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This article has been written from a “first principles” perspective, in order to objectively explain the dynamic and physiological structures of the preparation of a 100-meter sprinter. This is Part 2 of a two-part series. Part 1 in the series covers Sprint Dynamics and Physiology.

Weight Training

Weight training is only one form of presenting external resistance to be overcome, sustained, or yielded against for the purposes of any variety of muscular adaptations. It also happens to be a convenient means of presenting external resistance that may be manipulated, and quantified, in an incremental fashion.

As with any other form of human movement, the biodynamic, bioenergetic, and biomotor implications of the number and size of the muscles involved in the work and the movement amplitudes, coupled with the intensity, duration, and force-velocity characteristics of work, are the primary factors that determine the neuromuscular cost of training.

In the context of T&F sprint preparation, nearly any and all conventionally understood weight training must, necessarily, classify as a general exercise (although, from a strictly neuromuscular perspective, there are weight training exercises that represent a class of specialized exercises up to the point in a sprint when the muscle contractile velocities become so much higher than what any conventional weight work allows). The word “general” is stated so as to be distinguished from more specific forms of training stimuli, particularly, in this context, in terms of force-velocity characteristics.

When considered against both the biodynamic and the biomotor structure of the sprinter’s motion during the 100m event, the difficulty in achieving a specific training stimuli in most weight rooms is understandable. The exceptions to this exist as specialized exercises for the block start, and also potentially the first couple of steps.

General and specific do, in fact, suggest a polarity; when, in truth, a continuum exists upon which any conceivable preparatory stimuli falls. It is within this continuum that we may define derivative motion categories based upon their relationship to the competitive action(s). Such was the work of Russian throws coach Anatoly Bondarchuk in defining what he refers to as specialized developmental (more specific) and specialized preparatory movements (less specific and unconstrained by kinematic criteria), which fill the void often left by coaches who think only in terms of general OR specific.

If we accept Bondarchuk’s more accurate classification of motion, we see that all preparatory motion is then contextualized by the biodynamic/bioenergetic/biomotor structure of the competition motion it’s intended to develop.

Consider the kinematics of the block start:

A paper by Čoh, Tomažin, and Štuhec [3] illustrates the “block velocity” (the velocity of the sprinter immediately subsequent to the front foot separating from the block), of a 10.14sec 100m sprinter, as being 4.18 ± 0.19 m/s. Contrast this against any conventional barbell exercise, for example, such as a barbell snatch, and note that the “block velocity” of this sprinter is nearly twice the maximum vertical velocity of the barbell measured in a variety of elite Olympic weightlifters. Thus, while the joint positions of the ankles, knees, and hips share more similarities when compared between, for example, the starting position in the blocks and the snatch, their kinematic motion attributes vary substantially.

Herein lies a valuable lesson for those who celebrate “velocity-based training” in the weight room. While it is prudent to understand the dynamics of all preparatory motion, any velocity-based weight training is only as relevant, in terms of its direct transfer to sport, as the proximity in which the dynamics of the weight training relate to the dynamics of the sport maneuvers. Otherwise, any degree of velocity-based weight training that does not directly transfer to some specific aspect of sport might contribute to a greater proportion of specialized neuromuscular stress. In any case, whether there be a specific, specialized, or general context, all preparation must be accounted for.

As kinetics enter the equation, a recent presentation by Morin [6], based upon his contribution to research led by Mendiguchia et al. [4], clarifies the important distinction to be made between the initial force a sprinter is able to develop, and their maximum velocity, during a sprint.

*These metrics are readily quantifiable from the film capture of a 30m sprint on an Apple iPhone or iPad, capable of video recording at 240fps, via the MySprint App (available on the Apple App store).

Force Velocity Characteristics

In a force vs. velocity graph of a sprint, we are able to observe the varying F(v) characteristics of sprinters/athletes that provide essential information necessary for the individualization of preparation. As historically known by athletics coaches around the world, if only by way of more parochial modes of athlete monitoring, different sprinters of comparable speed achieve their speed via different F(v) output profiles. We may often observe that the sprinter who is more force-dominant is also the one who is more impressive in a weight room. Alternatively, the sprinter who is less impressive in the weight room often demonstrates more remarkable reactive/elastic ability as quantified in terms of contact times associated with jumps/bounds and sprints themselves.

However, the fastest sprinters all, necessarily, generate a greater magnitude horizontal ground reaction force vector [5]. This keen insight highlights the fact that the means by which athletics coaches, and coaches of any other sport, conceptualize “strength” training mustn’t be isolated to that which is achievable in a weight room via conventional exercise.

Sprinters, and athletes in general, differ in their F(v) profile, and it is therefore important to understand the nature in which each of these types of sprinters must be prepared differently in order to most effectively correspond to their latent abilities. It is also important to address limiting factors that are most objectively measured by technological resources, such as the MySprint App, that provide insights to the relevant metrics of the sport action, and not a generalized preparatory motion.

Such is the root of the problem with many misdirected attempts to solve various sports training problems in a weight room. In these cases, it is not a deeper investigation into what can be achieved in a weight room that resolves the issue; it is a deeper investigation into the dynamics of the sport motion that reveals the limiting factor and, often, the solution mandates a more specific approach to problem-solving.

As the sprinter’s velocity increases with each step subsequent to block clearance, up until he/she reaches maximum velocity, it is clear that no conventional weight training exercise can come remotely close to qualifying as a specific sprint training stimulus from a neuromuscular perspective for an elite sprinter. In order for an already moderately high- to elite-level sprinter to derive a more specific neuromuscular stimulus from “weights” (again, not counting block clearance and initial acceleration), the resistance must be presented to the sprint action itself. In this way, sleds, chains, tires, and resisted sprint training devices such as the 1080 Sprint, Exergenie, Run Rocket, and Isorobic Exerciser prove to be exceptionally valuable resources.

It is clear that no conventional weight training exercise can come remotely close to qualifying as a specific sprint training stimulus from a neuromuscular perspective for an elite sprinter.

Outside of resisting the sprint motion itself, we may see the disparities begin to grow in terms of what most specifically transfers from a neuromuscular perspective as the sprinter rises in qualification. It is in this context that we may recognize the evidence for the finite relevance of a sprinter, along with any other athlete, striving to improve strength in a weight room beyond the point in which it has plateaued.

Biological Maturation

For example, a novice/young sprinter will, up to a certain stage, possess less of a differential between his/her kinematic/kinetic outputs when comparing a block start, for instance, with a barbell exercise. In this way, the neuromuscular specificity of motion is in some proportion to each athlete’s stage of biomotor preparation. For this reason, the barbell work that might have more of a specific neuromuscular transfer earlier in an athlete’s career will, eventually, shift further into general territory as preparation rises. This is true predominantly in cases in which the competitive action is heavily dependent upon velocity.

As any athlete/coach should know, once the athlete is of complete biological maturation, the amount of time that individual is capable of continuing to lifter heavier and heavier weights, via intelligently planned training, is limited to perhaps three or four years. Beyond that, any gains become fractional unless that athlete has the luxury of increasing their bodyweight. Now, in the case of an athlete whose competitive demands are lifting weights, fractional improvements may be all that’s needed to continue to win competitions. In much the same way, any high-level sprinter is, at best, similar to the strength athlete, improving fractionally (by hundredths of a second, maybe a tenth or so, once they’ve reached the elite international level). That same high-level sprinter would be remiss, however, to expend valuable and finite adaptive reserves seeking the same fractional improvements in lifting weights as the Olympic weightlifter or powerlifter, because the neuromuscular demand of doing so presents far too great a competitive stress against the sprinter’s most important aspect of preparation—sprinting.

Indeed, as they get better, there is less room to get better, and the stimuli required to advance preparation for an elite athlete (who is a product of balanced preparation) is nearly invariably a specific one. In this way, it is, with very few exceptions, work on the track, not off of it, that has the most relevance to the already elite sprinter. The exceptions to the rule are those who, for whatever reason, were not recipients of holistic and well-balanced preparation and, thus, achieved a certain high level of speed in spite of the fact that they are relatively untrained in other elements of preparation (jumps, throws, weights, etc.).

All stated, the fact that the neuromuscular character of most barbell work is significantly slower than the sprinter’s horizontal velocity does not mean that neuromuscular adaptations achievable in a weight room are insignificant. Nor does this suggest that it is futile for a sprinter to utilize heavy weight training as a general neuromuscular stimulus. On the contrary, there are a variety of beneficial adaptations from weight training that effectively supplement a sprinter’s preparation, regardless of how elite that sprinter may be; and the placement of that form of training, relative to when the track work occurs, is of critical importance.

As the sprinter rises in their level of qualification, assuming for a moment that every aspect of their preparation advances relatively proportionally, their neuromuscular outputs increase on every preparatory element that allows for it. For example, as the sprinter’s max velocity increases, so often does their force-velocity profile on explosive jumps, throws, and weight training exercises, to a point, provided that all of those preparatory elements are part of their training.

Temporal Placement of Weight Training

As neuromuscular outputs rise, however, so does the structural and neuromuscular cost of performing those exercises.

For this reason, while a novice sprinter may not be at risk, and may actually benefit from performing explosive lower body weight training prior to sprinting, a high-level sprinter may pay a dear price. Thus, the temporal placement of weight training, depending particularly upon the type of weight training, is of paramount importance and strongly relative to the biomotor preparation of the sprinter.

A useful analog to describe this reality is to consider the comparison between a family automobile and a race car. Consider the family car as the analog to an average untrained person and the race car as the analog to a high-level sprinter. The family car is built for day-to-day use; however, pressing the accelerator all the way to the floor yields a slow response (slow acceleration) and never results in a remarkably high velocity. The net result of “flooring” the accelerator on the family car is of moderate consequence because the output of the car is low. For this reason, the toll this takes on the family car is less.

The race car, on the other hand, is not built to sustain the same high-frequency wear and tear as the family car. However, when the driver presses the accelerator to the floor, the response is tremendous (very large and fast change in acceleration-jerk) and the reachable maximum velocity dwarfs what the family car is capable of. As the output of the race car is fantastically higher than the family car, it is capable of much higher performance. Yet, the forces the entire car must endure are also exceptionally higher and render the race car less “durable” than the family car.

In conclusion, if coaches accept the family car and race car to represent opposite ends of a performance continuum, then coaches are encouraged to identify where on that continuum the analog of those cars are represented by their athletes. (It is a foregone conclusion that no “family cars” are selected for sprint events. However, the relevance of the car analogy is stated to assist coaches in understanding the non-uniform implications of training sequencing relative to the individual outputs of each athlete.)

The highest output athletes are often capable of generating high outputs on a variety of motions. Thus, a high output leg weight training exercise done earlier in the day, or immediately before a high output sprint (particularly a sprint that involves a longer acceleration or reaching maximum velocity), presents a significantly higher risk factor for the “race car” than the “family car.”

This cautionary message applies to coaches/athletes of any sport whose preparatory rehearsal includes sprint efforts.

As the fundamental necessity to individualize preparation rises, and as an athlete’s preparation rises, the logistical challenges presented by the school, university, and amateur environments often make difficult the job of many coaches who would like to individualize the preparation of their athletes; yet these coaches are often short-staffed and constrained by factors outside of their control. It is therefore stated that once a sprinter reaches higher output levels, they would benefit more greatly from reserving the performance of any weight training involving the legs (and of any significant neuromuscular intensity and/or exhaustive fatigue) to after they perform their sprint work. This suggestion applies equally to any high school/secondary school-age sprinter, and beyond, and any athlete who is particularly fast in any other sport that includes sprint efforts.

Put simply, in the case of sprinters and sprint sports, it is more advisable to go from the track/field to the weight room, then to precede sprint work with lifting weights.

Indeed, the “dosage” of any preparatory stimulus is intrinsic to the many “it depends” responses that are necessary to answer the most specific questions in this regard. In this way, all sprint coaches, and indeed all coaches of all sports, are encouraged to this foundational information seriously and integrate it into further understanding of their tradecraft in order to more effectively individualize what has been generalized here.

The “first principles” perspective was utilized to write this article in order to demonstrate one method of preserving objectivity, as well as how to efficiently examine the fundamental basis of any problem solving—which begins with understanding the very structure of the problem itself.

Anyone who is keen to engage in objective discussion of this sort is encouraged to consider a membership in the Conclave on globalsportconcepts.net, which was created to foster unlimited creative freedom in the rational solving of sport training problems and the evolution of coaching as a whole, as inspired by the work of theoretical physicists Neil Turok and David Deutsch.

Since you’re here…
…we have a small favor to ask. More people are reading SimpliFaster than ever, and each week we bring you compelling content from coaches, sport scientists, and physiotherapists who are devoted to building better athletes. Please take a moment to share the articles on social media, engage the authors with questions and comments below, and link to articles when appropriate if you have a blog or participate on forums of related topics. — SF

 

References:

1. ATP5B ATP synthase, H+ transporting, mitochondrial F1 complex, beta polypeptide [Homo sapiens (human)] Gene ID: 506, updated on 19-Mar-2017.
2. Berg J.M., Tymoczko J.L., Stryer L. Biochemistry. 5th edition. New York: W H Freeman; 2002. Section 18.4, “A Proton Gradient Powers the Synthesis of ATP.”
3. Čoh M., Tomažin K., Štuhec S. “The Biomechanical Model of the Sprint Start and Block Acceleration.” University of Ljubljana, Faculty of Sport, Ljubljana, Slovenia. FACTA UNIVERSITATIS Series: Physical Education and Sport Vol. 4, No 2, 2006, pp. 103 – 114
4. Mendiguchia J., Martinez-Ruiz E., Edouard P., Morin J.B., Martinez-Martinez F., Idoate F., Mendez-Villanueva A. “A Multifactorial, Criteria-based Progressive Algorithm for Hamstring Injury Treatment.” Med Sci Sports Exerc. 2017 Mar 8. PMID 28277402
5. Morin J.B., Edouard P., Samozino P. “New Insights into Sprint Biomechanics and Determinants of Elite 100m Performance.” New Studies in Athletics. 2013.
6. Morin J.B., 2017 IOC workshop: “Sprint Acceleration Force-Velocity Profile and Hamstring Injury Management: Win-Win?” YouTube.com. Mar 2017.
7. Robergs R. A., Ghiasvand F., Parker D. “Biochemistry of exercise-induced metabolic acidosis.” American Journal of Physiology. 2004.
8. Smith, J. “Applied Physiology- Anaerobic Supply Mechanisms.” Freelapusa. 2014.
9. Smith, J. Applied Sprint Training. 2014.
10. Smith, J. The Governing Dynamics of Coaching. 2016.
11. Hommel, H. “Biomechanical Analysis of Selected Events at the 12th IAAF World Championships in Athletics.” Berlin, Aug. 2009.

This article has been written from a “first principles” perspective, in order to objectively explain the dynamic and physiological structures of the preparation of a 100-meter sprinter. This is Part 1 of a two-part series. Part 2 in the series covers Sprint Weight Training Considerations and Temporal Placement.

