LEADING ARTICLE
Sports Med 2009; 39 (2): 85-106 0112-1642/09/0002-0085/$49.95/0
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Determinants of Ski-Jump Performance and Implications for Health, Safety and Fairness Wolfram Mu¨ller Centre of Human Performance Research, Karl-Franzens University of Graz and Medical University of Graz, Graz, Austria
Abstract
Ski jumping puts high demands on the athlete’s ability to control posture and movement. The athlete has to solve extremely difficult optimization problems. These implicit decisions and the resulting control manoeuvres can be understood by means of computer simulations. Computer simulations based on wind tunnel input data can identify the determinants for high performance and answer many questions of training methods, safety and health, role of weight, fairness, optimized hill design, sport development, and changes to the regulations. Each of the performance determinants has to be seen in the context of all others in order to understand its importance; the predominant factors are: high in-run velocity, high momentum perpendicular to the ramp at take-off due to the jump and the lift force, accurate timing of the take-off with respect to the ramp edge, appropriate angular momentum at take-off in order to obtain an aerodynamically advantageous and stable flight position as soon as possible, choice of advantageous body and equipment configurations during the entire flight in order to obtain optimum lift and drag values, and the ability to control the flight stability. Wind blowing up the hill increases the jump length dramatically and decreases the landing velocity, which eases the landing, and vice versa for wind from behind. Improvements to reduce unfairness due to changing wind are urgently needed. The current practice of the judges to reduce the score when the athlete has to perform body movements in order to counteract dangerous gusts is irrational. The athletes should rather be rewarded and not punished for their ability to handle such dangerous situations. For the quantification of underweight it is suggested to use the mass index: MI = 0.28 m/s2 (where m is the jumper mass and s is the sitting height), which indirectly considers the individual leg length. The MI formula is similar to the body mass index (BMI) formula: the height is replaced by the sitting height s and a factor of 0.28 effects that the MI is equal to the BMI for persons with average leg length. The classification of underweight is not only a question of the cut-off point, as much it is a question of the measure for relative bodyweight used. Low weight is one of the performance determinants; however, it should be considered that very low weight can cause severe performance setbacks due to
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decreased jumping force, general weakness, reduced ability to cope with pressure, and increased susceptibility for diseases. In the past, several cases of anorexia nervosa among ski jumpers had come to light. The development toward extremely low weight was stopped in 2004 by new Fe´de´ration Internationale de Ski ski-jumping regulations, which relate relative body mass to maximum ski length. The 2006/7 and 2008/9 seasons showed that light athletes who had to use skis with just 142% of their height could still win competitions. A further increase of the borderline weight is being discussed. The current regulations are based on the well known BMI; the use of the MI instead of the BMI should be explored in future studies.
This article outlines the status of contemporary ski-jumping research and its historical roots and discusses the performance determinants and the implications for health, safety and fairness. For this purpose, a multidisciplinary view of the underlying research fields is developed that spans from biomechanics, aerodynamics and computer modelling to sports medicine, anthropometry and training methods. 1. Background Ski jumping as well as the Nordic Combined event have been Olympic disciplines since the very beginning of the Olympic Winter Games in Chamonix, France, 1924, when Jacob Tullin Thams (Norway; figure 1a) won the ski-jumping competition and Thorleiff Haug (Norway) obtained the gold medal in the Nordic Combined event. The ski-jumping technique has changed several times since then and many authors have analysed the in-run and take-off techniques as well as the flight styles in the various phases of the sport’s historical development. The first analytical model of the ski-jumping mechanics was developed by R. Straumann, 1927.[1] Today, World Cup ski-jumping events are held on three types of hills: (i) ‘normal hills’ are designed for jump lengths up to 110 m; (ii) ‘large hills’ for jumps >110 m; (iii) and ‘ski-flying hills’ for flights >185 m.[2] By means of computer simulations possible now,[3] ‘custom-made’ landing slopes can be optimized in terms of landing impact characteristics and height above ground. In Innsbruck, for example, computer simulations have been used to obtain a moderate ª 2009 Adis Data Information BV. All rights reserved.
increase of landing impact as a function of jump length when compared with conventional hill designs.[4] Other studies also indicate advantages of landing hill profiles differing from conventional designs.[5] The application of global positioning systems or other local position measurements for the determination of the flight paths of ski jumpers can also be applied advantageously for the design of modern jumping hills.[6] The ski-jumping world record has continuously increased over the years. The first to jump further than 100 m was Sepp Bradl (Austria) in 1936. In 1994, the 200 m line was exceeded for the first time by Andreas Goldberger (Austria), and B.E. Romo¨ren (Norway) reached 239 m in Planica (Slovenia) in 2005. The slope of the linear regression line of the world record development since 1936 is 1.9 m per year.[7] The modern ‘V technique’ was pioneered by Jan Boklo¨v (Sweden) in 1985. All world-class athletes have followed his example. During the flight phase, the skis are no longer held parallel to each other; thus, this flight style is called the V-style (or Boklo¨v-style).[8] Associated with this flight style is an increase in jumping length at a given in-run velocity, thus the aerodynamic features of this flight style are profitable when compared with the old parallel style. This is not only because lift and drag forces are advantageous, but also because this flight style enables the athlete to lean forward in a more pronounced way,[9] which also results in an aerodynamic improvement. Together with the take-off movement and the stabilization phase after take-off, it is the fine torque balance during the flight phase that Sports Med 2009; 39 (2)
Determinants of Ski-Jump Performance and Implications
a
b
Fig. 1. (a) Flight style in 1924. Jacob Tullin Thams, 1924, at the first Olympic Winter Games in Chamonix, France. Safety bindings were not available in those days; it is surprising that it took about 80 years until release bindings were developed for ski jumping, which were a matter of course in alpine skiing long before (IOC homepage gallery; reproduced with permission from the IOC/Olympic Museum Collections). (b) ‘V style’. Extreme flight styles as shown here by Christof Duffner (Germany) in Planica at the World Championships in ski flying were possible due to missing limitations of the front ski percentage in the regulations until 1994. Notice that the body-to-ski angle b is negative. A series of tumbling accidents occurred in this and in the seasons before due to the instability associated with such extreme flight styles (photo: W. Mu¨ller).
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makes ski jumping so difficult. The athlete has to solve extremely difficult sensorimotor tasks in ‘real time’ and even little mistakes in one of the crucial phases prohibit a good performance. Not many athletes have been able to remain continuously at a top performance level over a longer period. It is not at all surprising that even top athletes can ‘lose the feeling’ easily and can be pushed down from a top ranking from one season to another or even within a season. Possible reasons for ‘a loss of the feeling’ are manifold. Recently, in the case of Adam Malysz (Poland), it has been discussed that pronounced force training might have had negative effects on takeoff coordination.[10] After the introduction of the ‘Boklo¨v style’, athletes soon found out that they could lean forward in an even more pronounced way when mounting the binding further back on their skis. This led to extreme flight styles like the one shown by Christof Duffner (Germany, figure 1b) during the World Championships in ski flying in Planica, 1994. Note that the head of the athlete was approximately in the plane of the skis. Some of the athletes had used a front ski to total ski length ratio of up to 60% in order to be able to reach such extreme flight styles. The tumbling risk is high using such postures because the pitching moment can suddenly become unbalanced, e.g. due to a gust, and many worldclass athletes had severe tumbling accidents. Based on measurement of the aerodynamic forces and pitching moments associated with various flight positions in the large-scale wind tunnel of Railtec Arsenal, Vienna,[9] the author suggested limiting the maximum percentage of front ski to total ski length. From the following season on 1994/5, the percentage was limited to 57% by the Fe´de´ration Internationale de Ski (FIS). As a consequence, the pitching moment balance has been eased and only one tumbling accident occurred during the 1994/5 World Cup, compared with ten in 1993/4; tumbling accidents occurred very rarely since this regulation change was introduced. Comparative wind tunnel measurements have shown that both lift and drag have strongly increased due to flight style and equipment Sports Med 2009; 39 (2)
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changes.[9] In particular, the skis got broader and lighter and the jumping suits larger and were also made of thicker and stiffer material. For given initial conditions, the trajectory during the flight phase is determined only by the aerodynamic forces and the weight of the athlete with his or her equipment. The development towards larger aerodynamic forces has increased the importance of low weight as a performance factor[11] and this resulted in a substantial decrease of the athletes’ bodyweight, starting out from a mean body mass index (BMI) of 23.6 kg/m2 in the years 1970–5.[7,12,13] A dangerous disease associated with extremely low weight is anorexia nervosa.[14-17] Several severe cases among ski jumpers have become apparent and 22% of the ski jumpers[7] at the Olympic Games 2002 (Salt Lake City, USA) had BMI values below the WHO underweight borderline of 18.5 kg/m2.[18] The problematic development toward extremely low weight was predicted in 1995:[19] ‘‘Another urgent problem is the anorexia deliberately induced by the athletes (low weight increases the jump length).’’ In this paper, Mu¨ller et al. already suggested ‘‘a regulation relating ski length to bodyweight (a self-regulating approach).’’ Shorter skis, i.e. ‘smaller wings’, compensate for the advantage of very low weight and thus the attraction for athletes to be underweight is removed. It took several years to overcome the psychological barriers associated with the introduction of weight into the ski jumping regulations. However, a research project funded by the cooperation of the International Olympic Committee (IOC) and the FIS, which was conducted in the two seasons before and during the Olympic Games 2002, clearly showed how dramatic the situation had become.[7] At the FIS Congress in Miami, 2004, the FIS officials followed the concept to solve the problem by relating the weight – in terms of BMI – to the ski length.[20] The new regulations allow a ski length of 146% of body height for athletes with a weight to squared body height ratio of 20 kg/m2 or above, with the weight being measured with jumping suit and boots directly after the competition. This value corresponds to a BMI slightly above 18.5 kg/m2. The difference between the FIS ª 2009 Adis Data Information BV. All rights reserved.
value and the BMI value is about 1.3 kg/m2. Every 0.5 units below 20 kg/m2, the maximum ski length percentage (of the athlete’s height) is reduced by 2%. Currently, an increase of the cut-off point is being discussed because many coaches, athletes and officials are of the opinion that the current regulation is just a first step in the right direction. The author shares this view; in a publication of Mu¨ller et al.,[20] a heuristic example for the discussion of a BMI-based regulation is suggested (rounded values): 146% above a BMI of 21 kg/m2, 144% between 20 and 21, 142% between 19 and 20, 140% between 18 and 19, and 138% below 18 kg/m2. From today’s point of view, the slope of 2% per BMI could be used (instead of discrete steps) because the industry is able to produce a ski with any desired length. A well known methodical problem for all kinds of sports where relative bodyweight is determined in terms of BMI lies in the fact that the BMI ignores different body properties. For individual assessments, according to the WHO,[21] ‘‘care should be taken in groups with unusual leg length to avoid classifying them inappropriately as thin or overweight.’’ The recently suggested measure of mass index (MI) for relative bodyweight shows a way to modify the BMI in order to correct for deviations of the individual’s leg length from average.[7] 2. The Dynamics of Ski Jumping 2.1 In-Run and Take-off
Ski jumping puts high demands on the athlete’s ability to control posture and movement. During the in-run, the athlete tries to maximize acceleration by minimizing both the friction between skis and snow and the aerodynamic drag in order to obtain a maximum in-run speed (v0), which has a high impact on the jump length.[9,22] The reduction of aerodynamic drag in the in-run phase is primarily a question of the athlete’s posture and his or her dress, and can be optimized by means of wind tunnel measurements and feedback training forms. The friction between skis and snow is not well understood and the theoretical as well as the empirical basis for Sports Med 2009; 39 (2)
Determinants of Ski-Jump Performance and Implications
these complex problems of surface physics are not sufficiently developed for a scientifically guided friction minimization approach. The choice of the ski surface structure as well as the ski preparation with sophisticated wax mixtures is still a field of practical experience. Obviously, the position of the athlete on the skis, which is determined by the given biomechanical geometry of the athlete’s feet, legs and hips, by the jumping boots, and by the binding, is of relevance, and also how the athlete guides the skis in the track is considered to be important. Recently, Ettema et al.[23] have studied the dynamics of the in-run in ski jumping by means of simulation studies. In the curved path of the in-run, the force perpendicular to the ground was found to be 1.65 times the weight. This mechanical demand posed on the athlete determines the initial conditions for the take-off movement. Due to the curved form of the in-run just before the ramp, the athlete has to counteract the centrifugal force acting on him or her (as seen from his or her point of view) and this phase is immediately followed by the athlete’s acceleration perpendicular to the ramp due to the exerted muscular forces.[24-26] During this decisive phase of approximately 0.3 seconds in duration, the athlete has to produce a high momentum (equation 1): Z FðtÞdt ¼ pp0 ¼ m vp0 perpendicular to the ramp (where m is the mass of the athlete plus equipment; F = accelerating force including the lift force; pp0 is the linear momentum perpendicular to the ramp) through which an advantageous take-off angle of the centre of gravity can be obtained. The take-off velocity vector (v00) is given by: v00 = v0 + vp0, where vp0 is the velocity perpendicular to the ramp due to the athlete’s jump; v0 is the in-run velocity parallel to the track on the ramp. Simultaneously to the production of vp0, the athlete must produce an angular momentum L for the rotation in forward direction in order to obtain an advantageous angle of attack as soon as possible after leaving the ramp. During the ª 2009 Adis Data Information BV. All rights reserved.
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jumping phase, the athlete must anticipate the magnitude of the backward torque that will occur due to the air-stream in the initial flight phase so that his forward rotation will be stopped at the right moment. If the forward angular momentum is too low, the associated disadvantageous flight position reduces velocity and lift, and this would result in a bad performance. On the other hand, it is very dangerous to produce a too high forward angular momentum due to the high risk of tumbling accidents associated with it. The take-off movement is a crucial phase of a ski jump because the athlete has to combine several interrelated performance optimization tasks within a time span of approximately 0.3 seconds. Additionally, the accurate timing of the muscle groups involved in the jumping movement has to occur such that the take-off jump is completed as close at the edge of the ramp as possible. Due to the glide path, with respect to the profile of the landing hill, a too early take-off would result in a substantially decreased jump length. This also holds true if the timing is too late. In this case, the leg extension is not completed in time and the linear momentum perpendicular to the ramp pp0 is too far from the athlete’s maximum and, associated with this, the angular momentum L will also not be optimal. The flight trajectory is sensitive to both initial conditions, the angular momentum that largely determines the lift and drag forces after take-off, and to the linear momentum. It has to be pointed out that, particularly on large hills and ski-flying hills, it is not only the magnitude of the linear momentum that determines the jump length:[9,22,27] even a maximal value cannot lead to the success without accurate timing and production of an appropriate angular momentum. A timing mistake of only 0.05 seconds means that the edge of the ramp would be missed by >1 m. However, a very high jumping potential allows the athlete to correct for smaller mistakes before take-off and can therefore help in stabilizing a high performance level. Take-off forces have been measured in the field.[25,26] The force curves of different subjects showed different patterns[25] and this goes hand in hand with the results of kinematic analyses of Sports Med 2009; 39 (2)
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the take-off movement where significant differences between individuals were found.[28,29] Another approach to study details of the take-off movement is the combined measurement of electromyogram (EMG) activity and plantar pressure distribution.[30,31] The athletes also utilize the aerodynamic lift force during take-off: force plate measurements in the wind tunnel have shown that the vertical momentum was larger than that found from the calculation of the area under the force-time curve, which was due to the effect of the lift force. This additionally acting force also decreased the take-off time in the wind when compared with calm conditions.[24] Forceplate and EMG measurements have shown that the jumping technique depends on whether training shoes or jumping boots are used and therefore the latter should be preferred for simulation jump trainings.[32] However, the major impact of the high friction coefficient between shoes and floor (allowing the application of large tangential forces during the take-off movement) when compared with the very low value of skis on ice has not been considered.[33] Also, the weight of the skis was ignored in this study, which necessarily influences the resulting take-off velocity and which may also have a considerable impact on the jumping pattern. A high-intensity strength training with few repetitions and high mobilization of force in the concentric movement has shown that maximum force in a group of Word Cup ski jumpers could be increased without increase in bodyweight.[34] However, it has been described that the correlation between maximum force and maximum jumping height differs considerably between different ski jumpers[35] and it is also still being debated which load maximizes power output during various resistance exercises and how training at maximum power influences functional performance.[36] Production of a high momentum at take-off necessitates the ability of a ski jumper to obtain high power output (W/kg)[33] in the shortest time domain (below 0.4 sec) of the human power spectrum.[37] Many athletes use a board rolling down a slightly inclined road on small wheels which simulates low friction – comparable with the situation on the ramp – for ª 2009 Adis Data Information BV. All rights reserved.
exercising take-off jumps from these rolling platforms; they finally ‘land’ in a forwardoriented position similar to a flight position in ski jumping in the hands of the coach. Such exercises come quite close to the situation on the ramp, but at about 25 m/sec wind velocity in a real take-off, a noticeable lift force acts additionally, which reduces the jumping duration.[24] 2.2 The Flight Phase: Mapping Real-World Ski Jumping to a 2-Dimensional Computer Simulation Model
During the flight, the gravitational force (Fg), the lift force (Fl) and the drag force (Fd) act upon the athlete and his or her equipment (figure 2) and determine the flight path of the centre of gravity of a ski jumper with a given set of initial conditions and parameters (equation 2): r r Fg ¼ m g; F1 ¼ w2 cl A; Fd ¼ w2 cd A; 2 2 where w is the relative wind vector (w = u - v, u being the velocity of external wind, v the velocity of motion), cl and cd are the lift and drag coefficients, respectively, A is the reference area (cross section area), and g is the gravitational acceleration. L = cl A and D = cd A are called the lift and drag areas, which can be measured in a wind tunnel. The air density (r) is a function of y F1
ϕ
x Fd
m.g Fig. 2. Forces acting on a flying object. The only forces acting on an object, e.g. a ski jumper flying through the air, are the weight (m g), the lift force (Fl) and the drag force (Fd). j is the instantaneous angle of the flight path.
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the air pressure and thus decreases in the atmosphere with increasing altitude and temperature: r = p/RT (where p is the air pressure, T is the absolute temperature and R = 287 J K-1 kg-1 is the gas constant). The equations of motion consider all forces acting during the flight. These equations have already been used in the pioneering work by Straumann in 1927[1] and later in several analytical studies.[27,38,39] They can be solved numerically for a given set of initial conditions with any desired accuracy (equation 3): 1 . vx ¼ ðFd cos j F1 sin jÞ m 1 . vy ¼ ðFd sin j þ F1 cos jÞ g m . x ¼ vx . y ¼ vy The athlete’s position changes during the flight phase. The athlete can strongly influence the aerodynamic forces by changing his or her posture. He or she can affect the drag force, the lift force and the torque; the latter enables him or her to change flight position and angle of attack with respect to the air stream. The real problem with simulation studies of the flight path of a ski jumper is the difficulty obtaining accurate lift and drag area functions L(t) and D(t), respectively, which correspond to the changing postures of the athlete during the flight and to the time functions of the angles of attack of the body parts and the skis. Based on a simplified model that constrained the motion to that of a rigid body, it has already been shown by Remizov[39] that maximization of the jump length necessitates an increase of the angle of attack during the flight (a = angle of attack of the skis). Hubbard et al.[40] developed a four-segment dynamic model of ski jumping based on a Lagrangian formulation of the equations of motion of the body segments, in which the jumper is modelled as a collection of planar, rigid bodies. However, a satisfying prediction accuracy of the set of muscle joint torques of the athlete as a function of time in order to position him or herself in the air stream in a desired way also necessitates sophisticated windª 2009 Adis Data Information BV. All rights reserved.
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tunnel measurements of the pressure distribution on all body segments and on the skis in all positions the athlete goes through during the flight, which are still not available nowadays. Seo et al.[41] developed a computer simulation model based on wind tunnel data[42] and tried to find criteria for flight style optimization. They did not correct their wind tunnel data for blocking effects, used a model of a ski jumper with a hip angle (g) of 180, which is associated with high drag and low lift, and is thus aerodynamically disadvantageous when compared with 160–170,[43] the investigated range of V-angles was only 0–25 (during international competitions values of typically 30–35 have been measured[43,44]), and the high accuracy necessary for pitching moment measurements of a ski jumper is not analyzed sufficiently. Evidence for the appropriateness of the findings is not provided by the authors and a similarity of predicted optimum time courses of a, b and V-angles and also the predicted oscillating flight position cannot be found in the literature on field studies of ski jumping. Both time functions L(t) and D(t) of the whole system (athlete in his or her gear with skis) depend in a most complicated way on the body configuration of the athlete with respect to the plane of the skis, on the V-angle between the skis to each other, and on the skis’ angle of attack (a) with respect to the relative wind vector (w). Even small changes of the posture can have noticeable effects on L and D and thus on the flight trajectory. The often-made comparison of a ski jumper with a wing does not work at all: angles of attack of a wing range from about 0 to about 12, whereas a ski jumper’s body angle of attack is typically around 50:[9,43,44] A wing would stall at such high angles of attack and it makes more sense to compare the ski jumper with a flat plate or to a flat prism.[45,46] Since the calculation of the aerodynamic forces acting on a ski jumper by means of computational fluid dynamics (CFD) is far from the necessary accuracy for relevant predictions in competitive sports,[47] the only way to obtain accurate L and D values are measurements of the aerodynamic forces in a wind tunnel with a large cross-sectional area (in order to keep the blocking effect low). Sports Med 2009; 39 (2)
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For the design of appropriate wind tunnel studies the flight position angles of ski jumpers have to be measured from take-off through the entire flight phase until landing. A first approach using pan and tilt cameras was published in 1989.[48] Measurements of V-style ski jumping orientation angles in the field have been made for the first time during the World Championships in ski flying in Planica, 1994. Series of wind tunnel measurements of athletes in 54 different positions in a 5 · 5 m cross-sectional wind tunnel (Railtec Arsenal, Vienna) have led to a model of ski jumping that enables the investigator to take the very important positional changes of the athletes into account.[9,19] This first approach to a realistic mapping of V-style ski jumping has meanwhile been further developed using field data sets from several World Cup events[43] and from the Olympic Games, 2002.[44] In order to maximize positioning accuracy, a series of additional measurements with 1 : 1 models of ski jumpers in the large-scale wind tunnel as well as measurements with a model on a smaller scale (in the 1.5 · 1 m wind tunnel of Graz University of Technology, Graz, Austria) have been conducted in order to receive a very dense grid of data for all imaginable postures. The wind tunnel measurements with the scaled down model have also been designed for comparative studies with CFD approaches.[47] The computer model based on the sets of wind tunnel data allows the study of the impact of all variables, parameters and initial conditions that determine the flight path – and thus the jump length – of a ski jumper. In addition, wind u (wind vector) in the vertical plane can be selected with any direction and speed (w = u - v), with v being the velocity of motion along the path and w the relative wind. Insertion of the construction parameters of a given hill allows the calculation of jump lengths and also the landing impact (component of the linear momentum perpendicular to the landing slope) as a function of jump length for the particular hill. Although the computer model is 2-dimensional (2-D), it considers the V-angle of the skis to each other in terms of its effect on the lift and drag area. The simulation accuracy obtained by this approach for a ski jumper’s glide path calculation is limited only by ª 2009 Adis Data Information BV. All rights reserved.
the experimentally obtainable accuracy for the simulation input values. Recently, a detailed analysis has been presented for both absolute obtainable simulation accuracy and for comparative studies of the effects of parameter and initial value variations as well.[43] The model developed by Mu¨ller et al.[9,19,43] uses lift and drag area inputs and enables the calculation of the flight path; inversely, flight path data can be used to calculate lift and drag forces or areas as functions of flight time.[49] Until now, in scientific papers, not much attention has been paid to the final phase of the flight and to the landing, except for the landing impact.[3,4] The Expert Report for the FIS Technical Board[50] deals with aspects of landing in ski jumping, and in the FIS Bulletin the Committee for Ski-Jumping[51] has published an analysis on the telemark landing in which the role of the elastic properties of the skis for the compensation of the landing impulse is described. This report also contains criteria for the judges’ scoring and states that ‘‘the telemark landing in the classical style cannot be carried out any more due to the continuous development in ski jumping.’’ The Committee states that ‘‘from an aesthetic point of view, it would be a great loss if the application of the telemark landing would not be advocated any more.’’ Basic questions associated with scoring of ‘aesthetic performance’ in ski jumping remain unanswered, e.g. it is not clear what is meant by terms such as a ‘stable ideal flight posture’ or a ‘perfect movement’. 2.3. Computer Simulation: Results of Practical Relevance 2.3.1 Simulation Outputs
The 2-D computer-modelling approach described in the preceding section enables the investigator to predict the trajectory during the flight phase and to investigate the effects of parameter and initial value variations. Due to the high precision of simulation outputs obtainable when performing comparative studies,[43] many practically relevant questions can be answered reliably. Each simulation run results in the following outputs: jump length, landing velocity, landing velocity Sports Med 2009; 39 (2)
Determinants of Ski-Jump Performance and Implications
component perpendicular to the landing slope (which allows calculation of the landing impact in terms of ‘equivalent landing height’), height above ground (for every chosen jumping hill), velocity of motion, horizontal and vertical components of the velocity of motion, the flight trajectory as a function of the horizontal axis or of flight time, and the lift and drag forces acting on the athlete and his equipment during the entire flight. Of course, further variables, e.g. the linear momentum p(t), or the kinetic energy Ekin(t) can also be calculated and displayed graphically. 2.3.2 Simulation Inputs: Choice of Initial Conditions and Parameters
All simulation results obtained depend on the complete set of initial conditions and parameters chosen: the in-run velocity, the velocity perpendicular to the ramp (due to the athlete’s jumping force), the ramp angle, the time functions L(t) and D(t) during flight, which mirror the flight style (the athlete’s posture during the flight) and the equipment tuning used, the wind speed and direction, and the weight of the athlete (with his or her equipment). Additionally, air density (which depends on altitude and temperature) and the ramp angle, as well as the parameters of the landing slope, have to be chosen with respect to the real-world situation to be mapped. 2.3.3 Jump Length Optimization Approaches
Of very high practical importance is the utilization of comparative computer simulations for the determination of all kinds of factors, which influence the flight path. Therefore, for example, it is not possible to correctly interpret any wind tunnel measurements associated with flight style or equipment changes correctly without applying the measured results to an appropriate computer simulation protocol. The coupled, non-linear differential equations cannot be worked out in one’s head in order to interpret measured data correctly, e.g. in terms of influence on the jump length; additionally, the profile of the jumping hill has to be considered too. This also holds true for all other factors that determine the jump length. Each phase of a ski jump (in-run, take-off ª 2009 Adis Data Information BV. All rights reserved.
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jump, early flight phase, stabilized flight phase and landing) has an impact on the subsequent phase. This is to be expected because the equations of motion[1,9] describe a continuous dynamic system. As has already been shown by Remizov[39] and by Denoth et al.,[27] optimization approaches that focus on separated phases of a ski jump may not be relevant: the discussion of the ‘optimum style’ has to include all parts of a jump. This is also supported by the field study results of Schmo¨lzer and Mu¨ller,[44] obtained during the Olympic Games 2002, which demonstrate that different athletes, e.g. S. Amann (Switzerland) and A. Malysz (Poland), used distinctly different styles, which both resulted in top performance. Virmavirta et al.[52] also found large differences of individual flight characteristics in the first flight phase in the same competition (the data collection of this study ends at a flight time of 1.5 seconds). Their finding of a large spread of flight characteristics (position angles, velocity development, height differences) even within the best group during the investigated first phase of the flight also indicates that all phases of a ski jump have to be included in optimization discussions, and additionally supports the concept that individual athletes find their optima in different ways.[28,44] The optimum flight style for one athlete might be disadvantageous or even impossible to obtain for others due to different motor abilities and different anthropometrical and aerodynamic characteristics of individual athletes, which have an important impact on the difficulty for the athlete to stabilize the flight, i.e. to regulate the net pitching moment close to zero[9,40] as soon as possible after take-off. However, based on the simulation of the entire flight trajectory, it may be very useful to change an input value (or a set of values) only in a distinct phase of the flight in order to get a general idea of the impact of this performance factor in dependence on the phase of flight. For example, it has been shown that an increase of the drag D in the first third of the flight diminishes jump length dramatically, whereas the same increase in D has only minor effect in the last third of the flight,[53,54] and high lift forces are important through the entire flight. This emphasizes the Sports Med 2009; 39 (2)
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a 0.90 0.85
2.3.4 Wind Tunnel Measurements: Mapping the Effects of Flight Position Variations
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0.75 0.70
D
0.65 0.60
β = 9.5° γ = 160° V = 35°
0.55 0.50 28
30
32
34 36 α (deg)
38
40
42
b 0.85 L
0.80 0.75 L,D (m2)
In the real world, the change of one parameter will influence the others: both L and D values enhance with increasing angle of attack, thus the demand to keep the drag area D low is associated with a reduced lift area L, when compared to L-values at high angles of attack. Similarly, in the later phases of the flight, it is advantageous to increase the absolute values of both lift and (necessarily) drag, which has already been found by means of optimization studies by Remizov.[39] It is very important to notice that the drag area D of a ski jumper increases continuously with increasing angle of attack a (figure 3a) and also with increasing body-to-ski angle b (figure 3b), whereas the lift area L shows a plateau.[9,43] Sets of aerodynamic data of V-style ski jumping obtained in a large-scale wind tunnel with athletes,[9,19] or with 1 : 1 models of athletes[43] can be applied to investigate all imaginable combinations of the time functions of position angles. The wind tunnel data sets described in the publications above allow finding L and D measurement values (or interpolated values) for many simulation protocols of practical relevance.[9,19,43,44] The detailed sets of wind tunnel measurements pffiffiffi with a scaled down model (equation 4) ½1 : 2 composed of mathematically describable structures in a smaller wind tunnel (measured at reciprocally increased wind speed as demanded by aerodynamic similarity laws)[47] were primarily designed for comparing CFD results to measured data. However, appropriate calibration of these values to results obtained from 1 : 1 measurements in a large-scale wind tunnel would further enlarge the data sets available for detailed optimization studies. The prediction accuracy of the computer model depends predominantly on the accuracy
L
0.80 L,D (m2)
predominant importance of a rapid transition from take-off to a flight position associated with low drag. The athlete can balance the lift to drag ratio by means of his flight position individually and should take care to keep the absolute value of drag small after take-off, although the associated lift value is below maximum in this case.
0.70
D
0.65 α = 35.5° γ = 160° V = 35°
0.60 0.55 0
2
4
6
8 10 β (deg)
12
14
16
Fig. 3. Wind tunnel measurements. Lift area (L) and drag area (D) measurements in a large-scale wind tunnel. (a) The curves give an example of L and D dependency on the angle of attack of the skis (a) at a given body-to-ski angle (b), hip angle (g) and angle (V) between the skis to each other. In order to cover all positions occurring in ski jumping, all relevant combinations of position angles had to be measured and these data were used as inputs for the computer simulation. Athletes and 1 : 1 models of athletes were positioned in 54 different postures. (b) The curves give an example of L and D dependency on the body-to-ski angle (b) at given angle of attack (a), hip angle (g), and angle of the skis to each other (V) [reproduced from Schmo¨lzer and Mu¨ller,[43] with permission. Copyright ª Elsevier 2002].
of the lift and drag input values. The obtainable simulation accuracy has recently been analysed in detail:[43] for example, a measurement error of 2 in a or in b results in jump length deviations (in a simulation using a K120 hill profile) of approximately 3.5 or 2.3 m, respectively. An erroneous increase in both L and D of 2% during the entire flight (compared with the reference jump values) increases the jump length by 1.6 m. Even in large-scale wind tunnels where blocking effects can be ignored, it necessitates a careful approach Sports Med 2009; 39 (2)
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simulation protocol: approach velocity v0 = 26 m/sec, vp0 = 2.5 m/sec, m = 65 kg, r = 1.15 kg/m3. This setting results in a jump length of 120 m, i.e. to the K-point of the hill profile in Innsbruck, which was used for this set of simulations. Lift and drag areas used for the definition of the reference jump A (and also for the reference jump P, which resulted from the field study during the Olympic Games 2002) are shown in table I.
when accuracy in L and D in the range of 1–3% is to be obtained. However, for comparisons of the effects of differing simulation protocols, reliable predictions can still be made when differences in the obtained jump lengths are much smaller than the errors discussed above. The exactness of the simulation output is determined by: (i) the exactness of the determination of the position angles in the field; (ii) the accuracy of the lift and drag measurements in the wind tunnel due to the obtainable positioning accuracy of the athlete (or model); and (iii) the minimization of blocking effects with objects by using large-scale wind tunnels with cross-sectional areas of ‡20 m2.
