An 8-factor model for evaluating crew race performance

ISSN 1750-9823 (print) International Journal of Sports Science and Engineering Vol. 02 (2008) No. 03, pp. 169-184

Print copy: An 8-factor model for evaluating crew race performance

1 Jeffrey L. Cornett: Director of Institutional Research, Valencia Community College. Department of Industrial Engineering and Management Systems, University of Central Florida. Former coxswain for Cornell University, Potomac Boat Club, and the 1971 US Pan-American Team.
2 Pamela McCauley Bush: Associate Professor, Department of Industrial Engineering and Management Systems, University of Central Florida.
3 Nancy H. Cummings: Assistant Professor, Departments of Physical Education and Athletic Training Education, Florida Southern College.

Abstract. There are no models in the current literature that offer a unified theory of all of the factors that define crew race strategy and race day performance. This paper proposes an 8-factor model of crew race performance and summarizes the related literature. The model suggests those factors that should be used to analyze and evaluate the performance of crews in actual races.
Keywords: crew race performance, race plan, rowing strategy, coxswain role, swing effect

1. Toward a Generalized Model of Crew Race Performance

Various researchers (including Zatsiorsky and Yakunin, 1991; Soper and Hume, 2004; and Atkinson, 2001) have developed biomechanical models to mathematically forecast boat speed as a function of the physics of racing cadence, force vectors, shifts in mass and momentum, and hydrodynamic resistance. Such models are useful to study the theoretical efficiency of energy usage, but ignore the human factors of the race competitors – including skill, level of exhaustion, and psychological motivations.

Computerized biomechanical models are deterministic in that they do not consider the uncertainties of race scenarios, intended strategies, the qualitative aspects of race day performance, and the situational aspects of how crews respond to their race positioning.

To evaluate how crews actually perform on race day, an 8-factor overall model of crew race performance is proposed (Figure 1). Each of the eight model components is further classified into four macro-categories:
• Base Capability defines the raw talent of a crew and their capabilities in using their equipment. It includes the two factors for Human Talent (H) and Biomechanics (B).
• Race Scenario defines the circumstances a crew faces on race day. It includes the two factors for the crew’s Physiology (P) and the Weather and Environment (W) on this particular day.
• Performance Execution is how well a crew actually performs relative to its base capability and the race scenario. It includes factors for the Quality of Execution (Q) and the effects of Race Psychology (R).
• Decisions made before and during a race also affect the race outcome. These include the coach’s pre-race Strategy and Race Plan (S) and the coxswain’s actual within-race application of Tactics and Contingencies (T).

Putting this all together, the performance of a crew in any given race is a function of the base capability of the crew when faced with a particular race scenario, combined with their performance execution of the decisions made before and during the race. This is a complete conceptual model of a crew’s performance. Each of these factors interacts with each other on race day in a complex way. Due to uncertainties, the results of a future race cannot be predicted, yet the results of past races can be studied and evaluated. The number of splits or race observations per crew affects how precisely races can be studied and analyzed. Collegiate competitions are typically reported with only one overall race time observation per crew. World championships report four quarterly splits. Other data may be available from audio or video records that provide more detailed data to interpret.

2. Base Capability:

Human Talent (H) The human talent of a crew is a function of anthropometrics, age, gender, health, talent and athletic experience. This base capability defines how fast a crew should be able to perform if well trained and conditioned to race. Talent and experience also define the reliability of how well the crew should perform on a consistent basis.

International rowing competitions are classified (US Rowing Referee Committee, 2008) according to boat type (number of rowers in the shell), gender, lightweight versus heavyweight (referred to as “openweight” for women’s competition), age, and experience level. The effect of size and gender on rowing performance is easily seen in championship race performance times.

Valery Kleshnev (Kleshnev, 2006) studied the results of World and Olympic championships from 1993 to 2004 according to the various boat classes. Kleshnev filtered out the best and worst times because they were presumed to have been significantly affected by weather conditions. Comparing the percentage difference in the average winning times, the expected variations by gender (M vs. W) and by weight class (L for Lightweight) can be calculated. For the 5 classes of Olympic events common to both genders, the average winning time for women averages 10.2% greater than for men. For the 3 classes of lightweight events, their winning times average 2.6% greater than their heavyweight (or openweight) counterparts.

Percent body fat is another differentiator between men versus women. Volker Nolte (Nolte, 2005) offers a guideline for elite competitors that men’s body fat should not exceed 8 percent whereas women’s should not exceed 14 percent. The combination of height, weight and body fat percentage substantially define body cell mass (muscles, brain, and inner organs) – about 52 percent for elite male heavyweights. Of this, 85 percent is muscle mass, and 75 percent of this muscle mass is used in rowing. Because body cell mass is determined by body growth and training, younger competitors are at a disadvantage – until about age 19.

Performance differences by gender and size has also been studied through ergometer experiments (Yoshiga and Higuchi, 2003). Using multiple regression, rowing performance is shown to correlate with height, body mass, fat-free mass, and VO2max. Male rowers outperformed female rowers, but the expected variation by gender was reduced to only about 4% when adjusting for differences in size and aerobic capacity. Other factors to explain performance differences by gender include haemoglobin concentration, testosterone levels, and the relative size of leg muscles.

Besides anthropometric and physiological advantages, differences in human talent can also be attributed to rowing capacity and skill factors (Smith and Spinks, 1995). Discriminant function analysis was performed on ergometer results comparing novice, good, and national level rowers. The most powerful predictor was propulsive power per kilogram of body mass. Other significant predictors included the skill factors of stroke-to-stroke consistency, and stroke smoothness. Because power and skill levels can be correlated with experience categories, having experienced rowers improves the reliability of a crew to perform consistently well.

In an overall model of crew race performance, the quality of execution is treated as a separate factor in determining how well a given crew performs in any given race. Nevertheless, size, gender and other anthropometric advantages provide an expected base line of crew performance. These anthropometric advantages can also be factors of intimidation that can affect race psychology and race plan strategy.

3. Base Capability: Biomechanics (B)

The base capability of a crew is also a function of rowing technique and how well the crew uses and interacts with its equipment. In recent decades, the sport of rowing has been extensively studied using biomechanical techniques driven largely by new instrumentation technology and a growing interest in sports biomechanics. As described by Volker Nolte (Nolte, 1991), biomechanics is interested in how the rower converts physiological capacity into moving the boat. Biomechanical considerations include ergonomics, kinematics and rowing style. Superior equipment can also be a contributing factor in winning a race. Crews respond well and perform better when they use better equipment, have it optimally rigged to fit them well, and are well trained in how to use their equipment.

3.1. Ergonomics and Equipment

Ever since the sport of crew originated in the 19th century, the designs of equipment manufacturers have continually evolved to better accommodate the ergonomics of rowing, to maximize the mechanical advantages of equipment designs, and minimize the hydrodynamic drag from the shell and oars impacting the water.