Determining the Overall Performance Level of a Sprinter

The overall performance level of a sprinter is objectively quantified by their competition PB factored against the remainder of their known levels of biomotor output (power, strength, etc.), all along the continuum of specific to general motion. It is most important to understand the force-velocity profile of the sprinter, while they are sprinting.

No sensible discussion in this regard may begin, however, before the dynamics and physiology of the sprint motion are understood, as these are the reference points that provide meaning to any aspect of preparation. Alternatively, the attempt to prepare for an objective that is not clearly understood, despite the fact that there is a great amount already known about the objective, cannot take place in any sort of cogent discussion.

Biodynamic Considerations

A general overview of the dynamics of a sprint—the 100m event, for example—is as follows:

Block Start

The sprinter positions themself in the starting blocks so as to optimize their joint positions relative to their state of preparation and propulsive ability (biomotor output).

Universally important for all sprinters, however (regardless of their stage of preparation) is that:

• Their lead foot is positioned behind the plum line of the crease in the same side hip.
• Block clearance concludes with relative complete extension through the hips, knees, and ankles. If the front foot is positioned in front of the vertical axis of the crease in the hip, the distance the hips must travel in order to reach full extension is increased. Alternatively, if the front foot is positioned too far behind the hip, the distance the hips have to travel to reach extension is too short and thereby inhibits the propulsive force capability of the legs and backside muscles.
• Their hips are positioned above the horizontal axis of their shoulders. The extension of the back and hips plays a substantial role in the propulsive force required to achieve extension out of the blocks (as fast as possible). If the hips are positioned below the level of the shoulders, the angular displacement required for the hips to achieve extension is reduced, thereby minimizing the achievable exit velocity. Alternatively, as the hips are positioned higher and higher, relative to the shoulders (by way of increasing the horizontal distance between the hands), the neuromuscular demand placed upon the spinal erectors and gluteals becomes greater and greater.
• All things being equal, most sprinters, regardless of level, will benefit from positioning their shoulders exactly above, or marginally in front of, their hands, in the set position. Positioning the shoulders too far forward of the hands increases the amplitude of lead arm travel during take-off and the percentage of body mass that is supported by the hands in set position.

Acceleration

This is the phase of the sprint that begins with block clearance and ends when the sprinter reaches their maximum velocity (in which acceleration ceases to occur).

The fastest sprinters achieve faster 10m splits up to the point of max velocity and the total distance they accelerate is longer than their slower counterparts (they reach max velocity farther from the starting line).

• By any objective measure, the mechanical efficiency of a sprinter’s acceleration is characterized by the smoothest (most gradual/incremental) possible kinematic transition from the position in the blocks to the full upright sprint position. This is a point that has well been expounded upon by ALTIS Head Coach Shanghai Pierre-Jean Vazel.
• With each step during acceleration, the angle created by the sprinter’s extension relative to the track becomes incrementally greater.
• The physical sensation of accelerating is pronounced resultant of the muscle/mechanical nature of output required to overcome the sprinter’s static inertia and increase their rate of acceleration. The muscles are working very hard and the increased GCT (and subsequent longer time of applied force), in comparison to max velocity, renders a situation in which the actual “feeling” of acceleration is distinct from max velocity.
• The entire spine from, skull to sacrum, is well-served to remain in the anatomical position from the first step to the finish line. This is most obviously indicated by the sprinter’s head position.
• If the sprinter prematurely raises their head, they will extend their cervical spine. Alternatively, if the sprinter forces their chin/head down, they will flex their cervical spine. While there is no statistically significant data demonstrating that minor cervical rotations in either direction have mentionable implications on acceleration, the preservation of the anatomical position remains a viable model.
• In this way, the sprinter’s line of sight, if you imagine Superman-like lasers emitting from their eyes, should be relatively perpendicular to the long axis of their spine. They should not become parallel with the track until the sprinter is in the upright position; in which the long axis of their spine is relatively perpendicular to the track.
• The angle of extension itself, relative to the track, must be dependent upon each sprinter’s neuromuscular output ability.
• As the angle of extension, relative to the horizontal, becomes less and less, the sprinter must necessarily possess greater propulsive force output ability (in the associated muscles of the back, hips, and legs) in order to prevent falling on their face while attempting to accelerate.
• For this reason, forcing an unnaturally low position of acceleration is in no way a useful tactic for any sprinter whose biomotor preparation is insufficient to optimize a low angle of extension. In such attempts, the sprinter’s lack of sufficient propulsive output renders their effort to “stay low and drive” inefficient because they are bent forward at the waist at toe-off.
• Alternatively, a sprinter who rises to the upright position too soon may truncate the attainable acceleration due to the increased demand placed upon the legs to accelerate amidst too great an angle of extension, relative to the horizontal.
• Positive Shin Angles
• The relationship between the knee of the lead leg and the ground, when the foot makes contact, represents the degree of mechanical advantage that is achieved during early acceleration. An accepted rule is that the athlete maintains positive shin angles for the first four to five steps of an acceleration [10]. This kinematic position of the shank most effectively facilitates the delivery of force in the horizontal direction upon ground contact when the angle of the athlete’s extension is increasing from block clearance.
• Each foot strike should occur in relative approximation to the vertical axis created through the sprinter’s center of mass (just in front of bottom dead center).
• The farther in front of the hips the foot makes contact, the longer the duration the foot will be on the ground, as the hips must pass over the foot before the recovery cycle of that leg begins.
• The ankle/foot is the end-stage transmitter of force upon ground contact.
• The stiffness of the foot/ankle, and associated musculotendinous structures, is characterized by their resistance to deformation.
• The less motion that occurs about the ankle during GCT, the lesser the loss of energy as heat and the greater its kinetic transfer.
• The sprinter dorsi flexes the first metatarsal prior to GCT, which facilitates the approximate anatomical position of the ankle prior to GCT. This generates a pre-stretch on the Achilles/gastroc that contributes to greater elastic return during GCT and, subsequently, shorter ground contact times.
• During front-side action, the motion of the knees should be thought of as forward, and not upward.
• As the optimal ground strike is marginally in front of bottom dead center, the more the sprinter rotates their femur upwards, the greater the “artificial” increase in stride length by increasing the duration of the recovery cycle; not due to greater force application during GCT, but via the increase of the amplitude of femoral rotation beyond what is optimal.
• In effect, exaggerated knee lift, regardless of whether it occurs during acceleration or in the upright position, begins to shift the sprint action into a bound in which ground contact times are proportionally longer. The result is a reduction in horizontal velocity.
• During the recovery phase, the sprinter should collapse the knee such that the lower leg is relatively parallel to the horizontal axis when it is under the hips.
• When this is achieved, the view from behind the sprinter, at the moment in which the lower leg is parallel to the horizontal axis, will reveal the entire sole of the shoe—the bottom of the foot is relatively perpendicular to the horizontal axis.
• The position of the pelvis is of principle importance during all phases of the 100m event.
• o If the pelvis is anteriorly rotated, relative to its anatomical position, the sprinter necessarily experiences greater amplitude backside leg motion, as made evident by the foot travelling farther behind and upwards following toe-off. This is informally referred to as “kicking out the back.” This is the net result of the curvilinear “apparatus” of the sprinter’s leg cycling (envisage this as an orbital ellipse); being rotated in the direction they are sprinting.
• o When this occurs, ground contact occurs farther in front of the hips resultant of their negative vertical displacement due to their anteriorly rotated position. This results in a longer ground contact patch, as well as greater braking ground contact forces directed at the musculotendinous structures of the posterior knee and distal hamstring.
• The action of the arms is one of the most consequential motion dynamics in sprinting.
• The hips naturally rotate, due to the momentum generated by the legs moving back and forth. The arms provide counter rotation, about the thoracic spine to the rotation of the hips, and this contributes to the sprinter’s momentum of forward locomotion.
• The dynamics of the arms themselves deserves special attention:
• The length of the lever, represented by the sprinter’s arms, is defined by the degree to which the elbow is flexed and in reference to the hands moving back and forth in a curvilinear path of motion. (Similarly, if we consider the sprinter’s hands to represent the point of applied force, then the lever arm is described by the distance between the center of the rotating axis of the glenohumeral joint and the hands. Alternatively, if we consider the lever arm of any of the muscles that move the humerus, then we must measure the distance between the center of the rotating axis of the glenohumeral joint and the attachment points on the humerus of the respective muscle’s tendon attachment.)
• The greater the degree of elbow rotation (flexion), the shorter the lever and less mechanical energy required to move the arms back and forth. Alternatively, the lesser of a degree of elbow rotation (keeping the elbow extended) lengthens the lever, requiring more mechanical energy to move the arms back and forth.
• As the dynamics of faster and faster sprinting include higher ground contact forces delivered in less and less time, you may observe the interplay between force and velocity, as they are intrinsic to the sprint action. For this reason, while a fully extended elbow allows the arm to generate more force, and thereby momentum, during the back swing, for example, it requires more time to do so. Alternatively, a fully flexed elbow during the backswing rotates more quickly yet produces less force, and momentum. For these reasons, a near-uniform observation of the world’s fastest 100m sprinters reveals that the angle of the elbow closes just beyond 90 degrees during front-side action and opens slightly more beyond 90 degrees during back-side action.
• The sprinter must overcome their static inertia in the blocks. Thus, the time of force application associated with ground contact force becomes shorter and shorter, up until the moment the sprinter reaches maximum velocity. Correspondingly, the greatest degree of elbow extension during backside arm action, and overall motion about the shoulder, is observed during block clearance and the initial steps of acceleration.

Maximum Velocity

• The sprinter reaches the full upright position.
• Ground contact times are the shortest (~.08sec for the fastest males).
• Locomotive velocity is the highest (+12m/s for the fastest males).
• Muscle contractile velocity is the highest.
• The period of maximum velocity is generally limited to a distance of 10m, and close to 20m in some of the elites.
• The vast proportion of motion dynamics referenced during acceleration hold true in the upright position, with the most significant distinction being the angle of the sprinter’s extension at toe-off (relative to the horizontal).
• As the sprinter has achieved the full upright position and their maximum velocity, what began as a higher proportion of muscle/mechanical work during early acceleration has now transitioned to greater reactive/elastic contribution. The physical sensation of max velocity sprinting is much different than early acceleration, as the time of applied force into the track is less than one-tenth of a second.
• During the front side action of the legs, knee height is generally optimized when the horizontal distance between the knee and hip is maximized (~90 degrees of hip flexion).
• If the femur rotates upward, reducing the angle at the hip, the sprinter runs the risk of losing hip height as a result of a break in the knee of the stance leg during ground support.
• Alternatively, if the femur fails to rotate to a position parallel with the horizontal axis, the stride length will be shortened.
• Whereas, relative extension through the hip and knee is achieved at toe-off.
• Upon toe-off, the greater the athlete’s ability to collapse the knee of the recovery leg, the greater their ability to mitigate the mechanical cost of bringing the leg forward. In effect, the sprinter is creating a shorter lever. When this is achieved, the result is the lower leg becomes relatively parallel to the horizontal axis as the knee comes forward, and this transitions into the foot of the recovery leg passing at or above the level of the knee of the support leg. Hence the term “toe over knee” [10].
• Despite the fact that the sprinter is in the full upright position, the greatest distinguishing characteristics of their dynamics at this point, relative to lesser talented and/or developed sprinters, is the horizontal ground reaction force vector and impulse of their foot strike [5].

Speed Endurance

Speed endurance, or your preferred colloquial equivalent, describes the period following the conclusion of maximum velocity that characterizes the sprinter’s ability to sustain the highest percentage of their maximum velocity through to the finish.

• The preservation of mechanical optimization is of the utmost importance through this period in which the sprinter’s internal physiological environment becomes increasingly less conducive to generating top speed.
• Speed endurance will be expounded upon in the next section.

Physiological Considerations

The two primary bioenergetic domains (anaerobic and aerobic) are differentiated based upon the biochemical substrates that they metabolize in order to synthesize adenosine triphosphate (ATP), which is essential to facilitate muscle contraction. Simply put, the human organism has two primary ways of synthesizing ATP—with and without oxygen.

To be taken literally, the (an)aerobic system conducts its operations void of oxygen. This system is subdivided into the anaerobic-alactic (no lactic acid) and anaerobic-lactic (with lactic acid) —(or glycolytic reflective of the process of anaerobic glycolysis: the breakdown of glucose via the anaerobic machinery).

The anaerobic-alactic system is recognized as the short-term system, or the ATP-CP system in reference to the breakdown of creatine phosphate (CP), whose energy release couples with other processes specific to the re-synthesis of adenosine triphosphate (ATP). This system, regarding continuous movement, is responsible for the shortest-duration and highest-intensity muscular outputs.

The anaerobic-lactic system, the medium term system, signifies the process of anaerobic glycolysis. Glycolysis refers to the breakdown of glucose (sugar), and the subsequent energy release is one of the mechanisms associated with ATP synthesis. Lactic acid is one by-product in the process of anaerobic glycolysis; hence, the anaerobic-lactic system. In the context of continuous movement, this system is responsible for medium duration and relatively high intensity muscular output [8].

While at no point during human motion is any single bioenergetic resource solely responsible for movement, in a 100m sprint, the overwhelming bioenergetic contribution stems from anaerobic alactic and anaerobic lactic processes. It is a question of contributing proportions.

As ATP-CP is intrinsic to the highest intensity muscular contractile dynamics, developing a sprinter’s alactic bioenergetic system, to their genetic ceiling, is of top importance.

No two steps during acceleration occur at the same velocity, as by definition, an acceleration is a change in velocity over time. Acceleration increases up to, and concludes, when maximum velocity is reached, and the point in which maximum velocity finalizes is the point in which the alactic processes cease to be the primary contributing bioenergetic resource.

At this point, anaerobic lactic processes begin to assume greater and greater proportions of the bioenergetic load. The results of which are greater and greater accumulations of blood lactate, along with greater proton production resultant of the muscles’ use of large quantities of ATP [2,7].

In 1961, Peter Mitchell proposed that electron transport and ATP synthesis are coupled by a proton gradient across the inner mitochondrial membrane…[2]. Mitochondrial ATP synthase catalyzes ATP synthesis, utilizing an electrochemical gradient of protons across the inner membrane during oxidative phosphorylation [1].

Every time ATP is broken down to ADP and P(i), a proton is released. When the ATP demand of muscle contraction is met by mitochondrial respiration, there is no proton accumulation in the cell, as protons are used by the mitochondria for oxidative phosphorylation and to maintain the proton gradient in the intermembranous space. It is only when the exercise intensity increases beyond steady state that there is a need for greater reliance on ATP regeneration from glycolysis and the phosphagen system. The ATP that is supplied from these nonmitochondrial sources and is eventually used to fuel muscle contraction increases proton release and causes the acidosis of intense exercise [7].