In-Run Velocity and Velocity Perpendicular to the Ramp
An increase of the in-run velocity v0 of 0.1 m/sec (0.36 km/h) increases the jump length by 1.6 m (open circles in figure 4a). The impact of the athlete’s jump (in terms of the velocity perpendicular to the ramp vp0 at take-off) on the performance is also shown in figure 4a (filled circles): an increase of vp0 of 0.1 m/sec results in 1.2 m jump length increase.
2.3.5 The Impact of the set of Parameters and Initial Values on the Jump Length
The simulations summarized here start out from the reference jump A[43] and the following Table I. Lift and drag areas of reference jumps A and P Lift area (m2)
Drag area (m2)
0
0.275
0.383
0.2
0.389
0.462
0.4
0.494
0.7
0.668
1.0
Time (sec)
a ()
b ()
g ()
V ()
0
63
115
0
7
49
135
13
0.506
14
43
145
20
0.605
25
26
155
31
0.738
0.626
30.2
16.4
159
35
1.2
0.766
0.644
32.6
13
159
35
1.5
0.774
0.662
34.8
10.4
159
35
2.0
0.786
0.697
36.1
10.3
158
35
4.0
0.795
0.732
37.1
10.8
161
35
5.5
0.784
0.686
36.2
9.3
164
35
0.6
0.707
0.590
26.3
21
154
27.5
1.1
0.710
0.556
26.4
18
156
29.6
1.4
0.760
0.636
29.8
16.4
157
31
2.0
0.791
0.675
31.9
14.8
155
31.2
2.3
0.797
0.704
32.1
16.1
154
31
2.7
0.823
0.799
36.4
15.3
154
30.3
3.3
0.838
0.859
38.4
15.0
157
29.1
3.6
0.848
0.881
38.9
17.3
158
28.2
Reference jump A
Reference jump P
For the computer simulation, linear interpolation was used in between the time steps and for the very first flight phase (t = 0, 0.2 and 0.4 seconds) identical values for both reference jumps A and P were used. a = angle of attack of the skis; b = body to ski angle; c = hip angle; V = V-angle.
ª 2009 Adis Data Information BV. All rights reserved.
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a 2.0
b 2.5
−10
3.0
−5
0
130
5
10
130
l (m)
l (m)
10
ΔD (%)
vp0 (m/sec)
120
120
110
110
ΔL (%)
v0 (m/sec) 25.0
5
26.0
−5
−10
27.0
0
c −4
−3
−2
−1
0
1
2
3
4
u (m/sec)
l (m)
130
120
110
m (kg) 55
60
65
70
75
Fig. 4. Simulation results. For the simulation of the effects of parameter and initial value variations on the jump length, the reference jump A[43] (see table I) had been used as a starting point. Characteristics of the starting-out protocol: approach velocity v0 = 26 m/sec; take-off velocity perpendicular to the ramp vp0 = 2.5 m/sec; mass of athlete with his/her equipment m = 65 kg. All simulations use the new profile of the jumping hill in Innsbruck (K = 120 m) (a) Jump length l, in-run velocity v0, velocity perpendicular to the ramp vp0. Slopes at l = 120 m: 16.2 m/ms-1; 12 m/ms-1, respectively. (b) Effects of shifts of L(t) and D(t) functions on the jump length. Slopes: L: 1.8 m/% ; D: - 1.2 m/%. (c) Mass (m), wind (u); u positive for wind blowing constantly up the hill (z = 130, with respect to x-axes) and negative for wind from behind (320). Slopes: -0.9 m/kg; 4.3 m/ms-1 (reproduced from Mu¨ller and Schmo¨lzer,[22] with permission).
Variations of vp0 in the simulation protocol show that a good jumping force of the athlete resulting in vp0 »2.5 m/sec is a necessity for a successful jump; however, a further increase of vp0 to a value as high as 2.8 m/sec, for example, would increase the jumping distance by 3.6 m only. Such an increase of jumping potential of the athlete from 2.5 to 2.8 m/sec would necessitate a very high training effort and this goal might not be attainable for all athletes.[25,34] At the high ª 2009 Adis Data Information BV. All rights reserved.
level of jumping potential most ski jumpers have, it is not so much a further increase of vp0 that would act as a main performance factor due to the better take-off angle associated.[9] More than this, it seems that it is the ability of the athlete to utilize the high jumping force for fine regulation of the take-off movement (which necessitates sufficient force and power reserve) in order to produce an optimum initial angular momentum.[29,52] Sports Med 2009; 39 (2)
Determinants of Ski-Jump Performance and Implications
Flight Style and Aerodynamic Features of the Equipment: L(t) and D(t) input functions
Figure 4b shows two examples out of an infinite number of imaginable modifications of the L(t) and D(t) functions: open circles indicate the results obtained when only the lift area L(t) is increased or decreased by a constant percentage during the entire flight in the range of -10% to +10% (with respect to the reference jump A): the slope is a 1.8 m increase in jump length per 1% increase in L. Filled circles indicate the reduction in jump length with analogously increased drag values only: the slope is -1.2 m per 1% increase in D (during the entire flight). Each change in the flight style or the equipment used can be expressed in a modification of the L(t) and D(t) input functions used, provided that the respective wind tunnel data are available. In addition to the angle of attack of the skis a and the body to ski angle b, the lift and drag areas also depend on the hip angle g used by the athlete:[43] the maximum lift L and a drag area D close to minimum have been measured at g = 160 in the wind tunnel. The mean g-values found in the field when studying the ten best athletes of several World Cup events were: 159, 159, 159, 158 and 161 at the flight times 1.0, 1.2, 1.5, 2.0 and 4.0 seconds, respectively. This excellent match of optimization prediction and field data found from the best ski jumpers in the world indicates the fascinating ability of top athletes to find out the optimum empirically, guided only by their proprioception and coaches’ advice. In the case of hip angle g, starting at the time of stabilized flight, there is very little space for individual style because a hip angle of 180, for example, would be associated with 5% increase in drag and 5% reduced lift, which would cost the athlete any chance to win. The V-angle (angle between the skis to each other) most athletes use today is about 35.[43] Smaller V-angles would reduce the backward rotating torque of the skis.[9] However, at the high altitude of Park City (2000 m), a mean V-angle of 30.5 was found (from t = 1.0 to t = 3.3 seconds), and the mean g-angle was only 155.5. This result has to be seen in connection with the larger b-angles the athletes used at this altitude.[44] ª 2009 Adis Data Information BV. All rights reserved.
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Air Density Effects on the Flight Style
Mapping of ‘real world’ ski jumping to a computer simulation model has to consider that both increased elevation (the air pressure p decreases exponentially with increasing altitude) and increased air temperature T decrease the air density according to r = p/RT, which has a proportional effect on the aerodynamic forces. For example, a change in temperature from -20C to 10C (i.e. from 253 to 283 K) at a given venue reduces the lift and drag forces by >10%. During the Olympic Games 2002, the effect of the low air density of 1.0 kg/m3 (due to the elevation of 2000 m of the venue in Park City) on the flight position during the entire flight has been investigated by Schmo¨lzer and Mu¨ller.[44] Reduced aerodynamic forces are associated with lower values of the backward-rotating torque due to the air flow: the athletes cannot lean forward in such an extreme way as they do at lower altitudes. They have to use larger b-angles in order to avoid flight instability and tumbling accidents. In fact, the mean b-angles from t = 1.0 to 3.6 seconds found in Park City[44] were noticeably larger than the values found in field studies during World Cup competitions at lower elevation.[43] The mean b-angle during the flight phase from 1 second on was 16.1, whereas the mean value found at lower elevations was 11.7. In the computer simulation of jumps at the Olympic venue (Park City jumping hill parameters; r = 1.0 kg/m3), the reference jump P protocol[44] derived from the field study in Park City resulted in a 4.4 m increase in jump length when compared with the reference jump A protocol result. In addition, the landing velocity when using protocol P was much lower, which eases the landing. The athletes not only increase their b-angle in order to obtain a stable flight in thin air (increase of the backward rotating torque acting on the athlete’s body due to increased L and D values), the altered position also leads to a better performance. Individual Flight Styles
The flight position angles differed markedly from one athlete to the other; however, the analysis of flight posture data of the Olympic medalists showed that they were able to reproduce their Sports Med 2009; 39 (2)
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individual flight style in an impressive way.[44] The gold medallist Simon Amann used a low angle of attack of the skis a and additionally a low body-to-ski angle b in order to keep the drag force low through the entire flight until the preparation for the landing. The silver medalist Adam Malysz, on the other hand, used a noticeably higher angle of attack a and also a low bodyto-ski angle b. Different athletes utilize the advantages associated with high lift and low drag in distinctly different ways. These results, obtained with Olympic medalists of 2002, support the opinion that ‘the optimum flight style’ applicable to all athletes does not exist at all. Analogous to this finding, it has been pointed out by Vaverka et al.[28] that the take-off movements of various world-class athletes also deviate remarkably from each other. This has also been found with high-speed video analyses when takeoff forces were measured in a wind tunnel using an inclined plane and rollers underneath the skis in order to mimic the ramp and the low friction coefficient between skis and snow.[33] The forcetime functions differed strongly from each other. It is remarkable that one (SH) of the two members of the Austrian National Team who participated in these wind tunnel jumps in 2001 won two Summer Grand Prix competitions a few days later although he had not won any event in the years before. The other one (WL) is still competing; he won the Four Hills Tournament 2008/9. In this publication[33] a comparison of the perpendicular velocity vp0 obtained by the two worldclass ski jumpers with jumping skis and with jumping boots in a wind tunnel can also be found. The Influence of Wind
The effect of wind blowing depends on the wind velocity u and on the direction the wind comes from (wind angle z, within the vertical plane). Wind blowing up the hill increases the jump length dramatically and decreases the landing velocity, which eases the landing; wind from behind the jumper reduces the jump length and increases the landing velocity. A simulation series, with athletes’ gear of 1994, and with wind directions from z = 0 (wind assumed to blow horizontally in the positive x-direction, i.e. the direction ª 2009 Adis Data Information BV. All rights reserved.
of the run-out) to z = 350 in anti-clockwise steps of 10 (using the parameters of the ski flying hill in Planica) allowed determination of the most advantageous and the most disadvantageous direction of a constantly blowing wind.[9] Wind blowing up the hill with u set to u = 3 m/sec resulted in a 16 m increase of jump length, whereas wind blowing down the hill reduced the jump length by 23.7 m. Seo et al.[41,42] have found by means of a computer simulation that tail or headwind of 1 m/sec (for simplicity the wind vector they used had only a horizontal component) reduces or increases the jump length by 4 m, when using the parameters of the Okurayama jumping hill (Sapporo). Using the hill parameters of Park City (K = 120 m), with athletes in the gear of 2002, the effect of a wind of u = 3 m/sec, blowing during the whole flight from an advantageous angle (z = 135) resulted in increased jump lengths of l = 120.0 m (compared with 106.2 m in calm conditions; m = 75 kg), of l = 128.7 m (compared with 115.9 m; m = 65 kg), and l = 136.8 m (compared with 125.7 m; m = 55 kg).[43] Using the same wind vector u in the simulation, a pronounced increase in jump length l occurred on all sizes of jumping hills: the jump length l was 128.9 m when using the Sapporo hill profile (K = 120 m), 178.9 m for the Kulm (K = 185 m), and 99.5 m for Villach (K = 90 m), whereas the jump lengths obtained in the respective simulations without wind were only 112.5 m, 158.5 m and 89.5 m, respectively. Additionally, the landing velocities vl decreased (Sapporo: vl without wind was 28.7 m/sec and 27.0 m/sec with wind; Kulm 30.3 and 28.4 m/sec; Villach 27.7 and 26.1 m/sec, respectively). These simulation studies impressively show the enormous effect of wind on the jump length and also on the landing velocity. The jump length dependency on the wind from both behind and front is shown in figure 4c. Also, in reality, jumps to or beyond the K-point of a hill can often be performed only with the help of wind blowing up the hill. Changing wind velocities during a competition raise the question of fairness and it has to be emphasized that just 1 m/sec difference in wind speed can easily decide between winning or losing.[9,22] Sports Med 2009; 39 (2)
Determinants of Ski-Jump Performance and Implications
3. Low Weight in Ski Jumping 3.1 Weight as a Performance Factor
The importance of being light for ski jumping performance has recently been investigated in detail:[9,20,22,43,44] body mass profoundly influences the jump length and the velocity of motion. The lighter athlete has the advantage of flying further, and additionally the touch down is eased due to a lower landing velocity. Figure 4c shows simulation results on how the jump length decreases when the weight increases. The slope is -0.9 m per kg mass increase. It has to be added that lower weight allows the athlete to lean forward in a more pronounced way, which results in an additional increase of jump length due to aerodynamic advantages associated with a lower body-to-ski angle b,[9,43] which might double the effect of low mass. This additional increase of jump length is hard to quantify because of the associated complex flight stabilization questions; such an attempt would need a modelling approach including questions of flight stabilization and pitching moment balance, based on the knowledge of the air pressure distribution on all surfaces of the jumper-ski system. 3.2 Assessment of Relative Bodyweight in Terms of Body Mass Index and Mass Index
The BMI = m/h2 (where m = body mass in kg and h = body height in m) is widely used to define appropriate bodyweight. The WHO[18] defines three grades of thinness: grade I, BMI from 17 to 18.49; grade II from 16 to 16.99, grade III for BMIs below 16. However, the BMI is a rough measure that does not distinguish between persons with alternate body properties. The Cormic index s/h, where s = sitting height and h = body height, characterizes the relative leg length, which should be considered for the classification of thinness or overweight.[21] According to Norgan,[55] Cormic index means range from 0.50 to 0.55 in different populations: correspondingly, the individual leg length affects relative bodyweight substantially. The BMI definition, the Cormic index C as a measure for the individual leg length, and ª 2009 Adis Data Information BV. All rights reserved.
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anthropometric data presented by Norgan[55] were the starting points for the deduction of a new measure for relative bodyweight: the MI, which takes the relative leg length of the individual into consideration.[7] In his analysis of anthropometric data, Norgan found an increase of BMI with increasing Cormic index: ‘‘Using the sitting height to body height ratio (Cormic index) C = s/h as an index of body shape in 158 groups (18 000 individuals), the regression coefficient of BMI on C was 0.90 kg/m2 per 0.01 C.’’ This increase mirrors the pronounced effect of individual leg length on BMI. The MI, which has recently been suggested,[7] is a modification of the BMI according to equation 5. !k C MI ¼ BMI C where C is the individual Cormic index s/h and ¼ 0:53; (equation 6) is a value chosen in the C middle of the Cormic index continuum. The intention was to define a new measure for relative bodyweight, which is termed MI, that is independent of the relative leg length and thus independent of the Cormic index. Using the regression coefficient of 0.9 kg/m2 per 0.01 C, as found by Norgan, k could be determined: k = 2.015 » 2, and thus the simple formula for the MI results as (equation 7):[7] m m MI ¼ 0:532 2 0:28 2 s s The unit is the same as for the BMI: kg/m2. With k = 2.0, the body height h does not appear in the final equation for the MI. In case further studies would imply using anthropometric data sets resulting in a value for the exponent ka2.0, both h and s would remain in the formula and the general term MI* would result (equation 8): k m C m k MI ¼ 2 ¼ 2k C h s=h h sk For practical purposes, this formula could be approximated by a simple term of the type MI* = BMI + f (MI - BMI), with f being an appropriately chosen constant factor. For k = 2.0 Sports Med 2009; 39 (2)
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(which was used for the definition of the MI) the factor f would equal 1.0 and MI* MI would result.
3.4 The Solution of the Underweight Problem in Ski Jumping
A solution of the underweight problem in ski jumping by means of new regulations, which would relate ski length to bodyweight – a selfregulating approach,[19] was suggested in 1995. A reduction of ski length for athletes who are too light can solve the underweight problem because then it is not attractive for the athletes to be underweight any more: shorter skis are smaller ‘wings’ for the athlete and this aerodynamic disadvantage compensates for the advantage of low weight. ª 2009 Adis Data Information BV. All rights reserved.
BMI (kg/m2)
A series of anthropometrical measurements on world class ski jumpers was made in Planica (World Cup 2000; pilot study), Hinterzarten (Summer Grand Prix 2000), and Salt Lake City (Olympic Games 2002).[7] The mean BMI values found in these studies were 19.8, 19.6 and 19.4 kg/m2, respectively. Figure 5a shows these results and compares them to results obtained by Vaverka[12,13] between 1970 and 1995. During the last 30 years, the mean BMI has decreased by approximately 4 kg/m2. The evaluation of the complete World Cup data set from Hinterzarten (the participation rate was 100%) showed that 16.3% of the athletes had a BMI <18.5 kg/m2, and 22.8% of the athletes investigated during the Olympic Games 2002 were below 18.5 kg/m2. The lowest BMI found among competing world-class ski jumpers was 16.4 kg/m2. In figure 5b, the difference of MI minus BMI for both data sets, Hinterzarten and Salt Lake City, is displayed. The measure MI, which considers the individual leg length of the athlete, deviates remarkably from the BMI values. The large deviations of the MI from the BMI up to 4 units underlines the importance of applying the new measure MI when individual relative bodyweight is to be assessed.
23.6
23 22 21.1 21
20.5 19.8
20
19.6
19.4
19 18 17 1970-5 n = 226
1986 n = 42
1993-5 2000 P n = 81 n = 56 Year
2000 H 2002 S n = 92 n = 57
b 4.0 3.0 MI - BMI (kg/m2)
3.3 Anthropometric Data of Ski Jumpers
a 24
2.0 1.0 0.0 −1.0 −2.0 −3.0 −4.0 1.60
1.65
1.70
1.75 1.80 Height (m)
1.85
1.90
Fig. 5. Data collection during World Cup and Olympic events. Relative bodyweight of ski jumpers. (a) Decrease of relative bodyweight of ski jumpers from 1970–5 to 2002. Mean body mass indexes (BMI) according to anthropometric measurements of Vaverka[12,13] and Mu¨ller et al.[7] (b) Differences between the BMI and the mass index (MI) values of all World Cup ski jumpers investigated at the Summer Grand Prix in Hinterzarten (2000 H in a) and during the Olympic Games in Salt Lake City (2002 S in a). The measure MI, which considers the individual leg length, deviates remarkably from the BMI in many cases and can be used advantageously for individual assessments of ‘relative bodyweight’. P = Planica.
Studies of the athletes’ anthropometric status on the one hand[7,20] and of the importance of weight in the context of aerodynamic forces acting on a ski jumper on the other,[9,43] formed the basis for the discussion of the final form of the changes to the regulations.[20] The FIS has meanwhile adopted the suggestion to solve the problem by relating maximum permitted ski length to relative bodyweight in terms of BMI, but the BMI borderline chosen by the FIS was lower than the one suggested.[20] In addition to the existing ski-length limitation Sports Med 2009; 39 (2)
Determinants of Ski-Jump Performance and Implications
(maximum ski length is 146% of body height), this percentage is now reduced for those athletes whose relation of weight (including the jumping shoes and the suit) to body height squared is <20 kg/m2. This corresponds to a correctly measured BMI of slightly >18.5 kg/m2 (18.5 kg/m2 is the WHO underweight cut-off point). Every 0.5 units below 20 kg/m2 the maximum ski-length percentage (of the athlete’s height) is reduced by 2%. For example, a value of 19 kg/m2 would result in a ski length of only 142% of the body height.
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flight position as soon as possible; choice of advantageous body and equipment configurations and angles of attack during the entire flight in order to obtain optimum lift and drag values; and, overall and most difficult, the ability to control flight stability. After the transition from take-off to the flight phase, the pitching moment has to be balanced close to zero[9] in order to avoid tumbling accidents; this can become extraordinarily difficult when gusts or other disturbing effects occur. 4.3 Wind, Fairness and Questionable Practices of the Judges
4. Conclusions and Recommendations 4.1 Mapping Real-World Ski Jumping to a Computer Simulation Model
Within the few seconds of a ski jump, the athlete has to solve extremely difficult control and optimization problems in order to produce a long jump. The basis for these implicit decisions and the resulting control manoeuvres can be understood by means of computer simulations, which include the effects of position changes during the flight by means of wind tunnel input data. Computer simulation can be applied for the interpretation of field study, laboratory, wind tunnel or theoretically obtained data in quantitative terms, e.g. their effects on the jump length or on the flight velocity. The predictions can also be used to answer many questions that arise during discussions of training methods, safety and health considerations, fairness in the sport, optimized jumping hill design, development of the sport, and changes to the regulations. 4.2 Predominant Demands for High Performance
Predominant demands for high performance are: high in-run velocity; high linear momentum perpendicular to the ramp at take-off due to the jumping movement and the utilization of aerodynamic lift; accurate timing of the take-off jump with respect to the edge of the ramp; appropriate angular momentum at take-off in order to obtain an aerodynamically advantageous and stable ª 2009 Adis Data Information BV. All rights reserved.
Wind blowing from the front increases the jump length enormously, whereas wind from behind reduces the jump length. Thus, changes in the wind speed or direction will make every contest one of a gamble with the wind. During the whole series of World Cup events in the course of a season, the advantages and disadvantages for an individual athlete may equalize; however, for single competitions and events such as the World Championships or Olympic Games, the wind can be an overwhelming factor causing severe unfairness. Solutions to this problem are urgently needed. One improvement would be a statistical model based on Bayes Theory for the prediction of the expected jump length for each individual athlete, which could advantageously be used for the appropriate choice of the in-run length during a competition.[56] This could serve as an unbiased criterion for the jury decision. However, even when using this optimized approach, some cases of exceptionally long jumps are to be expected due to the stochastic influences on the performance, e.g. a gust from in front during the flight combined with an extraordinary athletic performance. As soon as the athlete recognizes that the jump would go too far, he or she will try to interrupt the flight. This is easier said than done because the history of the flight largely determines the phase to come. Additionally, the athlete is not free in the choice of manoeuvres, which reduce lift and increase drag due to biomechanical demands regarding the compensation of the landing impulse. In such dangerous cases Sports Med 2009; 39 (2)
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of extremely long jumps, punishment of nontelemark landing is questionable. A possible approach to reduce the wind is building wind protection walls or nets that damp the wind gusts, but this is associated with high costs. CFD predictions may be a help for designing such devices. It is imaginable to perform competitions sporadically on smaller hills (e.g. for K = 90 m) in indoor facilities in the future, but such constructions for large or skiflying hills are out of reach. Another approach to reduce, but not to eliminate, the problem could be the correction of wind effects by means of computer simulations. This would only cause comparatively low costs: anemometers capable of measuring wind speed and direction mounted at the height of the glide path every 5–10 m would serve as input sources for the simulation. For winds blowing within the vertical plane, the prediction accuracy of 2-D simulations designed for this purpose would be capable of correcting the major part of the wind effect; however, the problem of proper consideration of gusts from the side would remain unsolved, and also an extension of the simulation model to 3-D would not be capable of solving these questions in a transparent way, particularly due to the presumably different reactions of different athletes to such gusts. In this respect, it has to be noticed that the current practice of the judges to reduce the score when the athlete has to perform body movements in order to counteract dangerous gusts is irrational. For his or her ability to prevent a tumbling accident by moving parts of his or her body and the skis immediately in the forced way to counteract dangerous destabilizing forces, the athlete should be rewarded and not punished. 4.4 The Mass Index Improves the Assessment of Underweight
The WHO Expert Consultation is aware of the fact that ignoring the individual leg length when using the BMI leads to severely inaccurate results[18] and on the other hand the BMI cut-off points defined by the WHO for under- and ª 2009 Adis Data Information BV. All rights reserved.
overweight should be retained as international classification criteria for relative bodyweight.[57] This discrepancy can be overcome by introducing the MI instead of the BMI. The MI considers the individual leg length. The MI formula is similar to the BMI formula: the height (h) is replaced by the sitting height (s) and a factor of 0.532 (»0.28) effects that the MI is equal to the BMI for a person with average leg length (C ¼ C ¼ 0:53). The MI value resulting for a person with long legs (short upper body and thus low Cormic index) is higher than the BMI, and vice versa. The measurements necessary to determine the MI are as simple as for the BMI: instead of the body height (h) the sitting height (s) has to be measured. It is to be expected that the application of this new measure to underweight athletes and also to anorexia patients will improve the accuracy of diagnosis. It is suggested that both values are determined, the MI and the BMI, because this would also allow the later determination of the modified index MI*, in case further investigations resulted in a value for k a 2. It is recommended to use the MI for a more expressive estimate of relative bodyweight instead of or in addition to the BMI. This is of particular importance for very light athletes with short legs, whose MI is lower than their BMI. The classification of underweight is not just a question of cut-off points; just as well it is a question of the measure for relative bodyweight used.[7]
4.5 Low Weight and Regulations in Ski Jumping
Low weight is one of the determinants of ski jump performance that has to be seen in the context of all others. Although there is an obvious advantage of low weight during the flight phase, it should be considered that very low weight can cause severe performance setbacks due to decreased jumping force, general weakness, reduced ability to cope with pressure, increased susceptibility for diseases and, in the worst case, anorexia nervosa, which is a very serious disease that can have lethal consequences.[14-17] Sports Med 2009; 39 (2)
Determinants of Ski-Jump Performance and Implications
The development toward extremely low weight was stopped in 2004 by means of new ski jumping regulations. Since then, the severe underweight problem in ski jumping has almost disappeared. Currently, an increase of the borderline value is being discussed, which would be a further improvement for two reasons. First, it would be advantageous from a health point of view because it would motivate more athletes to compete also with a weight several kilograms above the WHO underweight borderline (18.5 kg/m2). Secondly, from the fairness point of view, athletes who are not very thin by nature or due to dieting would get a better chance to win. This would enlarge the pool of possible winners and as a consequence the number of competing ski jumpers could increase. A higher borderline weight value does not mean at all that very light athletes could not win any more; they still have a good chance to be among the best because the disadvantage of their shorter skis is largely compensated by their lower weight. The 2006/7 season has shown examples of athletes who performed excellently with a ski length of only 142% of their body height, which corresponds to a BMI <18 kg/m2. The current FIS regulations are BMI-based (not MI-based) because the BMI is well known worldwide and it appeared to be too big a step to introduce new regulations based on a new measure. The introduction of ski jumping regulations that are based on the leg length corrected measure for relative bodyweight MI should be discussed as a second step. For the introduction of the MI, it would be necessary to measure the leg length instead of the sitting height due to the possibilities for fibbing in the latter case because it is practically not possible to force a person to sit upright in the most extended position. 4.6 Future World Record Developments
Despite the fact that the height difference between the edge of the take-off platform and the lowest point of the outrun is limited to the vertical height difference DH = 130 m, the competitive performance in terms of jump length has continuously increased, the present world ª 2009 Adis Data Information BV. All rights reserved.
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record being 239 m. This was possible due to the development of aerodynamically improved equipment, particularly due to larger ski areas, thicker and stiffer garments, an aerodynamically advantageous new flight style, the Boklo¨v- or V-style, and a substantial decrease of bodyweight. All these factors enabled flatter glide paths and thus longer jumps at a given maximum height between run-out and ramp. The recent changes to the regulations have reduced the possibilities for further increases of the flight path angle and it is to be expected that new records in ski flying will not occur so frequently any more. Without the height limitations at larger and steeper hills than the ones available today, much longer jumps are imaginable. From approximately 5 seconds flight time on the glide path angle of a ski jumper with contemporary equipment and flight style remains almost constant.[3] However, from a safety point of view, such developments should not be supported in competitive ski jumping and the current hill height limitation should be maintained in the future; this will also keep the costs of ski jumping hills within reasonable limits. 4.7 Individual Flight Styles
Different athletes use distinct jump length maximization strategies, which have to be seen in the context of their individual motor abilities, the aerodynamic features of the form of their bodies and the equipment tuning used as well as their anthropometric characteristics. Computer simulations of the flight path can be used as a powerful tool to distinguish between advantageous and irrelevant strategies in the training process and thus the development of an improved technique can be accelerated when compared with trial and error approaches. Forms of ‘flight trainings’ and ‘simulated take-off jumps’ in a wind tunnel are of high practical importance because modified flight styles can be tested there without the risk of accidents and the ‘flying time’ can be many minutes instead of a few seconds when compared with a real jump. Quantitative explanations and predictions concerning the complex flight stabilization problems have not Sports Med 2009; 39 (2)
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been made yet. Such attempts would need a highly sophisticated multi-segment modelling approach including questions of pitching moment balance in turbulent flow situations. The enormous difficulties to be expected with such an approach indicate the fascinating abilities of the athletes to work out individual solutions for such complex tasks including the anticipation of possible effects of chaotic phenomena of turbulent flow[45,47] and disturbances due to immediately occurring gusts. Of course, all of this has to be done by the athlete in ‘real time’. 4.8 Conclusion
Ski jumping has developed rapidly during the last two decades. The introduction of the V-style and various equipment developments changed the appearance of the sport substantially. Various fields of science, including biomechanics, aerodynamics, computer modelling and simulation, exercise physiology, motor control and motor learning, anthropometry and body composition form the basis for the analysis of ski jumping performance and for the prevention or correction of misleading developments of the sport. The determination of the impact of a single or a set of factors on the performance in the context of all other determinants necessitates computer modelling and simulation studies. Detailed research has been done in this field and this has led to a deeper understanding of physics of ski jumping and to a basis for discussing performance optimization strategies and training methods.[33,49,58] However, little is known about how the athletes manage to alter their posture appropriately during the flight in order to increase performance and how they solve the extremely difficult problem of flight stabilization. It is obvious that athletes make use of a detailed knowledge on aerodynamics of ski jumping and on the biomechanical characteristic of their bodies, which their motor control is based on, although the major part of this is ‘implicit knowledge’. Measurements of take-off forces and positions, field studies of the flight postures and aerodynamic and anthropometric measurements indicate that different athletes use differing ways ª 2009 Adis Data Information BV. All rights reserved.
for reaching high performance, depending on the individual characteristics of their bodies and their motor abilities. Ski jumping research contributed to solving the problem of tumbling accidents which appeared in combination with the introduction of the V-style and directed the way to overcoming the low weight problem. The approach to reduce maximum ski length for too light athletes was an important step into the right direction, but the BMI cut-off point of the current regulation is too low. A well known shortcoming of the BMI is that it ignores the individual’s leg length; this could be overcome in the future by means of the recently suggested MI. However, the establishment of the MI or a modified version of it necessitates further anthropometric studies on the decrease of BMI with increasing individual leg length in various groups of persons. The cooperation of the FIS and the IOC Medical Commission concerning the low weight problem in ski jumping should be seen as a role model for other sports where health or safety problems exist. The enormous influence of wind on the performance raises questions of fairness, particularly in the case of single events such as the Olympic Games; an increase of the number of runs would be a possible way to mitigate the problem. Additionally, the practice of the judges to punish athletes who have to counteract dangerous gusts cannot be supported from a rational point of view. Acknowledgements The author would like to thank all athletes and coaches for participating in the preceding projects, and the IOC, the FIS and the Austrian Research Funds FWF (15130 Med., 14388 Tec) for their support. The author also wishes to thank H. Ahammer and B. Schmo¨lzer for their comments and A. Fu¨rhapter-Rieger, G. Tschakert and T. Zarfl, who have contributed to the preparation of this manuscript. No funding was received for the preparation of this article, and the authors have no potential conflicts of interest that are directly relevant to the content of this article.