One of the first major innovations was the introduction of the sliding seat between 1857 and 1861. The sliding seat allows the legs to be used as the primary form of propulsion. The timing of force application by the legs, arms and trunk, along with the dynamics associated with shifts in mass-momentum, results in a distinct profile of shell speed and the associated forces over the duration of the stroke (Jones and Miller, 2002).

Racing shells have been adapted in hull geometry design (Tuck and Lazauskas, 1996) to better fit the varying sizes of crews, water displacement and the associated hydrodynamics. Water resistance can be categorized into hull, pitch and skin resistance (Soper and Hume, 2004). Skin friction represents 88% of the water resistance to boat propulsion.

Oar blades have been redesigned to better catch the water, improve the quality of rowing bladework, and minimize negative hydrodynamic effects. In 1991, the asymmetrical “hatchet” blade was introduced. Some of the reported benefits of the hatchet blade (Soper and Hume, 2004) include more stability with less vertical movement, greater peak compressive force at the catch, and less slippage of the blade at the catch.

Outriggers have become increasingly adjustable to allow them to be individually adapted to fit each rower and accommodate diverse rowing styles including the quality of an individual’s bladework. Rigging can be adjusted to raise or lower the oar blade relative to the water, thus better adapting to the wave height on race day. Rigging that is well-adapted to the rower’s anthropometrics improves performance (Barrett and Manning, 2004). Rigging adjustments also allow the mechanical loads or “gearing” to be adapted to the expected race duration (affected by weather conditions), race strategy and stroke rate pacing.

3.2. Kinematics of the Rowing Stroke

The rowing stroke can be defined in terms of four phases (catch, drive, finish, and recovery) in which different muscle actions are activated in a coordinated sequence (Mazzone, 1988). The drive phase is a sequence where the emphasis is on legs, body swing, and arm pull-through.

Rowing style affects the performance of a crew. Crews vary considerably in the kinematics of rowing style according to the beliefs of their coaches and the difficulty of training rowers to conform to a common style. Nevertheless, a review of the rowing biomechanics literature (Soper and Hume, 2004) revealed evidence to support several commonly held beliefs:

The race scenario is an important factor in race strategy. A well conditioned crew should bring with it 4. Race Scenario: Physiology (P)

Higher stroke rates and longer drive lengths result in greater average boat velocity. However, it is difficult to do both simultaneously, so crews must fundamentally choose between these two in their rowing and racing style.
• Drive to recovery ratios are strongly negatively correlated to stroke rate and average boat velocity. Therefore, increases in stroke rate and velocity are primarily associated with speeding up the recovery phase of the stroke.
• Rowers of different ability levels can be distinguished by elements of both power and skill – including power per kilogram of body mass, propulsive work consistency, stroke to stroke consistency, and stroke smoothness.
• As stroke rate increases, peak oar force occurs earlier in the drive phase. The ability to maintain peak oar force through the middle of the stroke may also be an indicator of performance level.
• Greater force on the oar handle is generated when the elbows are extended at the start of the drive, and also when the elbows are kept close to the trunk at the finish.
• A sequence of power using first the lower limbs, then the trunk, and finally the arms may be a more effective rowing stroke for achieving greater boat velocity.

Aside from applying force to the oar, a rower also must move his own bodyweight horizontally and vertically during a stroke. Only 75% of the power is used to pull the oar (Nolte, 1991), whereas 9% is used to support horizontal body movement and 16% is used for vertical body movement.

3.3. Timing of Force Application

The shape of the force power curve over the duration of a stroke has been widely studied. Jones and Miller (2002) found that rowers display individual or signature stroke profiles in terms of the shape of their force power curve over the duration of their stroke. Such individual stroke profiles are used to provide a basis for distinguishing the features of the stroke and classifying individuals.

The timing of force application during a stroke can vary according to coaching philosophy. Some biomechanical principles favor prompt application of power starting with a quick catch and a steep rise to maximum power early in the stroke (Schwanitz, 1991). His empirical research on boat speed compared to rowing style supports increased power emphasis on the early part of the drive. The position of the body in the early part of the drive is similar to that of a weightlifter at the beginning of a lift. Schwanitz interprets this position as allowing for a more synchronous whole-body effort incorporating leg, back and arm muscles. Emphasis on power at the middle or end of the drive would emphasize more isolated and smaller muscles. Early application of power also means that the force being applied with the oar is not at its most productive angle given the distribution of force along the two vectors of the horizontal plane.

Consideration of hydrodynamic drag and the associated benefits of maintaining a steady boat speed suggest that rapid force development at the catch and longer stroke maintenance at the finish should be emphasized instead of applying the highest peak force in the middle of the drive (Kleshnev, 1999).
On the other hand, a comparison of propulsive versus transverse forces on the oar suggests that force application is most inefficient at the catch and finish (Sanderson and Martindale, 1986). Even if you assume that exploding at the catch with power is a more effective technique for short rowing pieces, producing very steep force-time curves may be very costly in terms of lactic acid accumulation and energy production (Seiler, 1997). Therefore, sustaining this technique over the duration of a 2000-meter piece may not be a sound strategy if one wishes to conserve and pace the usage of the rower’s limited aerobic physiological resources.

Coaching philosophies and biomechanical principles differ as to rowing style. However, it is generally regarded that more experienced crews will row more skillfully and therefore more efficiently and effectively. It is also generally understood that having the best equipment and having it skillfully rigged provides a competitive advantage. The advantages of experience, skill and equipment can often offset the size advantages of larger, stronger crews. Indirectly, even just a crew’s beliefs about rowing style and the relative skill of their competitors can affect race strategy and the psychology of the race. The combination of human talent, equipment and skill-based biomechanical advantages defines the “base capability” for a crew.

4. Race Scenario: Physiology (P)

The race scenario is an important factor in race strategy. A well conditioned crew should bring with it the training, conditioning, and physiological capability needed to compete well, given its race strategy for the given race scenario. However, excellent human talent, rowing technique and proper equipment are not enough. The crew must also be fit and well conditioned for the race distance it is rowing. The crew must balance and spend its energy reserves over the duration of the race. This involves pacing the stroke rate and level of energy expenditure to match the gearing of the crew’s rigging so that the crew’s energy “budget” is used optimally over the 2000 meters.

From a physiological standpoint, the competitive goal is to find ways to deliver the most oxygen to muscles as fast as possible while balancing the pace of energy usage over the duration of the race. For short periods, muscles can work without oxygen through anaerobic respiration. For longer periods, oxygen is needed to sustain aerobic energy (along with the consumption of either glucose or fat). About 85% of the energy requirement during a crew race is supplied aerobically (Seiler, 2005), while the remainder is supplied via anaerobic pathways. Other research (Maestu, 2004) has yielded varying estimates, but the most recent research places the aerobic component around 85%.