This proton release that causes the acidosis (not to be confused with lactic acid) is what contributes to the “burning” feeling associated with anaerobic lactic work bouts. However, as elucidated upon by the referenced authors, the historical claim that the accumulation of blood lactate is what causes the “burn” of intense/prolonged exercise is false. To be clear, growing concentrations of blood lactate are closely associated with, yet not the cause of, the high level of muscle discomfort associated with the highest achievable intensities over ~20-60sec durations.

From a proportionality and contribution standpoint, you may objectively state that the shorter the distance over which a sprinter is able to accelerate, the greater the significance of speed endurance training. Alternatively, the longer the distance over which a sprinter is able to accelerate, the greater the significance of maximum velocity training. For this reason, speed endurance training, proportionally, tends to more positively benefit developing female sprinters and younger sprinters in general, as both of these populations, broadly speaking, reach maximum velocity in less time.

Every second of time spent after the sprinter is unable to sustain maximum velocity is a second of time spent in growingly rigorous physiological conditions. Thus, while the development of maximum velocity is the ultimate commodity for a 100m sprinter, any sprinter whose period of post-max velocity sprinting is longer in distance than the distance over which they are able to accelerate is a sprinter who will, in the short term, benefit more greatly from enhancing their ability to sustain the highest percentage of their existing max velocity via speed endurance training.

Alternatively, the more elite a sprinter becomes, the deeper into the race distance they accelerate and, as a consequence, reach a higher maximum velocity. This population of sprinters experiences less physiological stress, as the period over which they battle growing levels of acidosis is shorter in duration than their slower counterparts. Interestingly enough, however, is that the most profound impact on these sprinters’ performance is the continued development of maximum velocity—as incremental as it will be at this point. In these cases, even a relatively small increase in maximum velocity may pose a dramatic reduction in the lactic period.

An IAAF report on the 2009 World Championships in Berlin reveals that Usain Bolt reached his 12.35m/s at 70m and, by comparison, Powell reached his 11.90m/s at 60m [11]. Bolt, therefore, had less than 30m remaining to operate under growing physiological challenges, while Powell had the better part of 40m.

It must be pointed out that the highest maximum velocity does not always amount to the fastest race time. While this may sound peculiar, we must account for the significance of the start and reaction times. Bolt’s 12.35m/s is the highest maximum velocity ever recorded in a 100m competition (some research has him listed at over 12.4m/s at peak velocity). It also accompanied the fastest recorded time in a 100m competition (9.58). Had he stumbled out of the blocks, however, and tripped and fallen to the ground, he still may have recovered and reached 12.35m/s; however, it would have been much closer to the finish. While that would have further reduced the lactic period, it surely wouldn’t have mattered as everyone else would have already long since finished the race.

In regards to what did happen, not only did Bolt register a higher maximum velocity farther into the race, his 60m split bested the 60m world record by a full tenth of a second, and the reduced lactic period allowed him to remain within 2% of his maximum velocity through the tape. The result was the fastest 100m of all time [9].

The “first principles” perspective was utilized to write this article in order to demonstrate one method of preserving objectivity, as well as how to efficiently examine the fundamental basis of any problem solving—which begins with understanding the very structure of the problem itself.

Anyone who is keen to engage in objective discussion of this sort is encouraged to consider a membership in the Conclave on globalsportconcepts.net. This was created to foster unlimited creative freedom in the rational solving of sports training problems and the evolution of coaching as a whole, as inspired by the work of theoretical physicists Neil Turok and David Deutsch.

Definition of Terms

Acceleration: the 2nd derivative of position, 1st derivative of velocity, defined by the change in velocity over the change in time. Defined by meters per second squared (m/s^2).

Angular Acceleration: the rate of change of angular velocity. Described by Radians or Degrees per second squared. (Rad/sec^2 or Deg/sec^2).

Angular Displacement: defined by the angle through which an object moves on a circular path. Described by Radians or Degrees.

Angular Velocity: the rate of change of a rotating object. Described by Radians or Degrees per second (Rad/sec or Deg/sec).

Biodynamics: the study of physical motion or dynamics in living systems.

Bioenergetics: the study of the transformation of energy in living organisms.

Biomotor Outputs: biological motion possibilities regulated by the motor cortex.

Dynamics: the branch of mechanics that deals with the motion of bodies under the action of force (kinematics and kinetics).

First Principles: the fundamental concepts or assumptions on which a theory, system, or method is based.

Force: the product of some mass multiplied by some acceleration. Described by Newtons (kg x m/s^2).

GCT: ground contact time.

Impulse: a change in momentum. Described by Force x Time.

Inertia: the resistance of a physical object to a change in its state of motion. Implicit to Newton’s 1st Law, also known as his law of inertia, which states that objects at rest tend to stay at rest and objects in motion tend to stay in motion (in the same direction and at the same speed) unless acted upon by an unbalanced force.

Jerk: the 3rd derivative of position, 2nd derivative of velocity, 1st derivative of acceleration, defined by the change in acceleration over the change in time. Described by meters per second cubed (m/s^3).

Kinematics: the study of motion, change in position (and its derivatives: velocity, acceleration, and jerk), without consideration of mobilizing forces.

Kinetics: the study of motion and its mobilizing forces.

Lever: A simple machine consisting of a rigid bar pivoted on a fixed point and used to transmit force.

Lever Arm: the perpendicular distance between the axis of rotation and the line of action of the force.

Momentum: a quantity of a moving object’s motion, described by mass x velocity.

Quantitative Units of Measurement: length (meter), mass (kilogram), time (second).

Sprint Training: physical training intended to improve an athlete’s ability to sprint faster over a given distance.

Vector: in physics, any quantity that possess both a magnitude and direction.

Velocity: the first derivative of position characterized by change in position over change in time. Described by meters per second (m/s).

Since you’re here…
…we have a small favor to ask. More people are reading SimpliFaster than ever, and each week we bring you compelling content from coaches, sport scientists, and physiotherapists who are devoted to building better athletes. Please take a moment to share the articles on social media, engage the authors with questions and comments below, and link to articles when appropriate if you have a blog or participate on forums of related topics. — SF

References:

1. ATP5B ATP synthase, H+ transporting, mitochondrial F1 complex, beta polypeptide [Homo sapiens (human)] Gene ID: 506, updated on 19-Mar-2017.
2. Berg J.M., Tymoczko J.L., Stryer L. Biochemistry. 5th edition. New York: W H Freeman; 2002. Section 18.4, “A Proton Gradient Powers the Synthesis of ATP.”
3. Čoh M., Tomažin K., Štuhec S. “The Biomechanical Model of the Sprint Start and Block Acceleration.” University of Ljubljana, Faculty of Sport, Ljubljana, Slovenia. FACTA UNIVERSITATIS Series: Physical Education and Sport Vol. 4, No 2, 2006, pp. 103 – 114
4. Mendiguchia J., Martinez-Ruiz E., Edouard P., Morin J.B., Martinez-Martinez F., Idoate F., Mendez-Villanueva A. “A Multifactorial, Criteria-based Progressive Algorithm for Hamstring Injury Treatment.” Med Sci Sports Exerc. 2017 Mar 8. PMID 28277402
5. Morin J.B., Edouard P., Samozino P. “New Insights into Sprint Biomechanics and Determinants of Elite 100m Performance.” New Studies in Athletics. 2013.
6. Morin J.B., 2017 IOC workshop: “Sprint Acceleration Force-Velocity Profile and Hamstring Injury Management: Win-Win?” YouTube.com. Mar 2017.
7. Robergs R. A., Ghiasvand F., Parker D. “Biochemistry of exercise-induced metabolic acidosis.” American Journal of Physiology. 2004.
8. Smith, J. “Applied Physiology- Anaerobic Supply Mechanisms.” Freelapusa. 2014.
9. Smith, J. Applied Sprint Training. 2014.
10. Smith, J. The Governing Dynamics of Coaching. 2016.
11. Hommel, H. “Biomechanical Analysis of Selected Events at the 12th IAAF World Championships in Athletics.” Berlin, Aug. 2009.

L-citrulline, a non-protein amino acid, is made by the body and used as a supplement for exercise performance because of cardiovascular effects optimizing blood flow. In the body, citrulline is converted to L-arginine (another amino acid), which produces nitric oxide (NO), a small molecule that dilates blood vessels to allow working muscle to receive more oxygen and nutrients, and remove metabolic waste. This optimized blood flow may help performance and accelerate recovery.

Citrulline is supplied as a dietary L-citrulline malate supplement (a powdered capsule) or from watermelon, the only food source high in citrulline (with variable citrulline content depending on watermelon species and maturity).

Studies suggest citrulline may benefit exercise. However, there’s no recommendation for citrulline intake¹ , which means the appropriate amount of citrulline (g/day) to take and when to take it (before exercise or in the short term) is unclear. Therefore, studies investigating citrulline and exercise use different dosages and supplementation procedures.

In this article, I will discuss:

• Effects of citrulline and arginine in the body
• Studies investigating the various benefits of citrulline supplementation on exercise (while emphasizing dosage)
• Difficulties applying the science

Citrulline and Arginine

Citrulline converts to arginine in the kidneys. Arginine is an essential precursor for many important biological molecules, including NO and creatine. NO optimizes blood flow, which allows nutrients and oxygen to enter muscle and ammonia to be removed during recovery from exhaustive exercise.² Arginine is the main precursor of one of the two pathways to NO synthesis, and arginine blood levels are the limiting factor for NO synthesis in endothelial cells.³

Therefore, citrulline intake leads to more arginine and increased production of NO. There must be enough citrulline intake to increase arginine levels so that NO synthesis can occur. Citrulline blood levels (plasma) are the main barriers in the conversion of citrulline to arginine results.⁴

Citrulline intake leads to more arginine and increased production of nitric oxide. Share on X

Why not supplement with arginine? Arginine supplementation increases plasma arginine, but citrulline supplementation increases plasma arginine for a longer period of time. In fact, citrulline is more efficient at increasing arginine levels than arginine itself because arginine is mostly used by the liver, and citrulline is available for the whole body. Also, arginine supplementation of at least 10g can lead to diarrhea. Currently, citrulline doesn’t have any side effects, so it’s a preferred source over arginine.

Citrulline also is part of the urea cycle; an important pathway that removes ammonia from muscle and the liver.

Plasma Citrulline and Arginine Levels

The role of citrulline and arginine in stimulating NO synthesis was investigated in moderate- and severe-intensity cycling exercise on days six and seven during a seven-day supplementation period using 6g/day of arginine and 6g/day of citrulline.⁵ The study did not use g/kg/day, but based on the average weight of the participants, it was approximately 0.08g/kg.

The results were:

• Plasma arginine increased to a similar extent with arginine and citrulline supplementation, but plasma citrulline only increased with citrulline supplementation.
• Plasma nitrite (another substrate for NO) increased with arginine and increased with citrulline.
• Blood pressure was only lower with citrulline supplementation.
• Citrulline improved tolerance to severe-intensity exercise and the total amount of work completed.
• Arginine did not affect blood pressure or performance.

The study concluded that short-term citrulline supplementation (6g/day for seven days) may improve blood pressure and exercise performance.

Another study found improved blood flow after increasing NO, following 5.6g/day of citrulline for seven days. The improved blood flow correlated with increases in arginine levels.⁶

Further support of a dose of 6g citrulline (0.08g/kg) given to pre-professional male cyclists two hours before exercise found a 173% increase in plasma citrulline, a 123% increase in plasma arginine, and a significant increase in NO production.⁷ Creatinine also increased, which suggested that increased arginine may lead to creatine synthesis. This same dose (0.08g/kg) and a similar increase in citrulline and arginine levels were also found in 17 male professional cyclists.⁸

However, two other studies used a slightly higher amount of citrulline, 0.18g/kg (~12g of citrulline for a 150lb person). One study gave five equal dosages of citrulline at three-hour intervals within 12 hours to increase circulating levels of citrulline, which increased citrulline six times its original amount and doubled plasma arginine levels.⁹ The other study found an increase in plasma arginine and an elevenfold increase in plasma citrulline.¹⁰ Therefore, citrulline supplementation can be used to increase citrulline and arginine availability in the body.

Effect on Exercise

A study investigated the effect of different citrulline dosages and loading protocols on incremental treadmill tests to exhaustion after citrulline supplementation (9g 24 hours before test or 3g three hours before test).¹¹ Citrulline ingestion over 24 hours reduced time to exhaustion by 0.8% compared to citrulline supplementation three hours prior.

Citrulline supplementation of 2.4g/day for seven days and 2.4g of citrulline one hour before a 4-km cycling time trial reduced completion time by 1.5% in trained males.¹1 Citrulline also improved feelings of muscle fatigue and concentration immediately post-exercise.¹1

An 8g dose of citrulline has shown to increase resistance exercise performance in males,^12,13 , but due to physiological difference between sexes, the results could not be applied to females. Therefore, two recent studies have investigated citrulline in females.

One study found an increase in upper and lower body resistance exercise performance and lowered rating of perceived exertion during upper-body exercise when 8g of citrulline was taken one hour before.¹⁴ Another study found that one hour after consumption of 8g of citrulline, there was an increase in maximal and average grip strength and peak and explosive power in female master-level tennis players, which was suggested to improve tennis-match play.< sup>15

Effect on Muscle Soreness and Fatigue

Muscle soreness from an anaerobic weightlifting exercise was reduced by ~40% (24 hours after) and ~42% (48 hours after) for those taking 8g of citrulline.¹⁶ Citrulline delayed fatigue by enhancing more reps performed per set for each set (except the first two sets).

Another study providing 6g/day of citrulline for 22 days found reduced reports of fatigue from men who usually complained of fatigue.¹⁷

When Does Citrulline Stop Being Effective?

A dose-response study investigated citrulline loading (2g, 5g, 10g, and 15g) and found that higher amounts of citrulline increased plasma citrulline (tenfold at 2g and a hundredfold at 15g).¹⁸ After citrulline supplementation, plasma citrulline increased to a maximum and then decreased to baseline levels within three to five hours. Also, 15g of citrulline did not cause GI problems like arginine does at 10g.

The study concluded that saturation of citrulline begins to occur at 15g, and 10g may be an appropriate amount to use. This lower amount supports another study that found 12g of citrulline taken one hour before a time-to-exhaustion test on the bike did not provide ergogenic benefits in well-trained males.¹⁹

Difficulties With Applying the Science

Citrulline may provide various exercise benefits. However, the data is slightly inconclusive for us to apply the conclusions of these studies.

The dosage of citrulline in these studies ranged from: 2.4g to 15g.

It’s unclear how much citrulline we need. Even though there’s no recommendation on citrulline intake, it’s suggested that a 70kg person should be able to tolerate a daily dose of arginine from a normal diet (4-6g/day).²⁰ Therefore, the dose of 6g of citrulline could be the same as the daily intake of arginine.

Moreover, maybe the amount should be provided in grams per kilogram of body weight (similar to how protein requirement is determined to take into account larger and smaller athletes). If so, it appears that citrulline supplementation may be 0.08-18 g/kg/day.

