF] Smoll | Psychology of the Young Athlete, Stress-Related Maladies and Remedial Approaches[END_REF], and previous studies have already found that people use cigarettes, alcohol or cannabis to cope with stressors [START_REF] Ballion | Conduites délictueuses et consommation de substances psychoactives des lycéens (Delinquent Behaviours and Drug Use among High-School Students), report for the MAIF foundation and the French Monitoring Centre for Drugs and Drug Addictions (OFDT)[END_REF][START_REF] Holahan | Drinking to Cope, Emotional Distress and Alcohol Use and Abuse: A Ten-Year Model[END_REF][START_REF] Sorensen | Attitudes toward Smoking Cessation among Men and Women[END_REF][START_REF] Waldron | Patterns and Causes of Gender Differences in Smoking[END_REF].

Method

Data Source

The data were abstracted from the Survey on Elite Provençal Student-Athletes (SEPSA). The SEPSA survey is a pilot study, which is likely to be extended to the whole country in the years to come. In France, each year, the Ministry of Youth and Sports draws up three lists of the best young athletes and proposes that they enter specialized public centres administered at the regional level. The first list includes athletes who have already achieved significant performances in international competitions; the second list gathers young athletes with promising but not yet significant performances; the third one aims to supply athletes enlisted from the first two lists with valuable sparring partners. Each centre musters athletes practising the same sport, and organizes their training programmes, their school timetable, as well as their medical follow-up. The SEPSA survey targeted ESAs aged 16-24 and registered in one of these three lists, in the centres located in South-Eastern France (Provence, the Alps and the French Riviera), which is placed third among French regions for the number of such centres. Among these centres, five were not included in the survey because their ESAs were either aged over 24 or under 16. The remaining 40 centres gathered 616 ESAs aged 16-24 in 28 different sports. Between February and May 2002, ESAs answered a self-administered questionnaire within a training room, in the presence of a professional investigator who was responsible for presenting the survey to the pupils. In order to ensure confidentiality, anonymous questionnaires were directly collected in a ballot box.

Questionnaire

The questionnaire included 127 closed-ended questions dealing with demographic background, mental health, sporting activity, lifestyle and opinions towards sport. ESAs were also asked to report their sporting discipline, but we did not use it. Indeed, the sample size by discipline was sometimes very small, thus using this variable in the analysis would have threatened respondents' anonymity.

Six so-called 'risky behaviours' were assessed by the questionnaire :

• Current smoking: subjects smoking cigarettes at the time of the study, at least from time to time (including both daily and less than daily smokers). • Regular drinking: subjects drinking alcohol at least 10 times per month.

• Occasional use of cannabis: subjects smoking cannabis at least once during the last 12 months.

• Unsafe sexual intercourse: subjects having intercourse with a non-regular partner without using condom during the last 12 months.

• Drunk driving: subjects driving a vehicle while being drunk, at least once during the last 12 months.

• Non-use of seatbelt or helmet (for motorcycles): 'rarely', 'often', 'every time', versus 'never'.

In order to test whether or not sporting practice and risky behaviours were impelled by the same motives, the questionnaire included 12 questions dealing with ESAs' motivations to engage in sport (based on a four-point Likert scale). The potential influence of sporting peers on opportunities to behave in a risky way was measured by asking to respondents whether they had been out with athletes or with non-athletes during the last 30 days. Finally, the questionnaire included questions about psychological disorders which were measured with the GHQ-12 (General Health Questionnaire, 12-item version), a widely used instrument to detect psychological disorders, especially current depressive and anxiety symptoms. It has been validated in many countries, including France [START_REF] Goldberg | The Validity of Two Versions of the GHQ in the WHO Study of Mental Illness in General Health Care[END_REF]. In non-clinical populations, the GHQ-12 score is usually used as a continuous variable [START_REF] Banks | Validation of the General Health Questionnaire in a Young Community Sample[END_REF]. This score ranges from 0 (no psychological disorder) to 36 (acute psychological disorders).