The exact estimate of the aerobic/anaerobic mix may not be as important as how to apply this knowledge in developing race strategy. Coaches disagree on the ideal steady-state stroke rate and energy usage levels to adopt during the middle of a race. They also disagree on the desirability and frequency of use of big-10’s during the body of the race in order to make planned “moves” on other crews at key psychological points during the race. Although such moves may provide psychological advantages to crews as they strive to implement their race plans, these extra energy expenditures may just be borrowing from energy reserves otherwise rationed for use later in the race.

The concept of a maximal lactate steady state (MLSS) has been studied (Billat et al, 2003) as a bridge between biochemistry, physiology and sport science. MLSS is the highest point in the lactate turnover equilibrium – the point at which lactate appearance and disappearance are balanced. MLSS provides a basis for understanding the energy pacing possible in a 6 minute crew race, as well as the means and consequences of varying the pacing of energy usage throughout a race. For trained athletes, Billat estimates endurance time at MLSS to last about an hour. Crew races which average around 6 minutes are much shorter in duration. Therefore, the average energy pacing of a crew race is above the steady-state MLSS workload, and lactate levels should peak at the maximum possible level at the end of the race. Consequently, crew races are often referred to as 2000-meter “sprints” even though the high consumption of aerobic energy reveal crew races to also be an endurance sport.

The physiological science and the strategy behind a 2000-meter crew race is not simply about maintaining a steady-state effort, but rather about how much above the steady-state MLSS a crew should expend its energy, the timing of when to exceed this threshold, and whether deviating the pacing above the level of MLSS is somehow strategically or tactically warranted. The extra energy expended to support a big-10 or other tactical drives accelerates the timing of energy usage, but is not the sole rationale for a crew to row above the MLSS equilibrium. Somehow, the crew needs to burn its energy above this equilibrium so that all of its anaerobic energy is consumed.

The physiological means of obtaining the extra energy for a big-10 or similar drive may be enhanced by the “fight-or-flight” enzyme – glycogen phosphorylase. Glycogen reserves are a factor in exhaustion at MLSS (Billat et al, 2003), and glycolysis can be mediated by adrenogenic activity. Therefore, it is possible that the motivational psychology of calling a big-10 could lead to a fight-or-flight mental state triggering enzymes or adrenaline that would lead to a temporary burst in energy and resultant boat speed. This would explain why big-10 style moves could provide a temporary burst in speed when the rowers are already thinking they are rowing at full power and are already burning their energy at a rate above the relative comfort level of the MLSS steady state.

The eventual consequence of exercising above the steady-state critical power and beyond the endurance limit for this level of power is exhaustion. Continued exercise after exhaustion is only possible by reducing work rate and the corresponding power output to a level below the critical power (Coats et al, 2003). This work rate after exhaustion is reduced to a level that relies predominantly on aerobic energy transfer, and in so doing, allows exercise to be sustained.

In terms of racing strategy, a crew can race significantly above its level of critical power for only a limited duration. After achieving exhaustion, the crew is burnt out and will continue to row only at a sub-optimal level of power. Therefore, one of the keys to race strategy is to time the level of intensity and duration of power to achieve exhaustion near the end of the race. Otherwise, the loss of power and speed after exhaustion may offset the gain from rowing earlier in the race at a work rate above the critical power.

The amount of energy available above the level of critical power can be thought of as a constant and finite level of energy store (Fukuba et al, 2003). This is comprised of a phosphagen pool, an anaerobic glycolytic component, and an oxygen store. This pool of energy is modeled as a hyperbolic curve that is a function of power versus duration, and is considered to be the equivalent of the oxygen deficit or the subject’s anaerobic work capacity. This work capacity in excess of critical power can be utilized rapidly by exercising at a higher work rate, or may be sustained for longer durations by exercising at lower work rates. Research (Fukuba et al, 2003) also suggests that this excess work capacity is a fixed amount and not affected by the pattern of power variations – at least for power ranges in cycle ergometry from 100 to 134% of critical power. From the standpoint of race strategy, this supports the notion that crews could strategically vary their level of energy consumption during a race, and in a wide range of patterns, up until the point where their anaerobic energy store is cumulatively consumed and exhaustion sets in. What is also suggested by this research is that no part of the race should be rowed below the level of critical power if an optimal time is to be achieved.

5. Race Scenario: Weather and Environment (W)

Weather and environmental conditions are important aspects of the race day scenario. Unpredictable factors such as wave height can affect the ability of a crew to row well (including minimal splashing and a clean catch and finish). Headwinds and tailwinds or currents in the water also determine the effective distance of the race in terms of expected time and total strokes. A head wind slows the race. A tailwind speeds up the race. Other environmental influences affecting crew comfort level and expected race speed include temperature (air and water), water depth, water density, altitude, and air pollution.

Not all conditions affect each crew fairly. Some lanes may have advantages. Random events can occur including obstacles in the water or wakes from other boats on a lake or river. Even though courses are laid out to offset the curvature advantages of inside lanes, it is inevitable that turns in the course can affect perceived race positioning and therefore race psychology.

The referee handbook specifying the Rules of Rowing (US Rowing Referee Committee, 2008) specifies factors that must be considered before a race course is judged suitable for a registered regatta, including whether the course is uniformly sheltered from the wind, whether the course is free of any current, and whether any current that does exist is slight and equal across the course.

In recent years, all world rowing championships are raced on straight and narrow channels usually custom built for crew racing. Water current, water depth, and the random effects of other environmental factors are minimized. However, until the day that the first crew race is held on an indoor course, the effects of wind direction and speed are still uncontrollable factors that can shorten or lengthen race duration, and can have unfair effects on racing lanes.

Statistical regressions on winning times over 14 years (Kleshnev, 2006) reveals a gradual pattern of improving times in 13 out of 14 boat classes (women’s double sculls being the exception). Nevertheless, fluctuations in winning times are substantial and important in terms of race strategy. Winning times for both men’s and women’s 8’s competition can vary by 40 seconds or more from year to year. This is certainly due to environmental conditions – not due to the quality of crews varying this much each year.

Wind and weather can vary dramatically within a short period of time – sometimes just a matter of minutes. This is a critical aspect of the race scenario for which crews need to be prepared. Part of the challenge of each coach is to anticipate the rowing conditions their crews will actually face at race time. The gearing of the rigging and oars can be adjusted to affect the leverage and level of energy needed per stroke for a given stroke rate. A crew can be prepared (gearing, stroke rate plan, and expected pace of energy expenditure) for a race expected to last 6 minutes and yet experience race conditions that can be as much as 40 seconds faster or slower.

6. Performance Execution: Quality of Execution (Q)

One can define “performance” simply in terms of how well a crew placed in the race. Thus, winning is the best performance possible, and losing means their performance was the worst possible. Consider this to be the performance result.

For purposes of a crew race performance model, "performance execution" is defined based on the combination of the quality of execution (Q) of a crew and the effects of race psychology (R). A losing crew could still have rowed very well, and perhaps even better than was expected from them. A winning crew could have performed poorly in terms of their effort and execution, but still easily win a race due to the weakness of their competition. A crew’s quality of execution is determined not simply by winning or losing, but whether the crew executed according to its potential.