The amount needed must also consider citrulline bioavailability, which may be limited by intestinal absorption.²¹ Citrulline bioavailability is higher when contained in its natural form (e.g., watermelon), but further information on this is out of the scope of this article.

It’s also unclear when to supplement with citrulline (e.g., 1-2 hours before exercise or days prior to exercise).

Ultimately, there’s no conclusive evidence that citrulline supplementation enhances exercise performance.²² We need more science—especially using athletes—on the minimal amount of citrulline needed and when to take it, to better understand how we can effectively use citrulline to enhance exercise performance.

Since you’re here…
…we have a small favor to ask. More people are reading SimpliFaster than ever, and each week we bring you compelling content from coaches, sport scientists, and physiotherapists who are devoted to building better athletes. Please take a moment to share the articles on social media, engage the authors with questions and comments below, and link to articles when appropriate if you have a blog or participate on forums of related topics. — SF

References

1. Suzuki T, Morita M, Kobayashi Y, Kamimura A. Oral L-citrulline supplementation enhances cycling time trial performance in healthy trained men: Double-blind randomized placebo-controlled 2-way crossover study. J Int Soc Sports Nutr. 2016;13(1):6.
2. Sureda A, Pons A. Arginine and citrulline supplementation in sports and exercise: Ergogenic nutrients? Med Sport Sci. 2012;59:18-28.
3. Nussler AK, Billiar TR, Liu ZZ, Morris SM. Coinduction of nitric oxide synthase and argininosuccinate synthetase in a murine macrophage cell line. Implications for regulation of nitric oxide production. J Biol Chem. 1994;269(2):1257-1261.
4. Nussler AK, Billiar TR, Liu ZZ, Morris SM. Coinduction of nitric oxide synthase and argininosuccinate synthetase in a murine macrophage cell line. Implications for regulation of nitric oxide production. J Biol Chem. 1994;269(2):1257-1261.
5. Bailey SJ, Blackwell JR, Lord T, Vanhatalo A, Winyard PG, Jones AM. L-citrulline supplementation improves O2 uptake kinetics and high-intensity exercise performance in humans. J Appl Physiol. 2015.
6. Ochiai M, Hayashi T, Morita M, et al. Short-term effects of L-citrulline supplementation on arterial stiffness in middle-aged men. Int J Cardiol. 2012;155(2):257-261.
7. Sureda A, Córdova A, Ferrer MD, Pérez G, Tur JA, Pons A. L-Citrulline-malate influence over branched chain amino acid utilization during exercise. Eur J Appl Physiol. 2010;110(2):341-351
8. Sureda A, Córdova A, Ferrer MD, et al. Effects of L-citrulline oral supplementation on polymorphonuclear neutrophils oxidative burst and nitric oxide production after exercise. Free Radic Res. 2009;43(9):828-835.
9. Rouge C, Des Robert C, Robins A, et al. Manipulation of citrulline availability in humans. AJP Gastrointest Liver Physiol. 2007;293(5):G1061-G1067.
10. Thibault R, Flet L, Vavasseur F, et al. Oral citrulline does not affect whole body protein metabolism in healthy human volunteers: Results of a prospective, randomized, double-blind, cross-over study. Clin Nutr. 2011;30(6):807-811
11. Hickner RC, Tanner CJ, Evans CA, et al. L-Citrulline reduces time to exhaustion and insulin response to a graded exercise test. Med Sci Sport Exerc. 2006;38(4):660-666.
12. Wax B, Kavazis AN, Luckett W. Effects of supplemental citrulline-malate ingestion on blood lactate, cardiovascular dynamics, and resistance exercise performance in trained males. J Diet Suppl. 2016;13(3):269-282.
13. Wax B, Kavazis AN, Weldon K, Sperlak J. Effects of supplemental citrulline malate ingestion during repeated bouts of lower-body exercise in advanced weightlifters. J Strength Cond Res. 2015;29(3):786-792.
14. Glenn JM, Gray M, Wethington LN, Stone MS, Stewart RW, Moyen NE. Acute citrulline malate supplementation improves upper- and lower-body submaximal weightlifting exercise performance in resistance-trained females. Eur J Nutr. December 2015.
15. Glenn JM, Gray M, Jensen A, Stone MS, Vincenzo JL. Acute citrulline-malate supplementation improves maximal strength and anaerobic power in female, masters athletes tennis players. Eur J Sport Sci. 2016;16(8):1095-1103.
16. Pérez-Guisado J, Jakeman PM. Citrulline malate enhances athletic anaerobic performance and relieves muscle soreness. J Strength Cond Res. 2010;24(5):1215-1222.
17. Bendahan D, Mattei JP, Ghattas B, Confort-Gouny S, Le Guern ME, Cozzone PJ. Citrulline/malate promotes aerobic energy production in human exercising muscle. Br J Sports Med. 2002;36(4):282-289.
18. Moinard C, Nicolis I, Neveux N, Darquy S, Bénazeth S, Cynober L. Dose-ranging effects of citrulline administration on plasma amino acids and hormonal patterns in healthy subjects: the Citrudose pharmacokinetic study. Br J Nutr. 2008;99(4).
19. Cunniffe B, Papageorgiou M, OʼBrien B, Davies NA, Grimble GK, Cardinale M. Acute citrulline-malate supplementation and high-intensity cycling performance. J Strength Cond Res. 2016;30(9):2638-2647.
20. Wu G, Bazer FW, Cudd TA, et al. Pharmacokinetics and safety of arginine supplementation in animals. J Nutr. 2007;137(6 Suppl 2):1673S-1680S.
21. Bailey SJ, Blackwell JR, Williams E, et al. Two weeks of watermelon juice supplementation improves nitric oxide bioavailability but not endurance exercise performance in humans. Nitric Oxide. 2016;59:10-20.
22. Stear SJ, Castell LM, Burke LM, et al. A-Z of nutritional supplements: dietary supplements, sports nutrition foods and ergogenic aids for health and performance–part 10. Br J Sports Med. 2010;44(9):688-690.

My boss, Joe DeFranco, has always voiced his opinion on how variations of heavy sled sprint training can result in greater sprint performances, specifically with American football clients. I started out as a high school football client at DeFranco’s Training Systems in 2008, at the age of 17. At that time, we had a small facility with no access to any field space outside of the asphalt in the parking lot.

Joe had to get creative—and creative usually meant many variations of heavy sled runs. Sled pushes, sled sprints, sled drags… you name it! Joe always had a stopwatch around his neck and timed every repetition. No matter what resistance was on the sled, we had to give it our best effort and aim for the best time.

Even though we had no field space to run sprints, we all noticed that this heavy sled training was making us faster. Our ability to accelerate seemed to be drastically improving. However, due to the lack of field space, it was hard to determine if it was actually helping our sprint performance or not. Feeling a certain way is one thing, but what would the measurements and data show?

DeFranco’s gym then moved to a new location, with access to a 30-yard strip of turf. It wasn’t the best distance available for sprint training, but we could at least work on starts and 10-yard runs unloaded. Now, with the help of fully automatic timing (FAT), Joe could measure unloaded 10-yard runs and see if the heavy sled training was actually doing anything for performance gains. What Joe found was that 10-yard sprint times were improving after cycles of heavy sled training. In a sport where so much is measured by the ability to get off the ball, this appeared to have some important implications for American football players.

Horizontal Force Production

Recently, the works of JB Morin, Matt Cross, Pierre Samozino, Matt Brughelli, Scott Brown, and many others have attempted to make the historical concept of improving sprint performance with heavy sled running more accessible, and easier to understand and measure. One of the interesting concepts that I read from the researchers listed above was how elite level sprinters are able to produce more net horizontal force at higher velocities than lower level sprinters.

It’s important to explain what is meant by “net horizontal force.” With every stride, a sprinter produces force onto the ground that can be split into a vertical and a horizontal component in the sagittal plane of motion. The combination of vertical and horizontal forces in the sagittal plane is known as the resultant force, or simply the force that results from this combination. The net horizontal force describes the force being generated to propel the body horizontally. When running, ground contacts with more net horizontal force production will result in greater propulsion in the sagittal plane. Ground contacts with less net horizontal force production can result in inefficient braking forces from vertical influence. For anyone trying to sprint from point A to point B, net horizontal force production is certainly your friend.

As an athlete sprints, he/she will attempt to propel forward by putting force into the ground and producing net horizontal force and impulse, which is the force multiplied by the duration of the ground contact. However, as the athlete reaches maximum velocity, the net horizontal force production will inevitably drop and vertical forces will predominate in magnitude, notably because athletes must work against gravity, which is vertically oriented. As mentioned before, the best sprinters are able to prolong this inevitable drop and continue to produce higher ratios of force (net horizontal to resultant force) at faster and faster speeds. So, the question then becomes: If less-efficient athletes produce high ratios of net vertical force, how can we expose them to longer durations of net horizontal force production? For JB Morin and his colleagues, the answer may be found with heavy sled sprinting and trying to find an “optimal” sled load with which athletes and sprinters maximize their power and net horizontal force ratios in the sprinting motion.

Optimal Loading for Maximizing Power

A paper led by Matt Cross on “Optimal Loading for Maximizing Power During Sled-Resisted Sprinting” [1] was published in 2016 and took the sports performance world by storm. Traditionally, sled-resisted sprinting recommendations were such that no more than 10% of an athlete’s body weight should be on a sled or that no more than a 10% drop in velocity or split time should occur. However, in this paper, Cross and his colleagues recommended that, to achieve maximum power, movement velocity would have to be slowed down to 48-52% max velocity and sled loads should be in the range of 69-91% body weight for mixed-sport athletes and 70-96% body weight for sprinters! Surely that’s insanity, right?

Well, before we call these guys nuts, let’s consider what they are reportedly measuring and what these measurements mean. Below is a list of measurements that can be used to better quantify sprint performance. I give my best attempt to provide their definitions and a simplistic explanation of what they mean [2]:

V0 (m/s):

• Theoretical maximal running velocity if mechanical resistances against movement were to become nothing, or 0. This is slightly higher than the actual maximum velocity of the athlete.
• Basically, this describes the highest potential speed of the athlete and can be used to determine training loads as a percentage of max velocity.

F0 (N/kg):

• Theoretical maximal horizontal force production per unit of body mass. This corresponds to the initial push of the athlete into the ground during acceleration.
• Higher values indicate more horizontal force production.

P_max (W/kg):

• Maximum horizontal power capability, per unit of body mass, during sprint acceleration. This is the theoretical optimal combination of force and velocity, and can be graphically depicted as the apex of the power-velocity second-degree polynomial relationship (think of the highest point on a graph of a power curve).
• Ultimately, this is the goal of optimizing sprint training. Once the relationship between force and velocity is balanced for an individual, the aim then becomes producing as much horizontal power as possible.

F_opt (N/kg):

• The theoretical optimum force production to elicit maximum horizontal power. This is considered against the theoretical optimum velocity.
• This is how much force is necessary to optimize and maximize horizontal power output.

V_opt (m/s):

• The theoretical optimum velocity achieved to elicit maximum horizontal power. This is considered against the theoretical optimum force.
• This is how much running velocity is necessary to optimize and maximize horizontal power output. If an athlete produces his/her best efforts against a resistance that “forces” him/her to run at V_opt, then the power output will be maximal.

L_opt (kg):

• The theoretical optimum load (sled load) necessary to elicit maximum horizontal power.
• This is where it becomes important to test and train on the same type of surface so that friction forces do not interfere with accurate calculations. For example, a 150-lb sled on field turf may slide more easily than the same load on a rubber track surface.

RF (%):

• Ratio of force. This basically describes how much of the total resultant force applied onto the ground will result in net horizontal force production.
• Athletes who efficiently apply force so that higher net horizontal forces occur will have a higher value than those who produce higher ratios of vertical force. All things being equal, the higher this ratio, the more horizontally oriented the resultant ground reaction force, and the more effective the propulsion.

D_RF:

• The rate of decrease in ratio of force as speed increases linearly with increasing velocity during sprint acceleration. It’s inevitable that net horizontal force will diminish as max velocity is reached, and this value can determine the athlete’s ability to maintain net horizontal force production as speed increases.
• For example, Athlete A attains a value of 0.10 and Athlete B attains a value of 0.05. This means Athlete A will lose approximately 10% of net horizontal force with every 1 m/s increase in velocity, whereas Athlete B will only lose 5% of net horizontal force. Thus, Athlete B has more effective force application during acceleration.
• Note that this range of values is the range separating world-class sprinters from recreational runners.

So, according to this research, it seems that improving acceleration is heavily dependent upon finding the optimal combination of horizontal force and velocity (maximum horizontal power), improving the ratio of force production in the horizontal direction (avoiding unnecessary vertical force production), and maintaining horizontal force application for as long as possible as movement velocity increases with every step. It should be mentioned that during an unloaded sprint, maximal power is usually reached within one second, and the rest of the sprint acceleration puts the athlete at running velocities way beyond the optimal velocity (V_opt).

Sleds are a “no-brainer” for overloading the horizontal plane and improving horizontal force capabilities. Sprinting with a sled is also arguably the most specific strength training exercise that a sprinting athlete can do. Therefore, finding the individualized load for max power (the L_opt) would put the athlete in the best position to improve maximum horizontal power and the high sled loads could also lead to improvements in RF and lowering the percentage of D_RF. It just so happened that the research from Cross et al. (2016) found the L_opt to occur anywhere between 69-96% of body mass!

Sleds are a “no-brainer” to overload the horizontal plane and improve horizontal force. Share on X

I thought back on how Joe DeFranco found improvements in sprint performance using very heavy sleds in training. Was he improving upon the aforementioned measurements of efficient force production and aiding in the maximization of horizontal power without fully realizing it? Joe had only tested unloaded 10-yard sprints and longer sprints during times of combine/pro day training with potential NFL athletes, due to the space limitations of our old facility. Luckily for us, our current location has an entire football field and track directly across the street—a perfect location for sprint work of any distance. I also happen to have a 1080 Motion Sprint machine, which I like to call the “holy grail” of resisted sprint training devices. So, I thought to myself… Let’s give this heavy sled sprinting thing a try and see what happens!

The Case Study

At the time of this project, I had been training four NFL free agent clients: two linebackers and two running backs. I wanted to see how training at a constant distance of 20 yards against the individualized load of maximum power might affect their ability to run unloaded 20-yard and 40-yard sprints. The first step was figuring out what their unloaded sprint times were, which I tested using a FAT system. This means that the start and the finish both had to be fully automatic, with a motion sensor triggering the start and a laser triggering the finish. I wanted to eliminate human error as much as possible.

It should absolutely be noted that the most accepted margin of error between FAT and hand times (i.e., using a stopwatch) is 0.24 seconds [3]. It’s important that I elaborate on what this 0.24-second margin means. It means that if a player can run a 40-yard dash in 4.50 seconds when a coach uses a stopwatch, it’s very likely that the same exact sprint could register as 4.74 seconds on a FAT system. Therefore, it’s a safe bet to assume that the split times reported below should have 0.24 subtracted from them to get an estimate of what they might be if timed by hand.