Statistical Analysis

We described our sample for male and female ESAs separately, using Pearson's 2 to compare them. Then, a cluster analysis was performed to summarize in some synthetic profiles the variety of motivations to engage in sport. For the corresponding 12 questions, items measuring agreement were encoded from 1 ('strongly disagree') to 4 ('strongly agree'). Non-responses were replaced by mean values, and the resulting variables were transformed to Z-scores prior to clustering. The cluster analysis was based on the agglomerative hierarchical procedure (each observation begins in a cluster by itself, then the two closest clusters are merged to form a new one, and this procedure is repeated until only one cluster is left). At every step, clusters were less homogeneous but more easily interpretable. We selected a three-cluster partition, which was described with t-tests for motivation items, an ANOVA test for age and GHQ-12, and Pearson's 2 for proportions (for gender, competitive level and outings). We used multivariate logistic models to test the relationship between risky behaviours and motivations to do sport (with the three-cluster partition), outings with athletes or non-athletes, competitive level (national or regional versus international or Olympic), psychological disorders (GHQ-12 score), while controlling for usual confounding factors (gender and age). Our models were built by selecting step by step the more significant factors using the stepwise method (entry threshold p = 0.10), with gender and age forced into all models. Odds ratios (ORs) were computed from the regression coefficients and their levels of statistical significance were computed from the Wald's 2. Given the small size of our sample, only one decimal is reported for ORs. By definition, ORs are equal to 1 for reference values. Lastly, in the case of unsafe sexual intercourse, as previous studies showed that athletic participation and gender interact to influence adolescent sexual outcomes [START_REF] Kokotailo | Substance Use and Other Health Risk Behaviors in Collegiate Athletes[END_REF][START_REF] Miller | Athletic Participation and Sexual Behavior in Adolescents: The Different Worlds of Boys and Girls[END_REF][START_REF] Miller | Gender and Racial/Ethnic Differences in Predicting Adolescent Sexual Risk: Athletic Participation versus Exercise[END_REF][START_REF] Sabo | High School Athletic Participation, Sexual Behavior and Adolescent Pregnancy: A Regional Study[END_REF], two separate models were built: one for females, one for males.

Results

Description of the Sample

Among the 616 eligible ESAs, 147 were absent (most of them were on a sporting trip for a competition), 13 refused to participate or did not answer the demographic items and 458 (159 girls and 299 boys) completed the questionnaire correctly (response rate: 74.4%). As shown in Table 1, boys were slightly older than girls, and girls reported more symptoms of psychological disorders. One ESA out of two went in for competitive sport at the national level, and one out of three at the international or Olympic level. Concerning 'risky behaviours', boys were more prone to drink alcohol regularly, to use cannabis occasionally and to engage in unsafe road behaviour (drunk driving or non-use of seatbelt or helmet).

Motivations to Engage in Sport

The three-cluster partition summarizing motivations to engage sport is described in Table 2. Cluster 1 (n = 242, 52.8% of the sample) corresponds to respondents who rated almost all items higher than other ESAs, except for motivations that refer to sport as leisure ('for pleasure', 'for health', 'to meet people, to make friends'). The highest scores for items related to personal and social achievement ('to become really someone', 'to feel strong', 'to earn money') were observed in this cluster, ahead of items related to sporting success. This profile of motives can be labelled as an instrumental orientation toward sport. Males were overrepresented among those respondents. Respondents in Cluster 2 (n = 103, 22.5% of the sample) were characterized by high scores for items related to the competitive spirit ('performance', 'mounting the top of the rostrum', 'competition'), but they also highlighted the importance of pleasure. These respondents were less prone to agree with motivations related to personal achievement ('to develop one's body', 'to feel strong', 'to become really someone', 'to earn money'). These ESAs were older and more prone to go out with other athletes or to go in for competition at the international or Olympic level. Finally, Cluster 3 (n = 113, 24.7%) is characterized by higher scores for only two items ('pleasure', 'to meet people, to make friends'), and lower scores for items related to competitive spirit or personal and social achievement. Corresponding ESAs, who were more frequently females, who were younger and practised less frequently at the international or Olympic level, seemed to consider, above all, sport as a form of leisure.

Factors Associated with Risky Behaviours among ESAs

Once the effects of gender and age were controlled for, ESAs whose sporting practice was motivated by pleasure and sociability (sport as a form of leisure) were more prone to smoke cigarettes or to use cannabis occasionally, while those who considered sport as a means for personal and social achievement (instrumental orientation toward sport) were more likely to engage in unsafe road behaviour (drunk driving or non-use of seatbelt or helmet, OR < 1 for Clusters 2 or 3 versus Cluster 1). Lastly, girls who endorsed the competitive spirit were more prone to engage in unsafe sex. ESAs who went out recently with other athletes were more likely to be current smokers, regular drinkers (for boys only), or occasional users of cannabis. Those who went out with non-athletes were more prone to use cannabis occasionally, or to engage in unsafe sex (for boys only) or drunk driving. Competition at the international or Olympic level was positively associated with current smoking and regular drinking (among boys only). Finally, psychological disorders were positively correlated with current smoking and occasional use of cannabis. Lastly, as none of the three clusters is clearly oriented to thrill motivations, we directly tested the relationship between thrill motivations for sport and cannabis use in a separate model, by substituting the thrill motivation item for the three-cluster partition. We obtained no significant relationship between this item and cannabis use (p = 0.370).