The quality of execution of a race can be judged in three ways:
• Strategic Execution – whether the crew adhered to its race plan, and whether the race plan was the best choice for the race scenario.
• Technical Execution – whether the crew rowed mistake-free in terms bladework, steering, and other imperfections or misfortunes.
• Teamwork and Synchronization – whether the crew rowed in a coordinated manner, the rowers' styles blending well together, and possibly even achieving a “swing” effect.

6.1. Strategic Execution

A race plan is the combination of strategy and tactics planned for a race. The plan is established by the coach but needs to be implemented by the crew. The coxswain may need to react to unexpected race circumstances, and make appropriate tactical adjustments based on the contingencies for which the coxswain has been trained. If a crew executes the coach's strategy as planned and if the crew responds appropriately in terms of tactical contingencies, it has performed well in terms of strategic execution.

Although a crew can follow its race strategy and tactics perfectly – making all the moves and stroke rate changes exactly as planned – the crew may still fail to perform as well as expected relative to the competition. This does not necessarily mean that the crew failed in terms of strategic execution. Poor strategic execution is when a crew unintentionally deviates from its strategy and tactics. A failure to execute race tactics properly could include settling too high or too low off of the racing start. It could also include timing errors on the part of the coxswain, such as accidentally changing stroke rates at the wrong time during a race. The consequence of the coxswain calling a sprint much sooner than planned could be that the crew becomes totally exhausted before the race ends -- thus resulting in suboptimal physiological performance.

Coaches sometimes allow a coxswain tactical discretion as to whether to call up the stroke rate at the end of a race earlier or later than planned, or not at all if the crew seems safely in the lead. If such contingent tactical choices fail to achieve the desired effect, this can also be viewed as a failure in race strategy execution.

Sometimes, the coach has chosen the wrong race plan for the crew given the race scenario that day. A strong head wind could make the race last much longer than originally planned. A tail wind can have the opposite effect. The coach has geared the rigging of the crew and chosen a race plan based on a set of assumptions about the race conditions. Should the actual race conditions be different than planned, the crew or coxswain might or might not choose to deviate from their race plan. Regardless of the cause, executing the wrong strategy and tactics for the specific race day scenario can be viewed as a failure in strategic execution.

6.2. Technical Execution

Many things can go wrong that are the result of technical errors on the part of the rowers or the coxswain. At elite levels of international competition, no major errors are expected, yet even minor errors could still produce the difference between winning and losing in a very close race.
Technical errors in rowing style can be subtle and difficult to perceive by race observers, but still affect the feel of the boat. This includes sloppy blade work, imperfect timing and synchronization, and a boat that is not well balanced (occasional tilting toward the port or starboard side).

Major technical errors can have more dramatic consequences for a crew, but seldom occur in elite competition. These can include catching a full or partial "crab" (when the rower’s oar gets stuck in the water at the finish of a stroke), collisions with course obstacles, interference with another crew, jumping the start, broken equipment, and injuries to rowers. Misfortunes can dramatically affect crew performance and race times. Some rules infractions can even result in the crew being disqualified from the race.

6.3. Teamwork and Synchronization

Synchronization of the various elements of the rowing stroke and the timing of force application has been studied among rowers to explore the potential advantages and even the disadvantages of each rower being perfectly in synch with each other. Most coaches agree that good synchronization is highly desirable.

Research suggests that successful rowing performance is influenced by the consistency of intra-stroke fluctuations in boat velocity and that wider fluctuations are associated with less successful technique (Soper and Hume, 2004). Fluctuations in boat speed occur throughout each intra-stroke phase of the rowing cycle. As stroke rates increase, intra-stroke fluctuations in boat velocity significantly increase. The fluctuations tend to be asymmetrical around the average boat speed with greater reductions in boat velocity than the increases. The non-linear relationship between hydrodynamic drag and boat speed causes stroke-rate fluctuations to be sub-optimal in terms of the energy expenditures needed.

Kleshnev (1999) studied blade efficiency and hydrodynamic drag effects as a function of stroke rate and the timing of power during a stroke. He found that increasing stroke rates led to an increase in velocity variation and therefore a measurable loss of efficiency.

One of the most widely cited studies on rowing coordination and consistency is by Wing and Woodburn (1995). They defined three important components to crew coordination: having a common periodicity (cycle of activity), good synchronization (correspondence of phase), and similar force-time profiles. Wing and Woodburn illustrated how crew exhaustion affects the force-time curve. The Wing and Woodburn study examined the similarity of rowing styles among the crew and how consistently the rowers maintained these styles over time. They offered the interpretation that greater synchronization results in less wasted effort. The wasted effort is due to the inefficiencies of turning moments associated with poorly synchronized strokes and the unequal forces produced by each rower.

6.4. The "Swing" Effect

According to the US Rowing web site (US Rowing Nomenclature, 2008), “Swing is a hard-to-define feeling when near-perfect synchronization of movement occurs in a shell, enhancing the performance and speed of the crew.” This effect is rarely achieved even amongst the best of crews. The concept of swing is controversial in that not all coaches and crews even believe in this effect. Instead, they attribute such unexpected speed to simply exceptional effort (or psychological affect). Although nobody has yet provided a scientific explanation for swing, the rowing literature contains many references to swing as an unusual performance enhancing experience that is generally associated with good synchronization among the crew.

For purposes of elite crews competing at the world championship level, it remains a matter of speculation as to how often elite crews ever experience a dramatic swing effect, or whether they routinely experience it and just don't notice it as anything unusual. However, there are personal accounts of unusually good performances. For example, Lesley Thompson-Willie (Thompson-Willie, 2005) described her experience as a Canadian national team coxswain winning a gold medal at the 1992 Olympics. She describes rhythm and ratio (time on the slide compared to time with the blade in the water), the feel of the shell, and how the “boat will have a certain glide beneath you that is hard to describe.” She says there are only a few times that she has ever felt this in a race, but that the 1992 Olympic final was one race where “everything felt perfect.”

7. Performance Execution: Race Psychology (R)

Simply rowing a good, technical race does not mean the crew performed to its potential. The quality of a crew’s performance relative to its potential can be evaluated in terms of their psychological commitment to the race and their ability to focus on producing their best effort. The crew’s effort and commitment to the race is a function of the psychology of their competitive positioning as the race progresses. The ability of a crew to make their best effort is also a function of how well the athletes focus on what they need to be doing and not be distracted by counterproductive thoughts.

7.1. Motivation and Effort

The effort a crew puts into a race is a function of how close the race is and whether the rowers are motivated to make the effort they are capable of exerting. Sometimes, holding back in a race is appropriate – such as when a crew is conserving energy for a future race. Sometimes, when winning a race, a crew may delay or withhold its sprint because it is unnecessary and increases the risk of technical errors or exhaustion. Influences on motivation and effort include race importance, perceived conditions in a race, the morale and character of the crew, and race psychology.