When planning the heavy sled training, I decided that I was going to follow the loading guidelines outlined in Cross et al. (2016) and then perform four weeks of training using this “optimal load” once a week. To do this, I had to find the load that corresponded to 48-52% of max velocity for each individual. I decided to base this on average velocity rather than peak velocity; the average velocity would paint a clearer picture of improvements over the entire 20-yard sprint rather than an instantaneous moment.

The absolute beauty of the 1080 Sprint machine is the amount of information that it gives you. Not only does the machine time every sprint for you using FAT, but you can see peak and average force, power, and velocity, in addition to a graphic representation of every step of that particular sprint, after every sprint performed. The resistance that the machine provides has a wide range of possibilities.

The absolute beauty of the 1080 Sprint machine is the amount of information that it gives you. Share on X

Preliminary measurements consistently showed that there is a friction coefficient on the machine of about 35% when considering field turf surface. This means that for each setting on the machine, the number should be divided by 0.35 to get a more accurate depiction of what a similar-feeling load on a regular plate-loaded sled would be. So, if we are on a turf football field and the setting on the machine is 10kg, it is likely that it would feel the same as a sled loaded with about 29kg (or 63lbs). Below is a basic table of load conversions using this friction coefficient:

Video 1. Sprints in gear 1 – 3kg, 8kg, 15kg

To find the maximum average velocity (V0) over 20 yards, I had each player run a 20-yard sprint against incremental load settings on the 1080 Sprint: 3kg, 8kg, 15kg, 20kg, 22kg, 24kg, 26kg, 28kg, and 30kg (the highest the machine goes). I then took the average velocity data obtained from the 1080 Sprint and plotted the numbers against the load being used at each point along an XY scatter plot in Excel. I made sure that the formula was then displayed so that I could view where the y-intercept would occur. This number would theoretically correspond to V0, as long as the coefficient of determination (R^2) was higher than 0.96. All of my guys had R^2 values of at least 0.97, so I felt confident that the velocity numbers were an accurate representation of their theoretical maximums.

Once I had these numbers on hand, the 1080 Sprint could tell me the rest. I simply went into the testing data on the 1080 Motion Web App and found the load that corresponded to 48-52% average velocity. For example, one of my running backs had a V0 of 8.25 m/s, so I looked for the load that had him running between 3.96-4.29 m/s.

Since the 1080 Sprint can provide real-time feedback on every repetition, I decided that if any of the players began running faster than 52% of their estimated V0 during the session, I would adjust the load setting upwards by 2kg. This allowed me to auto-regulate the process and always ensure that the players were sprinting against the load that would put them in the 48-52% range. However, if any of the players reached the 30kg setting (the highest setting currently allowed by the 1080 Sprint machine), then they would simply just try and run faster against that load each subsequent repetition.

Training Schedule

The 1080 Sprint sessions were performed once a week. Before working with the 1080 Sprint, the athletes performed low-volume, unloaded sprint work of 10-20 yards immediately following warmups to maintain a velocity-specific stimulus to unloaded sprinting. I then had them each do four 25-yard sprints against their individual optimum load. I had them do 25 yards to ensure that 20 yards were sprinted at full effort. Lastly, I had them perform some extra sprint work against lighter loads to feel for any potentiation from the heavy loads. These lighter load sprints were performed for 25-45 yards with the intention of staying around 85-90% of unloaded split times.

Video 2. 45 yard sprints with 1080 Sprint.

Looking at the Results

So, what happened? Well… a lot.

The first note worth mentioning is that by Week 4, three out of four of the players were using the 30kg setting, meaning they reached the highest available resistance granted by the 1080 Sprint machine. The other player, Linebacker #2, stayed at a constant load setting from Weeks 1-4 (28kg). The progression is laid out in Figure 7.

Video 3. Sprinting with week 4 optimal load.

The paper by Cross et al. (2016) assumed that the optimal loading range would occur somewhere between 69% and 96% body mass and my data showed that all of my football players fell within this range (71-80%) in Week 1. Again, despite upwards of a 14% increase in load as seen with Running Back #2, the mean velocity stayed between 48% and 52% of V0. This indicates that the players whose loads increased over four weeks could maintain their horizontal velocity in the face of increasing resistances. They were becoming more powerful.

Of course, it’s easy to comprehend that more power was being generated, but I wanted to see how changes might have occurred with maximum velocity (V0), maximum relative horizontal force (F0), and maximum relative horizontal power (P_max). Here are the pre-testing and post-testing numbers.

All of the players improved their relative maximum power after four weeks. Both of the running backs did so through greater proportional rises in V0, while the linebackers were the opposite, seeing rises in power through greater proportional rises in F0.

I should make sure to state that this particular four-week program was not based upon individual deficiencies in force-velocity profiling. All of the athletes performed the same routine, once a week for four weeks. However, since the overall training program included a combination of unloaded sprints, sprints against load of maximum power, and lighter resisted sprints, it’s possible that everyone was able to improve upon their force-velocity deficiencies.

For example, the linebackers may have been deficient in their ability to produce net horizontal force, and based on relative force (N/kg), they both produced far less than the running backs in the pre-testing period. Their improvement in F0 may have stemmed from the heavy resisted runs. The running backs both made larger improvements in V0, which may have been a result of exposure to the light-resisted runs and unloaded runs.

One area that all players showed improvement in was the ability to accelerate at each 5-yard segment over 20 yards against their individual optimum load. During the first two weeks, most of them began decelerating between 15 and 20 yards. But by Weeks 3-4, all of them were able to continue accelerating or maintain acceleration every 5 yards. This was likely an indication of improvement in RF and D_RF.

George Petrakos has referred a concept known as the Maximum Resisted Sled Load (MRSL). The MRSL is the highest load an athlete can use for a 20-meter sprint and show no deceleration at any 5-meter segment. In my case, I was measuring yardage, but from what I was seeing, it is likely that the L_opt will tend to be very close to the load corresponding to 100% MRSL as described by Petrakos. It may be possible to prescribe different training loads based on L_opt in similar ways to what Petrakos describes for loads based on MRSL.

Improvements in Sprint Times?

So, great. Everyone’s maximum horizontal power improved. Cool. But what about their sprint times? Isn’t it all meaningless if sprint times didn’t improve?

Well, let’s look at the pre- and post-testing data as it relates to split times in the 10-, 20-, and 40-yard dash. Unfortunately, one of the players (Linebacker #2) finished his time at the facility before being able to re-test his sprint times. So here is the FAT sprint time data for Linebacker #1, Running Back #1, and Running Back #2.

Did sprint times improve? Yes, they did. Between these three players, a range of 0.10-0.33 seconds of improvement in FAT sprints was found after only four weeks of training. Will my findings be fully consistent with yours? Who knows, but decreases in split times at 10, 20, and 40 yards definitely occurred. It is likely that a combination of factors came together that led to these results.

Here are some considerations based on the program I did:

• Even if only 10-20 yards of distance, performing unloaded sprints at full speed after warming up allowed the players to experience sprinting against their own body mass and preserve coordination.
• Developing the ability to accelerate every 5 yards for 20 yards against horizontal resistances of 77-94% body mass likely improved each player’s RF and D_RF, allowing them to produce more net horizontal force at increasing velocities and accelerating or maintaining speed throughout the entire 40-yard dash.
• Performing the light resisted sprints for upwards of 45 yards also likely played a role in improving upon D_RF for each player. Having the light resistance allowed for very high efforts against relatively long distances with lower risk of CNS fatigue or potential injury.

In terms of really improving sprint performance, it would have been more optimal for me to calculate a force-velocity profile for each player and then prescribe loading parameters from there. However, my goal was to see if four weeks of sprinting against individualized loads of maximum power could improve sprint performance in NFL players and my results showed that it can, indeed.

So, there you have it. More practical evidence that sprinting against the load of maximum power (L_opt) can improve sprint performance and may be best-served as a complement to unloaded sprints and light-loaded sprints (i.e., 10-15% decrement in split time). Consider using maximum power sled sprinting as a—dare I use a pun? —powerful tool in your training tool box.

P.S. Based on my findings, 20-yard split times against L_opt were between 60% and 66% of unloaded 20-yard split times. So, although the 48-52% velocity decrement is reported by Cross et al. (2016), those numbers do not correspond to numbers based on split times. More research needs to be done to determine whether an actual normative range based solely on split times can be recommended.

Special thanks to JB Morin and Matt Cross for helping with the accuracy of the interpretation of their work.

References

1. Cross, M.R., Brughelli, M., Samozino, P., Brown, S.R., Morin, J.B. (2016). Optimal Loading for Maximizing Power during Sled-Resisted Sprinting. Int J Sports Phys & Perf. 1-25.
2. Morin, J.B., & Samozino, P. (2016). Interpreting power-force-velocity profiles for individualized and specific training. Int J Sports Phys & Perf. 11(2): 267-272.
3. Smith, J. (2014). Applied Sprint Training. Amazon.

Since you’re here…
…we have a small favor to ask. More people are reading SimpliFaster than ever, and each week we bring you compelling content from coaches, sport scientists, and physiotherapists who are devoted to building better athletes. Please take a moment to share the articles on social media, engage the authors with questions and comments below, and link to articles when appropriate if you have a blog or participate on forums of related topics. — SF

There are a number of factors within a particular environment that can contribute to the performance outcome of an athlete’s effort. Weather is often an intractable and unpredictable variable in outdoor competitions. The event venue—such as World Championship or Olympic stages, as compared to local or regional events—can greatly affect both the mental and physical performances of the athlete, especially when comparing indoor to outdoor events. Wind resistance has a physical effect on running speed and metabolic cost, as does the altitude of the venue. These factors, as well as timing method, were investigated by Hollings, Hopkins, and Hume (2012) for their effect on elite male track athlete performance.

Timing and Venue

Human error is a major factor in the timing of events, and it led to the evolution of automatic timing systems. Stopwatch-timed sprint events are notoriously biased toward a 0.24-second faster time on average, due to the timer’s anticipation of the athlete’s finish (Hollings et al., 2012). Hand-timing in distance events is associated with delayed times of greater than 0.14s, again from timer bias. The 200-meter, 400-meter, 800-meter, and 1500-meter events are typically run slower indoors than outdoors, likely due to the tight turns of the track, and there being twice as many bends to run.

The standard of competition was found to affect performance based on the event distance, since there are varying outcome goals per respective race (Hollings et al., 2012). Sprint and hurdle events raced their fastest times at the highest stage venues, while middle- and long-distance events raced slower due to the tactical nature of the race on that level of competition. Similar findings occurred at altitude, where the sprinters performed much better due to less air resistance. Distance appeared to be a greater challenge, due to the reduced oxygen content of the air at altitude. The advantage of running sprints at altitude is greatly outweighed by the disadvantage in the distance events.

Weather

Heat exchange between an athlete and their environment is directly impacted by the ambient temperature of the environment, as well as its moisture density, or humidity. Effective evaporation is limited in heavy humid conditions due to increased sweat vaporization, and thus reduces heat loss in the athlete (Hayes, Castle, Ross, & Maxwell, 2014). In dry heat conditions, the evaporative requirement of the athlete cannot be matched by the environment’s evaporation potential. In either case, the athlete is at risk for hyperthermia and significant physiological performance stress.

Ideally, an athlete should train for the environment in which the performance will take place. Share on X

There was a comparison made between hot, humid environments and hot, dry environments for their respective conditional effects on intermittent-sprint exercise performance (Hayes et al., 2014). The conditions were matched for heat stress to create the most controlled and accurate analysis of the two experimental groups, in relation to the temperate environment control group. The proposed hypothesis was that hot, humid conditions would produce greater physiological strain than hot, dry conditions and result in impaired sprint performance.

The results of the study concluded that sprint performance was impaired, but not significantly more so in one condition over the other when heat stress was matched between the two. Ideally, an athlete should train for the environment in which the performance will take place. For example, training in dry heat would be optimal preparation for a championship event in Arizona. However, this is not always possible, so aiming to match the heat stress is a valid way to achieve similar gains in training. Preparing in Georgia heat and humidity for a dry heat Arizona competition may require training to be conducted in the early morning hours, when the humidity and temperatures are lower, to equilibrate the demands of GA and AZ running. As an example, training in 65 degrees and 40% humidity often feels equal to dry 80-degree training.

Another study specifically investigated the effects of dry and humid heat stress on heat loss capacity in different age categories (Larose et al., 2014). As age increases, sweat rate has been found to decrease, making it more difficult for older adults to effectively expend the heat retained during exercise. Heat loss capacity was measured by both direct (evaporative) and indirect (metabolic) calorimetry in 60 males, ages 20-70, in 35˚C and both 20% and 60% relative humidity.

The hot, humid conditions caused an attenuated heat loss capacity in all age categories. The relative core temperature, heart rate response, and perceived thermal discomfort level all increased with age. This corresponded with a decrease in heat-loss capacity in the middle-age and older populations. No age group differences were observed in dehydration status, percent change in body weight, or local sweat rate and blood flow (Larose et al., 2014). Participants aged 40-70 stored 60-85% [in dry heat] and 13-38% [in humid heat] more than the 20-30 year age group.

It’s important to consider the physiological effects of prolonged exercise in heat, especially for the lack of efficient thermal regulation in the older age populations. All in all, these findings are helpful for athletes to know and apply across multiple race performances, in order to gauge true comparisons in performance.

Since you’re here…
…we have a small favor to ask. More people are reading SimpliFaster than ever, and each week we bring you compelling content from coaches, sport scientists, and physiotherapists who are devoted to building better athletes. Please take a moment to share the articles on social media, engage the authors with questions and comments below, and link to articles when appropriate if you have a blog or participate on forums of related topics. — SF

References

1. Hayes, M., Castle, P. C., Ross, E. Z., & Maxwell, N. S. (2014). “The influence of hot humid and hot dry environments on intermittent-sprint exercise performance.” International Journal of Sports Physiology and Performance, 9: 387-396.
2. Hollings, S. C., Hopkins, W. G., & Hume, P. A. (2012). “Environmental and venue-related factors affecting the performance of elite male track athletes.” European Journal of Sport Sciences, 12(3): 201-206.
3. Larose, J., Boulay, P., Wright-Beatty, H. E., Sigal, R. J., Hardcastle, S., Kenny, G. P. (2014). “Age-related differences in heat loss capacity occur under both dry and humid heat stress conditions.” Journal of Applied Physiology, 117(1), 69-79.

Over the years, sports coaching and training has become much more scientific. Today, training decisions and process are expected to at least have some form of evidence base, and the expectation for coaches and support staff (and even athletes) to be scientifically literate has increased. The advent of the internet has also led to an increase in PubMed warriors (myself included), and there is an ever-increasing number of scientific papers out there that can be found and cited to support a person’s position.