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2. Gasser HH. Grundlagen fu¨r die Projektierung einer Schisprungschanze [online]. FIS. Available from URL: http:// www.fis-ski.com/ [Accessed 2008 Apr 17] 3. Mu¨ller W. Biomechanics of ski-jumping: scientific jumping hill design. In: Mu¨ller E, editor. Science and skiing. London: E&FN Spon (Chapman & Hall), 1997: 36-48 4. Mu¨ller W, Schmo¨lzer B. The new jumping hill in Innsbruck: designed by means of flight path simulations. Proceedings of the IVth World Congress of Biomechanics. Calgary (AB): University of Calgary. Faculty of Kinesiology, 2002 Aug 4–9 5. Gilbertson WK. Ski jump profile optimization: incorporating blunt body ground effect [dissertation]. Boulder (CO): University of Colorado, 2003 6. Blumenbach T. GPS-Anwendungen in der Sportwissenschaft: Entwicklung eines Messverfahrens fu¨r das Skispringen [dissertation at the Bayerische Akademie der Wissenschaften; Reihe C 591]. Mu¨nchen: C. H. Beck Verlag, 2005 7. Mu¨ller W, Gro¨schl W, Mu¨ller R, et al. Underweight in ski jumping: the solution of the Problem. Int J Sports Med 2006; 27: 926-34 8. Schwameder H, Mu¨ller E. Biomechanische Beschreibung und Analyse der V-Technik im Skispringen. Spectrum 1995; 1: 5-36 9. Mu¨ller W, Platzer D, Schmo¨lzer B. Dynamics of human flight on skis: improvements on safety and fairness in ski jumping. J Biomech 1996; 29 (8): 1061-8 10. Starosta W. The cause of fluctuations of performance in ski jumping. Leistungssport 2004; 34 (2): 15-9 11. Mu¨ller W, DeVaney TTJ. The influence of body weight on ski jumping performance. In: Haake E, editor. The engineering of sport. Rotterdam: Balkema, 1996: 63-9 12. Vaverka F. Somatic problems associated with the flight phase in ski-jumping. Studia i monografia AWF we Wroclawiv 1994; 40: 123-8 13. Vaverka F. Research reports. Olomouc: University of Olomouc, 1987 and 1995 14. Becker AE, Grinspoon SK, Klibanski A, et al. Current concepts: eating disorders. N Engl J Med 1999; 340: 1092-8 15. Sudi K, O¨ttl K, Payerl D, et al. Anorexia athletica. Nutrition 2004; 20: 657-61 16. Sullivan PF. Mortality in anorexia nervosa. Am J Psychiatry 1995; 152: 1073-4 17. Sundgot-Borgen J. Eating disorders in female athletes. Sports Med 1994; 17: 176-88 18. WHO Expert Committee. Physical status, use and interpretation of anthropometry. Technical Report Series 1995; 854: 364 19. Mu¨ller W, Platzer D, Schmo¨lzer B. Scientific approach to ski safety. Nature 1995; 375: 455 20. Mu¨ller W, Gro¨schl W, Schmo¨lzer B, et al. Body weight and performance in ski jumping: the low weight problem and a possible way to solve it. In: 7th IOC Olympic World Congress on Sport Science; 2003 Oct 7-10; Athens, 43 D 21. WHO Expert Committee. Physical status, use and interpretation of anthropometry. Technical Report Series 1995; 854: 355
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22. Mu¨ller W, Schmo¨lzer B. Computer simulated ski jumping: the tightrope walk to high performance. Proceedings CD. IV World Congress of Biomechanics. Calgary (AB): University of Calgary. Faculty of Kinesiology, 2002 Aug 2–4 23. Ettema GJC, Braten St, Bobbert MF. Dynamics of the inrun in ski-jumping: a simulation study. J Appl Biomech 2005; 21: 247-59 24. Virmavirta M, Kiveska¨s J, Komi PV. Take-off aerodynamics in ski jumping. J Biomech 2001; 34 (4): 465-70 25. Virmavirta M, Komi PV. Measurement of take-off forces in ski-jumping:. part I and II. Scand J Med Sci Sports 1993; 3: 229-43 26. Kaps P, Schwameder H, Engstler C. Inverse dynamic analysis of take-off in ski-jumping: In: Mu¨ller E, Schwameder H, Kornexl E, et al., editors. Science and skiing. London: E&FN Spon (Chapman & Hall), 1997; 6: 72-87 27. Denoth J, Luethi SM, Gasser H. Methodological problems in optimisation of the flight phase in ski jumping. Int J Sport Biomech 1987; 3: 404-18 28. Vaverka F, Janura M, Elfmark M, et al. Inter- and intraindividual variability of the ski-jumper’s take-off. In: Mu¨ller E, Schwameder H, Kornexl E, et al., editors. Science and skiing. Austria: E&FN Spon (Chapman & Hall), 1996: 61-71 29. Arndt A, Bru¨ggemann GP, Virmavirta M, et al. Techniques used by Olympic ski jumpers in the transition from take-off to early flight. J Appl Biomech 1995; 11: 224-37 30. Virmavirta M, Komi PV. Plantar pressure and EMG activity of simulated and actual ski jumping take-off. Scand J Med Sci Sports 2001; 11: 310-4 31. Virmavirta M, Perttunen J, Komi PV. EMG activities and plantar pressure during ski jumping take-off on three different sized hills. J Electromyogr Kinesiol 2001; 11: 141-7 32. Mu¨ller E, Benko U, Raschner C, et al. Specific fitness training and testing in competitive sports. Med Sci Sport Exerc 2000; 32 (1): 216-20 33. Mu¨ller W. Performance factors in ski jumping. In: No¨rstrud H, editor. Aerodynamics of sports. Centro Internazionale di Scienze Meccaniche (Udine), CISM Series, vol. 506. Wien, New York: Springer, 2008: 139-60 34. Hoff J, Berdahl O, Braten S. Jumping height development and body weight considerations. In: Mu¨ller E, Schwameder H, Kornexl E, et al., editors. Science and skiing II. Hamburg: Dr. Kovac Verlag, 2001: 403-12 35. Bruhn S, Schwirtz A, Gollhofer A. Diagnose von Kraft- und Sprungkraftparametern zur Trainingssteuerung im Skisprung. Leistungssport 2002; 5: 34-7 36. Cronin J, Sleivert G. Challenges in understanding the influence of maximal power training on improving athletic performance. Sports Med 2005; 35 (3): 213-34 37. Mu¨ller W, Hofmann P. Performance factors in bicycling: human power, drag, and rolling resistance. In: No¨rstrud H, editor. Aerodynamics of sports. Centro Internationale di Scienze Meccaniche (Udine), CISM Series, vol. 506. Wien, New York: Springer, 2008: 49-91 38. Koenig H. Theorie des Skispringens angewandt auf die Flugschanze in Oberstdorf. In: Uhrentechnische Forschung. Stuttgart: Steinkopf Verlag, 1952: 235-53 39. Remizov LP. Biomechanics of optimal flight in ski-jumping. J Biomech 1984; 17: 167-71
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40. Hubbard M, Hibbard RL, Yeadon MR, et al. A multisegment dynamic model of ski-jumping. Int J Sport Biomech 1989; 5: 258-74 41. Seo K, Murakami M, Yoshida K. Optimal flight technique for V-style ski jumping. Sports Engineer 2004; 7 (2): 97-103 42. Seo K, Watanabe I, Murakami M. Aerodynamic force data for a V-style jumping flight. Sports Engineer 2004; 7 (1): 31-9 43. Schmo¨lzer B, Mu¨ller W. The importance of being light: aerodynamic forces and weight in ski jumping. J Biomech 2002; 35: 1059-69 44. Schmo¨lzer B, Mu¨ller W. Individual flight styles in ski jumping: results obtained during Olympic Games competitions. J Biomech 2005; 38: 1055-65 45. Reisenberger E, Meile W, Brenn G, et al. Aerodynamic behaviour of prismatic bodies with sharp and rounded edges. Exp Fluids 2004; 37: 547-58 46. Flachsbart O. Messungen an ebenen und gewo¨lbten Platten. In: Ergebnisse der Aerodynamischen Versuchsanstalt zu Go¨ttingen 4, 1932; 96-100 47. Meile W, Reisenberger E, Mayer M, et al. Aerodynamics of ski jumping: experiments and CFD simulations. Exper Fluids 2006; 41: 949-64 48. Yeadon MR. A method for obtaining three-dimensional data on ski jumping using pan and tilt cameras. Int J Sport Biomech 1989; 5 (2): 238-47 49. Mu¨ller, W. Computer simulation of ski jumping based on wind tunnel data. In: No¨rstrud H, editor. Aerodynamics of sports. Centro Internationale di Scienze Meccaniche (Udine), CISM Series, vol. 506. Wien, New York: Springer, 2008: 161-82 50. Ranta MA, von Hertzen R. Landing in ski-jumping, an expert report for the FIS Technical Board. Oberhofen, Schweiz: FIS, 1999 Apr 23
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51. Hochmuth G. Telemark landing. FIS Bull 1999; 137 (2): 29-43 52. Virmavirta M, Isolehto J, Komi P, et al. Characteristics of the early flight phase in the Olympic ski jumping competition. J Biomech 2005; 38: 2157-63 53. Schmo¨lzer B, Mu¨ller W. The influence of lift and drag on the jump length in ski jumping. In: Book of abstracts. First International Congress on Skiing and Science, 1996 Jan 7-13. St. Christoph a. Arlberg: E. Mu¨ller. p. 274. 54. Mu¨ller W. Physics of ski jumping: the lift and drag forces in the early flight phase have a pronounced impact on the performance. Canadian Society for Biomechanics. Proceedings of the Ninth Biennial Conference, Simon Fraser University; 1996 Aug 21–24; Vancouver (BC), 246-7 55. Norgan NG. Population differences in body composition in relation to the BMI. Eur Clin Nutr 1994; 48: 10-27 56. Jelmini S. Modelling of distance in ski jumping using statistical models with random parameters [dissertation]. Lausanne: Swiss Institute of Technology, 2000 57. WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and interventions strategies. Lancet 2004; 363: 157-6 58. Meile W, Mu¨ller, W. Ski-jumping aerodynamics: modelexperiments and CFD-simulations. In: No¨rstrud H, editor. Aerodynamics of sports. Centro Internationale di Scienze Meccaniche (Udine), CISM Series, vol. 506. Wien, New York: Springer, 2008: 183-216
Correspondence: Dr Wolfram Mu¨ller, Human Performance Research, Karl-Franzens University of Graz, Max-MellAllee 11, 8010 Graz, Austria. E-mail:
[email protected]
Sports Med 2009; 39 (2)
Sports Med 2009; 39 (2): 107-127 0112-1642/09/0002-0107/$49.95/0
REVIEW ARTICLE
ª 2009 Adis Data Information BV. All rights reserved.
Sea-Level Exercise Performance Following Adaptation to Hypoxia A Meta-Analysis Darrell L. Bonetti and Will G. Hopkins Institute of Sport and Recreation Research, AUT University, Auckland, New Zealand
Contents Abstract. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Study Selection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Data Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Meta-Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Exercise Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Physiological Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Abstract
107 109 109 109 112 116 116 117 121 125
Adaptation to living or training in hypoxic environments (altitude training) continues to gain interest from sport scientists and endurance athletes. Here we present the first meta-analytic review of the effects on performance and related physiological measures following adaptation to six protocols of natural or artificial hypoxia: live-high train-high (LHTH), live-high train-low (LHTL), artificial LHTL with daily exposure to long (8–18 hours) continuous, brief (1.5–5 hours) continuous or brief (<1.5 hours) intermittent periods of hypoxia, and artificial live-low train-high (LLTH). The 51 qualifying studies provided 11–33 estimates for effects on power output with each. protocol and up to 20 estimates for effects on maximal oxygen uptake (VO2max) and other potential mediators. The meta-analytic random-effect models included covariates to adjust for and estimate moderating effects of study characteristics such as altitude level and days of exposure. Poor reporting of inferential statistics limited the weighting factor in the models to sample size. Probabilistic inferences were derived using a smallest worthwhile effect on performance of 1%. Substantial enhancement of maximal endurance power output in controlled studies of subelite athletes was very likely with artificial brief intermittent LHTL (2.6%; 90% confidence limits –1.2%), likely with LHTL (4.2%; –2.9%), possible with artificial long continuous LHTL (1.4; –2.0%), but unclear with LHTH (0.9; –3.4%), artificial brief continuous LHTL (0.7%; –2.5%) and LLTH (0.9%; –2.4%). In elite athletes, enhancement was possible with natural LHTL (4.0%; –3.7%), but
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unclear with other protocols. There was evidence that these effects were mediated at least partly by substantial placebo, nocebo and training-camp effects with some protocols. Enhancing protocols by appropriate manipulation of study characteristics produced clear effects with all protocols (3.5–6.8%) in subelite athletes, but only with LHTH (5.2%) and LHTL (4.3%) . in elite athletes. For VO2max, increases were very likely with LHTH (4.3%; –2.6%) in subelite athletes, whereas in elite athletes a ‘reduction’ was possible with LHTH (-1.5%; –2.0%); changes with other protocols were unclear. Effects on erythropoietic and other physiological mediators provided little additional insight into mechanisms. In summary, natural LHTL currently provides the best protocol for enhancing endurance performance in elite and subelite athletes, while some artificial protocols are effective in subelite athletes. Likely mediators include . VO2max and the placebo, nocebo and training-camp effects. Modification of the protocols presents the possibility of further enhancements, which should be the focus of future research.
When an athlete ascends from sea level to moderate altitude, the shortage of oxygen (hypoxia) initially impairs endurance training and performance. After a few weeks at altitude, training and performance recover to some extent as the athlete adapts. If the athlete then returns to sea level, do the adaptations lead to enhancement of endurance performance? Coaches have long thought so, but studies aimed at this question appeared to be inconclusive, leading researchers to suspect that any benefit from adaptation to hypoxia was offset by loss of endurance fitness consequent to the reduction in training intensity.[1] The focus of research on altitude training then moved from this traditional ‘live-high trainhigh’ approach (LHTH) to live-high train-low (LHTL), in which athletes live and sleep at altitude, but descend regularly to lower altitude for training sessions.[2] LHTL appeared to be more successful, and interest has grown in the use of nitrogen houses, hypobaric chambers, altitude tents or hypoxic inhalers to adapt to hypoxia and train normally without having to travel up and down a mountain.[1] Researchers have investigated three such approaches to artificial LHTL: (i) continuous exposure to a simulated moderate altitude for periods of 8–18 hours per day (artificial long continuous LHTL); (ii) continuous exposure to a simulated moderate-high altitude for 1.5–5 hours per day (artificial brief continuous ª 2009 Adis Data Information BV. All rights reserved.
LHTL); and (iii) intermittent exposure to a simulated high altitude for <1.5 hours per day (artificial brief intermittent LHTL). The same devices have also been used to simulate moderate altitude while the athlete exercises continuously or intermittently for at least 0.5 hours per session (artificial LLTH). While there is general agreement that adaptation to some forms of hypoxia can enhance sealevel performance, there has been considerable debate recently about the physiological mechanisms.[3-5] Gore and Hopkins[4] provided a rationale for understanding the mechanisms underlying the effects on maximal performance of differing durations. Exercise intensities below . maximal oxygen uptake (VO2max) [>10 minutes’ duration] are sustained essentially by aerobic . power, whereas exercise intensities above VO2max are sustained by a combination of aerobic and anaerobic power. Aerobic power consists of three . components: (i) VO2max; (ii) the fraction of maximal uptake that can be sustained during the exercise; and (iii) economy or efficiency of conversion of oxygen consumption into power output.[6] Changes in endurance performance following adaptation to hypoxia could therefore be due to changes in any of these three components, along with any changes in the contribution of anaerobic power for supramaximal exercise. Researchers who are interested in the mechanisms Sports Med 2009; 39 (2)
Performance with Adaptation to Hypoxia
underlying the performance effects of hypoxic adaptation measure one or more of these components or the more fundamental physiological variables underlying them. There has been no previous meta-analytic review of the effects on performance or related physiological measures following adaptation to any of the artificial or natural forms of altitude training. The current review addresses this deficit. There were sufficient studies to allow us to metaanalyse separately the effects on performance of the six natural and artificial altitude protocols. By far the most popular potential mechanism . variable has been VO2max, and we have also been able to meta-analyse this variable with all six protocols. Researchers have long argued that . enhancements in VO2max are mediated by erythropoiesis,[1,5,7-9] so measurements of erythropoietin, reticulocytes, red cell mass, haemoglobin mass, haemoglobin concentration and ferritin have also been reported. We have been able to meta-analyse haemoglobin concentration for LHTH and artificial brief intermittent LHTL, but we had to meta-analyse haemoglobin mass and red cell mass by combining them across all protocols. We were able to perform only a graphical analysis for erythropoietin, reticulocytes and ferritin due to the small number of estimates for these variables. Mechanisms underlying anaerobic power are less popular with researchers, and only the indirect measure of anaerobic power represented by peak blood lactate following an exercise test was reported in sufficient studies for metaanalysis in LHTH and artificial brief intermittent LHTL.
1. Methodology 1.1 Study Selection
Searches of PubMed, SportDiscus and Google Scholar were performed for studies published in English up to and including April 2007. Reference lists in review and original research articles identified were also examined. The primary focus of the meta-analysis was performance. We therefore included studies of performance meaª 2009 Adis Data Information BV. All rights reserved.
109
sured at or near sea level (<1000 m). Studies published only as conference abstracts were not excluded. We included studies with measures of oxygen consumption directly related to endurance performance, but studies reporting haematological or other parameters not directly related to performance and lacking a performance measure were excluded. Several studies were excluded for poor reporting of data or for not assessing performance at or near sea level.[10-25] Other reasons for excluding studies were: a performance enhancement of 19% in 5 mmol/L lactate speed in elite runners, when other measures of performance increased by 0.6% and 1.1%;[26] the only uncontrolled study in LLTH and with only five athletes;[27] and poor compliance with training, a non-specific performance test, and an uncertain post-exposure test time in an uncontrolled study of the brief continuous LHTL protocol.[28] The descriptive statistics for the 51 qualifying studies are shown in table I. 1.2 Data Extraction
The study estimates for the treatment effect were calculated for estimates without a control group by dividing the mean post-score by the mean pre-score for the experimental group and expressing the ratio as a percentage; for estimates with a control group, the post-/pre-score ratio in the experimental group was divided by the post-/ pre-score ratio in the control group before converting to a percentage. Percentage change in performance time in time trials was converted to change in mean power output by multiplying by an appropriate factor derived from powervelocity relationships.[73] For running, the factor was -1; for cycling, the factor was -2.5; for swimming, the factor was -2.0, which was an index x derived from first principles[73] by fitting the power-velocity relationship P = kVx to published data.[74] For any exercise modality, the percentage change in time to exhaustion at a constant power was converted to percentage change in power output in an equivalent time trial of the same duration by multiplying by a factor derived from models for the power-duration relationship of human performance, as follows: for Sports Med 2009; 39 (2)
110
ª 2009 Adis Data Information BV. All rights reserved.
Table I. Characteristics of study groups included in the meta-analysis sorted by protocol and first author Subjects
Sample sizea
Design
Competitive level
Training phase
Hypoxic (h/d)b
Exposure/ intervention daysc
Altitude level (m)d
Runners
8M, 2F; 14M, 5F
C
Elite
?
24
28
1640
Runners
9M, 5F; 6M, 3F
C
Elite
?
24
28
1750
Burtscher et al.[30]
Runners
10M; 12M
C
Subelite
?
24
12
2315
Friedmann et al.[31]
Boxers +Fee
9M
U
Subelite
Off-season
24
18
1800
Boxers -Fee
7M
U
Subelite
Off-season
24
18
1800
Gore et al.[32]
Cyclists
8M
U
Elite
?
24
31
2690
Ingjer and Myhre[33]
Skiers
7M; 7M
U
Elite
Competitive
24
21
1900
Jensen et al.[34]
Rowers
9M; 9M
C
Elite
Competitive
24
21
1822
Levine and Stray-Gundersen[35]
Runners
10?
U
Subelite
?
24
28
1200 2500
Study
Hypoxia device
Live-high train-high Bailey et al.[29]
Runners
9?
U
Subelite
?
24
28
Levine and Stray-Gundersen[2]
Runners
9M, 4F; 9M, 4F
C
Subelite
Competitive
24
28
2500
Miyashita et al.[36]
Swimmers
12M, 8F
U
Elite
Competitive
24
21
2300
Pyne[37]
Swimmers
14M, 8F
U
Elite
Competitive
24
21
2102
Rusko et al.[38]
Skiers
14M; 7M
C
Elite
?
24
22
1700
Saunders et al.[39]
Runners
10M; 13M
C
Elite
?
24
20
1750
Svedenhag and Saltinj[40]
Runners
5M; 4M, 2F
C
Elite
?
24
14
2000
Svedenhag et al.[41]
Skiers
5M, 2F
U
Elite
?
24
30
1900
Live-high train-low Dehnert et al.[42]
Triathletes
6?; 10?
C
Subelite
?
~18–24
13
1956/800
Levine and Stray-Gundersen[2]
Runners
9M, 4F; 9M, 4F
C
Subelite
Competitive
~18–24
28
2500/1200
Stray-Gundersen and Levine[43]
Runners
6?
U
Subelite
?
~18–24
28
2500/1200
Stray-Gundersen et al.[8]
Runners
8F, 14M
U
Elite
Competitive
~18–24
27
2500/1200
Wehrlin et al.[44]
Orienteers
5M, 5F
U
Elite
Pre-season
~18–24
24
2456/1000
Witkowski et al.[45]
Runners
8M, 4F
U
Subelite
?
~18–24
28
1780/1250
Runners
8M, 4F
U
Subelite
?
~18–24
28
2085/1250
Runners
8M, 4F
U
Subelite
?
~18–24
28
2454/1250
Runners
8M, 4F
U
Subelite
?
~18–24
28
2805/1250
Clark et al.[46]
Cyclists, triathletes
9M; 10M
C
Subelite
?
9–10
20
2650
N2 house
Cyclists, triathletes
10M; 10M
C
Subelite
?
9–10
20/24
2650
N2 house
Continued next page
Bonetti & Hopkins
Sports Med 2009; 39 (2)
Artificial long continuous live-high train-low
Sample sizea
Study
Subjects
Gore et al.;[47] Hahn et al.[48]
Triathletes
6M; 6M
C
Elite
Hahn et al.[48]
Cyclists
5F; 6F
C
Elite
Hinckson and Hopkins[49]
Runners, triathletes
11M; 11M
X
Subelite
?
Hinckson et al.[50]
Runners
8M, 2F
U
Subelite
?
Martin et al.[51]
Cyclists
5F; 6F
C
Elite
?
Mattila and Rusko[52]
Cyclists
5M
U
Elite
Competitive
Nummela[26]
Runners
6M, 2F; 10M
C
Elite
Roberts et al.[53]
Cyclists
14M, 5F; 14M, 5F
X
Subelite
Rusko et al.[38]
Skiers
9M, 3F; 8M, 2F
B?
Saunders et al.[39]
Runners
10M; 13M
C
Design
Competitive level
Training phase
Hypoxic (h/d)b
Exposure/ intervention daysc
Altitude level (m)d
Hypoxia device
?
8–10
23
3000
N2 house
?
8–10
12
2650
N2 house
8
25
2500–3500
N2 tent
10
24–30/30
2500–3500
N2 tent
8–10
12
2650
N2 house
18
11
3000
N2 house
?
16–17
17
2200
N2 house
?
8–10
5–15
2650
N2 house
?
?
12–16
25
2500
N2 house
Elite
?
9–12
19/25
2000–3100
N2 house
Performance with Adaptation to Hypoxia
ª 2009 Adis Data Information BV. All rights reserved.
Table I. Contd
Artificial short continuous live-high train-low Basset et al.[54]
Skiers, skaters
7M, 5F; 7M, 5F
X, B
Subelite
Off-season
3
6/19
3650
N2 tent
Katayama et al.[55]
Runners
6M; 6M
C
Subelite
?
1.5
9/19
4000
Chamber
Katayama et al.[56]
Runners
8M; 7M
C
Subelite
Competitive
3
14
4000
N2 tent
Gore (2006); Rodriguez (2007)[57,58]
Swimmers
3M, 3F; 4M, 3F
C
Subelite
?
3–5
9
4000–5500
Chamber
Gore et al.;[57] Rodriguez et al.[58]
Runners
2M, 3F; 3M, 2F
C
Subelite
?
3–5
9
4000–5500
Chamber
Artificial brief intermittent live-high train-lowf Bonetti et al.[59]
Kayakers
10M; 10M
X
Subelite
Competitive
0.5/1
15/19
3600–6000
Inhaler
Bonetti et al.[60]
Cyclists
18M; 9M
C
Subelite
Competitive
0.5/1
15/19
3600–6000
Inhaler
Hamlin and Hellmans[61]
Multisport athletes
5M, 7F; 8M, 2F
C, B
Subelite
?
0.75/1.5
15/19
3400–5000
Inhaler
Hinckson et al.[62]
Rowers
2M, 5F; 1M, 4F
C, B
Elite
?
0.9/1.5
15/19
3600–6000
Inhaler
Julian et al.[63]
Runners
7M; 7M
C, B
Elite
Competitive
0.75/1.5
20/26
3600–5000
Inhaler
Wood et al.[9]
Hockey players
15M; 14M
C, B
Subelite
Competitive
0.6/1
15/19
3600–6000
Inhaler
Dufour et al.[64]
Runners
9M; 9M
C
Subelite
Pre-season
0.2–0.33/0.33
12/40
3000
Inhaler
Hendriksen and Meeuwsen[65]
Triathletes
12M; 12M
X, B?
Subelite
Pre-season
2
10
2500
Chamber
Katayama et al.[66]
Non-athletes
7M; 7M
C
Trained
?
0.5
10/12
4500
Chamber
Morton and Cable[67]
Team sports
8M; 8M
C
Trained
?
0.17/0.5
9/19
2750
N2 house
Live-low train-high
111
Sports Med 2009; 39 (2)
Continued next page
Bonetti & Hopkins
Numbers separated by ‘/’ indicate live-high and train-low altitudes.
Groups with and without iron supplementation.
Altitude level estimated from arterial oxygen saturation in each study using the figure at www.high-altitude-medicine.com/SaO2-table.html.[72]
d
e
f
ª 2009 Adis Data Information BV. All rights reserved.
B = blind; B? indicates blinding uncertain (assumed not blind); C = controlled trial; F = female; M = male; U = uncontrolled trial; X = crossover; ? indicates uncertain.
Numbers separated by ‘/’ indicate sum of time in bouts of hypoxia and sum of recovery time per session.
Numbers after ‘/’ indicate intervention period, if longer than exposure period.
b
c
Inhaler 3200 18/40 0.5 Competitive Subelite C, B? 6M; 5M, 1F Cyclists
Data separated by ‘;’ are controlled trials with sample size in experimental and control groups.
Ventura et al.
[71]
a
Inhaler 2500
Chamber 2300 20/20–26
15/33
?
2
Subelite
Subelite C
C, B 3M, 5F; 3M, 5F
4M; 4M Cyclists
Swimmers Truijens et al.[70]
Terrados et al.
?
0.21/0.5
Inhaler 3000 14/47 0.2–0.5/0.5 Cyclists, triathletes
Table I. Contd
[69]
Roels et al.[68]
11M; 11M
C, B?
Subelite
Pre-season
Exposure/ intervention daysc Subjects Study
Sample sizea
Design
Competitive level
Training phase
Hypoxic (h/d)b
Altitude level (m)d
Hypoxia device
112
supramaximal tests (<7.5 minutes), the factor was 0.50/T, where T is the time in minutes;[75] for submaximal tests (>7.5 minutes), the factor was approximately 1/15.[73] Percentage change in time to exhaustion in incremental tests was converted to percentage change in peak power by multiplying by a factor 1-f, where f was the power of the first stage of the test expressed as a fraction of the peak power, under the assumption that the load increased linearly to maximum. A spreadsheet containing all study estimates can be obtained from the authors. 1.3 Meta-Analyses
The main outcome from a meta-analysis is a weighted mean of values of an outcome statistic from various studies, where the weighting factor is usually the inverse of the square of the sampling standard error of the statistic. The standard error is derived from either the confidence interval or p-value of the statistic or from standard deviations of change scores in control and experimental groups. Unfortunately, 55% of the study-estimates for performance that would have otherwise qualified for inclusion in our metaanalyses did not have sufficient information to derive the standard error; for estimates from studies other than of intermittent artificial LHTL, the figure is 71%. The main problem was reporting of statistical significance or nonsignificance as a p-value inequality without any further inferential information. To exclude all these studies from the meta-analyses would have resulted in unacceptable bias, akin to the publication bias that arises from failure of authors to submit studies with non-significant outcomes or failure of journal editors to accept them. We therefore performed the meta-analyses with a weighting factor derived from the sample size for each study estimate. The factor was (study sample size)/(mean study sample size). To calculate the sample size equivalent to that of a parallelgroups controlled trial with equal-sized groups when the groups were of unequal size n1 and n2, we assumed equal standard error e in each group. The standard error of the difference in means between groups is therefore e2/n1 + e2/n2, and for Sports Med 2009; 39 (2)
Performance with Adaptation to Hypoxia
groups of equal size n the standard error is 2e2/n. It follows that the effective sample size = 2n = 4 n1n2/(n1 + n2). An uncontrolled trial is equivalent to a controlled trial in which the control group has a mean of zero and an infinite sample size, but as n1 - ¥, 4n1n2/(n1 + n2) - 4n2, so the sample size for uncontrolled trials was inflated by a factor of 4 to make it equivalent to that of a controlled trial. To ensure studies with different numbers of estimates would have equal weighting, each study’s weighting factor was divided by the number of estimates it provided and multiplied by the mean number of estimates in all the studies contributing to the meta-analysis. The resulting meta-analysed effect is equivalent to that produced in a random-effect meta-analysis in which the between-study variance far outweighs the error variance in each study estimate, so the confidence interval must be more conservative (wider) than would be provided by the usual random-effect analysis. An assumption underlying our analyses is that the dependent variable giving rise to the study estimates has the same error of measurement in all studies, but violation of this assumption will result only in minor differences in the weight given to each study; the main differences in weight arise from differences in effective sample size. The meta-analyses were performed with the mixed modelling procedure (Proc Mixed) in the Statistical Analysis System (Version 9.2, SAS Institute, Cary, NC, US). Percentage effects were converted to factors (= 1 + effect/100), log transformed for the analysis, then back transformed to percentages. Study characteristics were the fixed effects in the model; these were included as main effects only because of the limited number of study estimates. We limited the characteristics to those that were included in most studies and that might be expected on physiological or psychological grounds to moderate the effect of hypoxia: competitive status (elite vs subelite); design characteristics (uncontrolled vs controlled trial, non-blind vs blind trial); sex (males as a fraction of the sample); training phase (competitive vs non-competitive or unknown); altitude level or its equivalent for artificial altitude (m); hours of hypoxia per day (for LHTH, LHTL and artificial ª 2009 Adis Data Information BV. All rights reserved.