If race psychology does not influence the dynamic performance of a crew, then every race might as well just be a time trial with crews rowing in isolation! Each crew would just execute its ideal race plan for
today’s race scenario. Theoretically, a crew should know the ideal race plan to minimize its time over a 2000-meter race distance. Psychology is believed to contribute to both the level of effort and quality of effort needed to achieve a superior performance.

Klavora (1980) discusses the psychological basis of racing and compares the advantages and disadvantages of the “even-paced” racing strategy to the “early-lead” racing strategy. Rowing is the only sport where the athletes do not face the forward direction (except for the coxswain). The lead crew is in an authoritative position since the rowers can observe their trailing opponents and can react to their tactical intentions. They can counteract an opponent’s attack so as to hold onto the lead. According to Klavora, “In these instances “extra” energies which, in normal circumstances, are not available to competing athletes, are mobilized in the oarsmen of the leading crew.”

Compare this to the even-paced strategy where a crew must row from behind in the race. Even pacing is the most economical way to row a race from a physiological standpoint. However, according to Klavora, it generates substantial psychological disadvantages. Not being able to see what is going on in the race, the rowers cannot directly judge who is leading the race, the distance they are lagging, and whether they are still within striking distance. Although the coxswain’s job is to inform the crew, “hearing does not mean believing.” Rowers are tempted to look around and peek over their shoulders – which can lead to disturbing the crew’s rhythm and balance.

According to Klavora, there are few rowers with a “strong enough personality to take the beating of rowing in the tail in the early phases of a race.” However, by rowing more economically, they may be able to overtake their opponents in the second half of the race. Overtaking opponents one by one can be “psychologically devastating for the tiring opposition, who are desperately trying to hold onto their lead.”

Jennifer Johnson (1989) wrote about the psychology of pushing through the pain: “When the legs are screaming at the rower to stop … how does he keep going?” She defines the purpose of sports psychology as to “help the athlete push beyond the limitations imposed by the rational mind.” She cites the example of Kris Karlson, 1988 world women’s lightweight sculling champion, who said that when she reaches the point where she feels she cannot go on, she often notices that she is moving on other people. “Wow! They are dying more than I am.” She starts “getting psyched” and manages to block out how dead she is and starts to focus on how she is winning. Johnson’s experience illustrates how crew races can truly be psychological competitions.

7.2. Concentration and Focus

Baltzell and Sedgwick (2000) interviewed elite level rowers and their strategies toward optimizing performance through their ability to cope with competitive pressure before and during the competition. They developed a “coping-excellence model of elite rowing” that blends intrinsic and extrinsic motivation along with habits of excellence. Extrinsic motivation is the desire to achieve external success – such as earning a place on a team, winning races or medals, and receiving the coach’s praise. Intrinsic motivation is the innate desire to perform well and be in control while working toward their goal – such as to improve fitness, rowing technique, and rhythm. The habit of excellence reflects the principle that rowers race the way that they practice. Figure 2 summarizes the coping scenarios most commonly suggested by those elite rowers who were interviewed.

This research emphasized the importance of having a race plan and focusing the rowers’ mindset on what they can control including rowing efficiently, keeping relaxed and rowing with good rhythm. Pulling hard is necessary but most effective when it was a previously habituated response. Before the race, attention should be on the race plan which can be supplemented using mental skills such as imagery and goal setting. Baltzell and Sedgwick concluded that rowers need to be highly motivated to optimize their speed and performance, need to build habits of excellence into their daily practice, and that personal enjoyment and intrinsic motivation are more effective when coping with high levels of competitive pressure.

Research in other sports examines other ways that athletes experience psychological stress. Bar-Eli et al (1992) investigated how high levels of arousal can lead to anxiety that negatively affects tennis player performance. They defined a “psychological performance crisis” as when an athlete has difficulties performing a task in competition due to extreme physical and psychological arousal. They were able to correlate impaired motor performance with high levels of stress.

Tate et al (2006) studied techniques for modelling the relationship between athletic performance and levels of psychological affect (i.e. arousal). They proposed that there is an optimal range of affect within which an individual athlete’s performance is enhanced. They termed this the “Individual Zone of Optimal Functioning (IZOF)” and likened it to how athletes will sometimes characterize themselves as being “in the zone” when competing. Being too high or too low on the affect scale can lead to suboptimal or even dysfunctional performance.

The need to achieve a balance in the optimal amount of motivation and the need to focus on the most effective types of motivators has led many authors to research and propose how to train athletes psychologically. Waitley et al (1983) described how the Eastern Europeans were the first to employ sports psychologists on the staffs of their national teams. They state the goal of sports psychology is to “optimize competence through the development of psychological skills that will permit athletes to enhance performance and gain maximal satisfaction.” To address the need for balance, they recommended a variety of stress reduction techniques that could be taught to athletes by sports psychologists, including active rest, deep muscle relaxation, biofeedback, and assertiveness training. Assertiveness training means being “brain-controlled” rather than “emotion-controlled.” Consequently, they advocate training techniques in imagery training, cognitive reconstruction, and mental rehearsal.
According to Horsley (1989), asking rowers to “concentrate” is too vague. To improve their concentration, they must be given specific information and taught to work on specific skills – both mental and physical. The attentional demands of rowing are constrained by the brain’s limited capacity for processing short term memory, and by individual attentional style narrowly focusing on internal or external sources. Internal focus when racing includes awareness of lactate build-up, muscle tension, breathing control, task-related thoughts, and awareness of rowing technique. External focus when racing includes awareness of the coxswain, his/her instructions, race officials, the boat and water, teammates, other crews, and other coxswains. Problems occur when rowers become overly distracted with external cues or become overloaded with internal cues (perhaps due to anxiety). Horsley advocates that rowers need to practice calming their minds and develop strategies to focus on appropriate cues. He suggests using both off-water and on-water concentration drills.

During unsuccessful performances, athletes may have programmed their own failure through self-doubt and negative statements. They are looking for an excuse for their potential poor performance telling themselves they don’t row well in a cross wind, the rigging is wrong, or they ate the wrong food. To overcome this self-doubt, Johnson (1989) recommends techniques of self-talk, countering, thought stopping, and visualization.

Nideffer (1981) is another advocate of attentional control training in sports psychology. He recommends a technique that focuses concentration on the internal center of gravity of the body. He asserts that the average individual can be taught to control the inter-relationship between thought processes, centering attention, and physiological arousal.

Butler et al (1993) advocate “performance profiling” as a means of facilitating an understanding of the way an athlete perceives his/her ability and preparation for performance. Although the sport they studied was boxing, performance profiling addresses what they consider two fundamental aspects of applied sport psychology: self-awareness and goal setting.

Joy (2005) advocates a scheduled yearly mental training cycle to include five meditative training techniques: quiet sitting, visualization, relaxation, concentration, and mindfulness. Joy believes these meditative practices should be practiced both on land and on the water beginning with the first practice, and that this training leads to “flow” (analogous to swing) and peak performance. According to Joy, “Mental training enhances the flow and power of physical movement by allowing efficient release of energy.” It involves “a total integration of body movements with the shell, blades, and water, along with a heightened awareness and concentration.”