Central to most scientific research is the idea of statistical significance. As part of my day job, I run education programs for fitness professionals from personal trainers to those in elite sports. When I’m presenting data, a common question will be, “Is it significant?” The unsaid assumption is that, if it is not statistically significant, the results are worthless.

Researchers determine statistical significance through the use of p-values, which you will have come across if you’ve read scientific papers. Typically, if the p-value is said to be less than or equal to 0.05 (represented as p≤0.05), then the effect is said to be statistically significant. If it’s greater than 0.05 (i.e., p>0.05), then it’s said not to be statistically significant.

The Problems With P-Values

There are a few problems with the p-value method, and these often lead to issues in understanding research that are common with sports science practitioners. This, in turn, can lead to the misinterpretation of research and, therefore, the misuse of research paper findings.

Let me illustrate this with an example. Let’s say I want to test the effects of caffeine on vertical jump height. What I would likely do is gather a group of athletes—let’s say 40—and get them to do two trials: one with caffeine, one without. I collect the data and then analyze it using a t-test, and I come up with the following conclusion:

Subjects jumped significantly higher (p<0.05) in the caffeine trial than the placebo trial.

From this, I can conclude that caffeine improves jump height. But what is the p-value really telling us? Have a think and come up with your own explanation of what the p-value actually is. Seriously, do it—I’m not going anywhere.

Thought about it? Great! I also asked this question on Facebook, and got answers from a number of coaches and support staff—people who I would consider scientifically literate. Far and away, the most common description of the p-value was that it tells us “whether the effect is down to chance or not.” So, in the caffeine example, a p-value of <0.05 tells us that there is a less than 5% chance of these results being down to chance. Why 5%? Well, it’s arbitrary, but it’s a nice round number that has caught on, so it gets used.

Here’s the problem: That explanation is not quite correct.

To explain why, I need to explain what really happens when most people do an experiment. The common method used in research is that of Null Hypothesis Significance Testing (NHST). This means that we have two hypotheses: the null hypothesis and the alternative hypothesis. So, in my caffeine example, my hypotheses are:

1. Caffeine improves vertical jump height (alternative hypothesis).
2. Caffeine does not improve vertical jump height (null hypothesis).

When I’m calculating the p-value, what I’m really doing is deciding whether or not I can reject the null hypothesis. Scientific research is set up to disprove the null hypothesis. If p≤0.05, I can reject it, if its >0.05, I can’t. Rejecting the null hypothesis means that there is a difference between the groups.

What the p-value actually tells us is the probability of getting a result as extreme as this, and the null hypothesis being correct. When p=0.05, this means there is a 5% chance of getting a result as extreme as this and the null hypothesis being correct. If p=0.01, there is a 1% chance of getting a result that extreme and the null hypothesis being correct. Essentially, we’re getting the probability of falsely rejecting the null hypothesis, which is a false positive, known as a Type-I error.

P-Values and the Size of the Effect

So far, perhaps we have been largely arguing about semantics. Let me introduce the next question for you. Returning to my caffeine trial, I add an additional group of athletes. The first group take 3mg/kg or placebo. The second group take 6mg/kg or placebo. Here is the main finding:

Subjects jumped significantly higher in both the 3mg/kg (p=0.04) and 6mg/kg (p=0.004) caffeine trials compared to placebo.

My question to you is this: Is 6mg/kg of caffeine more effective than 3mg/kg of caffeine at improving vertical jump height?

Again, when I asked this on Facebook, most people answered yes. The correct answer is: you can’t tell. This is the main issue with p-values and NHST; they don’t tell you the size (or, more correctly, the magnitude) of the effect. So, while we can be more confident about correctly rejecting the null hypothesis in the 6mg/kg trial, we can’t be more confident that the effect was greater.

The main issue with p-values and NHST is that they don’t tell you the magnitude of the effect. Share on X

To repeat, the p-value tells us nothing about the size of the effect. Something having greater significance does not necessarily have a greater effect. This is important when it comes to translating science into practice. Whilst 6mg/kg caffeine might significantly (p<0.05) improve vertical jump height, if the size of this effect is just 0.1cm, this might not have any real-world effect. For example, if you’re a high jumper, you can only move the bar higher in 1cm increments, so jumping 0.1cm higher has no real-world impact for you.

Similarly, I once read a research paper examining the use of a specific type of training on mood. Mood was determined by a questionnaire, with each person scoring themselves out of 10. The training significantly improved (p<0.05) mood, but the average improvement in the training group was 0.2. Given that the scale used was 1, 2, 3 … 10, an improvement of 0.2 means that you would need five subjects to get a real-world improvement of 1 (i.e., going from 1 to 2, or 9 to 10). So how effective is the training really?

The scientific community is starting to wake up to the issues with p-values and NHST, and I am certainly not the first person to notice it. The American Statistical Association released a statement on this last year. Sports scientist Martin Buchheit, from Paris Saint Germain Football Club, recently authored a great editorial on the subject. In recent years, the godfather of statistical analysis in sports science, Will Hopkins, has proposed the use of Magnitude Based Inferences (MBIs) to help practitioners understand the true size of the effect of an intervention, in order to determine whether it is useful or not. Journals are starting to slowly move away from just the reporting of p-values, requiring effect sizes to also be used.

All of this allows for the better use of science in sport. Right now, my concern is that athletes and coaches only look for statistical significance, and not real-world significance. An effect can be statistically significant due to a large sample size, but have no real-world effect. Conversely, an intervention can have no significant difference in terms of statistics (usually due to a small sample size), but have a large real-world effect. More pertinently, when comparing two different interventions, the difference in p-values between them doesn’t really tell us anything about the magnitude of these effects, which is more important.

Something that has a greater significance does not necessarily have a greater effect. Share on X

Finally, to complicate things further, recent research has illustrated that there is a significant amount of inter-individual variation in response to an intervention, such that even if an intervention has no statistically significant effect for the average between groups, the effect can be huge for individuals within a group. As confusing as this might be, having a working knowledge of what a p-value is, and knowing the limitations of it, are crucial to successfully translate science into practice.

Recommended Reading:

The problem with p values: how significant are they, really?

Since you’re here…
…we have a small favor to ask. More people are reading SimpliFaster than ever, and each week we bring you compelling content from coaches, sport scientists, and physiotherapists who are devoted to building better athletes. Please take a moment to share the articles on social media, engage the authors with questions and comments below, and link to articles when appropriate if you have a blog or participate on forums of related topics. — SF

 

In an earlier blog entry, we discussed practical steps to take to enhance the accuracy and reliability of data collection in strength training, practice, recovery, and competition. The core concept of enhancing data reliability centers on the steps that coaches, trainers, practitioners, and sports scientists will take once the data is collected and organized, and primary analysis begins. With so many tools and methods available to collect data in sports, practice, and training, what can coaches and sports scientists actually do to start assessing and improving performance?

A practical starting point is assessing how data is being collected and organized in research conducted on sports performance. Popular technology such as global positioning systems (GPS), accelerometers, and heart rate monitors (HRM) are increasingly prevalent in research performed on team sports performance. However, the concepts used by researchers with these tools can be broadly applied to physical activities beyond competition and practice alone.

In 2014, Dellaserra et al., published a review of integrated technology (IT) use in team sports that highlighted opportunities, challenges, and future directions for their use in performance analysis [1]. Understanding the foundational methodology of data organization of both objective and subjective metrics is a key first step to effective performance analysis. As they organize all objective and subjective data, each practitioner will ultimately work to identify key performance indicators (KPIs) matching their program’s goals and philosophies. Forming KPIs is an essential portion of the sports science analysis process, and should be done on an individual process. The KPIs of one particular university, team, or facility may be totally different than the KPIs from another, yet each set will optimally serve each institution based upon their needs.

This second installment of the sports performance article series outlines and adapts the main points of the Dellaserra et al. review to provide useful steps as to how data can be organized once you and your staff have started collecting data on performance, training, recovery, or readiness. Consider the following elements once you start data collection: summarization, quantification, comparisons, and bioenergetic demands.

Summarization

After data is collected during activity, practitioners can begin organizing it into categories. This process may also be provided or supplemented by using the software accompanying the specific IT devices used. Data collected manually can be input to existing athlete management systems (AMS) or simple spreadsheets. Summarization can occur each day post-activity as data is uploaded via interfaces provided by each manufacturer of the tech devices used. Practitioners will then be able to produce summaries from the data collected during the activity of that day.

The basic descriptive summaries of totals, minimums and maximums, means, medians, and all other values of parameters measured can be classified by the following categories and subcategories:

Individual Parameters

1. By individual player: for example, player A, player B, etc.

Grouping Parameters

1. By position groups: such as all offensive linemen, freshman midfielders, or transfer point guards.
2. By depth chart standing: such as all first-string players, all second-string players.
3. By depth chart and position: such as all first-string guards, all redshirt pitchers.

Activity Parameters

1. By drill or practice sequence: such as Individual or Grouping parameters during a specific drill. For example: player A during 1-on-1 drills, first-string wide receivers during 7-on-7 drills, or all central defenders during set-piece drills.

Chronological Parameters

1. By day of the week: such as Monday practice, “game day minus 2,” walk-through session, summer conditioning, etc.
2. By week of the season: such as week 1 of pre-season camp, or week 3 of the season versus opponent “X,” or bye week.
3. By portion of the season: such as daily doubles or pre-season, post daily doubles preseason, in-season, post-season conference championship, playoffs, and bowl preparation.
4. By time of year: such as off-season training periods, off-season practice periods, pre-season camp, in-season.

Injury Status

1. By injury designation: such as pre- and post-injury, by rehab process or phase of treatment.

Quantification

Summarization of information can be applied to many parameters reported by IT devices or manual data collection in ways that produce discrete or continuous data. Remember, discrete data is data that can only occupy one distinct and separate value, like the number of pitches thrown (because you can’t throw half a pitch), number of repetitions completed in an exercise (because half reps are not counted in a certain test), or sleep quality rating on a scale (because you only offer a 5-point scale on sleep quality). Continuous data can be measured and take on any value on a scale possessing intervals between whole values, such as a bodyweight of 185.5lbs (because there are weight intervals between 185 and 186 pounds), or running a 40-yard dash in 4.44 seconds (because there are other time intervals between 4.40 and 4.50 seconds) [2,3].

Metric quantification has been part of strength & conditioning programs for decades. Share on X

The data points serve as quantifications of these parameters and are represented by numbers in the form of maximums, minimums, ranges, totals, and averages. As noted above, these quantifications can apply to both activity parameters and chronological parameters dealing with various individuals or groupings. Specific parameters may differ by software or methodology; however, several main themes include:

• Maximums – acceleration rate, speed, heart rate, heart rate recovery, heart rate variability, skin/core temperature, mechanical intensity, perceived exertion, jump height or distance, weight lifted for specific reps, and physiological intensity.
• Minimums – speed, heart rate, skin/core temperature, mechanical intensity, physiological intensity, sprint time, and training intensity.
• Ranges – heart rate, skin/core temperature, mechanical intensity, and physiological intensity.
• Totals – acceleration count, distance covered, distance covered within speed ranges, time spent within speed ranges, time spent within heart rate ranges, caloric expenditure, volume of weight lifted, mechanical load, physiological load, and training load.
• Averages – acceleration, speed, heart rate, heart rate recovery, heart rate variability, skin/core temperature, mechanical intensity, physiological intensity, and training intensity.

Keep in mind that metrics quantification has been part of strength and conditioning programs for decades. The quantifying method answers questions such as: How much weight was lifted? How fast was the agility test performed? How many repetitions were performed? Objectively quantifying workloads is an essential element of measuring progress in physical performance. As introduced in Part 1 of this blog series, there are times when more subjective data is collected. In this case, the same procedures apply to organizing subjective metrics such as surveys and RPE scales.

Comparisons

Quantifications allow the practitioner to create a framework of what occurred during the measured activity. As these figures are summarized, comparisons can be made to assess the importance and implications of the data. Practitioners can use various quantifications of the individual, grouping, activity, and chronological parameters to facilitate these comparisons, which may include:

Intra-Subject Comparisons

1. Using the quantification of individual parameters to compare session-to-session values of maximums, minimums, ranges, totals, and averages of various performance markers for an athlete.
2. Using quantifications of individual parameters of an athlete to compare performance prior to injury, leading up to injury, and during the rehabilitation and return to play protocols for an athlete.
3. Using quantifications of individual parameters to compare various chronological parameters, including values reached during the off-season, pre-season, and various portions of the competitive season.

Inter-Subject Comparisons

1. Using the quantification of individual parameters to compare like values of maximums, minimums, ranges, totals, and averages of various performance markers between two athletes. This may include one starter versus another starter of the same position, or one starter versus a non-starter of the same position. This could also compare athletes of different positions.
2. Using the quantification of grouping parameters to compare like values of maximums, minimums, ranges, totals, and averages of various performance markers between two groups, activities, or chronological parameters.
3. Using quantification of individual or grouping parameters to compare like values of performance markers achieved during training activities, practice activities, and competition.

Bioenergetic Demands

Quantified information of individual parameters of performance markers can allow practitioners to gain insight into the bioenergetic demands of the activities measured. Practitioners with a strong background in bioenergetics and exercise physiology will recognize the main energy system contributions to various types of activities. These may include anaerobic qualities such as alactic power and alactic capacity, or aerobic qualities such as aerobic capacity and aerobic power.

While understanding the various movement qualities and demands of each position, the practitioner may be able to gain insight from data on the distribution of workloads of the various bioenergetic systems. As information is gathered from training, practice, and competition settings, practitioners can compare workload distribution of each setting to determine if training and practice are preparing the athlete for the bioenergetic demands of the actual competition. When assessing the physiological or bioenergetic demands of an activity for an athlete, the practitioners may consider the following elements:

• Number of bouts: The number of repetitions during each training session, drill, or game taken by the athlete. This information is used to help establish volumes of training within specific intensity parameters with such movements as accelerations, speed, changes of direction, and other movement classifications that can be associated with specific bioenergetic demands.
• Duration of bouts: The length of time each repetition lasts during each training session, drill, or game. The information is used to further help establish volumes of training within specific intensity parameters. As more information is gathered from the number and duration of each bout, a clearer picture of the bioenergetic demands of the activity can be formed.
• Timing or frequency of bouts: The time interval between repetitions during each training session, drill, or game. Information on the timing and frequency of bouts during these activities can help further describe the intensity of the activity when other mechanical or physiological parameters are known. This is done by establishing the length of rest intervals between bouts, given the demands of each bout. The demands are further described by parameters of the activity that include speed, acceleration, or heart rate.

Criterion Performance

As bioenergetic concepts are used when monitoring changes and types of performance, practitioners must have data to serve as a reference point to make necessary comparisons. The establishment and use of criterion performances for individual athletes in each activity may assist the practitioner in this process. These criterion performances may deal with both generalized and specific data, such as maximums, minimums, ranges, and totals of physical performance markers.