Discussion

First, we have to acknowledge some limitations of our study. Data were extracted from a pilot survey carried out in only one French region, and must not be considered to represent a sample of the global French ESAs population (even if ESAs are recruited from the national level before being dispatched in regions according to the sport they practise). Moreover, because of the sample size, we did not separate systematically males and females in the analysis, nor take into account the heterogeneity of sporting disciplines. Our first hypothesis was related to motives for practising sport and engaging in 'risky behaviours'. A cluster analysis on motivations reported by ESAs enabled us to identify three profiles. ESAs who considered sport as a leisure by emphasizing the importance of pleasure and sociability in sport were more likely to smoke cigarettes or cannabis (these two uses being closely connected), and we did not find the expected relationship between the search for thrills in sport and cannabis use. This result may illustrate the so-called 'normalization' of adolescent recreational drug use: most of the time, contemporary cannabis users are not daredevils, they do not consider themselves as risk-takers. They rather regard their consumption as convivial and safe leisure [START_REF] Parker | Illegal Leisure: The Normalization of Adolescent Recreational Drug Use[END_REF][START_REF] Peretti-Watel | Neutralisation Theory and Denial of Risk: Some Evidences from Cannabis Use among French Adolescents[END_REF]. We also found that ESAs who were more prone to endorse motivations related to personal and social achievement, as well as motivations related to sporting success, were more likely to engage in risky behaviours on the road. The boundaries between the sporting field and the outside world may have become blurred for these ESAs who mixed sporting and non-sporting achievement: their propensity to risk-taking on the road could mean that they have transposed values from the sporting field (speed, competition) into the 'real world'. Lastly, among girls the competitive spirit was linked to unsafe sex, but this result should be interpreted cautiously because the corresponding subsample size is very small. Second, we tried to address the topic of opportunities. Even if this hypothesis seems more relevant to the case of informal practice (Peretti-Watel et al., 2002a), our data illustrated the 'opportunity effect' for cannabis use: outings with athletes or non-athletes increased the risk of engaging in such use. But in the case of alcohol drinking among male ESAs, athletes who reported outings with athletes were more prone to drink regularly, while those who reported outings with non-athletes were not. This result points out the ambiguity of the 'opportunity effect'. Indeed, people are not passively exposed to their environment: they choose whether or not they jump at the opportunity, and they may also bring about the opportunity [START_REF] Osgood | Routine Activities and Individual Deviant Behavior[END_REF]. This observation relates to the first hypothesis about sporting socialization and masculine values perpetuated in 'male disciplines': outings with other athletes may provide an ESA with opportunities to drink alcohol, and he/she may jump at this opportunity because he/she has been socialized among peer athletes who value this behaviour. Previous studies have already observed a relationship between alcohol drinking and the practice of a 'male sport', like rugby or strength and combat sports (Peretti-Watel et al., 2002a;[START_REF] Quarrie | The New Zealand Rugby Injury and Performance Project: Alcohol Use Patterns within a Cohort of Rugby Players[END_REF]. On the contrary, in our sample unsafe sex (for males only) and drunk driving were only related to outings with non-athletes.

Third, competition frequently induces anxiety among young athletes, so drug use may be a means to alleviate this anxiety. The GHQ-12 score, assessing psychological disorders, and especially anxiety and depressive symptoms, was positively linked to current smoking and occasional use of cannabis, and ESAs who competed at the international or Olympic level were more prone to smoke cigarettes or (for males only) to drink alcohol regularly. Nevertheless, the GHQ-12 is probably a poor tool to assess anxiety among athletes: in the future, we should use sport-specific instruments. Moreover, the relationship between drug use and competition can also be interpreted with reference to the 'opportunity effect', since international competition means travelling abroad. Despite these empirical limitations, the relationsip between anxiety, competition and drug use illustrates the 'integrative' use of so-called 'recreational' drugs: they can be used to increase efficiency or simply to 'hold out' and to manage stress [START_REF] Ehrenberg | Le Culte de la performance[END_REF]. Lastly, further research is needed to improve the theoretical conceptualization of the relationship between sporting practice and so-called 'risky behaviours'. As both activities are frequently supposed to express common motives and values, we have to investigate what meaning the individual gives to his/her behaviours. Such a conceptualization should question the current definition of 'youth risky behaviours', because this definition merges deliberate risk-taking as well as behaviours that are not considered as dangerous by those who engage in them. This is well-illustrated by cannabis use: we did not find the expected relationship between thrill motivations for sport and cannabis use, probably because adolescents do not experience this use as a search for thrills.

Conclusion

The SEPSA survey allowed us to study risky behaviours among a specific and homogeneous population of high-level young athletes, registered in public centres that provided them with a high standard of sporting facilities, training programmes and medical follow-up. We found some support for the tested hypotheses, even if some results could be interpreted in various ways: sporting activity and risky behaviours may be both impelled by similar motives and values; sporting activity may provide opportunities to engage in risky behaviours; 'recreational' drugs may be used as 'integrative' drugs, to alleviate the anxiety sharpened by competition.

Tables Table 1 Description

  of the SEPSA Subjects: Age, Psychological Disorders, Outings, Competitive Level and 'Risky Behaviours' According to Gender

Table 2 Cluster Analysis on Motivations to Do Sport among ESAs, Description by Gender, Age and Sporting Characteristics Table 3 Logistic Regressions Predicting Risky Behaviours among ESAs, Based on Gender, Age, Psychological Disorders and Sporting Characteristics

 2

Acknowledgements

This study was supported by the Regional Council of South-Eastern France (Provence, the Alps and the French Riviera) and the Regional and Departmental Authority on Youth and Sports of South-Eastern France (DRDJS PACA).