Joy witnessed the power of this technique in 1984 as practiced by coach Neil Campbell with the Canadian men’s eight in winning the Olympic gold medal. He attributed their oneness of body, mind and spirit to allow these rowers to relax, focus, and enjoy the competitive moment.

8. Decisions: Strategy and Race Plan (S)

A student once asked, “What is there to learn about rowing strategy other than to just pull hard?” To an untrained eye, there would appear to be no strategy at all to a 2000-meter sprint. The coach trains the crew to begin with a racing start, lower its stroke rate to a sustainable rate in the middle 1000 meters, and then sprint at the end of the race. If the crew pulls as hard as it can and executes its coach’s stroke rate pacing plan, the crew should finish in its optimum race time. Theoretically, it is just that simple.

8.1. Coaching Philosophies

US national team coach Mike Teti is an expert on racing strategy having coached the US men’s eight to the Olympic gold in 2004 (the first US gold in the eight since 1964) and then repeating as the world champion in 2005. According to Teti and Nolte (2005), strategy is a skillful plan to reach a goal, and tactics are the means to implement the strategy. They believe a coach needs to use any information available about their competitors in order to create a winning strategy. A coach also must consider many other factors including the importance of the race, the level of competition, and even weather.

However, they consider the most important factor in choosing a race strategy is to adapt the race plan to your athletes and to set realistic goals. When adapting your strategy to your crew and setting realistic goals, Teti and Nolte assert, “Winning a bronze is much better than racing for victory and coming in fourth.”

In training for competition, the crew should already have figured out the crew’s most effective stroke rate. According to Teti and Nolte (2005), the adrenaline that comes with a major race may cause the crew to row higher than planned. If the crew is within one stroke per minute of plan, they believe no adjustment is needed. If off by more than that, an experienced coxswain should then make an adjustment.

Teti and Nolte (2005) believe that a race plan should reflect not only technical and physiological capabilities, but also psychological strategy. Rowers often say their most memorable race is when they rowed an even pace at the beginning of the race and then rowed through the competition to win at the end. Nevertheless, Teti and Nolte believe in the racing philosophy of a fast start and trying to take the lead early. They advise that you always need to stay with the leaders because it is difficult to get big margins back. They also suggest that a crew that gains a one-length lead by the 1000-meter mark can “get brave” knowing they only have to hang on to their lead for another 2 minutes and 38 seconds.

Racing information on your opponents is available in terms of official results and 500-meter splits. Stroke rates can be taken from the shore. Teti and Nolte (2005) believe, “You have to interpret and use this information to the best of your ability.” Split times give coaches an idea of competitor speed distribution throughout the race. Because other coaches also study race results, Teti and Nolte favor using different race profiles in the heats and the finals so as to throw off those competitors who are studying them.

8.2. Gearing and Pacing Strategy Hydrodynamic Resistance

Gearing and Pacing Strategy Hydrodynamic resistance to the flow of the boat is a function of skin drag, form drag, wave drag, and forces resulting from poor technique (Jones and Miller, 2002). The predominant source of resistance is shell skin friction with the water. The laws of fluid dynamics show this skin friction to be proportional to the velocity of the boat squared while the metabolic power consumed in moving the boat is related to the velocity of the boat cubed (Secher, 1993).

Consider an example by Nolte (1991) calculating the water resistance effects of maintaining a constant velocity of 5 meters per second compared to a speed distribution that spends half the time at 4 meters per second and the other half at 6 meters per second. Although both of these scenarios average 5 meters per second, the latter results in 4% greater boat resistance.

Thus, the metabolic cost to the rower is minimized by maintaining a constant boat speed. This would encourage race strategies that maintain a constant speed over the course of a race while minimizing or eliminating racing starts and sprints. Greater hydrodynamic resistance could also argue against the use of big-10 drives due to the extra energy needed to increase velocity while making a temporary drive in the body of the race.

Mechanical gearing is part of the overall race strategy as planned by the coach. For longer races or for races to be rowed at higher stroke rates, the gearing leverage can be lightened to reduce the work per stroke and thus balance the physiological energy expended according to the capabilities of the rowers, the expected time duration of the race, and the planned total number of strokes.

In spite of the non-linear effects of hydrodynamic drag, the goal of crew racing is to row at the fastest overall average speed so as to achieve the fastest time possible. Effectively using all of the available anaerobic and aerobic energy resources argues against using a constant speed throughout a race. Furthermore, the race plan must factor in the psychological advantages of leading in a race or staying within striking distance of the leader.

8.3. Strategy Profiles

Klavora (1979) defines the basic principle of the even-pace strategy is to start at the highest stroke rate that can be sustained throughout the race so that the last remnants of energy to reach the maximum possible oxygen debt is used up in the last stroke of the race. The crew would begin with a moderately fast start, quickly settle into an optimal racing rhythm, and would not sprint at the end of the race.

According to Klavora, although an even-pace strategy has been proven by physiologists to be the most economical, it is very hard to achieve in actual practice. He cites examples of world championship crews that demonstrated an even-pace strategy. The even-pace strategy was demonstrated based on their official 500-meter split times. However, his data do not show whether the crews actually took a racing start or a finishing sprint. Nevertheless, he concludes that, “it is obvious that whenever even pacing has been followed it has brought success to crews employing this racing plan at an elite level of competition across a variety of events.” He goes on to explain that the even pace strategy may work better at the highest level of competition because such crews know their physical capabilities perfectly, and it takes years of experience to learn to row at a consistently even pace.

Pacing studies have been published (Garland, 2005) that examine how average stroke rates and speeds vary among the four 500-meter quartiles of race. However, these patterns do not isolate the pacing and speed effects of racing starts and final sprints (roughly 250 meters each) since these are blended into the results of the 500-meter segments.

Another study (Kleshnev, 2001) examined 12 “patterns of race strategy” defined as the quartiles where crews are at their fastest and at their slowest relative to the other crews, and the relationship of this fast-slow pairing to how well the crews finished in the race. For example, a 1-4 pattern means the crew performed its best relative to the other crews in the first 500 meters and performed its relative worst in the last 500 meters. This study shows no single best strategy and raises many unanswered questions about race dynamics, the effort sustained by losing crews, and the statistical validity of comparing times of winners against time averages that include crews that were hopelessly out of contention. Also hidden from this study are the timing and discrete effects of drives and counter-drives as crews execute tactical moves to gain or hold the lead.

9. Decisions: Tactics and Contingencies (T)

Strategy decisions are made by the coach before the race in the form of a race plan. The coach drills this race plan into the crew – possibly allowing for race contingencies. The coxswain serves as the “agent” of the coach in executing the race plan. Tactical decisions are made by the coxswain during the race – either closely following the race plan or making tactical adjustments on the fly depending on the contingencies the crew experiences during the race.