113
long-duration LHTL); minutes of hypoxia per day (for the remaining protocols, not counting minutes spent in normoxia between intervals of hypoxia); count of days when any exposure to hypoxia occurred; total count of treatment days, including any days resting from exposure; ratio of exposure/treatment days; day post-exposure when performance was tested; training intensity on a 1–4 scale (for LLTH); type of performance test (submaximal vs maximal); and duration of maximal exercise tests (minutes). Missing values for sex of nine and ten subelite runners experiencing LHTH[35] and of six subelite runners experiencing LHTL[43] were assigned the mean value of proportion of males for their protocols. Competitive status was deemed elite if the athletes were in a national team and competing at international level. The four points of the training-intensity scale for LLTH were: above . . VO2max, 4; around VO2max, 3; around anaerobic threshold, 2; below anaerobic threshold, 1. Postexposure test day and duration of maximum exercise tests were log transformed before analysis and included as simple linear predictors. Supplementary analyses (not shown in table II) were also performed, where possible, with postexposure test day included as a quadratic or cubic polynomial in ·/‚ standard deviation units, to investigate the possibility of peaks or troughs in performance. An effect of a study characteristic is not shown in the tables for one or more of the following reasons: there was insufficient variation in the characteristic between-study estimates to estimate the effect; collinearity with other study characteristics prevented its estimation; and the small number of study estimates limited the analysis to only a few characteristics. To compare effectiveness of protocols on performance, the meta-analysed effects are shown for subelite athletes (all protocols) and elite athletes (four protocols) and are adjusted to 100% controlled trials and 100% maximal tests. For all the other study characteristics, we could not adjust to the same common value, so the effects on performance for each protocol are shown evaluated at the mean values of the study characteristics for that protocol. Sports Med 2009; 39 (2)
Effect
Natural altitude protocols
Artificial altitude protocols
live-high train-high
live-high train-low
live-high 8–18 h/d, continuous, train-low
Elite
(1.6; –2.7)
4.0; –3.7
(0.6; –2.0)
Subelite
(0.9; –3.4)
4.2; –2.9
1.4; –2.0
Elite
5.2; –4.1
4.3; –4.1
(4.0; –5.5)
Subelite
4.5; –4.1
4.6; –3.3
4.8; –5.3
3.5; –3.5
3.6; –2.1
6.8; –4.9
Study characteristics changed by +1 SD or -1 SD for enhanced protocol
+ Altitude - Days exposure + Test day
- Altitude - Test day
+ Altitude + Hours hypoxia - Days exposure
- Altitude - Test day
+ Exposure ratio - Test day
- Altitude - Train intensity + Days exposure + Test day
References
10
5
9
4
6
7
Study groups
13
9
10
5
5
7
Study estimates
33
13
17
11
33
19
Subjects/estimate
16 – 7
12 – 6
17 – 9
15 – 5
20 – 6
17 – 5
live-high 1.5–5 h/d, continuous, train-low
live-high <1.5 h/d, intermittent, train-low
(0.7; –2.5)
2.6; –1.2
114
ª 2009 Adis Data Information BV. All rights reserved.
Table II. Meta-analysis of effects on sea-level mean power output following adaptation to hypoxia experienced in studies with various protocols of natural and artificial altitude. Effects of mean and enhanced protocols are those predicted for controlled trials and maximal tests. Effects in parentheses are unclear (>5% chance of enhancement and >5% chance of impairment); otherwise bold indicates ‡50% chance of enhancement, italic indicates ‡50% chance of impairment, and plain font indicates ‡50% chance of trivial effect. These probabilistic outcomes are computed with reference to a smallest important change of 1%
live-low trainhigh 0.5–2 h/d
Effect of mean protocola (%); – 90% CLb (0.2; –1.8) (0.9; –2.4)
Effect of enhanced protocolc (%); – 90% CL (1.2; –2.5)
Study characteristics (mean – SD)
Effective subjects/estimate
36 – 22
41 – 11
20 – 9
15 – 5
20 – 6
17 – 5
Elite athletes (%)
54
33
50
0
33
0
Controlled trials (%)
46
11
85
100
100
100
Blind trials (%)
0
0
0
20
67
14
Males (%)
84
61
80
72
81
90
Competitive phase (%)
31
33
10
20
67
14 43
54
56
90
60
0
78
100
85
100
71
92
Altitude level (m)
2030 – 410
2400 – 290
2890 – 420
4530 – 840
6000
2750 – 310
Hours of hypoxia per day
24
~18–24
11 – 3
23 – 6
27 – 1
18 – 7
Minutes of hypoxia per day Days of exposure
210 – 84
40 – 9
47 – 48
9–3
16 – 2
14 – 4 Continued next page
Bonetti & Hopkins
Sports Med 2009; 39 (2)
Phase unknown (%) Maximal tests (%)
Effect
Natural altitude protocols
Artificial altitude protocols
live-high train-high
live-high train-low
live-high 8–18 h/d, continuous, train-low
Total period of treatment (d)
23 – 6
27 – 1
19 – 7
14 – 5
20 – 3
30 – 13
Exposure/treatment ratio (%)
100
100
96 – 7
76 – 32
82 – 8
55 – 27
Post-exposure test dayd
9.1 ·/‚1.9
5.4 ·/‚ 2.2
2.2 ·/‚ 2.7
4.4 ·/‚ 1.9
6.4 ·/‚ 2.3
2.8 ·/‚ 1.9
Duration of maximal testsd (min)
6.9 ·/‚2.4
11 ·/‚ 1.3
5.6 ·/‚ 2.3
5.2 ·/‚ 3.0
6.1 ·/‚ 2.5
3.9 ·/‚ 4.9
Subelite-elite
(0.7; –3.8)
(0.3; –2.2)
(0.8; –3.2)
Uncontrolled-controlled
3.3; –3.6
-2.6; –3.0
(-1.6; –3.4)
live-high 1.5–5 h/d, continuous, train-low
live-high <1.5 h/d, intermittent, train-low
live-low trainhigh 0.5–2 h/d
2.3 – 1.1
Training intensity (1–4)
Effects of study characteristics (%); –90% CL 2.4; –2.8
(-1.4; –4.5)
Blind-not blind (-0.3; –3.9)
Female-male Competitive-unknown phase
(0.5; –3.8)
Submaximal-maximal test
(0.0; –1.6)
1 SD altitude level
1.2; –1.6
-3.3; –2.4 -0.1; –1.0
1.5; –2.5
(-0.3; –1.8) -2.3; –2.5
(1.4; –3.2) (-0.9 –2.5)
0.8; –1.8
1 SD hours hypoxia
(0.4; –2.3)
1 SD minutes hypoxia 1 SD days exposure
Performance with Adaptation to Hypoxia
ª 2009 Adis Data Information BV. All rights reserved.
Table II. Contd
-1.8; –1.7
-1.0; –1.7
2.4; –2.5 0.6; –1.2
1 SD exposure/treatment ratio
(-1.2; –2.5)
1 SD training intensity 1 SD post-exposure test day
0.5; –0.7
1 SD duration of max. test
3.0; –2.5
-0.2; –0.3
(0.1; –2.1)
-0.5; –0.8
-0.4; –0.6
1.2; –1.5
-0.9; –1.2
-0.3; –0.6
0.6; –1.3
-0.2; –1.1
Random variation (%); –90% CL or ·/‚90% CL factor 2.7; –2.3
1.3; –1.3
1.0; –1.9
2.2; –3.5
-0.6; –0.9
2.4; –3.1
Standard error of measurement
2.4; ·/‚1.7
0.7; ·/‚2.2
2.2; ·/‚1.9
1.2; ·/‚ 1.9
3.2; ·/‚1.3
2.8; ·/‚1.5
a
Effects are the means predicted for controlled trials and maximal tests, but are otherwise evaluated at the mean values of the study characteristics for which effects are shown.
b
90% CL: subtract and add this number to the observed effect to obtain the 90% CL for the true (large-sample) effect.
c
Effects are the predicted means in maximal tests adjusted to –1 SD away from the mean for selected study characteristics shown.
d
SD shown as ·/‚ factor derived from log-transformed times.
CL = confidence limits.
115
Sports Med 2009; 39 (2)
Between-study SD
Bonetti & Hopkins
116
In most models, it was possible to include a random effect to estimate pure between-study variation in the effect of the treatment, expressed as a standard deviation. In principle, this measure of between-study variation is free of sampling variation arising from error of measurement in the dependent variable, but use of sample size as the weighting factor does not produce clean partitioning of random error into pure betweenstudy variation and residual error. The standard deviation representing the residual error in such models is the standard error of a study-estimate with the mean sample size (n) of the metaanalysed estimates; this standard error was multiplied by O(n/8) to provide an estimate of the mean standard error of measurement of the dependent variable. When there were insufficient study-estimates to include a pure between-study random effect, the residual random effect is shown as the between-study standard deviation. For each outcome measure, a novel funnel plot of the inverse of an estimate’s weighting factor (y-axis) versus the value of the estimate’s random effect (x-axis) was examined qualitatively for evidence of outliers (points judged visually to be more than about 4 SDs of horizontal scatter away from the centre of the plot) and publication bias towards positive effects (positive trend in the scatter). This procedure did not result in exclusion of any estimates. We reported uncertainty in the meta-analysed estimates as 90% confidence limits, and we made probabilistic magnitude-based inferences about the true (large-sample) values of outcomes, as described elsewhere.[76] In brief, an outcome was deemed unclear if its confidence interval overlapped the thresholds for smallest worthwhile positive and negative effects; equivalently, effects were unclear if chances of the true value being substantially positive and negative were both >5%. The magnitude of a clear effect was reported as the magnitude of its observed value, sometimes with an estimate of the probability the true value was substantial. The probabilities for each meta-analysed effect and for pairwise comparisons of effects were derived using a published spreadsheet.[77] The thresholds for smallest effects on performance were assumed to ª 2009 Adis Data Information BV. All rights reserved.
be –1%, which is an approximate average across a . range of sports.[78-80] Smallest effects on VO2max and exercise economy were also assumed to be –1% because the relationship between these measures and endurance performance[6] implies that, other things being equal, a 1% change in either of these measures would result in a similar change in performance. We also assumed a smallest effect of . 1% for haemoglobin or red-cell mass because VO2max is effectively proportional to haemoglobin mass in a cross-sectional study of athletes.[81] For haemoglobin and peak lactate concentration, there is no direct relationship with performance; effects were therefore standardized by dividing by the mean between-subject standard deviation of these variables in the studies that contributed to their meta-analyses, and a modified Cohen scale was used to make inferences.[82] 2. Results 2.1 Exercise Performance
The meta-analysed outcomes for the six protocols of natural and artificial altitude are shown in table II. Substantial enhancement of power output in subelite athletes was very likely with artificial brief intermittent LHTL, likely with LHTL, possible with artificial long continuous LHTL, but unclear for LHTH, artificial brief continuous LHTL and LLTH. Comparisons between the protocols for subelite athletes revealed that LHTL was likely better than all protocols, with the exception of artificial brief intermittent LHTL, where the difference was unclear. Artificial brief intermittent LHTL was possibly better than artificial long continuous LHTL, artificial brief continuous LHTL and LLTH. All other differences between protocols were unclear. Enhancements of mean power in elite athletes were likely with LHTL, but unclear for all other protocols. In comparison with the other protocols in elite athletes, LHTL was likely better than artificial long continuous LHTL and artificial brief intermittent LHTL. All other differences between protocols were unclear. Sports Med 2009; 39 (2)
Performance with Adaptation to Hypoxia
Several of the study characteristics listed in table II moderated the effects of hypoxia; performance was better in controlled relative to uncontrolled studies for LHTL, but the opposite was observed for LHTH; subelite athletes had a clear enhancement in performance relative to elite athletes with artificial brief intermittent LHTL, and submaximal exercise performance was clearly impaired relative to maximal with artificial long continuous LHTL. Effects for blinding, competitive phase and sex were unclear for the few protocols where these effects could be estimated. Post-exposure test day had a substantial clear positive linear effect for LLTH and trivial or unclear effects with the other protocols. Quadratic or cubic effects of post-exposure test day (not shown in table II) could not be modelled with the two shortest protocols of artificial altitude, and the polynomials revealed little curvature with LHTL (<0.3% over ·/‚SD2 either side of the mean time). However, relative to the effect at the mean post-test time, LHTH showed some evidence of enhancement at very short times (1.8% at ‚SD2 or ~2.5 days; 90% confidence limits –4.7%) followed by impairment (-1.5% at ‚SD or 5 days; –1.9%), enhancement (1.4% at ·SD or 17 days; –1.9%) and impairment (-2.3% at ·SD2 or 33 days; –8.9%); artificial long continuous LHTL showed a peak at the mean posttest time with a relative impairment of 1–2% (~–4.5%) either side of the mean (at ·/‚SD or 1 and 6 days); and LLTH showed a trough at the mean time with relative enhancements at ‚SD or 1.5 days (3.8%; –5.3%) and at ·SD or 5 days (1.0%; –1.4%). The moderating effect of study characteristics provides an avenue for enhancing each protocol, as shown in table II for the effects on performance after changing selected characteristics by – or ·/‚1 SD. Improvements in power output were observed in subelite athletes for all protocols after these theoretical enhancements, the increase ranging from 0.4% for LHTL to 5.9% for LLTH. The resulting effects were all clearly beneficial for subelite athletes, but beneficial effects for elite athletes were clear only for LHTH and LHTL. Alterations to the altitude level, days of exposure and daily exposure hours had the biggest ª 2009 Adis Data Information BV. All rights reserved.
117
contribution to the enhanced protocols, whereas effects for other characteristics were generally trivial or unclear. Modifying test duration by one SD would also have produced substantial enhancements in performance, especially for LHTH, but this characteristic was not included because the mean duration of tests was reasonably similar across the protocols, and changing the performance test does not represent a change to an exposure protocol. 2.2 Physiological Measures
. The meta-analysed effects on sea-level VO2max are shown in table III. There was a very likely enhancement with LHTH and a possible enhancement with LLTH in subelite athletes. The trivial effect for artificial LHTL with predominantly subelite athletes is very unlikely to have arisen from a substantial true positive effect. The unclear effects for the remaining . two artificial protocols represent changes in VO2max that were either unlikely to be positive (brief continuous LHTL) for subelite athletes or were possibly positive (brief intermittent LHTL) for predominantly subelite athletes. For elite athletes, there was a possible ‘impairment’ with LHTH, but an unclear effect for LHTL. It was not possible to estimate effects for elite athletes alone in the other protocols. . Study characteristics moderating VO2max are also shown in table III. The most interesting effect of characteristics. with the natural protocols was the increase in VO2max with increasing time post-exposure (clear for LHTH, unclear for LHTL), indicating that there is more benefit at . least for VO2max around 2 weeks after the intervention period. The trivial effect in artificial long continuous LHTL can be converted into a positive effect by increasing the hours of exposure; there is also a possibility of less benefit from ‘more’ days of exposure, even though the mean number of days of exposure is already about a week less than for the natural protocols. A reduction in training intensity with . LLTH would promote a further increase in VO2max. The remaining effects of study characteristics on . VO2max were unclear. Sports Med 2009; 39 (2)
Effect
Natural altitude live-high train-high
118
ª 2009 Adis Data Information BV. All rights reserved.
Table III. Meta-analysis of effects on sea-level maximal oxygen uptake following adaptation to hypoxia experienced in studies with various protocols of natural and artificial altitude. Effects of mean protocol are those predicted for controlled trials. Effects in parentheses are unclear (>5% chance of increase and >5% chance of decrease); otherwise bold indicates ‡50% chance of increase, italic indicates ‡50% chance of decrease, and plain font indicates ‡50% chance of a trivial effect. These probabilistic outcomes are computed with reference to a smallest important change of 1% Artificial altitude live-high train-low
continuous long hypoxia (8–18 h/d), train-low -0.5; –1.4
continuous brief hypoxia (1.5–5 h/d), train-low
intermittent brief hypoxia (<1.5 h/d), train-low
live-low, train-high (0.5–2 h/d)
Effect of mean protocola (%); –90% CLb Elite
-1.5; –2.0
(6.4; –11.2)
Subelite
4.3; –2.6
(6.4; –9.4)
(0.1; –2.8)
References
12
5
5
4
3
8
Study groups
15
9
6
5
3
8
Study estimates
20
10
7
6
5
10
Subjects per estimate
15 – 7
12 – 6
20 – 10
15 – 5
19 – 5
16 – 5
1.1; –2.0e
(-1.1; –3.5)
Study characteristics (mean – SD)c
Effective subjects per estimated
33 – 19
41 – 11
20 – 10
15 – 5
19 – 5
16 – 5
Elite athletes (%)
57
33
33
0
33
0
Controlled trials (%)
43
11
100
100
100
100
Blind trials (%)
0
0
0
20
33
13
Males (%)
87
61
75
72
100
91
Competitive phase (%)
29
33
0
20
100
13
Phase unknown (%)
57
56
100
60
0
50
Altitude level (m)
1990 – 400
2400 – 290
2680 – 160
4530 – 880
6000
2970 – 680
10 – 2
Hours of hypoxia per day
210 – 90
35 – 8
45 – 46
Days of exposure
23 – 6
27 – 1
18 – 6
9–3
17 – 3
14 – 4
Total period of treatment (d)
23 – 6
27 – 1
19 – 7
14 – 5
21 – 4
28 – 14
Exposure/treatment ratio (%)
100
100
96 – 9
76 – 33
78 – 1
59 – 27
8.0 ·/‚ 1.8
4.8 ·/‚ 2.1
1.2 ·/‚ 2.3
4.4 ·/‚ 2.0
4.7 ·/‚ 2.5
2.9 ·/‚ 2.2
Minutes of hypoxia per day
2.1 – 1.1
Training intensity (1–4) Post-exposure test dayc
Effects of study characteristics (%); –90% CL 0.3; –2.4
Competitive-unknown phase
(1.3; –2.7)
Subelite-elite
5.5; –2.4
1 SD altitude level
0.3; –1.2
(-2.7; –9.3) (-0.0; –5.2) (0.0; –2.3) Continued next page
Bonetti & Hopkins
Sports Med 2009; 39 (2)
Uncontrolled-controlled
Insufficient within-study clusters to estimate error of measurement; between-study SD includes within-study sampling variation. e
CL = confidence limits.
Derived by adjusting all sample sizes to those of controlled trials with equal numbers in control and experimental groups. d
90% CL: subtract and add this number to the observed effect to obtain the 90% CL for the true (large-sample) effect. b
ª 2009 Adis Data Information BV. All rights reserved.
c SD shown as ·/‚ factor derived from log-transformed times.
2.5; ·/‚2.6 2.9; ·/‚1.8 Standard error of measurement
Effects are the means predicted for controlled trials, but otherwise evaluated at the mean values of the study characteristics for which effects are shown.
3.3; ·/‚2.5e 1.7; ·/‚1.9e 3.8; ·/‚1.7e 1.8; –2.4
Random variation (%); –90% CL or ·/‚90% CL factor
Between-study SD
(1.1; –2.8) 1.0; –1.0 1 SD post-exposure test day
1 SD training intensity
0.5; –1.3 1 SD days exposure
1 SD hours exposure
119
a
2.6; ·/‚2.2e
2.1; –2.8
-1.2; –2.1
(1.1; –4.3) -0.9; –1.7
continuous long hypoxia (8–18 h/d), train-low
2.5; –1.9
Artificial altitude
live-high train-high
Effect
Table III. Contd
live-high train-low Natural altitude
continuous brief hypoxia (1.5–5 h/d), train-low
(-0.5; –3.3)
intermittent brief hypoxia (<1.5 h/d), train-low
(1.0; –2.2)
live-low, train-high (0.5–2 h/d)
Performance with Adaptation to Hypoxia
Haemoglobin mass (including red-cell mass) and exercise economy were meta-analysed for all studies collectively because of the lack of study estimates. Effects for haemoglobin mass were unclear, but an increase in exposure days and possibly an increase in altitude would produce a clear increase, whereas delaying the test day by >1 SD (>10 days) would offset the increase. The effect on exercise economy was trivial, but a substantial increase could accrue from reducing exposure days and increasing altitude (table IV). Haemoglobin concentration and peak lactate could be meta-analysed only for LHTH and brief intermittent artificial LHTL. For the interpretation of magnitude, the average pre-test betweensubject standard deviation for haemoglobin concentration was 6.2%, while that for peak lactate was 21%. Haemoglobin concentration demonstrated a likely moderate increase for LHTH and a possible small increase for artificial brief intermittent LHTL. The moderating effect of post-exercise test day shows that the increase in haemoglobin concentration was lost 3–4 weeks after exposure. The effect for peak lactate was unclear with LHTH, but an increase in altitude would produce a clear small to moderate decrease, whereas delaying the test day would produce a similar (but unexpected) decrease. Peak lactate showed a trivial decrease for artificial LHTL. The effect for peak lactate in artificial brief intermittent LHTL was trivial, but the uncertainty allows for the possibility of a small negative true effect. Effects for other physiological measures that could not be meta-analysed due to insufficient data are shown in figure 1. Erythropoietin was elevated during the hypoxic interventions and possibly showed a small elevation afterwards. Reticulocytes appeared to be elevated in a few studies during the intervention. The scatter in the plot for ferritin makes any conclusion about trend difficult. . Plots of performance versus VO2max, haemoglobin or red-cell mass and exercise economy are shown in figure 2. An estimate of the strength of the relationship between performance and each of these variables (in units of percentage change Sports Med 2009; 39 (2)
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120
Table IV. Meta-analysis of effects on sea-level haemoglobin (Hb) or red-cell mass (Hb mass), exercise economy, Hb concentration, and peak lactate in an exercise test following adaptation to hypoxia experienced in studies with various protocols of natural and artificial altitude. Effects in parentheses are unclear (>5% chance of increase and >5% chance of decrease); otherwise bold indicates ‡50% chance of increase, italic indicates ‡50% chance of decrease, and plain font indicates ‡50% chance of a trivial effect. These probabilistic outcomes are computed with reference to a smallest important change of 1% for Hb mass, 1% for economy, and 0.20 of baseline between-subject SD for Hb concentration and peak lactate Effect
Effect of mean protocolc(%); – 90%CLd
Hb mass, where measureda
Economy, where measuredb
Hb concentration LHTH intermittent brief hypoxia, train low
Peak lactate LHTH
(1.3; –2.4)
0.4; –1.3
4.8; –2.7
(0.7; –5.7)
2.3; –1.2
intermittent brief hypoxia, train low -3.5; –4.7
Study characteristics (mean – SD) References
12
14
5
4
5
5
Study groups
14
15
7
4
7
7
Study estimates
18
19
8
8
9
14
Subjects/estimate
15 – 7
19 – 5
16 – 9
22 – 5
19 – 8
24 – 3 24 – 3
Effective subjects/estimate
25 – 9
31 – 27
32 – 11
22 – 5
35 – 23
Elite athletes (%)
46
33
57
20
43
0
Controlled trials (%)
62
80
43
100
57
100
Blind trials (%)
0
7
0
60
0
50
Males (%)
74
91
83
92
82
90
Competitive phase (%)
15
53
29
80
29
75
Phase unknown (%)
62
40
43
0
43
0
Altitude level (m)
2540 – 970
3410 – 1460
1900 – 280
6000
1990 – 320
6000
21 – 7
20 – 6
24 – 5
16 – 2
22 – 6
15
Total period of treatment (d)
21 – 7
24 – 6
24 – 5
20 – 4
22 – 6
18 – 2
Exposure/treatment ratio (%)
100
86 – 22
100
83 – 9
100
84 – 9
Post-exposure test daye
3.9 ·/‚ 2.6
3.3 ·/‚ 2.8
9.1 ·/‚ 2.1
4.3 ·/‚ 2.3
8.3 ·/‚ 2.2
5.9 ·/‚ 2.3
(-1.8; –4.0e)
37 – 7
Minutes of hypoxia per day Days of exposure
35 – 6
Effects of study characteristics (%); – 90% CL 1 SD altitude level
(1.5; –2.6)
0.6; –1.6
1 SD exposure days
2.7; –2.7
-0.8 –1.6
1 SD post-exposure test day
-0.9; –1.0
(0.1; –1.4)
-12.4; –7.0 (1.4; –7.7)
-3.3; –3.9
0.6; –1.3
-10.5; –7.7
3.6; ·/‚1.8f
1.7; ·/‚1.7f
8.7; ·/‚1.7f
-0.6; –2.1
Random variation (%); –90% CL or ·/‚90% CL factor Between-study SD
4.6; –2.2
-1.0; –2.5
Standard error of measurement
2.1; ·/‚1.9
6.0; ·/‚1.7
3.6; –4.8 7.3; ·/‚1.5
a
Number of estimates: LHTH, 10; LHTL, 3; artificial long continuous LHTL, 3; artificial brief continuous LHTL, 2.
b
Number of estimates: LHTH, 4; LHTL, 3; artificial long continuous LHTL, 3; artificial brief continuous LHTL, 3 artificial brief intermittent LHTL, 5; LLTH, 1.
c
Effects are the predicted means evaluated at the mean values of the study characteristics for which effects are shown.
d
90% CL: subtract and add this number to the observed effect to obtain the 90% CL for the true (large-sample) effect.
e
SD shown as ·/‚ factor derived from log-transformed times.
f
Insufficient within-study clusters to estimate error of measurement; between-study SD includes within-study sampling variation.
CL = confidence limits; LHTH = live-high train-high; LHTL = live-high train-low; LLTH = live-low train-high.
in performance per percentage change in the variable) is provided by the slope of the regression line for each protocol (not shown in the ª 2009 Adis Data Information BV. All rights reserved.
. figure). The only clear slopes were for VO2max with LHTH (0.49 %/%; 90% confidence limits –0.29%/%) and LHTL (0.22; –0.13%/%). Sports Med 2009; 39 (2)
Performance with Adaptation to Hypoxia
121
a
3. Discussion
100
50
0
−50
// b
Effect (%)
100
50
0
−50
// c
40
20
0 −20 −40
// 0
10
0 Time (d)
Hypoxia
10
20
Post-hypoxia
Natural altitude: Live-high train-high Live-high train-low Artificial altitude: Live-high 8−18 h/d continuous, train-low Live high 1.5−5 h/d continuous, train-low Live-high <1.5 h/d intermittent, train-low Live-low, train high 0.5−2 h/d
Fig. 1. Individual study-estimates of effects on (a) erythropoietin, (b) reticulocytes and (c) ferritin sampled in blood during and following exposure to hypoxia with the various protocols of natural and artificial altitude.
ª 2009 Adis Data Information BV. All rights reserved.
In this first meta-analysis of sea-level exercise performance following adaptation to hypoxic exposure, we observed clear enhancements in endurance power output of 1–4% in subelite athletes with LHTL and with two of the artificialaltitude protocols (long continuous and brief intermittent LHTL). In elite athletes, the enhancements were clear only with LHTL. Modification of study characteristics might result in clear enhancements of 3–7% with all protocols in subelite athletes, but effects in elite athletes would be clear only for LHTH and LHTL. Following the development of the LHTL approach, the use of LHTH has received little support from sport scientists. There is enough uncertainty in our estimates of the effect of LHTH to allow for enhancements in elite and subelite athletes with this protocol. Furthermore, our estimates are for controlled trials, whereas athletes in an altitude camp would experience the equivalent of an uncontrolled trial, giving a possible further increase of ~3% (table II). The LHTH protocol also showed effects of postexposure test day consistent with anecdotal reports of coaches that performance is enhanced immediately after altitude and peaks again several weeks later. Taken together, these results provide reasonable support for what is still a widely accepted practice among many elite coaches and athletes. LHTH was also one of only two protocols that produced clear enhancements in endurance performance for elite athletes with appropriate manipulation of study characteristics. These moderating effects show that it may be better for athletes to go to higher altitudes (~2400 m) for shorter periods (~16 days) around 2–3 weeks before an important competition. Our results provide good evidence for the effectiveness of LHTL, which was clearly better than all but one protocol in subelite athletes (brief intermittent LHTL) and elite athletes (LHTH). The only moderating effect of study characteristics with LHTL was unexpected: uncontrolled trials showed a clear negative effect relative to controlled trials. According to conventional wisdom, uncontrolled studies should show ‘larger’ Sports Med 2009; 39 (2)
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122
a
b
c
Effect on performance (%)
10 5 0 −5 −10
−10 −5 0 5 10 Effect on maximal oxygen uptake (%)
Natural altitude: Live-high train-high Live-high train-low
−10 −5 0 5 10 Effect on Hb or red-cell mass (%)
Artificial altitude: Live-high 8−18 h/d continuous, train-low Live-high 1.5−5 h/d continuous, train-low
−10 −5 0 5 10 Effect on exercise economy (%)
Live-high <1.5 h/d intermittent, train-low Live-low train-high 0.5−2 h/d
Fig. 2. Individual study-estimates of effects on performance plotted against maximal oxygen uptake, haemoglobin (Hb) or red-cell mass, and exercise economy with the various protocols of natural and artificial altitude.
enhancements due to the so-called ‘training-camp effect’, which in principle is adjusted for in a controlled trial. What may happen in reality is that subjects in the control group of a controlled trial experience less of the training-camp effect, because they do not train as hard. There could also be a contribution from a ‘nocebo effect’, whereby subjects in a control group perform worse, because they know they are in the control group. There is evidence of a nocebo effect in the classic natural LHTL study of Levine and StrayGundersen[2] that is especially clear when the data are presented graphically as percentage changes (see figure 1 in Baker and Hopkins[83]). Indeed, data for the effect of uncontrolled versus controlled LHTL studies came entirely from this study. Therefore, our meta-analysed effect of ~4% for controlled studies needs to be interpreted with caution. When performance is predicted for uncontrolled studies (as previously mentioned, the way athletes train), the effect becomes a more realistic ~1.5%. The only design that avoids the nocebo problem is a blind trial, which is not possible with natural LHTL. Further research with controlled trials is warranted to assess the potential of LHTL. Artificial LHTL with long continuous exposures was developed to simulate LHTL, and our analysis provides some support for its efficacy. ª 2009 Adis Data Information BV. All rights reserved.
The limitation with this approach appears to be insufficient exposure to hypoxia because the moderating effects of study characteristics show that the effect on performance can be increased by increasing altitude and adding daily exposure hours. This result is consistent with the suggestions of researchers who believe that at least 12 hours of daily exposure is critical for the success of this protocol.[7,84] Another substantial moderating effect was a reduction in performance with increasing days of exposure, similar to that with LHTH. This result for both protocols seems counter-intuitive, although a ready explanation is a short-acting placebo effect. The only other moderating effect was a substantial downward adjustment for submaximal performance, which again implicates a placebo effect. More studies are needed to clarify the role of placebo effects with this and other protocols. At the opposite end of the spectrum of daily hypoxic exposure, artificial LHTL with brief intermittent exposures was one of the best protocols in subelite athletes. The moderating effects of study characteristics provided only marginal improvements of 1%, mainly through maximizing the exposure days in the intervention period. The equivalent altitude of this protocol is already at the limit for ethical approval, so there is no option to investigate higher altitudes. Alteration Sports Med 2009; 39 (2)
Performance with Adaptation to Hypoxia
of the hypoxic and normoxic intervals is a possible avenue for improvement, although we have found no clear difference between the effects of 3- and 5-minute intervals.[60] The clear difference between the effect on subelite and elite athletes suggests that the waves of hypoxia are less effective in elite athletes, possibly because elite athletes experience more hypoxia in their muscles from higher intensities of training compared with subelite athletes. With the remaining two forms of artificial altitude exposure, the uncertainty in the metaanalysed estimates was too large for their trivial magnitudes to be clear, although clear enhancements were possible with adjustment of appropriate study characteristics. With brief continuous LHTL, the average altitude appears to have been too high, since a reduction in altitude by 1 SD could produce a substantial enhancement in performance. Reducing the altitude may seem an implausible way to enhance this protocol, but the reduction by 1 SD would bring the altitude to ~3700 m, which is still well above that of the other continuous protocols and which could conceivably provide a sufficient hypoxic stimulus without the negative sequelae of continuous exposure to high altitude. A reduction in altitude along with a reduction in training intensity would also enhance performance with LLTH, but the main enhancement for this protocol would come from the more reasonable strategy of increasing days of exposure. LLTH also showed evidence that performance could be better either side of the mean post-exposure test day (~3 days), but it seems to us that this protocol is the least likely to produce performance enhancement. A study characteristic not included in the above discussion of the individual protocols was test duration, because altering this characteristic does not alter the exposure protocol. There are nevertheless implications for the effects on aerobic versus anaerobic performance. Our results demonstrate that performance could be improved in LHTH and brief intermittent LHTL with tests of longer duration. In all other protocols, performance could be better by a trivial margin for shorter tests. The average test duration in all protocols was 4–11 minutes, making all ª 2009 Adis Data Information BV. All rights reserved.