Criterion performances can be established from daily best marks to monitor acute fatigue, while using all-time best marks might allow insight into states of chronic fatigue or overall physical preparedness levels. These criterion values may also be of importance during the rehabilitation process, for practitioners to use performance markers prior to injury to compare with the athlete’s current progress. Factors being assessed during training, practice, or competition will fall into one of the following categories:

Training

1. Warm-up periods and all related rehabilitative or restorative protocols (stretching, range of motion, submaximal corrective exercises).
2. Speed training and all variations surrounding sprinting, mechanics of sprinting, and drills related to the development of speed and agility during locomotion.
3. Plyometric training and all variations, including jumps in all planes of motion, the shock training method, medicine ball throwing variations, and any other exercise focusing on optimizing the amortization phase.
4. Resistance training and all variations, including strength training, power development, hypertrophy, and specialized endurance using free weights, machines, bands, chains, and any other added outside force on the body.
5. Conditioning and all variations, including all activities focusing on the development of the cardiac system and aerobic energy system.

Practice

1. All practice warm-up periods.
2. Specialized work periods such as kickoff in football, free throws in basketball, set pieces in soccer, etc.
3. Individual position-related drills, such as only running backs practicing hand-offs, or only goalies defending shots, or only point guards practicing ball handling.
4. Group drills, including 7-on-7 offense versus defense, 3-on-3 drills in basketball, small-sided games in soccer.
5. One-on-one competition periods, including centers versus centers in basketball, wide receivers versus corners in football, or counter attack defending in soccer.
6. Team activities featuring live action in various situations such as two-minute hurry-up offense in football, final-play simulation in basketball, or sudden death in hockey.
7. Non-situation-specific drills, including form tackling, free throws, rondo-passing, and other general skill development sessions.

Competition

1. Pregame warm-up periods.
2. Special-activity periods and specific designations such as kickoff or punt return, free throws or inbounds, set pieces, or power plays.
3. Offensive or defensive drives, possessions, or efforts.
4. Chronological considerations such as quarter or half, or specific time range of a game such as the last six minutes of competition in a half or game.

Practitioners must find the exact physical and physiological parameters to monitor for each individual during periods outlined above, before electing to establish a criterion performance for these activities. Practitioners should use applied knowledge of factors affecting performance of the activity to determine when an individual athlete has achieved a personal best in a meaningful activity. The establishment of these criterion performances also allows the practitioner to lay the framework for future analysis of relationships between physical and physiological markers with objective performance outcomes.

Pay considerable attention to the relevant performance markers within a specific training program. Share on X

Establishing individual criterion performances also allows practitioners to compare athletes within the same group during the same activities. These comparisons might lend insight into strengths and weaknesses of each athlete as they compare to others in the same position group. This information is useful when designing training programs with the understanding of the underlying physical and physiological mechanisms that contribute to the activity.

Considerable attention should be paid to the relevant markers of performance within a specific training program. Establishing KPIs also allows practitioners to focus on more-efficient data collection, rather than opening the floodgates to never-ending data streams. There is absolutely a time and a place for expanding performance analysis to include more data, but strengthening the fundamentals of your data collection process must take precedent. The creation of program-specific KPIs lies at the heart of sports science, but this presents a question at the heart of the matter:

What is an operational definition of sports science that captures its true effect on performance?

This definition fits well, and gives consideration to the measurement factors outlined above:

Sports Science: The discovery, interpretation, and communication of meaningful data affecting athletic performance.

Discovery: through measurement, assessment, and monitoring of athletes.

Interpretation: by field experts with content knowledge of the relevant subdomains of the sport, training, rehabilitation, nutrition, and psychology.

Meaningful: content knowledge supplied by field experts influencing the practice of “modeling” within the performance process for athletes.

While endless measurements, assessments, and monitoring of athletic performance can take place, much of the data is useless without context. This highlights the need for educated practitioners with a diverse knowledge base to help “connect the dots” of data across fields. As the data stream comes in from all areas, defining a method for analysis becomes paramount. The simple procedure of the Performance Analysis Progression will meet the needs of practitioners looking to establish a foundation for their analysis program:

Performance Analysis Progression

I developed this procedure during my time as the sports science coordinator at a Division I university. Many coaches, scientists, and researchers have their own customized approaches to looking at their data, and I developed it out of the need to look at multiple streams of data concurrently, within context.

There is a simplistic progression in the order of asking questions about the data you collect, which also highlights the importance of collecting, organizing, managing, and visualizing your data via an AMS system. Consider the following progression when looking at the data from your athletes and, based on the previous descriptions of each step, define what it is you are looking for from your data:

 

 

 

 

As multiple data streams are combined to analyze performance and a system is put in place for making inquiries, managing data streams by connecting data sources and endpoints will enable practitioners to streamline this process. This element highlights the importance of an AMS that allows for customization to meet the needs of the users and the organization.

In my own experience, which is shared by countless practitioners, I grew tired of spending more time in front of a computer than out with athletes. Many strength coaches can likely relate to the reality of juggling many responsibilities at once beyond our primary duties. This includes: collecting endless streams of questionnaires at the last minute, sifting through piles of workout sheets to find one or two particular numbers, and connecting Google documents and downloading data. This is in addition to attempting to manage countless other streams that may or may not offer clarity. Not only did this process drive the need to create program-specific KPIs, but it also highlighted the need to streamline and simplify the process to meet foundational needs of sports science.

Out of this chaotic experience, I designed my own AMS, Voyager. I was familiar with other methods of managing data streams at a very high level, as well as other athlete management systems serving a high level of functionality, but I needed a simplified system for foundational sports science methodology. As I have mentioned before, the “bells and whistles” are necessary at a certain point, but there is great power in establishing a firm foundation of connectivity between athletes, strength coaches, sports medicine, sports nutrition, sports psychology, team managers, and sports scientists alike.

Analyzing the effectiveness of a training program, treatment modality, recovery intervention, or nutritional plan is not an attack on the practitioner implementing the program, but rather, it is part of the pursuit of performance that is a necessary trait of an elite coach.

As we highlight the need for connectedness and a streamlined process made possible by athlete management systems, the practicality of the Performance Analysis Progression is revealed. Think of the take-home concepts from the Performance Analysis Progression: What are we looking at? Why are we looking at it? What is it saying? What should we do? Did what we do actually work? The last question is troubling for some, because it requires that we—as strength and conditioning coaches, sports medicine practitioners, and sports scientists—assess the methodology we implement on athletes.

Nearly every good coach I have worked or spoken with over the last 15 years assesses their own performance and will not hesitate to improve their own training process. The best coaches I have worked or spoken with are tireless in their assessment of the performance of their athletes, and embrace the need to continue to refine their abilities as a professional. Therefore, sports science, as defined above, is part of a natural process that allows for the advancement of athletic performance and coaching ability. Analyzing the effectiveness of a training program, treatment modality, recovery intervention, or nutritional plan is not an attack on the practitioner implementing the program, but rather, it is part of the pursuit of performance that is a necessary trait of an elite coach.

During this current offseason period, I employed the Performance Analysis Progression while utilizing Voyager to organize in-house KPIs, Omegawave data, and training and wellness data, in addition to custom sports medicine assessments. I should also mention we had the opportunity to utilize a Freelap timing system and received exceptional, timely support from Christopher Glaeser on how to maximize use of the system to meet our specific needs. This process helped connect me with a strength and conditioning staff, a sports medicine staff, remote coaches, and a group of NFL-draft hopefuls and NFL off-season athletes.

While employing a new system and methodology creates many challenges, the connectivity and streamlining of our process based on the methodology outlined in this article allowed our team of professionals to further examine our own process. Within two weeks, we were able to identify needs for athlete education, project future adjustments in training load, and visualize progress for our athletes and their agents, coaches, and staff. We placed a particular emphasis on maximizing our use of the Omegawave Coach system, a system I have used as both an athlete and a coach. There are many ways to apply information from Omegawave screenings, wellness questionnaires, and athlete interaction to inform the training process, which will be the focus of a final article in this sports science methodology series.

Since you’re here…
…we have a small favor to ask. More people are reading SimpliFaster than ever, and each week we bring you compelling content from coaches, sport scientists, and physiotherapists who are devoted to building better athletes. Please take a moment to share the articles on social media, engage the authors with questions and comments below, and link to articles when appropriate if you have a blog or participate on forums of related topics. — SF

 

References

1. Dellaserra, C.L., Yong, G., & Ransdell, L. (2014). “Use of integrated technology in team sports: a review of opportunities, challenges, and future directions for athletes.” Journal of Strength & Conditioning Research (Lippincott Williams & Wilkins), 28(2): 556-573. doi: 10.1519/JSC.0b013e3182a952fb.
2. “Discrete and continuous random variables.” (2017). Khan Academy. Retrieved 20 March 2017.
3. “CaSQ 3B – Numerical Data: What’s The Difference Between Discrete and Continuous?” (2017). Retrieved 20 March 2017.

Introduction, Central and Chronic Fatigue

Sentient organisms are capable of perceiving the status of their moment-to-moment existence. Humans have the ability to compare and contrast the present moment with past experiences, and to rate the quality of such experiences. Many people exist in a state referred to as “chronic fatigue syndrome,” where they are inescapably held in a condition of feeling reduced energy to perform tasks and receive no joy during consciousness.

Anyone who has exercised to the point where they are reaching towards the upper limits of physiology has perceived acute fatigue, which decreases the ability to maintain a specific intensity level of output. According to Taylor et al. (2016), fatigue is a biological warning that signals an organism to reduce physiological output and move towards a resting state. Fatigue can result in performing a task slower or with less coordination, or in the complete inability to perform a task. Fatigue is objectively known to be involved with the performance of a task when there is an increase in EMG readings associated with that task.

Subjectively, fatigue can be said to be present when the participant perceives muscle pain, overall discomfort, or increased effort to perform a task (Taylor et al., 2016). Taylor et al. (2006) stated that factors leading to reduced performance resulting from fatigue are present at every level of the brain-muscle pathway. Meeusen and De Meirleir (1995) reported that exercise clearly alters neurotransmission, and does so by changing the concentration of different neurotransmitters. Such a change in neurotransmission has a direct impact on fatigue.

Chaudhuri and Behan (2004) stated that mechanical work output is a dependent variable affected by many factors. Internal (limbic system) and external (incentives) sources of motivation; feedback from motor, sensory, and cognitive systems; and environmental factors (external…temperature, internal…homeostatic state), are the primary factors that influence work output. The ability to execute and maintain voluntary activity depends on the smooth flow of afferent, interneuron processing, and efferent nervous activity in the primary sensory and motor systems. Any subsystem involved with the relay of information can contribute to fatigue.

Individuals with normal levels of internal and external motivation, and proper sensory and motor functioning, may still have reductions in work output due to limitations such as endocrine abnormality or autonomic dysfunction. Those who display abnormal levels of exertional fatigue, muscular fatigue, and exercise intolerance are categorized as having chronic fatigue syndrome, and are most likely displaying symptoms of neurological disease that is the result of mutation within the mitochondrial DNA. Central fatigue and chronic fatigue syndromes share many common threads, and are often grouped together within the literature (Chaudhuri and Behan (2004). Chaudhuri and Behan (1998) reported that approximately 80% of patients with chronic fatigue had a previous infection that likely led to presentation. These individuals typically have very low levels of motivation, experience anhedonia, struggle with sleep (apnea and hypoxia may be factors), and suffer from depressive symptoms.

Central and chronic fatigue syndromes share many common threads, and are often grouped together. Share on X

According to Chaudhuri and Behan (2000), central fatigue syndrome presentation is the result of hypothalamic, pituitary, and diencephalon abnormalities. Barron, Noakes, Levy, and Smith (1985) reported that development of sudden and profound central fatigue in athletes due to overtraining involves hypothalamic and neuroendocrine factors. In hypothalamic-pituitary driven cases of central fatigue, changes in body weight and sleep pattern are typically present. Often, centralized fatigue syndromes are the result of diseases that affect the basal ganglia and connected circuitry of the amygdala, thalamus, and frontal cortex. Typically, the connection between the prefrontal cortex and the thalamus is disturbed in these conditions.

Bruno, Crenidge, and Fick (1998) reported that in post-viral central fatigue circumstances, damage will take place in dopaminergic pathways, the reticular activating system, the midbrain, the brainstem, lenticular nuclei, the basal ganglia, thalamus, hypothalamus, and cortical motor areas. Reductions in leptins, substance P, and prostaglandins are associated with hypothalamic-pituitary central fatigue. Fatigue levels can also be impacted by circulating proinflammatory cytokines. These cytokines can be activated when there is a decrease in corticotropin-releasing factor and decreased circulating cortisol.

According to Steinman (1993), acute stress seems to be helpful, based on the fact that there will a spike in cortcotropin-releasing factor, which has an antagonistic effect on the T-helper-1 cell response (a proinflammatory cytokine); however, prolonged chronic stress seems to downregulate this system. This system is downregulated in patients with chronic fatigue syndrome (Scott and Dinan, 1999), post-traumatic stress disorder (Yehuda, 2002), fibromyalgia (McEwan, 1998), and postpolyomyelitis (Bruno, Sapolsky, Zimmerman, 1995). According to Yehuda et al. (2002), low cortisol concentrations following significant psychological or physical trauma were predictive of post-traumatic stress disorder. Low cortisol levels ultimately could impact the glucocorticoid receptors within the hypothalamic-pituitary network by increasing binding sensitivity. Such a change in receptor sensitivity could heighten a state of constant vigilance and feed into a constant stress response.

According to Buckwald, Herreld, and Ashton (2001), the concordance rate among monozygotic twins for the development of centralized fatigue syndrome symptoms was approximately 50%. This suggests that a genetic element, along with environmental factors, is at play in development of this condition. Therefore, certain individuals would be more at risk in terms of moving into a chronically fatigued state due to stress and other environmental influences (e.g., infection).

In susceptible individuals, environmental stressors will cause changes in the hypothalamic-pituitary-adrenal axis and the norepinephrine system. Shannon, Flattem, Jordan et al. (2000) reported that depleted norepinephrine levels, or decreased sensitivity of the receptor for norepinephrine, results in fatigue and depression in animals. Chaudhuri and Behan (2004) stated that organisms have opposing directional responses regarding development of fatigue with acute versus chronic stress experience, where chronic stress moves animals towards chronic fatigue syndromes.

Changes in synaptic receptor sensitivities to corticotropin-releasing factor, serotonin, and norepinephrine establish the nature and severity of the fatigue experience. In humans, prolactin is also secreted under stressful circumstances from the anterior hypothalamus. Dopamine is known to have an antagonistic effect on prolactin secretion. This has led some to believe that dopamine may have a role in combatting elements of the chronic fatigue response system (Chaudhuri and Behan, 2004).