9.1. Responsibility for the Race Plan

Crews usually begin with a racing start for roughly 250 meters and then settle to row the body of the race at a lower stroke rate. Most crews will call up the stroke rate with about 500 meters to go, and finish with a full sprint over roughly the last 250 meters.

Yasmin Farooq is a former world champion coxswain for the US women’s eight. In Figure 3, she summarizes a typical race plan (Farooq, 1992) along with the goals she would communicate to her crew at each phase of the race.

Tactics may include big-10's or drives at one or more points in the race in order to try to make a move on the other crews. Drive tactics vary from coach to coach. Some do not use them – preferring that the crew row at a steady level of effort throughout the body of the race. Other coaches plan one or more drives lasting 10, 15, or 20 strokes each. Other crews plan to take drives at the discretion of the coxswain as circumstances seem to warrant -- often to achieve a psychological effect to inspire your own crew and to discourage your opponents.

9.2. Coxswain Duties

According to Farooq (1992), the coxswain has five primary duties:
2.Technical coaching (assisting the coach)
3.Help practices to flow smoothly
4.Motivating the crew and teamwork
5.Racing and strategy

According to MacDonald (1980), the coxswain’s job is to implement the race strategy that has been formulated by his coach. If the race does not go according to plan, the coxswain may have to formulate and alter strategy in the midst of a race. In this case, MacDonald compares the role of the coxswain to that of a quarterback calling an audible at the line of scrimmage.

When your own crew is moving on the competition, MacDonald cautions that the coxswain must describe that movement in a way that guarantees it will continue. He states that coxswains can actually destroy movement by getting the crew so excited that the rowers lose their sense of pacing and begin to rush.

When the other crew begins to move on you, MacDonald describes the coxswain’s challenge as to prevent the crew from panicking and to find a solution to the internal problems that may be hurting performance. In any case, the coxswain should never lie to the crew or his/her credibility is lost forever.

Although the coach’s race strategy is drilled into the crew, teams rarely practice adapting to unexpected deviations from strategy, such as how to adjust the stroke rate after settling at the wrong pace after the racing start. Likewise, unexpectedly trailing or unexpectedly leading in a race present new race scenarios for the coxswain to consider and for which the coxswain should be prepared to make adjustments. McArthur (2005) cites an example of one of his crews unexpectedly finding themselves in front of the rest of the field by a wide margin. It was such a shock to them that they did not know what to do, and so they never really settled into the race, and were overtaken by the other crews in the last 500 meters.

The judgment needed to deal with unplanned contingencies requires situation awareness, the courage to act independently, and the experience to know how to adjust race tactics and perhaps even adapt to an entirely new race strategy. The coxswain must know the available options and be able to assess the relative risks from deviations from plan. Tactics include adjusting stroke rate, taking big-10 drives, and communicating effectively to the crew to adapt to circumstances while keeping them motivated.
It was Farooq’s experience that before each race, the crew would map out its race strategy. They would also plan a backup strategy in case the crew is not where it should be at a certain time. Everyone (coach, rowers and coxswain) knows the backup strategy before the race begins.

Farooq also advises that your arsenal of motivational and technical tactics should include only a few key points in the race where you want to make moves on the competition. Most moves are decided and discussed days or even weeks before the race between the coach and team. Together, the team discusses the technical focus for each move and sometimes the motivational focus. Rarely are more than two moves planned for a race, but that leaves the coxswain the flexibility for spontaneous moves on the competition.

Farooq describes one move they used in the 1990 world championships. They labeled this the “flex” as an abbreviation for flexing a little muscle. It was meant to be the best 10 strokes of their race and was used only once per race. It was also exercised only once each practice.

9.3. Tactical Race Decisions

The amount of leeway and judgment permitted a coxswain varies based on the coach's philosophy. Coxswains may have discretion as to when to call up the stroke rate at the end of the race – earlier or later than planned, and perhaps not even take a sprint at all if it is not needed. These tactics are available to the coxswain to experiment with and adapt to race circumstances, or the coach could instruct the coxswain to blindly follow the preplanned race strategy.

A crew typically uses a racing start for the first 20 or 30 strokes (roughly 250 meters) before setting down to cruising speed and finishing with a sprint. This is evidenced by studying the splits of world championship competitions, including a recent study of the 2000 Olympics and 2001-2002 world championships (Garland, 2005). Compared to the average velocity over the 2000 meters, the quarterly splits show relative velocities of 103.3%, 99.0%, 98.3%, and 99.7%. The pattern of results from Garland’s study show close consistency between the proportional time splits for men vs. women and for winning crews vs. losing crews.

Garland’s study included all of the qualifying rounds as well as the finals competition. Because qualifying rounds were included, he chose to include only those race results where there was evidence that the crews made a good attempt to complete the course in the shortest possible time. The data suggested that 41% of all regatta races should be excluded due to abnormal race patterns.

He interpreted two reasons for needing to exclude these races: 1) the crews overestimated their ability and set off at a pace that was too fast to sustain, or 2) there was a “deliberate tactical decision” to slow down to conserve energy for further rounds of the competition. He could not tell how often each of these reasons occurred, but one can assume that any deliberate tactical decisions were made as contingencies during the race due to the crew being comfortably ahead or hopelessly behind. In many race circumstances, it makes sense for losing crews to save their energy for their next race, and for winning crews to conservatively just sit on their lead without taking a full sprint.

10. Putting the Model to Use

The performance of a crew in any given race is a function of the base capability of the crew when faced
with a particular race scenario, combined with their performance execution of the decisions made before and during the race. The proposed 8-factor model of crew performance provides a conceptual framework for researching and interpreting crew race performance in terms of these eight separate factors.

This model can be put to use in analyzing and interpreting the results of historical race performances. Recommendations for applying this model include:
• Data: Gather detailed race performance data to use as a basis for performance analysis. Gather data from public and private sources (such as video race records) that go beyond the official race finish times and beyond the 500-meter splits reported for international races.
• Analysis: Use the tools of statistical analysis to assist in race analysis, but do not simply rely on computational results to interpret race performance. There are many confounding variables in a race, and the best statistical fit may not accurately portray what truly differentiated the crews from each other.
• Evaluation: Use the data to guide subjective evaluation of race results according to the eight factors. Reported race times must be reconciled in your evaluations, but the factors that led to the observed performance may be subjectively interpreted (perhaps guided by statistical analysis).
• Experimental Designs: Consider comparable crews to study, sometimes even evaluating the same identical crew in separate races. This controls the variability of some of the factors in the model and allows isolated factors to be evaluated more precisely.

The explanation for differentiated crew performance should be entirely explainable through the interpretation of these 8 factors as applied to a single race or to pairs of races involving the same crew(s). All times and time splits need to be reconciled while isolating the separate effects in seconds of each of these 8 component factors.