123
tests highly aerobic, but with only one of the protocols (LLTH) would a 1-SD reduction in test duration make the tests substantially anaerobic. More studies with shorter tests are needed to clarify the effect on anaerobic performance. Insights into the practical application of the findings of the meta-analyses can also be gleaned from a consideration of the between-study standard deviations (table II). These standard deviations represent unexplained variation in the mean effect of the protocol from study to study; as such, their magnitude is the typical deviation from the meta-analysed mean effects that a researcher or practitioner can expect to experience in another study using the mean protocol with a group or squad. For natural and artificial brief intermittent LHTL, these standard deviations in combination with the uncertainties in the mean effects imply that most researchers and practitioners will observe substantial enhancements in performance with a group or squad of subelite athletes. A beneficial outcome is less certain for elite athletes with the natural protocols and for subelite athletes with artificial long continuous LHTL; for the remaining protocols with subelite or elite athletes the outcomes could be good, bad or indifferent. However, if the enhanced protocols are as good as shown, the influence of the between-study standard deviation could be nullified for all protocols. The standard errors of measurement estimated from the meta-analyses (table II) do not have an immediate practical application, but they do provide evidence that the uncertainties in the meta-analysed mean effects and in the moderating effects of study characteristics are trustworthy. These uncertainties are estimated from a combination of the between-study standard deviations and the standard errors of measurement, so it is important that the standard errors of measurement estimated from the meta-analysis are realistic. The low value for natural LHTL (0.7%) is a reflection of the fact that almost all of the performance tests in these studies were time trials with runners. This value and the other values for error of measurement, given their uncertainties, are within the normal range for tests of endurance performance.[73] Sports Med 2009; 39 (2)
124
It is important to understand that some individual athletes may obtain no benefit or even impairment in performance from adaptation to hypoxia, even with those protocols that are clearly beneficial. Meta-analysis cannot address the question of individual responses to treatments until researchers provide complete inferential information about experimental and control groups in the form of confidence limits, exact p-values, or best of all, standard deviations of change scores. Such information would also allow the use of the inverse of sampling variance instead of sample size as a weighting factor in the meta-analysis, which would result in more trustworthy and probably narrower uncertainties in the meta-analysed mean and between-study standard deviations. Turning to the analysis of potential mechanism variables, it is clear from the findings in table III that adaptation to hypoxia can result in enhancements in maximal oxygen uptake. The usual mechanism suggested for an increase in this variable is erythropoiesis, which would effect a change in haemoglobin or red cell mass with a resulting increase in blood volume, cardiac output or oxygen-carrying capacity. Our meta-analyses provide limited evidence for this mechanism: the meta-analysed effect on haemoglobin mass was unclear on average, although extra exposure to hypoxia and a higher altitude level could result in a substantial increase. The meta-analysed effects on haemoglobin concentration provide some additional indirect evidence for an increase in haemoglobin mass, but an alternative explanation for the increase in haemoglobin concentration often mentioned by researchers is a dehydrating effect of acclimatization to altitude.[1] Direct evidence of erythropoiesis from levels of erythropoietin and reticulocytes could not be provided by meta-analysis, due to insufficient data, but it is reasonably clear from figure 1 that these variables increase transiently to some extent in some studies. Any erythropoiesis that did occur was not accompanied by clear reductions in ferritin, although supplementation with iron in most studies would probably . offset any reduction. Do the changes in VO2max mediate the changes in . performance? The pattern of the effects on VO2max in table III across different protocols for ª 2009 Adis Data Information BV. All rights reserved.
Bonetti & Hopkins
elite and subelite athletes and for the moderating effects of study characteristics does not mirror closely the effects on performance in table II. On the other hand, . the relationships (slopes) between changes in VO2max and performance were clear for the natural-altitude protocols and. were of a magnitude that might be expected if VO2max was a primary mediator, given the attenuating effects that error of measurement in this variable would have on the slopes. Unfortunately, blinding was not possible with these protocols, and therefore placebo and nocebo effects may have contributed to the relationships. A positive relationship between changes in haemoglobin mass and changes in performance - the expected outcome if the . changes in VO2max were mediated by erythropoiesis was not observed (figure 2), although error of measurement with haemoglobin mass (which manifests as a large between-study coefficient of variation, table IV) could also attenuate a true substantial relationship. Thus, our analyses have not resolved the issue of whether . VO2max is the primary mediator of performance following adaptation to hypoxia. There were insufficient data to meta-analyse the effects of exercise economy for each protocol, but a single analysis for all protocols and the relationship between exercise economy and sealevel performance (figure 2) provided little evidence for this mechanism. The only other physiological variable we meta-analysed, peak lactate concentration, is not a contender as a primary mechanism of performance enhancement, but an increase in peak blood lactate would indirectly implicate buffering capacity. However, placebo and nocebo effects on performance in altitude and control groups could also lead to an increase in peak lactate. For the two protocols we metaanalysed, a substantial increase in peak blood lactate was either unlikely (LHTH) or very unlikely (brief intermittent LHTL), so an increase in buffering capacity is presumably not involved with adaptation to these protocols. Other mechanisms therefore need to be identified, particularly for the artificial LHTL protocols, where gains in performance appear to be due at least partly to placebo. or nocebo effects and where an increase in VO2max may not contribute. Sports Med 2009; 39 (2)
Performance with Adaptation to Hypoxia
The suggestion of a change in cardiovascular regulation resulting in more cardiac output to exercising muscle[4] is plausible, but will be hard to investigate. 4. Conclusions Meta-analysis cannot adjust for the confounding effects of unknown or unquantified study characteristics. Furthermore, the simplistic nature of linear modelling, the exclusion of interactions between predictors, and the inevitable presence of substantial random error and systematic bias with some predictors all conspire to prevent the meta-analytic model from fully accounting for confounding effects even of the study characteristics included in the model. Nevertheless, our method of estimation of confidence intervals based on weighting by sample size is conservative, so our analyses must provide some evidence of the efficacy of adaptation to hypoxia for physical performance. Subelite athletes can experience endurance performance enhancements with adaptation to natural altitude exposure and to brief intermittent and long continuous protocols of artificial altitude exposure. For elite athletes, enhancements in endurance performance were possible only with the natural LHTL protocol. The enhancements with . natural altitude could be mediated in part by VO2max, but placebo effects, nocebo effects, training-camp effects and other mechanisms may be involved with these and the artificial protocols. Perhaps the most important outcomes of our analyses are the suggestions for enhancement of the protocols, some of which should be the focus of future research using double-blind designs, performance measures with smaller errors of measurement, and putative physiological mediators. Reviewers and editors should ensure that studies accepted for publication contain complete inferential information about the effects in treatment and control groups. Acknowledgements The literature reviews in the theses of Erica Hinckson and Matt Wood provided a valuable starting point for this review.
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Chris Gore provided useful publication lists and feedback on a draft version. The only funding for this study was provided by our institutional employer as salaries. There are no conflicts of interest.
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34. Jensen K, Nielsen TS, Fikestrand A, et al. High-altitude training does not increase maximal oxygen uptake or work capacity at sea level in rowers. Scand J Med Sci Sports 1993; 3: 256-62 35. Levine BD, Stray-Gundersen J. Altitude training does not improve running performance more than equivalent training near sea level in trained runners [abstract]. Med Sci Sports Exerc 1992; 24: S95 36. Miyashita M, Mutoh Y, Yamamoto Y. Altitude training for improving swimming performance at sea level. Jpn J Phys Fitness Sports Med 1988; 37: 111-6 37. Pyne DB. Performance and physiological changes in highly trained swimmers during altitude training. Coach Sport Sci J 1998; 3: 42-8 38. Rusko HK, Tikkanen H, Paavolainen L, et al. Effect of living in hypoxia and training in normoxia on sea level . VO2max and red cell mass [abstract]. Med Sci Sports Exerc 1999; 31: S86 39. Saunders PU, Telford RD, Pyne DB, et al. Improved running economy in elite runners after 20 days of simulated moderate-altitude exposure. J Appl Physiol 2004; 96: 931-7 40. Svedenhag J, Saltinj B. Aerobic and anaerobic exercise capacities of elite middle-distance runners after two weeks of training at moderate. Scand J Med Sci Sports 1991; 1: 205-14 41. Svedenhag J, Piehl-Aulin K, Skog C, et al. Increased left ventricular muscle mass after long-term altitude training in athletes. Acta Physiol Scand 1997; 161: 63-70 42. Dehnert C, Huetler M, Liu Y, et al. Erythropoiesis and performance after two weeks of living high and training low in well trained triathletes. Int J Sports Med 2002; 23: 561-6 43. Stray-Gundersen J, Levine BD. Altitude acclimatization normoxic training (high/low) improves sea level endurance immediately on descent from altitude [abstract]. Med Sci Sports Exerc 1994; 26: S64 44. Wehrlin JP, Zuest P, Hallen J, et al. Live high-train low for 24 days increases hemoglobin mass and red cell volume in elite endurance athletes. J Appl Physiol 2006; 100: 1938-45 45. Witkowski S, Karlsen T, Resaland G, et al. Optimal altitude for ‘living high–training low’ [abstract]. Med Sci Sports Exerc 2001; 33: S292 46. Clark SA, Aughey RJ, Gore CJ, et al. Effects of live high, train low hypoxic exposure on lactate metabolism in trained humans. J Appl Physiol 2004; 96: 517-25 47. Gore CJ, Hahn AG, Aughey RJ, et al. Live high: train low increases muscle buffer capacity and submaximal cycling efficiency. Acta Physiol Scand 2001; 173: 275-86 48. Hahn AG, Gore CJ, Martin DT, et al. An evaluation of the concept of living at moderate altitude and training at sea level. Comp Biochem Physiol A Mol Integr Physiol 2001; 128: 777-89 49. Hinckson EA, Hopkins WG. Changes in running endurance performance following intermittent altitude exposure simulated with tents. Eur J Sport Sci 2005; 5: 15-24 50. Hinckson EA, Hopkins WG, Fleming JS, et al. Sea-level performance in runners using altitude tents: a field study. J Sci Med Sport 2005; 8: 451-7
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51. Martin DT, Hahn AG, Lee H, et al. Effects of a 12-day ‘live high train low’ cycling camp on 4-min and 30-min performance [abstract]. Med Sci Sports Exerc 2002; 34: S157 52. Mattila V, Rusko H. Effect of living high and training low on sea level performance in cyclists [abstract]. Med Sci Sports Exerc 1996; 28: S156 53. Roberts A, Clark S, Townsend N, et al. Changes in performance, maximal oxygen uptake and maximal accumulated oxygen deficit after 5, 10 and 15 days of live high: train low altitude exposure. Eur J Appl Physiol 2003; 88: 390-5 54. Basset FA, Joanisse DR, Boivin F, et al. Effects of shortterm normobaric hypoxia on haematology, muscle phenotypes and physical performance in highly trained athletes. Exp Physiol 2006; 91: 391-402 55. Katayama K, Matsuo H, Ishida K, et al. Intermittent hypoxia improves endurance performance and submaximal exercise efficiency. High Alt Med Biol 2003; 4: 291-304 56. Katayama K, Sato K, Matsuo H, et al. Effect of intermittent hypoxia on oxygen uptake during submaximal exercise in endurance athletes. Eur J Appl Physiol 2004; 92: 75-83 57. Gore CJ, Rodriguez FA, Truijens MJ, et al. Increased serum erythropoietin but not red cell production after 4 wk of intermittent hypobaric hypoxia (4,000-5,500 m). J Appl Physiol 2006; 101: 1386-93 58. Rodriguez FA, Truijens MJ, Townsend NE, et al. Performance of runners and swimmers after four weeks of intermittent hypobaric hypoxic exposure plus sea level training. J Appl Physiol 2007; 103: 1523-35 59. Bonetti DL, Hopkins WG, Kilding AE. High-intensity kayak performance after adaptation to intermittent hypoxia. Int J Sports Physiol Perform 2006; 1: 246-60 60. Bonetti DL, Hopkins WG, Kilding AE, et al. Cycling performance following adaptation to two protocols of acutely intermittent hypoxia. 12th Annual Congress of the European College of Sport Science; 2007 Jul 12-15; Jyva¨skyla¨ 61. Hamlin MJ, Hellmans J. Effect of intermittent normobaric hypoxic exposure at rest on hematological, physiological and performance parameters in multi-sport athletes. J Sports Sci 2007; 25: 431-41 62. Hinckson EA, Hopkins WG, Downey BM, et al. The effect of intermittent hypoxic training via a hypoxic inhaler on physiological and performance measures in rowers: a pilot study. J Sci Med Sport 2006; 9: 177-80 63. Julian C, Gore C, Wilber R, et al. Intermittent normobaric hypoxia does not alter performance or erythropoietic markers in highly trained distance runners. J Appl Physiol 2004; 96: 1800-7 64. Dufour SP, Ponsot E, Zoll J, et al. Exercise training in normobaric hypoxia in endurance runners: I. Improvement in aerobic performance capacity. J Appl Physiol 2006; 100: 1238-48 65. Hendriksen IJ, Meeuwsen T. The effect of intermittent training in hypobaric hypoxia on sea-level exercise: a crossover study in humans. Eur J Appl Physiol 2003; 88: 396-403 66. Katayama K, Sato Y, Morotome Y, et al. Ventilatory chemosensitive adaptations to intermittent hypoxic exposure with endurance training and detraining. J Appl Physiol 1999; 86: 1805-11
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67. Morton JP, Cable NT. The effects of intermittent hypoxic training on aerobic and anaerobic performance. Ergonomics 2005; 48: 1535-46 68. Roels B, Millet GP, Marcoux CJ, et al. Effects of hypoxic interval training on cycling performance. Med Sci Sports Exerc 2005; 37: 138-46 69. Terrados N, Melichna J, Sylve´n C, et al. Effects of training at simulated altitude on performance and muscle metabolic capacity in competitive road cyclists. Eur J Appl Physiol 1988; 57: 203-9 70. Truijens MJ, Toussaint HM, Dow J, et al. Effect of highintensity hypoxic training on sea-level swimming performances. J Appl Physiol 2003; 94: 733-43 71. Ventura N, Hoppeler H, Seiler R, et al. The response of trained athletes to six weeks of endurance training in hypoxia or normoxia. Int J Sports Med 2003; 24: 166-72 72. Hackett PH, Roach R. High-altitude medicine. In: Auerbach PS, editor. Wilderness medicine. 3rd ed. St Louis (MO): Mosby, 1995: 1-37 73. Hopkins WG, Schabort EJ, Hawley JA. Reliability of power in physical performance tests. Sports Med 2001; 31: 211-34 74. Toussaint HM, Hollander AP. Energetics of competitive swimming: implications for training programmes. Sports Med 1994; 18: 384-405 75. Hinckson EA, Hopkins WG. Reliability of time to exhaustion analyzed with critical-power and log-log modeling. Med Sci Sports Exerc 2005; 37: 696-701 76. Batterham AM, Hopkins WG. Making meaningful inferences about magnitudes. Int J Sports Physiol Perform 2006; 1: 50-7 77. Hopkins WG. A spreadsheet for combining outcomes from several subject groups. Sportscience 2006; 10: 51-3 78. Pyne D, Trewin C, Hopkins W. Progression and variability of competitive performance of Olympic swimmers. J Sports Sci 2004; 22: 613-20 79. Hopkins WG. Competitive performance of elite trackand-field athletes: variability and smallest worthwhile enhancements. Sportscience 2005; 9: 17-20 80. Paton CD, Hopkins WG. Variation in performance of elite cyclists from race to race. Eur J Sport Sci 2006; 6: 1-7 81. Schmidt WF, Prommer N, . Heinicke K, et al. Impact of total hemoglobin mass on VO2max [abstract]. Med Sci Sports Exerc 2007; 39 (5): S3 82. Snowling NJ, Hopkins WG. Effects of different modes of exercise training on glucose control and risk factors for complications in type 2 diabetic patients: a meta-analysis. Diabetes Care 2006; 29: 2518-27 83. Baker A, Hopkins WG. Altitude training for sea-level competition. Sportscience 1998; 1 [online]. Available from URL: http://sportsci.org/traintech/altitude/wgh.html. [Accessed 2009 Jan 18] 84. Rusko HK, Tikkanen HO, Peltonen JE. Altitude and endurance training. J Sports Sci 2004; 22: 928-45
Correspondence: Prof. Will G. Hopkins, School of Sport and Recreation, AUT University, Private Bag 92006, Auckland 0627, New Zealand. E-mail:
[email protected]
Sports Med 2009; 39 (2)
Sports Med 2009; 39 (2): 129-145 0112-1642/09/0002-0129/$49.95/0
REVIEW ARTICLE
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The Epidemiology and Aetiology of Injuries in Sailing Vernon Neville and Jonathan P. Folland School of Sport and Exercise Sciences, Loughborough University, Loughborough, UK
Contents Abstract. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Injury, Epidemiology, Severity and Mechanisms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Injuries by Sailing Class. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.1 Olympic Class Sailing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.2 Novice and Recreational Sailing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.3 Disabled Sailing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.4 Windsurfing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.5 America’s Cup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.6 Offshore Racing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Environmental Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Sex Differences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Injury Prevention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Coaching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Sports Science and Medicine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Strength and Conditioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Protective Clothing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Design and Ergonomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Recommendations for Injury Definition and Methodology of Injury Surveillance in Sailing . . . . . . . . . 4.1 Injury Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Recurrent Injury Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Injury Severity Definition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Sailing Exposure Definition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Methodological Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Abstract
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Sailors are at risk of injury and an understanding of the risks and causes of injury are important in helping to reduce their frequency and severity. Injuries are specific to the class of sailing. In elite Olympic-class sailing the incidence of injury is ~0.2 injuries/athlete/year, with the lumbar and thoracic spine and the knee most commonly injured. Poor hiking technique and inadequate leg strength are thought to predispose the knee to injury. Injuries in novice and recreational sailing are predominantly acute in nature with contusions and abrasions typically occurring as a result of collisions with the boom or other equipment during manoeuvres. The only report of injuries in Paralympic-class sailing found a high rate of ~100 injuries/1000 days of sailing, likely due to severe sailing conditions. The majority of injuries were
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chronic in nature, predominantly sprains and strains of the upper extremity. The risk of windsurfing injury ranged from 1.1 to 2.0 injuries/person/year, with the majority of injuries being acute, typically due to impact with equipment. Severe injuries are frequent, with competitive male windsurfers often admitted to hospital for treatment. Chronic lower back injuries are also common in windsurfers and may be related to prolonged lordosis (lumbar extension) of the spine while ‘pumping’ the sail. In professional big-boat sailing, America’s Cup studies have reported an incidence of ~2.2 injuries/1000 hours of sailing, with one study reporting a higher incidence of injury during fitness training sessions (8.6 injuries/1000 hours of fitness training). The main cause of injury seems to be non-specific overuse, with joint and ligament sprains and tendinopathies being the most common. Grinders and bowmen are at greatest risk of injury, with the repetitive nature of ‘grinding’ a contributing factor. In round-the-world offshore racing, 1.5 injuries/person/round-the-world race (amateur), and 3.2 injuries/person/race (professional) have been reported, with the majority being impact injuries (e.g. contusions, lacerations, fractures and sprains). Helmsmen experience mostly upper-limb overuse injuries as a result of ‘steering’, while mastmen and bowmen are at greater risk of acute injuries. Illnesses and non-injuryrelated complaints account for a large proportion of medical conditions in these events. Sailors of all classes and abilities seem to be at risk of injury, particularly from acute impacts with equipment that might be reduced by wearing protective clothing and more ergonomic boat design. High repetition activities, such as hiking, pumping, grinding and steering, are major causes of overuse injury in experienced sailors. Informed coaching of correct technique and appropriate progression of physical and technical developments are required. Competitive sailors should undergo regular health screening with specific strengthening of high-risk muscle groups, synergists and stabilizers. The scarcity of analytical studies of sailing injuries is a major concern, and there is a need for thorough prospective studies.
Sailing is enjoyed worldwide by an estimated 16 million people. Although sailing has been an important mode of transport for over 5000 years, the earliest record of modern day sailboat racing was the first America’s Cup challenge in 1851, around the Isle of Wight. In fact, sailing boasts the oldest competing trophy in modern sport, with the America’s Cup predating the modern Olympics by 45 years. Much of the appeal of sailing is that although it requires some basic technical knowledge, it is not restricted by age, sex or disability and can be enjoyed by most individuals. The public profile and popularity of competitive sailing has increased over the last two decades with greater sponsorship, ª 2009 Adis Data Information BV. All rights reserved.
commercialization and media interest, particularly in prestigious events such as the America’s Cup, the Volvo Ocean Race and Olympic sailing. This has led to a progressive elevation in the standard of competition, a rise in professionalism at elite levels of sailing, and higher physical, technical and mental demands placed upon competitive sailors. Sailors are at risk of injury[1] and an understanding of the risks and causes of injury are important in helping to reduce the burden of sailing injuries (whether financial, performance or health). This article reviews the literature on the epidemiology of sailing injuries and provides information on the risks, distribution and Sports Med 2009; 39 (2)
Sailing Injuries
mechanisms of injury in each of the main sailing classes, as well as suggesting injury prevention strategies. A search was performed using online databases, SportDiscus and MEDLINE, with the terms ‘sail’, ‘sailing’, ‘yachting’, ‘windsurfing’ or ‘boardsailing’ and ‘injury’, ‘injuries’ or ‘medical’. Studies were limited to the English language literature between 1980 and 2008, although non-English publications that have been translated were included. A total of 213 articles were retrieved, from which relevant articles were selected. Due to the scarcity of the published literature, the preliminary results of an ongoing internet-based study[2] were also incorporated. The majority of studies are descriptive in nature and based predominantly on retrospective reports, hence few have reported incidence rates. In addition, there is considerable variation in research methodology, particularly in the definition and classification of injury, which makes comparison between studies difficult. In some classes (Olympic and Paralympic class sailing), there is only one report available, hence the results may be specific to the cohort studied rather than to the class in general. The demands placed on any sailor depend largely on the class of boat, their position on board and the prevailing weather conditions. Therefore, the causal factors predisposing sailors to injury are difficult to determine, as a large proportion of sailing injuries are insidious and occur as a result of complex interactions of various risk factors. The risk of injury is the result of an interaction between the sailors’ physical, technical and mental characteristics and the environment. Many of the activities with a high risk of injury are specific to the class of sailing, such as ‘hiking’ (Olympic class sailing), ‘pumping’ (Olympic class sailing and windsurfing), ‘grinding’ (big-boat sailing) and offshore ‘steering’ (offshore racing). Therefore, this article considers the different sailing classes in series and reviews the type, anatomy, severity and inciting mechanisms of injury, before two underlying variables of environmental conditions and sex are examined. Finally, ways to reduce or prevent injury are discussed, prior to some methodological recommendations for future research of sailing injuries. ª 2009 Adis Data Information BV. All rights reserved.
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1. Injury, Epidemiology, Severity and Mechanisms 1.1 Injuries by Sailing Class 1.1.1 Olympic Class Sailing
Sailing was included in the inaugural modern Olympic Games in Athens in 1896. Since then, the design (class) of boat has evolved considerably with nine one-design class boats currently used for the 11 Olympic sailing events. The term ‘one-design’ implies that the boats in each class are identical, having been manufactured under strict International Sailing Federation (ISAF) specifications, ensuring that the skill of the athlete is tested rather than the design of the boat. The Olympic class boats are sailed with one, two or three crew, depending on the class. An Olympic regatta usually consists of two races per day for 5–7 days, with each race lasting 30–75 minutes, depending on the class and wind strength. Hence, the physiological demands are high, and fatigue can be a limiting factor on performance, particularly in stronger wind conditions.[3,4] One of the most physically demanding activities during Olympic class sailing is ‘hiking’, whereby the sailor leans as far out of the boat as possible, in order to counter balance the heel force exerted by the wind, thereby increasing the speed of the boat (figure 1). The aerobic fitness of Olympic class sailors, particularly the dynamic ‘hikers’ in the Laser and 470 class, as determined . by maximum oxygen consumption (VO2max), is similar to that of other elite team sport athletes (~60 mL/kg/min).[5] In addition, elite ‘hikers’ have high leg extensor strength and endurance, with the peak (r = -0.71) and mean (r = -0.83) anaerobic power of the lower limb significantly correlated to performance in Laser sailors.[6] To date, there are few quantitative injury data available on Olympic class sailing, which is surprising considering the increased interest in the physical demands of this sport over the past decade. Although no data are available on the nature of injury, based on the type of injuries reported, elite sailors appear to be predominantly at risk of overuse injuries, particularly strains and sprains.[7,8] A retrospective questionnaire of 28 elite New Zealand Finn, Tornado, Sports Med 2009; 39 (2)
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Fig. 1. Olympic class sailors hiking off the side of the boat to counter the heel angle caused by the force of the wind, with the knee and lumbar spine at risk of chronic injury (courtesy of Horton & Nichol).
Laser, Europe, 470 and Mistral sailors in preparation for the 1996 Olympic Games[7] reported an incidence of 0.2 injuries/athlete/year and identified the lumbar spine as the most commonly injured body part (45%), followed by the knee (22%), shoulder (18%) and arm (15%). Similar results were reported in a physiotherapy evaluation of elite Brazilian Olympic class sailors in preparation for the 2004 Athens Olympics,[8] where the most common body parts affected by pain and discomfort were the lumbar spine (53% of athletes), thoracic spine (41%) and the knee (34%). The force exerted on the footstrap of Laser sailors while hiking in 15 knots of wind can be in excess of 800 N.[9] This force is translated mainly to the knee, quadriceps muscles and lumbar spine,[9] thereby increasing the stress and associated risk of injury to these structures.[7,10,11] The positions at greatest risk of knee injury are those that require sustained hiking; for example, the helmsman in the one-person classes (Laser, Finn and Laser Radial) and the crew in two- or threeperson classes (Star, Yngling and 470). Poor hiking technique and inadequate leg strength are thought to predispose the knee to injury.[7,10,11] In Finn sailing, it is extremely difficult to maintain a straight-leg position when hiking, hence increased shear force is exerted on the knee joint,[11] whereas in Laser sailing, hiking is performed predominantly in a straight-leg position, thereby reducing the shear force,[12] but increasing ª 2009 Adis Data Information BV. All rights reserved.
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the moment load on the knee and lumbar spine.[5,11,13,14] Furthermore, foot placement may be important, as internal rotation of the leg is considered to promote overdevelopment of the vastus lateralis muscle, which may increase lateral tracking of the patella and predispose the athlete to chronic knee pain such as chondromalacia patella.[15] Although fatigue and discomfort of the quadriceps, due to partial ischaemia, is commonly reported,[16] there are few data to suggest increased risk of quadriceps injury in hiking sailors. One result of quadriceps overdevelopment is an imbalance in hamstring/ quadriceps strength ratio,[5,17] with a lower ratio (0.34) found in elite hikers compared with elite tennis (0.44) or volleyball athletes (0.46).[5] A low ratio and relatively poor knee flexor strength may predispose the knee to overload and injury, due to impaired ability to stabilize the knee joint in reducing the anterior-posterior shear forces and the bone-on-bone stress forces.[5,17] Blackburn[18] suggested that weakness or fatigue of the abdominal musculature during hiking can result in greater hip flexor (such as the iliopsoas) muscle activity and loading. This tends to promote lumbar lordosis, with high compression forces on the posterior aspect of the vertebrae and intervertebral discs whilst these structures are also subjected to prolonged posterior shear force, thereby increasing the potential risk of chronic injury. In addition, maximal trunk extensor strength has been related to hiking performance,[17] and the relationship between trunk flexors and extensors may be important to performance and possibly injury prevention, although further research is required. Other body parts at risk of injury include the neck and shoulder regions due to sustained cervical flexion during hiking and shoulder protraction while trimming.[4,7] 1.1.2 Novice and Recreational Sailing
The incidence of injury in novice dinghy sailors has been reported as 0.29 injuries/1000 hours of sailing[19] or 0.4 injuries/person/year, which is similar to that of elite Olympic class sailors (0.2 injuries/person/year).[7] Furthermore, 0.7 injuries/person/year has been reported in Sports Med 2009; 39 (2)
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predominantly intermediate-level recreational sailors.[2] The objective of the novice sailor is skill development, whereas the elite sailor is focused on increasing performance; hence the physical[6] and mental[20] demands and the subsequent risks of injury may be different. Many of the injuries reported in both novice dinghy sailing and recreational sailing are acute in nature.[2,19,21] A study of 536 novice dinghy sailors undertaking a sailing course in Kiel, involving 36 hours of practical instruction,[19] found contusions and bruises (61%, 146/238) as the most common types of injury, followed by abrasions and cuts (32%, 75/238). Similar types of injuries were reported in the preliminary findings of a North American online survey of mainly intermediate-level sailors,[2] with bruising (32%) and lacerations (30%) being the most common injuries, followed by sprains (18%) and fractures (6%). The upper limb (40%) was the most injured body region in novice sailors. At particular risk were the hands and fingers (35%) and the head (32%), as a result of impact with and use of equipment.[19] Similarly, a 3-year retrospective study of predominantly recreational sailing injuries treated at a hospital in Kiel, Germany,[21] found the hand (31%, 27/86) and the head (22%, 19/86) as the most frequently injured body parts. Identifying the causes of injury is important in determining appropriate preventative strategies. In novice and recreational sailing,[2,19,21] the majority of injuries occur as a result of collisions with the boom or other equipment (14–31%), usually during manoeuvres such as tacking and jibing. Other causes include slipping or falling on the deck (7–29%), docking or casting off at the harbour (8–19%), capsizing (5–11%) and handling the sheets/ropes (6–9%).[2,19,21] In novice sailors, the helmsman is the position at greatest risk of injury, due to the proximity to the boom.[19] The most severe injuries happen as a result of collisions with the boom (head lacerations and concussions),[19,21,22] while other severe injuries occurred during lowering of the keel in the boat (finger lacerations and fractures).[19] 1.1.3 Disabled Sailing
Paralympic class sailing is growing in popularity, with over 150 000 internationally registered ª 2009 Adis Data Information BV. All rights reserved.
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competitive sailors. Disabled sailing was first introduced to the Paralympic Games in 1996 as a demonstration sport, and for the 2000 Games, the Sonar (three-person keelboat) and 2.4mR (single-person keelboat) were included as official medal events. A new two-person keelboat, the SKUD-18, debuted at the 2008 Paralympic Games in Beijing. The popularity of sailing amongst disabled athletes was evident at the 2004 Paralympic Games in Athens, where 69 athletes from 15 different countries participated in the two one-design classes. Only one report is currently available on the incidence and distribution of Paralympic class sailing injuries.[23] In a 5-day survey of 24 teams competing at the 1999 World Championships for Disabled Sailing in Cadiz, Spain, Allen[23] documented a total of 25 injuries (the majority being minor), with a rate of approximately 100 injuries/1000 days of sailing. The surprisingly high incidence could be attributed to the strong winds and rough sea conditions during the regatta.[23] The majority of injuries were chronic in nature (68%, 17/25) with sprains and strains being the most frequent types of injury. The crew were at greatest risk of injury (96% of all injuries) in the three-person class, with equal distribution between the fore-deck and mid-deck positions. The upper extremity was the most frequently injured body region (60%, 15/25), likely due to an increased reliance on the upper extremity as a consequence of lower limb or spinal cord disability. Injuries in disabled sailors have also been reported in amateur offshore racing.[24] An alldisabled crew, competing in the BT Global Challenge, experienced a greater number of ‘medical cases’ (15%, 106/685) than any of the other 13 competing able-bodied crews, with the majority being trauma-related injuries.[24] 1.1.4 Windsurfing
Boardsailing (windsurfing) originated in the late 1960s, and became one of the fastest growing sports in the world in the 1980s and 1990s, with more participants than all other types of sailing combined. Windsurfing joined the Olympic Games in 1984 and is regarded as one of the most exciting and athletically demanding sailing Sports Med 2009; 39 (2)
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events. In 1993, the ISAF allowed ‘pumping’, whereby the sailor dynamically pulls and pushes the sail in order to increase the rate of air flow, and thus board speed (figure 2). This has resulted in high (near maximal) physiological demands during racing.[25-27] Race duration is usually 30–40 minutes, with 2–3 races per day during Olympic regattas. A recent study of ten elite [25] reported an oxygen uptake of 87% sailors . of VO2max (56.5 – 5.9 mL/kg/min), a heart rate of 91% maximum heart rate (178 – 5 beats/min) and high blood lactate values (10.2 – 1.5 mmol/L) during racing.[25] McCormick and Davis[28] found an incidence of 0.22 injuries per 1000 hours in 73 recreational sailors interviewed off the Galveston coast, although only 15% of these injuries were classed as ‘significant’, i.e. requiring medical treatment. The results of a recent survey[29] reported competitive wave/slalom sailors as having the greatest risk of injury (2.0 injuries/person/year) followed by recreational sailors (1.2 injuries/person/year) and elite raceboarders (1.1 injuries/person/year). The majority of windsurfing injuries are acute (69%[30] to 78%[31]), mainly as a result of impact with equipment. Elite[31] and recreational sailors[28] seem to be at risk of similar types of injury, with abrasions (23–63% of sailors), lacerations (29–59%) and strains (19–59%) being most frequent. A recent survey of 107 windsurfers over
Fig. 2. Windsurfer pumping the sail rhythmically to increase the flow of wind over the sail and thus board speed. Prolonged exposure can increase the risk of overuse injury to the lumbar and thoracic spine and upper extremity (ª Richard Langdon/Skandia Team GBR).