Central Fatigue from an Acute Work Perspective, Examining Neurotransmitters

Newsholme et al. (1987) were the first to report on the notion of central fatigue within acute mechanical work circumstances. They stated that central fatigue was a serotonin-mediated phenomenon, where rising concentrations of serotonin led to an increased perception of lethargy, sleepiness, and reduced motivation. The subsequent investigation has led to mixed results in validating the serotonin-centric hypothesis of central fatigue. However, the consensus scientific perspective points to neurotransmitter concentrations and specific receptor binding activity in specific parts of the brain as being the driver of demonstrable organism fatigue resulting from central pathways. The following section will analyze the findings within the literature on the impact of serotonin, norepinephrine, and dopamine on central fatigue in acute work output.

Neurotransmitters are the chemical messengers that relay information from one neuron to the next within the central nervous system. In regards to acute central fatigue models, monoamine neurotransmitters, which include serotonin, norepinephrine, and dopamine, are believed to be the key players. According to the meta-analysis done by Taylor et al. (2016), the result of central fatigue is either decreased voluntary muscle force or maintenance of the same muscle force via a compensatory strategy somewhere in the neural system.

The primary factor leading to central fatigue is an exercise-induced alteration in monoamine neurotransmitter concentration in specific parts of the brain. The most difficult factor in determining the exact role of the monoamine neurotransmitters on fatigue is that each monoamine causes different responses depending on which region of the brain the binding to receptors is taking place in. To provide clarity on this topic, it becomes prudent to analyze each neurotransmitter and determine what the critical regions of the brain are for receptor binding on fatigue.

Serotonin

Perhaps the two most threatening environmental factors on survival of an organism are temperature and pH. Humans belong to the category of animals known as homeotherms, which have to maintain a relatively constant body temperature, often via internal heat production. Regarding monoamine neurotransmitters and internal heat production, Soares, Coimbra, and Marubayashi (2007) found that increased concentration of serotonin in the preoptic area is associated with greater heat production during exercise. Newsholme et al. (1987) first reported that central fatigue was a serotonin-based phenomenon. Newsholme, Blomstrand, and Ekblom (1992) claimed that the mechanism of central fatigue was elevated tryptophan levels that led to increased serotonin concentrations, and that serotonin precipitated feelings of lethargy and reduction of motivation.

Soares, Lima and Coimbra (2003), and Soares, Lima, and Coimbra (2004) found that increased hypothalamic tryptophan levels precipitated fatigue and were related to a rise in core temperature marked by increased internal heat production and decreased ability to dissipate heat to the environment. Gisolfi and Moura (2000) and Zhang et al. (1997) found that dissipation of heat from the body is more important for temperature regulation compared to heat production during exercise conditions. Nielsen et al. (1997), and Walters et al. (2000) reported that CNS drive towards exercise work output was reduced by elevated core temperature, and that hyperthermia led to subjective discomfort and lethargy. This decrease in CNS outflow and motivation was believed to be a safeguard against allowing dangerously high brain temperatures to occur. Rodrigues et al. (2003) showed that heat storage rates in rats was the main limiting factor in running performance with thermo-neutral and hot environments.

Coimbra and Migliarini (1986), Ferreira, Marubayashi, and Coimbra (1999), Lin et al. (1998), and Santos, Leite, and Coimbra (1991) all determined that the preoptic area and the anterior hypothalamus are the two parts of the brain most responsible for thermoregulation. These parts of the brain are also critical for evaluating and regulating external thermal inputs with metabolically produced heat. Lin et al. (1998) found that injecting serotonin into the preoptic area and hypothalamus of rats increased core body temperature. Soares, Coimbra, and Marubayashi (2007) reported that rats injected with tryptophan into the right cerebral ventricle showed decreased running performance along with increased core temperature and increased concentrations of serotonin in the preoptic area and hypothalamus, compared to rats injected with saline (control condition).

Furthermore, Soares, Coimbra, and Marubayashi (2007) showed that serotonin concentrations were also increased in the hippocampus of rats injected with tryptophan, compared to control. Running time to fatigue was directly correlated with serotonin concentrations in the hippocampus, through a mechanism that seemed to have nothing to do with hyperthermia. Overall, a fatiguing mechanistic cascade appears to exist between tryptophan levels in the brain, which act as a precursor to serotonin, which binds to the preoptic area and the hypothalamus for a heat production element inducing fatigue, as well as a separate serotonin effect on the hippocampus, which has some other fatigue-inducing response. Serotonin concentrations in these areas appear to have a linear relationship with the onset of fatigue.

Serotonin concentrations in these areas appear to have a linear relationship with fatigue onset. Share on X

Regarding the mechanism by which serotonin concentrations in the hippocampus impact fatigue, conclusive evidence still remains elusive. Meeusen et al. (1996), Takahashi et al. (2000), and Wilson and Marsden (1996) found a relationship between serotonin levels in the hippocampus and locomotion. Serotonergic neurons that descend from the hindbrain to the spinal cord appear to be involved in central pattern generator (CPG) neural activity controlling locomotion. Without serotonin being present in these neurons, locomotion capabilities are lost.

Soares, Coimbra, and Marubayashi (2007) found that rising concentrations of serotonin in hippocampal neurons lead to a linear increase of fatigue in running rats, and reported that mechanisms need further elaboration. Soares, Coimbra, and Marubayashi (2007) reported that their findings call into question which neurons associated with the serotonergic system are responsible for fatigue. Perhaps there is interplay between multiple systems; perhaps only one set is truly responsible. Consideration of the fact that other neurotransmitters in other parts of the brain may be modifying these effects as well must be considered.

Sharples, Koblinger, Humphreys, and Whelan (2014) reported that monoamines promote locomotion and influence the rhythmicity of locomotion. This influence occurs via binding to the corticospinal tract as well as hind brain regions, and motoneurons. Cotel, Exley, Cragg, and Perrier (2013) found that serotonin contributes to fatigue primarily through binding to motoneurons. According to Johnson et al. (2004), and Perrier, Rasmussen, Christensen, and Petersen (2013), synapses from descending tracts of serotonergic neurons are adjacent to the dendrites and cell bodies of motoneurons. Serotonin binds to the 5HT2 and 5HT1A receptors in the motoneuron system. 5HT2 receptors are excitatory and appear to be the receptor that allows serotonin to be involved with promoting locomotion. When serotonin levels reach very high levels, a spillover effect will be seen, and serotonin will begin binding to 5HT1A receptors (which are known to be inhibitory) and will prevent motoneuron firing.

When examining the effects of serotonin on fatigue from the perspective of the motoneuron, it appears as though there are two distinct ways in which this occurs. Fornal, Martin-Cora, and Jacobs (2006) showed that, in cats, there is an eventual decrease in concentrations of serotonin with prolonged exercise (removal of the excitatory stimulus at the dendritic binding site). Wei, Glaser, and Deng (2014), through indirect measures with humans, found that serotonin release increases in concentration as the force of muscular contraction increases in exercise (addition of the inhibitory axonal binding site). No studies on humans have directly measured serotonin concentrations in the brain; however, 5HT1A receptors are known to exist in humans, and D’Amico et al. (2015) showed that motoneuron excitability in humans can be reduced via 5HT1A agonist drugs administration.

Dopamine

The original hypothesis on central fatigue related primarily to the effects exerted by serotonin on the system; however, researchers also began to understand that other monoamines were powerful players in regards to fatigue. In 1972, Borg et. al, showed that administration of amphetamines improved performance during exercise. Bailey et al. (1993) demonstrated the importance of increased concentrations and binding of dopamine during exercise. Fatigue in rats was correlated with increased serotonin and reduced dopamine in the brain stem and midbrain. Davis and Bailey (1997) showed that the interaction between serotonin and dopamine influenced central nervous system fatigue. A low ratio of serotonin to dopamine favors improved performance and a high ratio decreases motivation and promotes lethargy, resulting in decreased performance (Davis & Bailey, 1997).

Researchers began to understand that other monoamines were powerful players in regards to fatigue. Share on X

Early studies where researchers administered amphetamines showed significant increases in performance in both animals (Gerald, 1978; Heyes et al., 1985) and humans (Wyndham et al., 1971; Borg, 1972). Watson et al. (2005) investigated the effects of bupropion, a dual dopamine and norepinephrine reuptake inhibitor, during cycling at 18 and 30.1 degrees Celsius. At 18 degrees Celsius, no difference was found between the placebo and bupropion trial. When subjects cycled at 30.1 degrees Celsius, the bupropion group performed 9% faster. Roelands, Hasegawa, Watson et al. (2008) applied the same cycling and temperature protocol and administered methylphenidate to our subjects. At 18 degrees Celsius, no difference in performance was observed between conditions. Methylphenidate administration improved cycling time trial performance in 30 degrees Celsius by 16% compared to placebo. The subjects receiving methylphenidate were able to reach significantly higher core temperatures compared to controls (40.1 vs 39.1 respectively).

Despite reaching significantly higher core temperatures, the experimental subjects reported the same subjective perception of thermal sensation and rating of perceived exertion (RPE) as control subjects. The researchers concluded that the increase in dopamine concentration has an effect on the internal safety switch of the brain regarding thermoregulation. The increased dopamine led to a state where the subjects ignored the potentially harmful effects of increased temperature as well as enhanced power production.

Foley and Fleshner (2008) stated that the reason that you’ll see improved performance in hot environments versus no change in ambient temperatures with dopamine administration is that enhanced dopamine levels will provide an increase in psychological motivation to work harder in a more stressful environment. Increased dopamine levels will offset the negative effects on motivation that are brought on by the stress of heat exposure (Del Arco & Mora, 2009). The mechanism of the ergogenic effect of increased dopamine concentrations is believed to be the result of stimulation of the ventral tegmental area. According to Burgess et al. (1991), rats that received stimulation to the ventral tegmental area demonstrated increased motivation to run on a treadmill compared to rats receiving an electric shock.

Norepinephrine

Roelands et al. (2013) reported that different neurotransmitter concentrations in the brain can lead to different pacing strategies during cycling time trial performance. Subjects with higher concentrations of dopamine in the brain demonstrated higher power output throughout time trial performance compared to control. Conversely, increased serotonin and norepinephrine led to decreased power output during time trial performance. The researchers stated that with increased serotonin, subjects were unable to end the time trial with an extra surge of power output (a kick). This led the researchers to speculate that serotonin may cut off access to some type of reserve capacity of substrate or perhaps decreases the motivation to increase power output (Roelands et al., 2013).

Serotonin, dopamine, and norepinephrine-dominated neuronal tracts innervate different areas of the hypothalamus, including the preoptic and anterior hypothalamus, which are considered the most critical regions of the brain for thermoregulation monitoring and control responses. Quan et al. (1991, 1992) have shown administering norepinephrine into the preoptic area of conscious guinea pigs decreased core temperature via reduced metabolic rate. Roelands et al. (2008) showed that subjects cycled 20% longer after given reboxitane, a norepinephrine reuptake inhibitor, to complete the same amount of work in comparison with the placebo trial. Subjects reported feeling colder before and during exercise under the reboxitane condition, and core temperature tended to be lower during exercise.

When studying the effects of norepinephrine on fatigue, part of the difficulty in the process relates to the fact that administration of drugs, such as reboxitane, exerts simultaneous central (brain) and peripheral (heart and vasculature) effects. Such combined effects can lead to confounds in elucidating the exact central role that norepinephrine plays (increased heart rate could expedite the experience of fatigue). Klass, Roelends, Levenez et al. (2012), Piacentini, Meeusen, Buyse et al. (2002), and Roelends, Goekint, Heyman et al. (2008) have all reported that norepinephrine reuptake inhibitor drugs have shown no effect or negative effect on reducing fatigue during exercise conditions with humans.

The negative effect on performance associated with pharmacologically increased norepinephrine levels is unexpected because of the positive associations between norepinephrine and levels of arousal and sensations of reward (Montgomery, 1997; Meeusen et al., 2006). Actions of norepinephrine on neurons alters serotonergic activity via binding to excitatory a1-adrenoceptors. Activation of the a1-adrenoceptors in the locus coeruleus causes action potentials in the serotonergic neurons in the brain stem and dorsal raphe (Szabo & Blier, 2001). Based on these interactions, the negative effects of norepinephrine on fatigue may be modulated through the serotonergic system of the brain.

Conclusions

Fatigue is a perceived experience that is unpleasant and is associated with decreased performance in physical tasks. Fatigue is induced by situations that are associated with a heightened stress experience. Elevated stress experiences that are short term stimulate organisms and have the ability to improve underlying physiology, and resiliency to future stressors. Stress experiences that are prolonged have deleterious effects on physiological systems. When animals exist in ceaseless stressful environments, they have the potential to develop chronic fatigue syndrome that has a stereotypical subjective component associated with lethargy and the inability to experience joy, as well as a chemical profile objectively linked to altered glucocorticoid levels and receptor abnormalities.

Exercise presents a specific environmental condition wherein the participant moves into an acutely fatigued state that results in the inability to sustain an absolute level of mechanical work output. Chronic and acute physiological state differences present themselves in the examination of many different systems, and are often extremely divergent from one another in terms of desirability on the impact of the overall status of an organism. Despite such differences, some of the mechanisms that lead to an overall state, such as fatigue, whether it be chronic or acute in nature, may find a common denominator.

From a reductionist perspective, central fatigue, whether chronic or acute, appears to involve specific neuronal circuitry, which is impacted by the concentration of specific neurotransmitters in the synapse as well as the binding sensitivity with receptors. While it is extremely early in the examination of the impact of neurotransmitters on fatigue, there appears to be a relationship with monoamine concentrations and perception of fatigue. Maintaining a situation where dopamine concentrations are protected and do not drop to diminished levels appears to be an effective strategy for preventing the movement towards a fatigued state.

Short-term stimulatory activity has the potential to increase dopamine levels. Dopamine appears to have a neuroprotective role against creating an unfavorable glucocorticoid level and aberrant receptor activity situation. Furthermore, dopamine acts in a rewarding manner, and reinforces the behavior that resulted in the initial secretion, thus making it likely that the behavior will be executed again. While this relationship of fatigue resistance and dopamine concentrations appears to be emerging based on the current body of literature, patience and skepticism should guide the overall thought-process regarding causative roles. Behavioral outcomes from neuronal activity driven through neurotransmitter communication systems are modulated by the interaction of many neurotransmitters working together in a concert-like fashion.

We are in the age of the brain in the body of knowledge of exercise physiology. However, this age is in its infancy, and our understanding is both superficial and lacking an overall integrated framework to guide our perspective on individual study findings. Future findings will be transformative in the way that we view chronic and acute states of fatigue, and new methods for manipulating the system will likely present themselves that lead to breakthroughs in physiological output.

Since you’re here…
…we have a small favor to ask. More people are reading SimpliFaster than ever, and each week we bring you compelling content from coaches, sport scientists, and physiotherapists who are devoted to building better athletes. Please take a moment to share the articles on social media, engage the authors with questions and comments below, and link to articles when appropriate if you have a blog or participate on forums of related topics. — SF

References

1. Barron JL, Noakes TD, Levy W, Smith C. Hypothalamic dysfunction in overtrained athletes. J Clin Endocrinol Metab 1985; 60: 803–06.
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