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a 2-year period[29] reported muscle strains as the most common injury in raceboard (45%, 34/76), wave/slalom (32%, 56/173) and recreational windsurfers (30%, 20/67). Cuts and grazes were also relatively common to wave/slalom windsurfers (17%) and ligament sprains (18%) to recreational windsurfers. Compression neuropathies in the forearm have also been reported in windsurfers[32,33] as a result of pumping.[34] The majority of injuries occur to the lower extremity and lower back regions.[29-31,35] Dyson et al.[29] found the thigh and calf (17%) and the ankle and foot (16%) to be the most frequently injured body parts, followed by the lumbar spine (11%) and shoulders (11%). Adverse interactions with equipment were responsible for a high proportion (45%) of all injuries,[30] for example falling with feet caught in the footstraps, or impact with the centre board, skeg or mast.[29-31,35] The occurrence of lower back injuries, particularly in light wind conditions,[36] is thought to be due to prolonged lordosis (lumbar extension) of the spine while pumping, and also the lumbar compression involved when sailing without the use of a harness, which attenuates the force transmitted through the spine.[36,37] Severe injuries are frequently reported in windsurfing,[29,35,38] with 21% (9/43) of competitive male windsurfers able to recall having been admitted to hospital for treatment at some point in their career.[35] These severe injuries included knee ligament sprains, vertebral disc herniations, lacerations, ruptured knee ligaments, shoulder dislocations, elbow epicondylitis, infected wounds, pneumothorax and fractured vertebra, with the majority associated with advanced manoeuvres of wave-jumping and wave-sailing. A total of 22 severe injuries requiring hospitalization occurred to windsurfers in the Aegean islands during the 1999 summer holiday season.[38] Fractures accounted for 51% (11/22) and shoulder dislocations 23% (5/22) of these injuries, and it was notable that alcohol was a contributing factor in 22% of these incidents. Windsurfers are also at risk of neuropathies; 23 cases of radial tunnel syndrome were reported by Ciniglio et al.[32] at an Orthopedic and Traumatology Clinic in Naples over a 3-year period. Sports Med 2009; 39 (2)
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The majority of these cases involved novice sailors, although some experienced sailors (4/23) had recurring symptoms whilst sailing in strong wind and rough sea conditions. Prolonged isometric contraction of the forearm in a pronated position seems to be a primary cause of this injury, and may be exacerbated by prolonged sailing with a constant grip position, infrequent rest, heavy wind conditions, a large sail area, and a heavy or large diameter boom.[32,33] 1.1.5 America’s Cup
The America’s Cup is regarded as the pinnacle of yacht racing. Races are in a ‘match-race’ format, whereby two boats race against each other around a pre-marked course for two laps, as opposed to the ‘fleet-race’ format used in the Olympic classes. Race duration is 1–2 hours and there are usually two races per day during the preliminary or qualifying rounds of the Challenger Series. All manoeuvres on board the 24-ton ‘big boat’ racing yachts are performed manually, without assistance from stored energy. The most demanding activity is grinding, whereby arm cranks are used to turn the winches that control the sails, and therefore the speed of the boat (figure 3). Work intervals are usually 5–60 seconds, with a mean work : rest ratio of approximately 1 : 5; however, this can be as little as 1 : 1 in close race situations; consequently, the physiological demands made of the athletes are high. The physiological demands are specific to the position on board, with bowmen and mid-bowmen having the greatest aerobic capacity (~58–61 mL/kg/min)[39,40] and typically performing the highest workload, whereas grinders and mastmen have the greatest upper-body strength and power (peak power during grinding: 929–1000 W).[39,41] There are few studies on injuries in America’s Cup sailing,[1,42,43] with only one study having reported exposure data.[1] During a 2-year prospective study of 35 elite professional sailors prior to and including the 2003 America’s Cup, Neville et al.,[1] reported an incidence of 5.7 injuries per 1000 hours of total exposure (sailing and strength and conditioning), which is similar to that of other elite non-contact team sports.[44] In addiª 2009 Adis Data Information BV. All rights reserved.
Fig. 3. America’s Cup grinding generates the power to turn the winches for trimming and hoisting sails. The high-repetition activity can increase the risk of lumbar spine and upper extremity injury (courtesy of Romolo Ranieri, ª Vernon Neville).
tion, the majority of injuries (67%, 148/220) were acute,[1] although earlier studies[42,43] reported a greater proportion of chronic injuries, which may be due to differences in injury definition, as injuries were categorized as either microtraumatic or macrotraumatic. When sailing and land-based training injuries were combined, joint/ligament sprains (27%) were the most common type of injury, followed by tendinopathies (20%), strains (12%) and contusions (12%).[1] However, during actual sailing, the most common type of injuries were contusions and sprains, occurring predominantly as a result of impact with hardware on the boat, as well as tendinopathies as a result of overuse.[1] The anatomical location most frequently injured was the lumbar spine (range 12–30%) and shoulder (15–18%), followed by the neck (8–13%), elbow (8–13%) and forearm (8–11%).[1,42,43] Sports Med 2009; 39 (2)
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The positions at greatest risk of injury were the bowmen (including the mid-bowmen, 3.2/1000 hours of sailing) and grinders (including the mastmen, 3.1/1000 hours of sailing).[1] Helmsmen, on the other hand, have few physical stressors, which was evident in the relatively low risk of injury (0.4/1000 hours of sailing).[1] Neville et al.[1] identified the main inciting events as non-specific overuse (24%), followed by impact with boat hardware (15%), strengthtraining (13%), cross-training (12%), and pulling and lifting sails (5%). In elite competitors, landbased training (strength and conditioning) frequently occurs at a higher intensity than practice sailing.[1] It is not surprising, therefore, that the incidence of injury during land-based strength and conditioning (8.6/1000 hours) was greater than that during sailing (2.2/1000 hours).[1] Strength-training and cross-training accounted for the majority of non-sailing injuries. Contrary to many other sports,[44-46] the incidence of injury during the America’s Cup competition period (4.7/1000 hours) was lower than during out-ofcompetition periods (6.2/1000 hours).[1] This lower risk was likely due to a reduced volume and intensity of training during the competition period, but could be compounded by a reluctance of athletes to indicate injuries for fear of jeopardizing their selection prior to competition. The severity of chronic injuries (predominantly tendinopathies and neuropathies) was significantly greater than the severity of acute injuries (6.1 vs 3.4 days absent/injury),[1] and this was attributed to the high-repetition activities involved. Following the 1987 America’s Cup in Fremantle, Australia, Miller[47] described ‘grinder’s elbow’ as a frequently occurring, complex, insidious, but poorly defined elbow injury. During the 2003 America’s Cup, Neville et al.[48] identified five cases of posterior interosseous nerve entrapment (PINE), which appeared to be the primary cause of grinder’s elbow. These five cases of PINE arose over a 2-year period in just one team of 35 athletes, with a mean consequence of 13 days absence from sailing and 37 days absence from strength and conditioning for each incident.[1] Similarly, wrist tenosynovitis resulted in 14.5 days’ absence from sailing per incident.[1] ª 2009 Adis Data Information BV. All rights reserved.
Other severe injuries included lumbar spine pathology (6% of days absent from sailing) and biceps tendinopathy (4%).[1] The activities that predispose to chronic forearm injury appear to be grinding and top handle winching,[49] which involve excessive repetitive elbow flexion and extension under load during prolonged gripping with forearm pronation. Poor grinding technique has also been suggested to be a risk factor[48,49] and might include an overreliance on the upper extremity as the force generator, rather than lower extremity and trunk involvement. Poor posterior shoulder strength is the main cause of posterior-anterior shoulder imbalance,[50] and may contribute to upper-limb overuse injuries. Shoulder overuse injuries, for example biceps tendinopathy, can often be attributed to sustained protracted position of the scapular,[51] which is common to grinding and trimming and can promote instability of the shoulder girdle.[51] The majority of activities performed in bigboat sailing require forward flexion of the spine with repetitive lumbar rotation (see figure 3), often with high loads,[1,42] thereby exposing the lumbar spine to prolonged strain and an increased risk of injury.[52] A high occurrence of illness-related medical conditions (35% of all medical conditions reported) have also been documented in the America’s Cup,[1] of which 40% were upper respiratory infections. 1.1.6 Offshore Racing
Offshore yacht racing can be described as ‘endurance racing’ where sailors live on board the yacht. Offshore races are usually between two points and may take from several days to as long as 8 months for round-the-world events. There are numerous types of round-the-world races, from non-stop to multiple stopovers, and they can be performed single-handed or involve crews of up to 14. Amateur events include the Clipper Round-the-World Race and the BT Global Challenge – arguably one of the most difficult races, as the boats are sailed the ‘wrong way’ round the world, from east to west, against the prevailing winds. Professional sponsored events include the single-handed Vende´e Globe, the Sports Med 2009; 39 (2)
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two-handed non-stop Barcelona World Race and the prestigious Volvo Ocean Race (previously known as the Whitbread Round-theWorld Race). Circumnavigating the ~30 000 nautical miles takes anywhere from 71 days in ‘Giant Trimarans’,[53] to 170 days in 20.4 m monohulls.[24] The Southern Ocean is regarded as having the toughest sailing conditions with sub-zero temperatures, winds regularly in excess of 40 knots, and waves reaching over 20 m. The gruelling nature of offshore racing is evident by the five fatalities in the Volvo Ocean Race since 1974. The incidence of injury in amateur offshore racing (BT Global Challenge 1996) has been reported as 0.37 injuries/1000 hours of sailing, or 1.5 injuries per person per round-the-world race,[24] which is less than the 3.2 injuries per person per race as reported in professional round-the-world racing.[54] Both the 1981 Whitbread[55] and the 1996 BT Global Challenge[24] reported similar types of injury, with the majority being impact injuries, such as contusions and lacerations (range: 38–47%), fractures (11–18%) and sprains (9–23%). During offshore racing, not all injuries occur on deck, with one-third of all injuries occurring below deck,[54] due to the time spent below and the violent and sudden movements of the yacht during heavy weather. In professional athletes,[54] the lumbar spine (21%) and shoulders (15%) are the most frequently injured body parts, whereas in amateur athletes,[24] hands and fingers accounted for a high percentage of all injuries (16%). All positions in offshore racing seem to be at risk of injury, ranging from 1.7 injuries per leg of the race (tactician) to 3.1 (mastman).[54] The nature of injury varies according to crew position, with helmsmen experiencing mostly upper-limb overuse injuries, while mastmen and bowmen are at greater risk of acute injuries.[54] Steering or helming is regarded as one of the least demanding activities in most sailing classes; however, in offshore racing, helmsmen have a relatively high risk of injury (2.6 injuries per race leg),[54] likely due to the arduous nature of steering in heavy weather conditions (figure 4). Spalding et al.[54] found that helmsmen were susceptible to upper-limb overuse ª 2009 Adis Data Information BV. All rights reserved.
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Fig. 4. Offshore helmsmen are at risk of upper extremity overuse injuries due to the arduous nature of steering in heavy sea conditions (ª Richard Mason).
injuries including rotator cuff impingement, wrist tenosynovitis and carpal tunnel syndrome. Interestingly, the three cases of carpal tunnel syndrome in helmsmen during the 2001 Volvo Ocean Race were all associated with the use of polished carbon steering wheels, as opposed to fabric grip wheels.[54] The smooth surface may require higher grip force and forearm stress, particularly when wet. Additionally, an upperlimb dominant steering technique was thought to predispose helmsmen to injury.[54] Illnesses and non-injury-related conditions accounted for a large proportion of medical conditions in these events: 1981 Whitbread 63% (103/163);[55] 1996 BT Global Challenge 56% (386/685);[24] 2001 Volvo Ocean Race 36% (173/485).[54] 1.2 Environmental Factors
The wind conditions play a major part in the physical and technical demands[56] and subsequent injury risk during sailing. Schaefer[19] noted a significant correlation between wind Sports Med 2009; 39 (2)
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strength and incidence of injury in novice sailors, such that 34% of injuries (81/238) occurred during 17–21 knots and <9% (21/238) occurred at 1–7 knots, even though the relative exposure was similar (22% and 24%, respectively). In recreational sailing,[2] 18% of the 1756 injuries reported in an internet-based survey were attributed to heavy weather. During offshore racing,[24,54] the legs with the roughest sea conditions in the southern ocean had significantly more injuries than other legs (p = 0.02[24]). Similar results have also been reported in windsurfing,[28] where the risk of acute injuries was greater when sailing in ‘hurricane’ conditions of 40 knots. In contrast, however, chronic lower back pain in windsurfers occurs predominantly during lighter wind conditions,[37] likely due to prolonged maintenance of a lordotic posture. Other health hazards as a result of exposure to the elements (sun, wind and salt water) include skin rashes and fungal infections,[24] acute sunburn or conjunctivitis, and chronic conditions such as skin cancer, cataracts and pterygium.[57] Windsurfers in some coastal regions may also be at risk of jellyfish stings (up to 26% of respondents in one report[28]). Large sea swell or rolling waves may also increase the risk of motion sickness (sea-sickness). 1.3 Sex Differences
There is little evidence to suggest any difference in the risk of injury between male and female Olympic class sailors. Of the 238 minor injuries incurred by novice dinghy sailors, Schaefer[19] reported that males (43% injured) and females (45% injured) had a similar risk of injury, with the nature and type of injuries also comparable. In windsurfing, Ullis and Anno[35] reported similar types of injuries for male and females, although males had a greater incidence of serious injuries. During strong wind conditions, female windsurfers have been found to have a greater incidence of injury (0.37/1000 hours; n = 22) than males (0.17/1000 hours; n = 51).[28] In America’s Cup sailing, a pilot study on an all-female team[43] showed a greater percentage of overuse injuries and muscle strains than an allª 2009 Adis Data Information BV. All rights reserved.
male team in 2003[1] (overuse, 68% vs 33%; strains 33% vs 12%, respectively). These differences may be attributed to differences in injury data collection and definition, but could also be due to differences in experience and the high absolute demands of this class subjecting females to higher relative loads. It may also be possible that the changes in class boat design between 1995 and 2003 could have contributed to these differences. 2. Injury Prevention Injury prevention can either aim to raise the capacity or resilience of the sailor, reduce the external stress, or a combination of both.[58] Appropriate support and guidance given to sailors can increase their capacity and ensure they are not exposed to excessive stressors. This may include coaching and physical preparation, sports science, sports medicine and psychological and social support. A survey carried out during the 2004 Paralympic Games[59] highlighted the need for education and training of coaches and organizers in order to improve their awareness of the specific needs of sailors with disabilities. Improved design and ergonomics of equipment and clothing aims to reduce the external stress placed on the sailor and reduce injury risk. Multidisciplinary support staff structures are common in professional sailing;[1] however, sailors at all levels would benefit from the support of informed personnel in the prevention of injuries. The literature on sailing injury prevention is predominantly descriptive[13,15,18,60,61] with few analytical data available. 2.1 Coaching
Informed coaching of sailors on the correct technique of high-risk activities is necessary, for example neutral foot position while hiking,[13] low frequency and high amplitude of pumping in windsurfing,[62] use of the lower-limbs during grinding[48,49] and whole-body offshore steering.[54] Appropriate progression of physical and technical development of sailors is also clearly Sports Med 2009; 39 (2)
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important to minimize the risk of demands exceeding capabilities. 2.2 Sports Science and Medicine
Competitive sailors should undergo regular health screening,[1,63] which should comprise both clinical (health) and kinesiological investigations (injury prevention and performance). Potential muscle or biomechanical imbalances should be identified and addressed,[5,17] as well as any pre-existing medical conditions.[1,64] Medical support in sailing should be encouraged to become involved in the prevention of injury and not only in the treatment of acute trauma. Physicians and sports scientists should be encouraged to provide informed medical advice on the prevention of sailing injuries, such as changing to a supinated hand grip position on the boom to relieve forearm neuropathy syndromes in windsurfing,[32] or using a ‘thumb-over’ grip when grinding with wrist tenosynovitis.[49] Sailing should be avoided when fatigued, and monitoring the volume and intensity of sailing and training can help in managing recovery and avoiding unnecessary fatigue. Nutrition and hydration are important in preventing fatigue and dehydration, which could potentially predispose an athlete to injury.[65,66] 2.3 Strength and Conditioning
Strengthening synergist and joint stabilizing tissues should be performed by sailors at all levels, for example rotator cuff, scapular and glenohumeral stabilizing, ankle and knee proprioceptive training and transversus abdominis and multifidus activation. The resilience of body regions at high risk of injury can be increased through specific strength training.[67] Specific muscle groups should be strengthened for particular types of sailing: forearm (grinders, windsurfers and offshore racing helmsmen); posterior shoulder (Olympic class and big-boat sailors); medial quadriceps and hamstring (hiking sailors); and lower back, abdominal and core stability (all sailors).[18,68] In addition, lower extremity strengthening should also be incorporated into the conditioning of grinders, windsurfers and ª 2009 Adis Data Information BV. All rights reserved.
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offshore helmsmen to increase force generation of the lower extremity and trunk for activities such as grinding, pumping and steering.[49,69] Muscle imbalances should also be corrected, such as posterior shoulder weakness in grinders[1] and hamstring strength in hikers.[5] Disabled athletes should also aim to increase muscle mass and stability of high-load body regions, such as the shoulders, arms and neck.[23] Furthermore, appropriate flexibility exercises to prevent malalignment from sustained postures should be routinely performed with the hip and knee flexors of hikers and the anterior chest and shoulder regions for windsurfers, offshore helmsmen and grinders. 2.4 Protective Clothing
Novice sailors can reduce the incidence of head and hand injuries by wearing protective clothing such as helmets and gloves. Head protection has been suggested for wave/slalom windsurfers,[29] and all windsurfers are recommended to wear shoes or booties for foot protection and wetsuits to avoid being stung by jellyfish.[28] Additionally, windsurfers are encouraged to use a harness with a good release system and also a neoprene lower back brace to provide additional trunk support.[29] Protecting against the harmful effects of ultraviolet light is extremely important and sailors should use high sun protection factor waterproof sunscreen as well as protective clothing such as hats, long sleeve shirts and sunglasses.[57,70] 2.5 Design and Ergonomics
To reduce windsurfing injuries, a faster and more effective mechanism of footstrap release[29] and a smaller boom grip size for females[31] have been suggested. In offshore racing, Spalding et al.[54] suggested increasing the friction of the steering wheel grip, adjusting the height and reducing the thickness of the wheel, and fitting a broader helmsman foot platform (to encourage whole-body movement when steering) may help to reduce the risk of chronic upper-limb and shoulder injuries in helmsmen. Improved ergonomics and design below deck may also significantly reduce Sports Med 2009; 39 (2)
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Table I. Summary of injury rates, nature, site and risk factors of common injuries in each of the main sailing classes Sailing class
Indicative injury rate
Common injuries nature/type
site
risk factors
Olympic
0.2 injuries/athlete/ year
Chronic strains and sprains
Lumbar spine Knee
Sustained hiking in a lordotic position Weakness of the abdominal muscles Shortness of the hip flexors Hiking foot placement (internal rotation) Low hamstrings/quadriceps strength ratio
Novice and recreational
0.3–0.4 injuries/ person/year
Acute contusions, bruises, lacerations and abrasions
Upper limb and head
Collisions with the boom or other equipment during manoeuvres Slipping or falling on deck
Paralympic
100 injuries/ 1000 d sailing
Chronic strains and sprains
Upper extremity
Inadequate upper-limb conditioning and muscle strength
Windsurfing
1.1–2.0 injuries/ person/year
Acute abrasions, sprains, lacerations and strains Chronic tendinopathies and neuropathies
Lower extremity Lumbar spine Upper limb
Impact with equipment Prolonged lordosis whilst pumping the sail Prolonged isometric contraction of the forearm during pronation
America’s Cup
2.2 injuries/ 1000 h sailing
Joint/ligament sprains Chronic tendinopathies
Lower limb and spine Upper limb
Impact with boat hardware and lumbar flexion with rotation under load Sustained grinding, gripping and top handle winching
Offshore sailing
1.5–3.2 injuries/ person/race
Acute contusions Chronic tendinopathies and neuropathies (helmsmen)
Upper limb Upper limb
Impact with boat hardware Prolonged repetitive steering
injury risk in this class.[54] Grinders may also benefit from improved grinding pedestal design, for example smaller handle diameter, custom handle shape and increased grip friction. Furthermore, optimizing the height of the pedestal could reduce the degree of lumbar flexion and minimize back stress. 3. Conclusions Sailors of all classes and level of ability seem to be at risk of injury, with hiking, pumping, grinding and steering being the main cause of chronic injury in experienced sailors. Novice and recreational sailors are at increased risk of head and hand injuries, and windsurfers lower limb and back injuries. Table I shows a summary of the incidence, nature and risk factors of injuries in the different classes of sailing. The scarcity of analytical studies on sailing injuries is a major concern, and it is remarkable that, to date, there have been no prospective analytical studies published in the English language literature on Olympic class sailing injuries. This review ª 2009 Adis Data Information BV. All rights reserved.
underscores the necessity for injury surveillance at all levels, from junior through to the elite Olympic classes. In order to increase the enjoyment and standard of sailing, it is recommended that informative scientific research be conducted. 4. Recommendations for Injury Definition and Methodology of Injury Surveillance in Sailing A major issue highlighted by this article is the variation in methodology and definition of injury used in the research literature. Schaefer[19] included all injuries, ‘‘not just major ones, but even little prangs’’, as did Price et al.:[24] ‘‘any medical incident that the boat medic recorded’’. Other studies used retrospective questionnaires[7,23,28-31,35,42] or internet-based surveys,[2,30,42] which rely on recall and may under-report injuries. Neville et al.[1] used a definition similar to that adopted in research of other professional sports, whereby ‘‘an incident occurring as a direct result of scheduled sailing or training causing pain, disability or tissue damage, resulting in at least one Sports Med 2009; 39 (2)
Sailing Injuries
treatment from a medical officer,’’ therefore excluding all minor ailments, medical advice, consultations, soft tissue massage or non-specific treatments that do not meet this criterion. Clearly there is a need for consensus on injury definition, data collection procedures and appropriate reporting indices in sailing, as has been achieved in other sports, such as soccer[71] and rugby union.[72] Furthermore, with the relatively high risk of overuse injuries in sailing, consensus as to the definition of acute and chronic injuries is also required. In the classification of sprains and strains, the nature of the injury should be based on the inciting events regardless of the type of injury. For example, an acute injury should be defined as an injury resulting from a specific, single traumatic event, whereas a chronic injury is defined as resulting from a gradual development of symptoms through overuse or prolonged exposure.[73] Researchers are encouraged to accurately collect exposure data in order to determine the actual risk and incidence of injury.[74] Severity statistics should also be included where possible in order to identify the injuries at greatest risk of effecting performance, participation or health.[75] Moreover, an understanding of the severity of injuries can enable the prudent distribution of preventative resources. The following recommendations have been established in order to standardize injury collection reports, and to minimize the current variations in injury definition and methodology. The aim is to provide a common protocol in order to encourage sailing injury research and to enable interstudy comparisons. The following sailingspecific guidelines have been based on reviews of, and are consistent with, the recent consensus statements for cricket,[76] soccer[71] and rugby.[72]
141
sailing or sailing-related activities should be referred to as a ‘time loss injury’. ‘Medical attention’ refers to an assessment by a qualified medical practitioner. ‘Future’ refers to any time after the onset of injury, including the day of the injury. Multiple injuries sustained by a sailor in a single event should be recorded as one injury with multiple diagnoses.[71] Injuries that are unrelated to sailing should not be included in sailing injury studies. The reporting and incidence of non-injury medical complaints should be highlighted or reported separately from physical injuries. ‘Time loss’ refers to the severity of the injury (see injury severity definition). 4.2 Recurrent Injury Definition
A recurrent injury should not be mistaken for a new injury and should be defined as: An injury of the same type and at the same site of a previously reported injury, which occurs after the sailors return to full participation from the existing injury. Acute contusions, lacerations and the like should not be recorded as recurrences. 4.3 Injury Severity Definition
The severity of an injury should be defined as: The number of days that elapse from the date of injury to the date of return to full participation in sailing, regardless of whether or not the sailing occurs on that day. The day of the injury should be recorded as day zero, and therefore if a sailor returns to full participation the day following an injury, the time loss severity would be zero days.[71,72] 4.4 Sailing Exposure Definition
4.1 Injury Definition
An injury should be defined as: Any physical complaint sustained by a sailor that results from sailing or sailing-related activities. Any injury that results in a sailor receiving medical attention should be referred to as a ‘medical attention injury’, and an injury that results in a sailor being unable to take a full part in future ª 2009 Adis Data Information BV. All rights reserved.
Sailing exposure should be included in studies where possible and defined as: The total number of hours of sailing, from when the first sail (mainsail or foresail) is hoisted until when the last sail is dropped. This should not include boat maintenance, rigging or boat preparation, which should be recorded separately. Furthermore, any land-based Sports Med 2009; 39 (2)
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142
SAILING INJURY REPORT FORM A. SAILOR IDENTIFICATION: Surname: Gender:
Firstname: male
Sailing experience:
female
Age:
Date of Birth:
no of years sailing:
no of days per year sailing:
Class of sailboat: Sailing position at time of injury:
helmsman
crew
bowman
other:
B. INJURY DESCRIPTORS: Date of injury:
Time of injury:
Temperature:
hot (>30°C)
moderate (15-30°C)
cold (<15°C)
other:
Conditions:
raining/wet
dry
TWS (knots):
Swell height (m):
Place of injury:
on -deck/board
below -deck
at -dock
other:
Type of sailing:
cruising
racing
maintenance
wave -jumping
board-slalom
Activity:
jybing
tacking
mark-rounding
boat/board rigging
other:
Cause of injury:
impact -boom grinding
impact -hardware steering
trimming pumping sails
pulling/lifting sails other:
hiking
Details of specific activity:
C. INJURY DETAILS: Nature of injury:
acute (resulting from a specific, single traumatic event) chronic (resulting from a gradual development of symptoms through overus e or prolonged exposure)
Type of injury:
joint/ligament sprain tendinopathy hernia
muscle strain/tear fracture joint dysfunction
contusion/haematoma dislocation spondylosis
laceration myofacial pain nerve injury
abrasion bursitis other:
Other medical conditions:
sunburn conjunctivitis
skin rash earache/otitis
fungal infection motion sickness
stings other:
Location:
head/face shoulder chest hand/fingers lower leg
cervical spine upper arm abdominals ankle
thoracic spine elbow sacrum/hip/pelvis foot
lumbar spine forearm upper leg
back wrist knee
Status of injury:
new injury
recurrent injury
pre-existing injury
Diagnosis confirmed by:
doctor athlete
physiotherapist parent
coach other
trainer
Severity (time loss):
minimal (1-3 days)
minor (4-7 days)
moderate (8-21 days)
severe (>21 days)
doctor
physiotherapist
massage
none
strapping
sutures
X-Ray/MRI/scan
surgery
Diagnosis of injury:
slight (0 days)
D. TREATMENT DETAILS: Referred to:
A&E/hospital other:
Initial treatment:
ice other:
Medication:
NSAIDS
analgesic
antibiotic
antihistamine
other:
Proposed therapy:
none required
rest
modified activity
physiotherapy
massage
other: Follow up treatments required:
No
Yes
Fig. 5. Sailing injury report form.
ª 2009 Adis Data Information BV. All rights reserved.
Sports Med 2009; 39 (2)
Sailing Injuries
training or strength and conditioning should also be recorded separately. 4.5 Methodological Considerations
Injury surveillance studies should, wherever possible, be prospective in design to minimize the recall error of retrospective studies.[71] Studies should not incorporate mixed definitions of injury, and it is suggested that medical attention injuries or time loss injuries be incorporated into incidence data rather than all injuries. Sailing exposure should also be included if possible, in order to understand the incidence and risks of specific injuries. Injuries should be classified by nature (acute or chronic), type, anatomical location and the cause or activity at the time of injury and if the injury was a recurrence. It is also suggested that any injury diagnoses be given by a qualified medical practitioner. An example of an injury report form for sailing is included (figure 5). Sailing exposure data should be accurately collected in order to determine the incidence of injury. The same definition of sailing exposure should be used in competitive sailing as recreational sailing, even though the actual time racing is usually far less than the total time sailing (with sails up). In addition, sailing, boat maintenance and land-based training exposures should all be reported separately. The incidence of injury should be reported as the number of injuries per 1000 sailing hours and the severity of injuries should be reported in days and grouped according to severity: slight (0 days absence from sailing); minimal (1–3 days); minor (4–7 days); moderate (8–21 days); severe (>21 days); and career ending. Acknowledgements No sources of funding were used in the preparation of this article and the authors have no conflicts of interest that are directly relevant to its contents.
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2. Nathanson A, Fisher G, Wallace R, et al. Sailing injury and safety survey 2006 [online]. Available from URL: http:// www.sailinganarchy.com/fringe/2006/injury%20survey.htm [Accessed 2008 Dec 1] 3. Castagna O, Brisswalter J. Assessment of energy demand in Laser sailing: influences of exercise duration and performance level. Eur J Appl Physiol 2007 Jan; 99 (2): 95-101 4. Spurway NC. Hiking physiology and the ‘quasi-isometric’ concept. J Sports Sci 2007 Aug; 25 (10): 1081-93 5. Bojsen-Moller J, Larsson B, Magnusson SP, et al. Yacht type and crew-specific differences in anthropometric, aerobic capacity, and muscle strength parameters among international Olympic class sailors. J Sports Sci 2007 Aug; 25 (10): 1117-28 6. Vangelakoudi A, Vogiatzis I, Geladas N. Anaerobic capacity, isometric endurance, and Laser sailing performance. J Sports Sci 2007 Aug; 25 (10): 1095-100 7. Legg SJ, Smith P, Slyfield D, et al. Knowledge and reported use of sport science by elite New Zealand Olympic class sailors. J Sports Med Phys Fitness 1997 Sep; 37 (3): 213-7 8. Moraes J, Nery C, Fontel E, et al. Multidisciplinary assessment of the Brazilian Olympic sailing team. In: Legg SJ, Mackie H, Cochrane D, editors. Human Performance in Sailing Conference Proceedings: Incorporating the 4th European Conference on Sailing Sports Science and Sports Medicine and the 3rd Australian Sailing Science Conference; 2003 Jan 9-10; Auckland. Auckland: Massey University, 2003: 92-5 9. Mackie HW, Legg SJ. Preliminary assessment of force demands in laser racing. J Sci Med Sport 1999 Mar; 2 (1): 78-85 10. Shephard RJ. Biology and medicine of sailing. an update. Sports Med 1997 Jun; 23 (6): 350-6 11. Newton F. Dinghy sailing. Practitioner 1989 Jul 8; 233 (1472): 1032, 5 12. Zheng N, Fleisig GS, Escamilla RF, et al. An analytical model of the knee for estimation of internal forces during exercise. J Biomech 1998 Oct; 31 (10): 963-7 13. Cockerill S. No pain: how to develop a hiking style that avoids the knee problems which put many out of the sport. Aust Sailing 1999; 09: 40-2 14. Mackie H, Sanders R, Legg S. The physical demands of Olympic yacht racing. J Sci Med Sport 1999 Dec; 2 (4): 375-88 15. Cockerill S, Taylor F. Hitch-hikers guide: ways we can improve our hiking-out style and avoid injury. Yachts Yachting 1998; 12/11/ (1353): 11-5 16. Blackburn M. Physiological responses to 90 min of simulated dinghy sailing. J Sports Sci 1994 08; 12 (4): 383-90 17. Aagaard P, Beyer N, Simonsen EB, et al. Isokinetic muscle strength and hiking performance in elite sailors. Scand J Med Sci Sports 1998 Jun; 8 (3): 138-44 18. Blackburn M. The stayed back: ideas and exercises to avoid problems with the sailing spine. Aust Sailing 1994; 02: 43-5; 67 19. Schaefer O. Injuries in dinghy sailing: an analysis of accidents among beginners. Sportverletz Sportschaden 2000 Mar; 14 (1): 25-30 20. Halliwell W. Delivering sport psychology services to the Canadian sailing team at the 1988 summer Olympic Games. Sport Psychol 1989 12; 3 (4): 313-9
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40. Bauer S. Coming about: can a bunch of guys with names like Rambo, Darling and Adam-12 bring back the America’s Cup? They’re working on it. Ultrasport 1986 07; 3 (6): 44-51 41. Pearson S, Hume P, Slyfield D, et al. External work and peak power are reliable measures of ergometer grinding performance when tested under load, deck heel, and grinding direction conditions. Sports Biomech 2007 Jan; 6 (1): 71-80 42. Allen A. Sports medicine injuries in the America’s Cup 2000. NZ J Sports Med 2005; 33 (2): 43-7 43. Allen JB. Sports medicine and sailing. Phys Med Rehabil Clin N Am 1999 Feb; 10 (1): 49-65 44. Hootman JM, Dick R, Agel J. Epidemiology of collegiate injuries for 15 sports: summary and recommendations for injury prevention initiatives. J Athl Train 2007 Apr-Jun; 42 (2): 311-9 45. Hagglund M, Walden M, Ekstrand J. Exposure and injury risk in Swedish elite football: a comparison between seasons 1982 and 2001. Scand J Med Sci Sports 2003 Dec; 13 (6): 364-70 46. Brooks JH, Fuller CW, Kemp SP, et al. A prospective study of injuries and training amongst the England 2003 Rugby World Cup squad. Br J Sports Med 2005 May; 39 (5): 288-93 47. Miller C. Treating the America’s Cup sailors/Suivi des equipages de l ‘ America’s Cup. Physician Sportsmed 1987 01; 15 (1): 172-6;8 48. Neville V, Molloy J, Wood I, et al. The pain of PIN. In: Legg SJ, editor. Human performance in sailing conference proceedings: incorporating the 4th European Conference on Sailing Sports Science and Sports Medicine and the 3rd Australian Sailing Science Conference [abstract]; 2003 Jan 9-10; Auckland. Auckland: Massey University, 2003: 65 49. Molloy J, Neville VJ, Wood I, et al. Posterior interosseous nerve entrapment. NZ J Sports Med 2005; 33 (2): 48-51 50. Kibler WB, Garrett WE Jr. Pathophysiologic alterations in shoulder injury. Instr Course Lect 1997; 46: 3-6 51. Kibler WB. The role of the scapula in athletic shoulder function. Am J Sports Med 1998 Mar-Apr; 26 (2): 325-37 52. Bono CM. Low-back pain in athletes. J Bone Joint Surg Am 2004 Feb; 86-A (2): 382-96 53. MacArthur E. Race against time. London: Penguin Books Ltd, 2006 54. Spalding TJ, Malinen T, Tomson M, et al. Analysis of medical problems during the 2001-2002 Volvo Ocean Race. NZ J Sports Med 2005; 33 (2): 38-42 55. Bugge M. The third Whitbread round the world race. Injury 1986 May; 17 (3): 196-8 56. Vogiatzis I, Spurway NC, Wilson J, et al. Assessment of aerobic and anaerobic demands of dinghy sailing at different wind velocities. J Sports Med Phys Fitness 1995 Jun; 35 (2): 103-7 57. Gentile DA, Auerbach PS. The sun and water sports. Clin Sports Med 1987; 6 (3): 669-84 58. van Mechelen W, Hlobil H, Kemper HC. Incidence, severity aetiology and prevention of sports injuries: a review of concepts. Sports Med 1992; 14 (2): 82-99 59. Allan JB, Alison B. Medical and scientific news: Athens research – part 3 [letter]. Paralympian 2006: 11
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60. Scott M. The fitness factor, part 2: Dinghy sailing strength and condition. Yachts Yachting 2001; 11/23/ (1430): 58-64 61. Crafer S. Stretching for success: part 2. Aust Sailing 2004; 09: 42-5 62. Guevel A, Hogrel JY, Marini JF. Fatigue of elbow flexors during repeated flexion-extension cycles: effect of movement strategy. Int J Sports Med 2000 Oct; 21 (7): 492-8 63. Allen JB, De Jong MR. Sailing and sports medicine: a literature review. Br J Sports Med 2006 Jul; 40 (7): 587-93 64. Fuller CW, Bahr R, Dick RW, et al. A framework for recording recurrences, reinjuries, and exacerbations in injury surveillance. Clin J Sport Med 2007 05; 17 (3): 197-200 65. Burke LM. Nutrition for open water sailing: an interview with Jeni Pearce, sports dietitian. Int J Sport Nutr Exerc Metab 2003 Jun; 13 (2): 244-9 66. Slater G, Tan B. Body mass changes and nutrient intake of dinghy sailors while racing. J Sports Sci 2007 Aug; 25 (10): 1129-35 67. Zelhof R. The power to hike and trim: six exercises to improve sailing strength in one-design sailboats. Sailing World 1990; 29 (5): SM7-9 68. Rovere GD. Low back pain in athletes. Douleurs lombaires chez les athletes. Physician Sportsmed 1987; 15 (1): 105-6; 15; 17 69. Kibler WB. Closed kinetic chain rehabilitation for sports injuries. Phys Med Rehabil Clin N Am 2000 May; 11 (2): 369-84
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70. Everett SA, Colditz GA. Skin cancer prevention: a time for action. J Commun Health 1997 06; 22 (3): 175-83 71. Fuller CW, Ekstrand J, Junge A, et al. Consensus statement on injury definitions and data collection procedures in studies of football (soccer) injuries. Br J Sports Med 2006 Mar; 40 (3): 193-201 72. Fuller CW, Molloy MG, Bagate C, et al. Consensus statement on injury definitions and data collection procedures for studies of injuries in rugby union. Br J Sports Med 2007; 41 (5): 328-31 73. Peterson L, Renstrom P. Sports injuries: their prevention and treatment. 3rd ed. Champaign (IL): Human Kinetics, 2001 74. Knowles SB, Marshall SW, Guskiewicz KM. Issues in estimating risks and rates in sports injury research. J Athlet Train 2006; 41 (2): 207-15 75. van Mechelen W. The severity of sports injuries. Sports Med 1997; 24 (3): 176-80 76. Orchard JW, Newman D, Stretch R, et al. Methods for injury surveillance in international cricket. Br J Sports Med 2005 Apr; 39 (4): e22
Correspondence: Vernon Neville, School of Sport & Exercise Sciences, Loughborough University, Loughborough, LE11 3TU, UK. E-mail:
[email protected]
Sports Med 2009; 39 (2)
Sports Med 2009; 39 (2): 147-166 0112-1642/09/0002-0147/$49.95/0
REVIEW ARTICLE
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Factors Modulating Post-Activation Potentiation and its Effect on Performance of Subsequent Explosive Activities Neale Anthony Tillin1,2 and David Bishop1,3 1 School of Human Movement and Exercise Science, the University of Western Australia, Crawley, Western Australia, Australia 2 School of Sport and Exercise Science, Loughborough University, Loughborough, Leicestershire, UK 3 Facolta` di Scienze Motorie, Universita` degli Studi di Verona, Verona, Italy
Contents Abstract. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Post-Activation Potentiation (PAP). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Mechanisms of PAP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Phosphorylation of Regulatory Light Chains. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Increased Recruitment of Higher Order Motor Units . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Changes in Pennation Angle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. PAP and Mechanical Power . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Acute Effects of PAP on Subsequent Activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 PAP versus Fatigue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Conditioning Contraction Volume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Conditioning Contraction Type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4. Subject Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.1 Muscular Strength . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.2 Fibre-Type Distribution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.3 Training Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.4 Power-Strength Ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Type of Subsequent Activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Abstract
147 148 148 148 149 152 152 153 156 157 158 160 160 160 161 161 162 163
Post-activation potentiation (PAP) is induced by a voluntary conditioning contraction (CC), performed typically at a maximal or near-maximal intensity, and has consistently been shown to increase both peak force and rate of force development during subsequent twitch contractions. The proposed mechanisms underlying PAP are associated with phosphorylation of myosin regulatory light chains, increased recruitment of higher order motor units, and a possible change in pennation angle. If PAP could be induced by a CC in humans, and utilized during a subsequent explosive activity (e.g. jump or sprint), it could potentially enhance mechanical power and thus performance and/or the training stimulus of that activity. However, the CC might also induce fatigue, and it is the balance between PAP and fatigue that will
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determine the net effect on performance of a subsequent explosive activity. The PAP-fatigue relationship is affected by several variables including CC volume and intensity, recovery period following the CC, type of CC, type of subsequent activity, and subject characteristics. These variables have not been standardized across past research, and as a result, evidence of the effects of CC on performance of subsequent explosive activities is equivocal. In order to better inform and direct future research on this topic, this article will highlight and discuss the key variables that may be responsible for the contrasting results observed in the current literature. Future research should aim to better understand the effect of different conditions on the interaction between PAP and fatigue, with an aim of establishing the specific application (if any) of PAP to sport.
1. Post-Activation Potentiation (PAP) Post-activation potentiation (PAP) or posttetanic potentiation (PTP) refers to the phenomena by which muscular performance characteristics are acutely enhanced as a result of their contractile history.[1,2] The difference between PAP and PTP is defined by the nature of the conditioning contraction. PTP is induced by an involuntary tetanic contraction, and PAP is induced by a voluntary contraction[3,4] performed typically at a maximal or near-maximal intensity. For simplicity, this article refers to all potentiation responses as PAP, and refers to the activity responsible for inducing PAP as a conditioning contraction (CC). The presence of PAP in skeletal muscle has been recorded by many studies in both mammals and humans,[5-17] prompting a discussion amongst recent review articles over the mechanisms of PAP[1,3] and its application to sports performance.[1-3,18] If effectively utilized, PAP could be implemented into a power-training routine to enhance the training stimulus of a plyometric exercise.[2,18] Inducing PAP prior to competition might also prove better than conventional warm-up techniques at enhancing performance of explosive sports activities such as jumping, throwing and sprinting.[10] Because of inconsistencies within the literature, research remains inconclusive on the possible benefits of PAP to explosive sports performance and/or training. The inconsistencies of past research are most likely due to the complex interaction ª 2009 Adis Data Information BV. All rights reserved.
of factors that influence acute performance following a CC.[1-3,18] This review discusses these confounding factors in greater detail, with the purpose of helping to inform and direct future research efforts towards establishing the application (if any) of PAP to performance/training of explosive sports activities. 2. Mechanisms of PAP It has been proposed that two principal mechanisms are responsible for PAP. One is the phosphorylation of myosin regulatory light chains (RLC),[1,3,4,11,12,19,20] and the other is an increase in the recruitment of higher order motor units.[1,10,20] There is also evidence to suggest that changes in pennation angle may contribute to PAP, and this possible mechanism is briefly introduced in this article. 2.1 Phosphorylation of Regulatory Light Chains
A myosin molecule is a hexamer composed of two heavy chains (figure 1).[21] The aminotermini of each heavy chain, classified as the myosin head, contain two RLCs,[9,21] and each RLC has a specific binding site for incorporation of a phosphate molecule. RLC phosphorylation is catalyzed by the enzyme myosin light chain kinase, which is activated when Ca2+ molecules, released from the sarcoplasmic reticulum during muscular contraction, bind to the calcium regulatory protein calmodulin.[1,5,13,21] RLC Sports Med 2009; 39 (2)
Post-Activation Potentiation, Theory and Application
ATP binding site
Fig. 1. One myosin molecule. Each myosin molecule is composed of two myosin heavy chains. Regulatory light chain (RLC)-2 represents a pair of RLCs positioned at the neck of a myosin head. Each RLC can incorporate a phosphate molecule, altering the structure of the myosin head. At each myosin head there is an actin and adenosine triphosphate (ATP) binding site.
phosphorylation is thought to potentiate subsequent contractions by altering the structure of the myosin head and moving it away from its thick filament backbone.[1,21] It has also been shown that RLC phosphorylation renders the actin-myosin interaction more sensitive to myoplasmic Ca2+.[13] Consequently, RLC phosphorylation has its greatest effect at relatively low concentrations of Ca2+, as is the case during twitch or low-frequency tetanic contractions.[1,3,4,22,23] An acute increase in RLC phosphorylation, and a parallel potentiation of twitch tension following tetanic stimulation of specific efferent neural fibres, has been reported by many studies in skinned animal models[5,7,9,13] (figure 2). Relatively few studies have attempted to measure a similar response in human skeletal muscle. Stuart et al.[8] recorded a significantly elevated phosphate content of RLC in the vastus lateralis muscle (p < 0.01), and a significant potentiation of twitch tension of the knee extensors, following one 10-second isometric maximal voluntary contraction (MVC; p < 0.05). There was also a positive but non-significant correlation between the extent of twitch potentiation and the amount of phosphate incorporated into individual RLC units, and between potentiation and percentage of type II muscle fibres (p > 0.05). Smith and Fry[24] also sampled muscle biopsies at the vastus lateralis, and analysed dynamic leg extension performance before and 7 minutes after a 10-second isometric MVC. The authors ª 2009 Adis Data Information BV. All rights reserved.
2.2 Increased Recruitment of Higher Order Motor Units
Research on animals has shown that an induced tetanic isometric contraction (caused by stimulating specific afferent neural fibres, which in turn activate adjacent a-motoneurons via an afferent neural volley; figure 3) elevates the transmittance of excitation potentials across synaptic junctions at the spinal cord. This accommodating state can last for several minutes following the tetanic contraction,[10] and as a Phosphate content Twitch potentiation
0.6
2.0 1.8
0.4
1.6 1.4
0.2
1.2 0
Twitch peak torque potentiation (post/pre)
RLC-2 Myosin heavy chains
reported no significant change in RLC phosphorylation or leg extension performance for the entire sample (p > 0.05). The subjects were then split into those who responded to the MVC with a significant increase, and those who responded with a significant decrease in RLC phosphorylation (p < 0.05), but no significant differences in leg extension performance were found between the groups (p > 0.05). Methodological factors and differences in fibre-type distribution between animals and humans may explain why an observed increase in RLC phosphorylation following a CC is not as consistent in humans as animals. Nevertheless, the significance of RLC phosphorylation in human skeletal muscle remains unclear, and Stuart et al.[8] suggest that other factors may provide the major contribution to PAP.
mol phosphate/mol RLC
Actin binding site
149
1.0 0
10
Tetanic contraction
20 70 130 Time (sec)
190
250
Fig. 2. The time-course of regulatory light chain (RLC) phosphorylation and twitch peak torque potentiation, following a 10-second pre-conditioning tetanus. Potentiation is represented as a ratio of the post-maximal voluntary contraction (MVC) peak torque value to the pre-MVC peak torque value (post/pre). These results indicate a possible relationship between RLC phosphorylation and twitch tension potentiation (reproduced from Moore and Stull,[7] with permission).
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Alpha motoneuron synapse
Spinal cord
Alpha motoneuron to agonist
Alpha motoneuron to synergist
Afferent neural fibre (la)
Alpha motoneuron to antagonist
Muscle spindle
Antagonist muscle
Agonist muscle
Synergist muscle
Fig. 3. The neural volleys of a Ia afferent fibre. An action potential generated at the Ia afferent neural fibre travels to the spinal cord, where it is transferred to the adjacent a-motoneuron of the agonist muscle. The action potential then travels directly to the agonist muscle, initiating the processes of muscular contraction.
result there is an increase in post-synaptic potentials, for the same pre-synaptic potential during subsequent activity.[25,26] Luscher et al.[26] proposed a possible mechanism underlying the elevated transmittance of action potentials across synaptic junctions at the spinal cord. For each parent neural fibre (i.e. Ia fibre) numerous synapses project onto each a-motoneuron. Activation of an a-motoneuron works in an all-or-none fashion, whereby presynaptic transmitter release must coincide with the post-synaptic receptor sensibility. Transmitter failure at various synaptic junctions is a common occurrence during normal reflex or voluntary responses, due to an autonomously protected activation reserve.[26,27] An induced tetanic contraction is suggested to decrease the transmitter failure during subsequent activity, via one or a combination of several possible responses. These include an increase in the quantity of neurotransmitter released, an increase in the efficacy of the neurotransmitter, or a reduction in axonal branch-point failure along the afferent neural fibres.[28] ª 2009 Adis Data Information BV. All rights reserved.
Hirst et al.[27] provided evidence to support a decreased monosynaptic transmitter failure during subsequent activity. They stimulated cat afferent neural fibres, and observed a 54% increase in excitatory post-synaptic potentials (EPSPs) for the same pre-synaptic stimulus, following a 20-second tetanic isometric contraction. Larger EPSPs represent greater depolarization of the a-motoneuron membrane, which would increase the likelihood of that a-motoneuron reaching the threshold required to initiate an action potential, and subsequently contract the muscle fibres of that motor unit. Luscher et al.[26] also measured EPSPs at cat a-motoneurons, in response to electrical stimulation. They found a significant positive correlation between motoneuron input resistances and EPSP amplitude, for a standard stimulus (r = 0.77; p < 0.01; figure 4a), where input resistance was associated with the size of the a-motoneuron (with a smaller input resistance representing a larger motoneuron). This suggests that monosynaptic transmitter failure is greater at larger motoneurons (those responsible for activation of higher order or fast-twitch motor units). Conversely, when a twitch was stimulated following a 10-second tetanic contraction, Luscher et al.[26] found a significant negative correlation between EPSP potentiation and motoneuron input resistance (r = -0.92; p < 0.001; figure 4b). This demonstrates that a tetanic contraction decreased the transmitter failure occurring primarily at larger motoneurons, which resulted in a considerable PAP effect at these motoneurons. If a CC could induce an increase in higher order motoneuron recruitment in humans, this effect might theoretically increase fast-twitch fibre contribution to muscular contraction, and therefore enhance performance of a subsequent explosive activity.[10] Previous studies have measured the H-wave in humans to investigate the effects of a CC on motoneuron recruitment.[10,29] The H-wave (H-reflex) is recorded at the muscle fibres using electromyography, and is the result of an afferent neural volley in response to single-pulse submaximal stimulation of the relevant nerve bundle (see figure 5 for more detail). An increase in Sports Med 2009; 39 (2)
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H-wave following a CC may therefore represent a decrease in transmitter failure at synaptic junctions, and a subsequent increase in higher order motoneuron recruitment. Gullich and Schmidtbleicher[10] stimulated the tibial nerve and measured changes in H-wave amplitude at the gastrocnemius before and after five 5-second isometric MVCs of the plantarflexors. They reported a depression in H-wave amplitude 1 minute
1st response to the electrical stimulation (M-wave) Muscle Spinal cord
Afferent neural fibres
Electrical stimulation Efferent neural fibres
a % Increase in EPSP amplitude
12
2nd response to the electrical stimulation (H-wave) Fig. 5. Elicitation of an M- and H-wave. Stimulation of a nerve with a single submaximal electrical impulse evokes two electrical responses at the muscle. The first response (M-wave) is the result of an action potential travelling directly down the efferent neural fibres (a-motoneurons). The second response (H-wave) is the result of an action potential travelling along the afferent neural fibres to the spinal cord, where it is transmitted to adjacent efferent neural fibres, and down to the muscle.
6
0 0
1
2
Larger motoneurons
3
4
5
Smaller motoneurons
Input resistance (MΩ) b % Increase in EPSP amplitude
140
70
0 0
1
2
Larger motoneurons
3
4
5
Smaller motoneurons
Input resistance (MΩ) Fig. 4. (a) The relationship between input resistances of cat motoneurons, and amplitude of their excitatory post-synaptic potentials (EPSP) in response to twitch stimulation of the adjacent afferent neural fibres. (b) The relationship between input resistances of cat motoneurons, and the percentage increase (potentiation) in EPSP amplitude, in response to a twitch stimulation of the adjacent afferent neural fibres, following a 10-second tetanus. Although EPSP amplitude is greatest at smaller motoneurons (those with greater input resistances), representing greater transmitter failure at larger motoneurons (a), potentiation is greatest at larger motoneurons (those with smaller input resistances), demonstrating a decreased transmitter failure at these motoneurons (b).[22]
ª 2009 Adis Data Information BV. All rights reserved.
after the MVCs (-24%; p < 0.05), but a potentiation of H-wave amplitude 5–13 minutes after the MVCs (+20%; p < 0.01). The H-wave, however, was not normalized to maximal M-wave (M-wave is the electrical counterpart of the activation of all motor units in the pool[30]). Therefore, other factors not relating to central activation, such as increased activity of the Na+-K+ pump at the muscle fibres,[12,14,28] may be responsible for the results that Gullich and Schmidtbleicher[10] observed. Nevertheless, other studies have reported a potentiation in normalized H-wave amplitude 3–10 minutes post eight sets of dynamic MVCs,[29] and 5–11 minutes post a 10-second isometric MVC.[31] Collectively, these results suggest that PAP increases H-wave amplitude in humans (albeit after sufficient recovery), and this may be the result of increased higher order motoneuron recruitment at the spinal cord. Whether or not a CC can enhance motoneuron recruitment and performance during a subsequent voluntary contraction is yet to be determined. The effect of isometric MVCs on subsequent voluntary motoneuron recruitment has been assessed using the interpolated twitch technique (ITT). The ITT can facilitate measurement of Sports Med 2009; 39 (2)
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motoneuron activation[32] by comparing maximal twitch amplitude at rest with that evoked when superimposed upon an MVC (for more detail of the ITT please refer to Folland and Williams[32] and Shield and Zhou[33]). Using the ITT, Behm et al.[34] reported a decrease in voluntary muscle activation following 10-second MVCs (p < 0.05). These results are in contrast to the proposed mechanism of PAP, but may demonstrate the dominance of central fatigue observed throughout this study (see section 4.2). Nevertheless, future research should consider using the ITT to investigate the mechanisms of PAP and their contribution to subsequent performance.
2.3 Changes in Pennation Angle
The pennation angle of a muscle (the angle formed by the fascicles and the inner aponeurosis) reflects the orientation of muscle fibres in relation to connective tissue/tendon.[35] The pennation angle will therefore affect force transmission to the tendons and bones.[35,36] The sum of the forces of all individual fibres being applied to the relevant tendon during muscular contraction is reduced by a factor of cosy (where y = pennation angle).[36] Consequently, smaller pennation angles have a mechanical advantage with respect to force transmission to the tendon.[35,36] Using ultrasonography, Mahlfeld et al.[37] measured resting pennation angle of the vastus lateralis before and after three 3-second isometric MVCs. Pennation angle immediately after the MVCs (15.7) had not changed from pre-MVC values (16.2); however, 3–6 minutes after the MVCs, the pennation angle had significantly decreased (14.4; p < 0.05). This change would only be equivalent to a 0.9% increase in force transmission to the tendons, but it is possible that this effect may contribute to PAP. Conditioning contractions, however, are also likely to increase connective tissue/tendon compliance,[38] and this may counter any increase in force transmission caused by a decrease in pennation angle. Nevertheless, the possibility that changes in muscle architecture contribute to PAP warrants further investigation. ª 2009 Adis Data Information BV. All rights reserved.
3. PAP and Mechanical Power Performance of explosive sports activities is largely determined by mechanical power.[10,39-43] Mechanical power can be defined as the rate at which force (F) is developed over a range of motion (d), in a specific period of time (t) [P = F · d/t], or as force multiplied by velocity (v) [P = F · v].[39,40,43] Accordingly, increasing the level of force at a given velocity will increase mechanical power, and this has been demonstrated in skinned rat/mouse models.[16,17,22] Similarly, decreasing the time over which a specific force is applied, without altering the distance over which that force is applied, will increase velocity, and consequently mechanical power. PAP could, therefore, increase force and/or velocity of the muscle contraction, which would enhance mechanical power and the associated sport performance. To date, there is little evidence that PAP can increase maximal force. This is consistent with the observation that increased sensitivity of the myosin-actin interaction to Ca2+ has little or no effect in conditions of Ca2+ saturation, such as those caused by higher stimulation frequencies (>20 Hz for tetanic, or 200 Hz for voluntary contractions).[9,22] Stuart et al.[8] also found that a 10-second isometric MVC of the knee extensors was unable to increase maximum unloaded velocity of subsequent dynamic contractions. Although PAP appears to have little effect at the extremes of the force-velocity curve (figure 6), it has been shown to increase rate of force development (RFD) of tetanic contractions elicited at any frequency.[9] An increase in RFD causes a less concave force-velocity curve (figure 6), resulting in a greater velocity for a specific force, or vice versa.[3,44] Therefore, PAP may enhance the performance of activities that require submaximal force and velocity production.[3,11] Typically, athletes participating in explosive sports activities will not produce maximal force because the mass they are attempting to move is often relatively small (e.g. body mass), but they must still overcome that mass so will not achieve maximal unloaded velocity either.[40] Consequently, PAP could benefit the performance Sports Med 2009; 39 (2)
Percentage of maximum unloaded velocity
Post-Activation Potentiation, Theory and Application
100
Increased RFD
0 0
100 Percentage of maximum force
Fig. 6. The relationship between force and velocity. The dotted line represents a less concave force-velocity curve due to an increase in rate of force development (RFD) [reproduced from Sale,[3] with permission].
of explosive sports activities by increasing RFD and thus mechanical power.[3,11] There is consensus over the existence of PAP, but if it is to be effectively utilized in performance and/or training, research must first confirm that PAP can be induced by an isometric or dynamic voluntary contraction, and then show that its benefits can be realized during a subsequent explosive sports activity. Unfortunately, measurement of both PAP and its effect on performance of a subsequent explosive sports activity in humans is inconsistent. Furthermore, little is known about the degree to which the proposed mechanisms underlying PAP may play a role in inducing an elevated neuromuscular response. 4. Acute Effects of PAP on Subsequent Activity The performance of explosive sports activities relies predominantly on the activation of large muscle groups (e.g. ankle, knee, hip and/or arm and ab/adductors). Therefore, studies assessing the effect of PAP on smaller muscle groups have been excluded from the following sections. Furthermore, it has been shown[45,46] and is widely accepted that contractions of maximal or near maximal intensity (>80% of dynamic or isometric MVC) optimize PAP.[4] Therefore, studies ª 2009 Adis Data Information BV. All rights reserved.
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assessing the effects of low-intensity contractions on subsequent performance have also been excluded from the following sections. Table I summarizes the studies that have investigated the effects of a voluntary CC on subsequent voluntary activity in humans. In agreement with the results produced by studies conducted on skinned mammalian models, research has consistently reported an enhanced twitch response following a CC in humans. Hamada et al.[12] elicited a twitch reflex at the femoral nerve prior to, 5 seconds after, and then every 30 seconds for 300 seconds after a 10-second isometric MVC of the knee extensors. Twitch Pt (peak torque) was significantly increased 5 seconds after the isometric MVC (+71%; p < 0.01); however, by 30 and 60 seconds after the isometric MVC, twitch Pt potentiation had decreased to +44% and +31%, respectively (p < 0.01). Potentiation continued to decrease at a more gradual rate for the remainder of the recovery period, but was still +12% 300 seconds after the isometric MVC (p < 0.01). Similar findings have been reported in other studies,[6,11,59] demonstrating that peak PAP is achieved immediately after a CC, but instantly begins to decrease. The decrease in PAP is rapid for the first minute, but then becomes more gradual resembling an exponential function (figure 7). Although an isometric MVC has been found to consistently enhance subsequent twitch tension, evidence to show that PAP can be effectively utilized to enhance the performance of subsequent voluntary contractions is not as convincing. Gossen and Sale[11] assessed movement mechanics of both twitch and submaximal voluntary contractions following a 10-second isometric MVC. While the MVC enhanced twitch Pt (p < 0.01), knee extension peak velocity following the MVC was significantly lower than knee extension peak velocity executed in a control condition (326.7 vs 341.6/sec; p < 0.03). These results suggest that although the 10-second MVC induced PAP, it also induced fatigue, and that the latter was more dominant during the voluntary contractions. It has been proposed, therefore, that it is the balance between PAP and fatigue that determines whether the subsequent Sports Med 2009; 39 (2)
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ª 2009 Adis Data Information BV. All rights reserved.
Table I. A summary of studies that have investigated the effects of a pre-conditioning contraction on a subsequent activity Study
Subjects
Pre-conditioning contraction (condition)
Volume
Rest interval
Performance test
Performance changes
Batista et al.[47]
10 UT M
Isovelocity MVC, knee extension
10 (30 sec RI)
4 min 6 min 8 min 10 min
Isovelocity knee extensions at all rest intervals
6% › Pt* at each rest interval
Behm et al.[34]
9 UT M
Isometric MVC, knee extension
1 · 10 sec 2 · 10 sec (1 min RI) 3 · 10 sec (1 min RI)
1, 5, 10, 15 min for all volumes
Isometric MVC knee extensions at all rest intervals
2 2 10-min post: 8.9% fl Pf* 15-min post: 7.5% fl Pf *
Chatzopoulos et al.[48]
15 UT M
Back-squat
10 · 1 rep 90% 1 RM (3 min RI)
3 min 5 min
30-m sprint 30-m sprint
2 3% fl 0–10-m sprint time*, 2% fl 0–30-m sprint time*
Chiu et al.[20]
24; 7 RT, 17 UT (12 M, 12 F)
Back-squat
90% 1 RM · 5 (2 min RI)
5 min 6 min 7 min 5 min 6 min 7 min
CMJ: 30% 1 RM 50% 1 RM 70% 1 RM SJ: 30% 1 RM 50% 1 RM 70% 1 RM
RT: 1–3% RT > UT* RT: 1–3% RT > UT* RT: 1–3% RT = UT RT: 1–3% RT > UT* RT: 1–3% RT = UT RT: 1–3% RT = UT
› , UT: 1–4% fl . › , UT: 1–4% fl . › , UT: 1–4% fl . › , UT: 1–4% fl . › , UT: 1–4% fl . › , UT: 1–4% fl .
Dynamic bench-press
3–5 RM
0–5 sec
Medicine ball BPT
2 GRF
French et al.
14 RT (10 M, 4 F)
Isometric MVC, knee extension
3 sec · 3 (3 min RI) 5 sec · 3 (3 min RI)
0–5 sec
CMJ DJ 5 sec C-sprint Isovelocity KE CMJ DJ 5 sec C-sprint isovelocity KE
2 5.0% › * (4.9% › GRF*) 2 6.1% › Pt * 2 2 2 3.0% fl Pt *
Gilbert et al.[51]
7 RT M
Back-squat
100% 1 RM · 5 (5 min RI)
2 min 10 min 15 min 20 min 30 min
Isometric MVC at all rest intervals
5.8% fl RFD 5.8% fl RFD 10.0% › RFD 13.0% › RFD* 2
Gossen and Sale[11]
10 UT (6 M, 4 F)
Isometric MVC, knee extension
10 sec
20 sec 40 sec
Dynamic KE Dynamic KE
2 2
[50]
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Continued next page
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10 RT M
Ebben et al.[49]
Subjects
Pre-conditioning contraction (condition)
Volume
Rest interval
Performance test
Performance changes
Gourgoulis et al.[15]
20 M (11 RT, 9 UT)
Back-squats
2 reps of: 20%, 40%, 60%, 80%, and 90% 1RM (5 min RI)
0–5 sec
CMJ
2.4% › RT + UT* RT: 4.0% › UT: 0.4% ›
Gullich and Schmidtbleicher[10]
Study 1: 34 RT (22 M, 12 F) Study 2: 8 RT
Isometric MVC, leg press Isometric MVC, plantarflexion
3 · 5 sec (5 min RI) 5 · 5 (1 min RI)
3 min, then every 20 sec. 8 jumps measured 1 min, then every 2nd min for 13 min
CMJ and DJ Isometric MVC, plantarflexion
3.3% › CMJ*. › DJ* 13% fl RFD 1 min post*. RFD 3 min post. 19% › RFD 5–13 min post*
Hanson et al.[52]
30 UT (24 M, 6 F)
Back-squats
4 reps of 80% 1 RM
5 min
CMJ
2
Jenson and Ebben[53]
21 RT (11 M, 10 F)
Back-squats
5 RM
10 sec 1 min 2 min 3 min 4 min
CMJ CMJ CMJ CMJ CMJ
4–13% fl * 2 2 2 2
Kilduff et al.[54]
23 RT M
Dynamic back-squats Dynamic bench-press
1 · 3RM 1 · 3 RM
15 sec 4 min 8 min 12 min 16 min 20 min 15 sec 4 min 8 min 12 min 16 min 20 min
CMJ CMJ CMJ CMJ CMJ CMJ Barbell BPT Barbell BPT Barbell BPT Barbell BPT Barbell BPT Barbell BPT
2.9% fl Pp* 2 6.8% › Pp* 8.0% › Pp * 2 2 4.7% fl Pp * 2 2.8% › Pp* 5.3% › Pp* 0.8% › Pp*
Magnus et al.[55]
10 UT M
Back-squats
90% 1 RM
3 min
CMJ
2
Rahimi[45]
12 RT M
Back-squats
2 · 4 reps of 80% 1 RM (2 min RI)
4 min
40-m sprint
3% fl 0–40 m sprint time*
Rixon et al.[56]
30 UT (15 M, 15 F)
Dynamic back-squats Isometric MVC back-squats
3 RM 3 · 3 sec (2 min RI)
3 min 3 min
CMJ CMJ
2.9% › JH *, 8.7% › Pp * 2 JH, 8.0% › Pp *
Robbins and Docherty[57]
16 UT M
Isometric MVC back-squats
3 · 7 sec (8 min between each set)
4 min
CMJ after each set of isometric MVC
2
Young et al.[58]
10 UT M
Back-squats
5 RM
4 min
LCMJ
2.8% › *
BPT = bench press throw; CMJ = counter movement jump; C-sprint = cycle sprint; DJ = drop jump; F = females; GRF = ground reaction force; JH = jump height; KE = knee extensions; LCMJ = loaded counter movement jump; M = males; MVC = maximum voluntary contractions; Pf = peak force; Pp = peak power; Pt = peak torque; RFD = rate of force development; RI = rest interval; RM = repetition maximum; RT = resistance/athletically trained; SJ = squat jump; UT = un/recreationally trained; › indicates increase; fl indicates decrease; 2 indicates no differences; * p < 0.05.
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Table I. Contd
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1.8 1.7 1.6 1.5 1.4 1.3 1.2 1.1 1.0 0
30 60 90 120 150 180 210 240 270 300 Time immediately after a 10-sec isometric MVC (sec)
Fig. 7. The time-course of twitch peak torque potentiation immediately after a 10-second isometric maximal voluntary contraction (MVC).[12] Potentiation is represented as a ratio of the post-MVC peak torque value to the pre-MVC peak torque value (post/pre).
contractile response is enhanced, diminished or unchanged.[2] 4.1 PAP versus Fatigue
The balance between PAP and fatigue and its effect on subsequent explosive contractions has been observed by several studies. Immediately after a CC, Gullich and Schmidtbleicher[10] and Gilbert et al.[51] reported a decrease or no change in isometric RFD, but following a sufficient recovery (4.5–12.5 minutes[10] and 15 minutes[51]) isometric RFD was significantly increased (+10–24%; p < 0.05). The same pattern of no change/decrease followed by an increase in counter-movement jump (CMJ) peak power (+7–8%; p < 0.05)[54] and 30-m sprint performance (2–3%; p < 0.05)[48] 8–12 minutes and 5 minutes, respectively, following a CC have also been reported. Collectively, these results suggest that although twitch studies have reported maximal PAP immediately after a CC (described in section 4; see figure 7), fatigue is also present early on. Furthermore, fatigue seems more dominant in the early stages of recovery and, consequently, performance of subsequent voluntary activity is diminished or unchanged. However, fatigue subsides at a faster rate than PAP, and potentiation of performance can be realized at some point during the recovery period. Figure 8 illustrates the PAP-fatigue relationship and ª 2009 Adis Data Information BV. All rights reserved.
shows how the net affect on subsequent voluntary contractions might be very different to the effect of a MVC on subsequent twitch contractions (represented in figure 7). There is also evidence that a recovery period may not be required to benefit from PAP, or that even with a recovery period performance of a subsequent voluntary activity may remain unchanged/diminished. French et al.[50] did not utilize a recovery period, but still observed a significant increase in both drop jump (DJ) height and isovelocity knee extension Pt (+5.0% and +6.1%, respectively; p < 0.05), immediately after three sets of 3-second isometric MVC knee extensions. Likewise, Gourgoulis et al.,[15] reported a significant increase in CMJ height (+2.4%; p < 0.05) immediately after two back-squats performed with 90% of one repetition maximum (1RM). Conversely, Chiu et al.[20] were unable to detect a significant improvement in peak power of three CMJs or three loaded squat jumps (SJ) [p > 0.05], even though they were performed after a recovery period of 5, 6 and 7 minutes, respectively, following five sets of one back-squat, with 90% 1RM. The three CMJs (5, 6 and 7 minutes post-activation), were executed with different
Peak PAP 2 Potentiation (post/pre)
Twitch peak torque potentiation (post/pre)
156
Performance
1
Peak fatigue Window 1
Window 2
0 Condition volume
Recovery time
Fig. 8. A model of the hypothetical relationship between postactivation potentiation (PAP) and fatigue following a pre-conditioning contraction protocol (condition).[3] When the condition volume is low, PAP is more dominant than fatigue, and a potentiation in subsequent explosive performance (post/pre) can be realized immediately (window 1). As the condition volume increases, fatigue becomes dominant, negatively affecting subsequent performance. Following the condition, fatigue dissipates at a faster rate than PAP, and a potentiation of subsequent explosive performance can be realized at some point during the recovery period (window 2).
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loads (30%, 50% and 70% of 1RM, respectively), which may have affected peak power output, and makes it difficult to compare differences in performance over the time-course. However, these results were supported by those of Mangus et al.,[55] who reported no change in CMJ height 3 minutes after one back-squat with 90% 1RM. Finally, Behm et al.[34] observed no change in isometric peak force immediately after three 10-second MVCs; however, after a 10- to 15-minute recovery period, maximal force had decreased (7–9%; p < 0.05). These contradictory findings suggest that the PAP-fatigue relationship and its effects on subsequent voluntary activity are multi-faceted. In summary, it has been suggested that following a CC an optimal recovery time is required to diminish fatigue and realize PAP; however, evidence is inconsistent in support of this theory. There are a number of possible explanations for the contrasting results produced by the aforementioned studies. The relationship between PAP and fatigue, and the overall effect of contractile history on subsequent performance, is influenced by a combination of factors.[2] These include: volume of the CC (e.g. sets, repetitions and rest interval between numerous sets); intensity of the CC (although there is consensus that maximal-intensity contractions optimize PAP), the type of CC performed (e.g. dynamic or isometric); subject characteristics (e.g. muscular strength, fibre-type distribution, training status or power-strength ratio), and the type of activity
Condition intensity Condition volume Condition type: • Dynamic • Isometric
Subject characteristics: • Muscular strength • Fibre type distribution • Training level • Strength-power ratio
157
performed after the CC.[1,2] Figure 9 illustrates the interaction of these complex factors and the following sections discuss them in more detail. 4.2 Conditioning Contraction Volume
The effect of the CC volume on the interaction between PAP and fatigue is highlighted by one particular study. Hamada et al.[14] used a fatiguing protocol of 16 5-second isometric MVC knee extensions, with each MVC separated by a 3-second rest interval. A twitch response was stimulated at the femoral nerve pre-MVCs, between each MVC, 1 minute after the MVCs, and then every second minute after the MVCs, for 13 minutes. Twitch Pt gradually augmented over the first three MVCs, peaking at a 127% increase from baseline values (p < 0.05). This demonstrates that PAP was more dominant than fatigue, after the first three MVCs when the MVC volume was small. For the remainder of the fatigue protocol, however, twitch Pt progressively decreased, and by the sixteenth MVC measured 32% below baseline-values (p < 0.05). This demonstrates that as the volume of MVCs continued to increase, so did the dominance of fatigue. Following the fatigue protocol twitch Pt gradually increased, and exceeded baseline values after 30–120 seconds of recovery (+32%; p < 0.05). This demonstrates that fatigue dissipated at a faster rate than PAP and, consequently, there was a potentiation in twitch Pt during the recovery
Mechanisms of PAP: • RLC phosphorylation • ↑ higher order motor-unit recruitment • ↓ pennation angle
Recovery time
Type of explosive activity
Performance
Mechanisms of fatigue: • Central • Peripheral
Fig. 9. The complex factors influencing performance of a voluntary explosive activity following a conditioning contraction (condition). Condition intensity, volume and type will affect individuals differently, depending on their subject characteristics. Collectively, these factors will influence the extent to which the mechanisms of post-activation potentiation (PAP) and fatigue are affected. The interaction between the mechanisms of PAP and fatigue will determine whether subsequent performance is potentiated, and the recovery period required to realize potentiation. Regardless of the previous interactions, however, the response of some explosive activities to the condition may be different to the response of other explosive activities. RLC = regulatory light chain.
ª 2009 Adis Data Information BV. All rights reserved.
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period. An adaptation of these results is presented in figure 10. These findings were supported in another study.[6] They recorded twitch tension in the dorsiflexors before and immediately after five isometric dorsiflexion MVC protocols, where each protocol differed in MVC duration (volume). Accordingly, each protocol induced a different level of PAP, with a 10-second isometric MVC eliciting the greatest potentiation (twitch Pt: after a 1-second MVC = +43%; after a 3-second MVC = +130%; after a 10-second MVC = +142%; after a 30-second MVC = +65%; after a 60-second MVC = +14%). Again, the important question is whether or not a similar effect will occur during performance of voluntary explosive activities? French et al.[50] assessed the effect of different CC volumes on performance of subsequent voluntary explosive activities. They measured a significant increase in isovelocity knee-extension Pt immediately after three 3-second isometric MVCs (+6.1%; p < 0.05), but reported a significant decrease in isokinetic knee-extension Pt immediately after three 5-second isometric MVCs (3%; p < 0.05). In contrast, Behm et al.[34] measured isometric MVC peak force after one, two and three sets of 10-second isometric MVCs, and the only effect reported was an 8–9%
Change in twitch torque (%)
140 120 100 80 60 40 20
decrease in peak force 10–15 minutes after three sets of MVCs. As discussed in section 3, PAP is not expected to enhance isometric peak force (which represents maximal force), so Behm et al.[34] may have observed potentiation had they measured voluntary RFD or dynamic performance. Additionally, the smallest CC volume used by Behm et al.[34] (10-second isometric MVC) is arguably larger than the smallest CC volume used by French et al.[50] (three 3-second isometric MVCs separated by 3 minutes), and may therefore have induced a greater degree of fatigue. Furthermore, due to the various other measurements taken by Behm et al.[34] during the recovery period (including high-frequency tetanic contractions, twitches, 30% isometric MVC and ITT), fatigue may have continued to accumulate, thus reducing any opportunity to realize PAP. The results of the four aforementioned studies[6,14,34,50] demonstrate the influence of CC volume on the PAP-fatigue relationship. They also present the possibility that PAP develops quicker than fatigue and may therefore be utilized immediately after a relatively low CC volume (window 1 in figure 8). In contrast, as the CC volume increases so does fatigue and its dominance in the PAP-fatigue relationship, and therefore a recovery period may be required before PAP is realized (window 2 in figure 8). The specific recovery period required for different CC volumes is yet to be determined and it is difficult to compare the results of individual studies because methodologies have not been standardized. If future research intends to infer the ideal warm-up and/or training protocol for optimizing PAP, CC volume and recovery between the CC and subsequent activity should be assessed together.
0 Fatigue protocol
−20 −40 0
4.3 Conditioning Contraction Type
Recovery period 2
7 Time (min)
Fig. 10. The time-course of knee extensor twitch torque during a fatigue protocol and throughout a subsequent 5-minute recovery period. The fatigue protocol consisted of 16 5-second MVCs separated by 3 seconds of recovery. A twitch contraction was recorded pre-fatigue protocol, between each MVC, 5 seconds post-fatigue protocol, and then every 30 seconds throughout the recovery period. Twitch torque is given as percentages of pre-fatigue values.[14]
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Although, to varying degrees, any type of contraction is likely to activate the mechanisms of PAP,[4] the degree of potentiation achieved is likely to be related to contraction type. Consequently, the use of different types of CC has probably contributed to the inconsistent results that have already been discussed. As past research Sports Med 2009; 39 (2)
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has typically used either isometric or dynamic CC, this article will only discuss the differences between these two types of contractions. Several studies have investigated the effects of isometric MVCs on subsequent explosive activity, and whilst two reported an increase,[10,50] others reported no change in performance.[11,34,57] Past studies have also used dynamic maximal/near maximal voluntary contractions to induce PAP, and again, some recorded potentiation of a subsequent explosive activity[15,45-48,54,58] and others did not.[20,49,52,53,55] These conflicting results (see table I for results) present no clear relationship between contraction type (isometric vs dynamic) and PAP-response, and only one study (to our knowledge) has directly compared isometric and dynamic CC with respect to their effects on performance of a subsequent explosive activity.[56] This study reported that while a significant increase in CMJ height (2.9%; p < 0.01) and peak power (8.7%; p < 0.001) was observed 3 minutes after three 3-second isometric MVC back-squats, no change in CMJ height (p > 0.05) but a significant increase in CMJ peak power (8.0%; p < 0.001) was measured 3 minutes after a 3RM dynamic back-squat set. The authors concluded that their isometric condition induced a greater PAP-response than their dynamic condition. The two conditions, however, were not matched with respect to volume or frequency, and as a result, it is difficult to make a direct comparison between their effects. Theoretically, different types of contraction would have different effects on neuromuscular fatigue.[60,61] Babault et al.[60] assessed neuromuscular fatigue during a dynamic contraction fatiguing protocol and an isometric contraction fatiguing protocol, where the two protocols were matched in terms of Pt decrement. The authors reported that early fatigue during the dynamic protocol was preferentially peripheral in origin (peripheral fatigue defined as a decrease in force generating capacity due to action potential failure, excitation-contraction coupling failure, or impairment of cross-bridge cycling in the presence of unchanged or increased neural drive[61]), while central fatigue (defined as a reduction in neural drive to muscle[61]) developed towards the ª 2009 Adis Data Information BV. All rights reserved.
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end of the dynamic fatiguing protocol. The isometric protocol, however, produced the opposite profile, whereby fatigue was firstly central and then peripheral in origin. Babault et al.[60] proposed that the difference in fatigue development between isometric and concentric contractions might be associated with muscle metabolite accumulation, which is suggested to activate and/or sensitize groups of small diameter (III and IV) afferent neural fibres.[60,62,63] This would in turn cause central fatigue by inhibiting a-motoneuron activation, and/or reducing the supraspinal descending drive,[60,63] and/or decreasing motoneuron firing rate.[64] The intermittent nature of dynamic contractions may favour blood flow, subsequently aiding the removal of metabolic by-products. Accordingly, metabolite accumulation would be greater during isometric contractions, resulting in greater central fatigue. Conversely, lactate accumulation has been reported to alleviate peripheral fatigue.[65] This might account for the slower development of peripheral fatigue during isometric contractions when compared with dynamic contractions.[60] If isometric and dynamic contractions can induce different fatigue responses, then it is fair to assume that they might also have different effects on the mechanisms of PAP. For example, the eccentric motion of dynamic contractions (but not isometric contractions) increases muscle spindle firing, activating group Ia neural fibres.[63] In turn, this might enhance the afferent neural volley at the spinal cord. Consequently, decreased transmission failure from Ia neural fibres to adjacent a-motor units, resulting in increased higher order motor unit activation during subsequent activity, might be greater after dynamic contractions. On the other hand, isometric contractions activate a greater number of motor units than dynamic contractions.[66] Consequently, more muscle fibres might be involved during an isometric contraction, and this might result in a greater percentage of RLC phosphorylation, and greater changes in muscle architecture. In summary, preliminary evidence suggests that isometric CCs may induce greater central fatigue, but are more likely to activate the peripheral mechanisms of PAP. In contrast, dynamic Sports Med 2009; 39 (2)
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CCs may induce greater peripheral fatigue, but are possibly more likely to activate the central mechanisms of PAP (table II). The manner in which these mechanisms interact has not yet been determined, but it is fair to assume that isometric and dynamic contractions will have different effects on subsequent explosive activities. The differences between isometric and dynamic contractions will also influence the volume and recovery period required to potentiate subsequent explosive activity. Future research should investigate the effects of contraction type on the mechanisms of PAP and fatigue, whilst standardizing CC volume and recovery period. It is also not known whether a CC of any type is more beneficial than conventional warm-up methods,[18] and although one study suggested that it is,[46] their results were specific to the individuals and protocols assessed. Future research should compare the potentiating effects of CC to conventional warm-up techniques. 4.4. Subject Characteristics
The subject characteristics that have been suggested to affect an individual’s PAP-fatigue response include muscular strength, fibre-type distribution, training level and power-strength ratio. These factors are discussed in more detail in the following sections.
squat loads of >160 kg, only recorded a 0.4% increase in CMJ height (p > 0.05). Similarly, Kilduff et al.[54] reported a correlation between muscular strength (absolute and relative) and CMJ peak power potentiation 12 minutes after a 3RM back-squat set (r = 0.63; p < 0.01). A possible explanation for these findings might be associated with subject fibre-type distribution. The positive linear relationship between muscular strength and percentage of type II muscle fibres is well documented (r = 0.5–0.93; p < 0.05),[67-69] and type II muscle fibres display the greatest increase in RLC phosphorylation following a CC.[7] Furthermore, subjects with a higher percentage of type II muscle fibres would presumably have a greater number of higher order motor units in reserve, which could be activated via decreased transmitter failure, following a CC. The combined effect of a greater RLC phosphorylation and a greater increase in higherorder motor unit recruitment would theoretically predispose individuals with a higher percentage of type II muscle fibres to a greater PAP response. Consequently, it could be speculated that the stronger subjects in the two studies discussed above[15,54] had a higher percentage of fast-twitch muscle fibres, and thus achieved a greater PAP response. 4.4.2 Fibre-Type Distribution
4.4.1 Muscular Strength
There is evidence to suggest that an individual’s muscular strength might partly determine their PAP response following a CC. Gourgoulis et al.[15] observed a 4% increase in CMJ height (p < 0.05) following five sets of backsquats in those subjects able to squat a load of >160 kg. Conversely, those subjects unable to
Table II. An illustration of the hypothetical effects of isometric and dynamic conditioning contractions on the central and peripheral mechanisms of post-activation potentiation (PAP) and fatigue Type of conditioning contraction
The mechanisms of PAP predominantly induced
The mechanisms of fatigue predominantly induced
Isometric
Peripheral
Central
Dynamic
Central
Peripheral
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Hamada et al.[14] provided evidence to support a relationship between fibre-type distribution and PAP. They separated their subjects into two groups: one with predominantly fast-twitch (type II) muscle fibres (T-II; n = 4), and a second, with predominantly slow-twitch (type I) muscle fibres (T-I; n = 4). They reported a greater Pt response in the T-II group during a 3-second isometric MVC (250.0 vs 171.0 N m; p < 0.01). Furthermore, in response to a fatigue protocol of 16 5-second isometric MVCs of the knee extensors, the T-II group showed significantly greater twitch tension potentiation during the early stages of the fatigue protocol (+127% vs +40% increase in Pt after the third MVC; p < 0.05). However, the T-II group also had a greater decrease in both twitch Pt and MVC Pt during the later stages of the fatigue protocol (p < 0.05). Therefore, although subjects Sports Med 2009; 39 (2)
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with a greater percentage of type II muscle fibres elicited a greater PAP response, they also elicited a greater fatigue response following a highvolume CC protocol. There are a number of possible reasons why Hamada et al.[14] observed a greater fatigue response in the T-II group. As stated, Hamada et al.[14] reported a greater Pt production in the T-II group during the early stages of the fatigue protocol. Therefore, according to the force-fatigue relationship,[70] a greater fatigue response in the T-II group would be expected. Additionally, a negative correlation has been reported between initial glycolytic rate and fatigue during intermittent exercise.[71] The specific task employed by Hamada et al.[14] (16 5-second isometric MVCs, with 3 seconds of rest between MVCs) would rely predominantly on a high anaerobic adenosine triphosphate (ATP) turnover rate, especially in subjects with a higher percentage of type II muscle fibres.[72,73] Therefore, although subjects with a higher percentage of type II muscle fibres are expected to produce a larger MVC Pt, due to a higher initial anaerobic ATP turnover rate, they are also likely to show greater Pt decrements, due to a greater utilization of anaerobic energy stores and the production of metabolites associated with fatigue.[74,75] 4.4.3 Training Level
An individual’s training level may also influence PAP and fatigue responses following a CC. Chiu et al.[20] separated a sample of 24 subjects into athletes who were training and participating in a sport at national and/or international level (RT; n = 7), and those who participated in recreational resistance training (UT; n = 17). Five sets of one back-squat with 90% 1RM and 5–7 minutes of subsequent recovery induced a 1–3% increase in CMJ and SJ height in the RT group. In contrast, the UT group reacted to the same condition with a 1–4% decrease in CMJ and SJ height. Chiu et al.[20] suggested that those subjects training at higher levels of resistance would develop fatigue resistance as an adaptation of their intensive training regimens, and were more likely to realize PAP. Chiu et al.,[20] however, did not ª 2009 Adis Data Information BV. All rights reserved.
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measure fibre-type distribution, so it is possible that a greater percentage of fast-twitch muscle fibres in the RT group also contributed to the effects observed in this study. 4.4.4 Power-Strength Ratio
There is also evidence to suggest that a subject’s power-strength ratio will influence their PAP response to a CC. Schneiker et al.[76] reported a significant negative correlation between power-strength ratio and potentiation of peak power during loaded CMJ, executed 2–4 minutes after one set of 6RM back-squats (r2 = 0.65; p < 0.05). Furthermore, when the sample of strength-trained subjects were separated into those with a power-strength ratio of <19 W/kg (group 1) and those with a power-strength ratio of >19 W/kg (group 2), group 1 had a significant negative correlation between power-strength ratio and peak power potentiation (r2 = 0.91; p < 0.05). In contrast, group 2 showed no relationship between power-strength ratio and peak power potentiation (p > 0.05). These results suggest that those subjects less able to effectively convert their strength into power are more likely to benefit from PAP than those that can. In addition, it appears that there may be a powerstrength ratio threshold above which subjects do not benefit from PAP. In summary, several subject characteristics have been suggested to affect an individual’s PAP-fatigue response, and this may partly explain the inconsistencies of past research. Evidence suggests that individuals most likely to benefit from PAP include those with a greater muscular strength, a larger percentage of type two fibres (although fatigue may also be greater in these individuals), a higher level of resistance training, and a smaller power-strength ratio. Further research, however, is required to validate these findings as well as determine the possible effects of other subject characteristics such as muscle and/or lever lengths. For coaches considering the implementation of CC prior to explosive activities (in training or performance), it may be pertinent to first assess each athlete’s susceptibility to PAP during the off-season period. Sports Med 2009; 39 (2)
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4.5 Type of Subsequent Activity
An additional explanation for the inconsistent results of past research is the different types of subsequent explosive activities used to determine the acute effects of PAP. The types of subsequent explosive activities employed by previous studies have included isometric MVCs,[10,34,51] isolated dynamic contractions (e.g. isovelocity knee extensions),[11,47,50] and compound ballistic activities (e.g. CMJ and DJ).[10,15,46,49,52-58] It is possible that a specific CC will not have the same effect on different explosive activities. With regard to differences between isometric and dynamic explosive contractions, previous studies have reported moderate to strong correlations between isometric and dynamic RFD (r = 0.65–0.75),[77] and moderate to strong correlations between isometric and dynamic peak force (r = 0.66–0.77).[77,78] These results indicate a clear relationship between tests measuring isometric and dynamic strength and power. There are, however, a number of differences in the neural and mechanical processes involved in isometric and dynamic activities. For example, the motor unit recruitment and rate coding for an isometric contraction will probably be regulated by the size principle,[79] whereby motor units are recruited in a hierarchical order of small, followed by higher order units. On the other hand, dynamic contractions might display a specific pattern of motor unit recruitment relevant to joint angle and position through the range of motion.[80] Additionally, the eccentric movement involved in dynamic contractions, but not isometric contractions, would result in a greater afferent (group Ia neural fibres) input from muscle spindles.[61,81] As a result, the a-motoneuron activation responses for isometric and dynamic contractions would be different.[82] Furthermore, utilization of elastic strain energy (stretchshortening cycle), stored in the muscles during an eccentric contraction, provides a significant contribution to overall performance of dynamic movements.[83-85] The stretch-shortening cycle, however, is not utilized during an isometric contraction and, consequently, isometric contractions may not reflect the muscles capabilities for ª 2009 Adis Data Information BV. All rights reserved.
dynamic situations.[82] Finally, PAP is greatest whilst the muscle is shortening[86] and extends to higher stimulation frequencies in concentric when compared with isometric contractions.[22] This suggests that PAP may have a performanceenhancing effect beyond what would be expected based on isometric contractions. It is also likely that whilst a specific CC might enhance performance of a particular dynamic activity, it might decrease or have no effect on the performance of a different dynamic activity. French et al.[50] analysed isovelocity knee extension, CMJ, DJ and 5-second cycle sprint performance before and immediately after three 3-second MVC knee extensions. They reported significant improvements in DJ height, DJ RFD and knee extension Pt (+5.0%, +9.5% and +6.1%, respectively; p < 0.05) after the MVCs, but found no significant effect in any of the other activities (p > 0.05). French et al.[50] used time-motion analysis to explain their results. They reported that the DJ and knee extension MVC had a muscle activation period of £0.25 seconds. In contrast, the CMJ and 5-second cycle sprint had a muscle activation period of ‡0.25 seconds. Explosive muscle actions have previously been defined as those that have an activation period of £0.25 seconds.[77] French et al.[50] therefore concluded that PAP was only effective in tasks defined as explosive muscle actions. The conclusions of French et al.,[50] however, should be interpreted with caution. Some studies have recorded a potentiation effect in CMJ performance, as well as other activities that otherwise might not fall under the above definition of explosive muscle action.[10,15,46,51,54,56,58] In addition, French et al.[50] only measured exercise performance immediately after the CC, and a rest interval may have been needed for a potentiation effect to be realised. Finally, the CC exercise was an isolation exercise targeting the knee extensors alone. The DJ may load the knee extensors to a greater extent than the CMJ and 5-second cycle sprint, which would explain the increase in DJ height/RFD. The CMJ and 5-second cycle sprint, however, may rely on the contribution of various other large muscle groups, which due to the kinematics of the CC, had not been potentiated. Sports Med 2009; 39 (2)
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These results therefore highlight the importance of closely matching the kinematics of the CC to those of the subsequent explosive activity. By doing so, an individual is more likely to activate the higher order motor units, phosphorylate the RLC and change the architecture of those muscle fibres specifically associated with the subsequent activity. The aim of recent research has been to establish the application of PAP to specific explosive sports activities. Explosive sports activities are dynamic in nature so, for the reasons discussed above, isometric responses to a CC should not be used to infer effects of the same CC on subsequent sports activities. If researchers are investigating the application of PAP to a training scenario, the reported effects of a CC on subsequent ballistic activities (e.g. CMJ and DJ) may be useful, as ballistic exercises are used in powertraining programmes. On the other hand, whilst PAP may sometimes be effective in enhancing performance of a ballistic exercise, it may not have the same ergogenic effect on performance of a specific explosive sports activity (e.g. sprinting, long jump). If PAP is to be utilized in competition, research must first determine its effects beyond those reported for ballistic training exercises. Two recent studies have shown that PAP can enhance performance of a specific explosive sports activity, reporting a decrease in sprint time (-3% over 10 m,[48] -2% over 30 m,[48] and -3% over 40 m;[45] p < 0.05) 4–5 minutes after the execution of near maximal (>80% 1RM) backsquats. Nevertheless, further research is required to establish the application of PAP to many different explosive sports activities. Furthermore, even if PAP is consistently shown to enhance performance of different explosive sports activities, several practical implications would need to be addressed to effectively apply PAP to a competitive scenario (such as the need for possible equipment in the warm-up area and the requirement to compete within the optimal recovery period following activation). As a result of these impracticalities, the application of PAP to performance has been challenged,[18] but with reported increases in performance by >3%, further investigation is warranted. ª 2009 Adis Data Information BV. All rights reserved.
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5. Conclusion It may be possible to effectively utilize PAP to enhance mechanical power and therefore performance and/or the training stimulus of an explosive sports activity. Evidence over the practical application of PAP to explosive activities is, however, inconclusive. The inconsistent results of past research appear to be due to the complex interaction of several factors that determine the degree to which the different mechanisms of PAP and fatigue are affected. Greater CC volumes and intensities are expected to induce greater levels of both PAP and fatigue. However, the rates at which PAP and fatigue develop and dissipate may differ, resulting in two windows of opportunity to potentiate performance; immediately after a low-volume CC, or after a specific recovery period following a high-volume CC. The type of CC may also have different effects on the mechanisms of PAP and fatigue. For example, isometric MVCs may induce central fatigue, but peripheral PAP, whilst dynamic MVCs may induce the opposite response. The interaction of these different mechanisms would, in turn, determine the optimal CC volume and recovery time required to potentiate (if at all) subsequent performance. Regardless of the above factors, an individual training at a higher level, with a greater muscular strength, a greater fast-twitch fibre distribution and a lower power-strength ratio may be more likely to benefit from PAP than an individual without these characteristics. When interpreting results, consideration should also be given to the specific application of PAP in sport. If the intention is to utilize PAP in competition, only the results of studies reporting the effects of a CC on performance of a specific explosive sports activity should be considered. Although standardization of these various factors provides future research with an extremely arduous task, the results of studies showing 2–10% increases in performance suggests further investigation of PAP may be worthwhile. It may be pertinent, however, for research to first establish how the mechanisms of PAP and fatigue interact under different conditions before applying PAP to sport. Sports Med 2009; 39 (2)
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Acknowledgements No sources of funding were used in the preparation of this review and the authors have no conflicts of interest that are directly relevant to the contents of the review.
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Correspondence: Mr Neale A. Tillin, School of Sport and Exercise Science, Loughborough University, Ashby Road, Loughborough, Leicestershire, LE11 3TU, UK. E-mail:
[email protected]
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