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Estimating the Maximal Lactate Steady State Power from an Incremental Test Using Lactate Pro LP1710

International Journal of Applied Sports Sciences 2009, Vol. 21, No. 1, 74-85.
Korea Institute of Sport Science
Received : 1 December 2008, Accepted : 10 February 2009

By: Asgeir Mamen, & Roland van den Tillaar Sogn og Fjordane
University College, Norway

The aim of the study was to explore how the Lactate Pro® LP1710 (LP1710) blood lactate analyser can be used to find the Maximal Lactate Steady State (MLSS) power from an incremental cycle test. Methods: Nine cyclists were tested.

They performed an incremental test to establish a power vs. blood lactate concentration (BLC) curve and find two threshold definitions: Lactate Breakpoint (LB) and Onset of Blood Lactate Accumulation (OBLA). Then several continuous load tests were performed to establish the power output that elicited MLSS power (WMLSS). Results: From the blood lactate curve of the incremental test a BLC of 2.7(0.6) mmol․ L-1 (or 1.7(0.6) mmo․ L-1 above resting BLC) equalled the WMLSS. The W, HR and VO2 from the LB and OBLA tests had a range of 91-93% (LB) and 103-111% (OBLA) of MLSS values. The LB produced the lowest power, 228(46) W, 18 W below WMLSS (p=0.005), OBLA the highest, 273(47) W, 27 W above WMLSS (p=0.007).

The oxygen uptake at LB was 70(5)% of VO2max, at MLSS 77(5)% and OBLA 82(3)%. Conclusion: From the results of an incremental test the WMLSS can be estimated with LP1710, if a fixed BLC of 2.7 ± 0.7 mmol․ L-1 is used.

key words: MLSS, OBLA, Lactate Breakpoint, blood lactate concentration, training 


The definition of the lactate threshold is the highest load a subject can endure over time without a rise in blood lactate concentration (BLC), often called Maximal Lactate Steady State (MLSS) (Weltman, 1995). This load can be found by performing several constant load tests, usually of 30 minutes duration (Beneke, 2003; Harnish et al., 2001). Such a method is time-consuming. Therefore, several indirect methods have been developed, mostly using an incremental protocol with step durations of 3-8 minutes (Weltman, 1995). These tests produce a power vs.BLC curve that can be used to define the point of threshold using some criteria.

Unfortunately, no agreement on criteria for threshold determination exists (Weltman, 1995). Two common ways of defining a threshold are the Lactate breakpoint (LB), which is the first increase in BLC > 1.0 mmol․L-1 seen in an incremental protocol (Davis et al., 1983) and the Onset of Blood Lactate Accumulation (OBLA), which Sjödin defined as a fixed BLC level of 4 mmol․L-1 (Sjödin & Jacobs, 1981b).

When comparing results of threshold tests, several factors need to be taken into consideration. point to stage duration and size of load increment as factors that may affect the test result, and recommend the use of 3-minute stages Bentley (Bentley, Newell, & Bishop, 2007). Foxdal have pointed out that if BLCs from incremental and continuous tests are to be compared, the stage duration of the incremental test should be eight minutes, to assure a steady state of lactate (Foxdal et al., 1996).

The measured BLC is dependent on sampling site and type of blood (El-Sayed et al., 1993; Feliu et al., 1999; Foxdal et al., 1990). Finger sampling produces higher BLC than ear lobe sampling (Feliu et al., 1999). El-Sayed found that the OBLA load calculated from venous blood was too high for the subjects to sustain with stable BLC, whereas the OBLA load from capillary blood could be endured in a BLC steady state (El-Sayed et al., 1993). Results from tests using different sampling sites should thus be compared with caution. When using resting BLC as a baseline for threshold determination, sampling site might seriously influence the result, as the difference between ear and finger sampling is greatest at low BLCs (Feliu et al., 1999).

Foxdal concluded that direct comparisons between BLC in capillary finger blood, venous whole blood and plasma could not be made (Foxdal et al., 1990). Even more caution should be exercised when different lactate analysers have been used as the BLC result is analyser specific. Medbø et al.(Medbø et al., 2000) and Buckley and co-workers (Buckley et al., 2003) have shown that the BLC will differ between lactate analysers, so that results from one brand of analyser are not always interchangeable with another analyser. Medbø et al. (2000) compared the LP1710 with several YSI 1500 sport analysers and found that the LP1710 blood lactate results was ~40% higher than the YSI results (Y(LP1710)=-0.21+(1.50․X(YSI)). A difference between analysers is especially important to be aware of for threshold determinations that use a fixed BLC (as OBLA). A difference between two analysers of 10% might satisfy the OBLA criterion in one analyser, but gives only 3.6 mmol․L-1 in the other. Threshold determinations based on a relative change in BLC (as LB), seem not to be as prone to analyser specificity (Buckley et al., 2003), neither should it be so sensitive to variation in sampling site as the calculations are not dependent on absolute BLC values.

The fitness level can also influence the result of an incremental test. Bentley found that well-trained and lesser-trained cyclists responded differently to incremental protocols with short (3 min) and long (8 min) step duration (Bentley et al., 2001). Results from a low BLC threshold (Lactate Threshold; first increment above resting BLC) differed between the two-step durations in the well-trained group, but not in the lesser-trained group. OBLA results were not affected by step duration in either fitness groups.

According to Bentley no threshold definition can be termed “best” (Bentley et al., 2007). The selection of a diagnostic test must be made according to specific needs, but coaches and athletes should be aware of how changes in the test protocol, analysers included, can influence the result.

Thus there is a need to investigate how a specific lactate analyser behaves with respect to specific lactate threshold tests and MLSS. The lactate analyser Lactate Pro® LP1710 (Arkray inc, Japan) (LP1710) has become popular due to both size and pricing. It is easy to use and requires little blood and consequently is well suited for both laboratory and field measurements. Several investigators have examined this analyser and found it accurate and reliable (Buckley et al., 2003; Mc Naughton et al., 2002; McLean et al., 2004; Medbø et al., 2000; Pyne et al., 2000; van Someren et al., 2005).

The aim of this study is therefore to investigate how the MLSS power can be estimated from the results of an incremental lactate profile test using capillary blood from the finger and the LP1710 in cycling.


Material and Methods


Nine male subjects participated in this experiment (Table 1). All were physically active with cycling as their main activity. This study complied with the requirements of the Helsinki declaration and with current Norwegian law and regulations. The subjects signed an informed consent that stated that they participated of free will, and could leave the project at any time without explaining why.



A resting blood lactate sample was taken from the fingertip before warming up.

The subjects performed an incremental lactate profile test with 5 min step duration and an increase in load of 30 W per step from a starting load of ~1.5 W kg-1 body mass, giving a slope of 6 W․min-1. BLC was measured at the end of each step, again by pricking a finger. There were no resting periods during the blood sampling. When BLC exceeded 4 mmol․L-1, the test was terminated and after a resting period of ~20 min, a maximal oxygen uptake test started. In this test load was increased by 30 W each min from a load of approximately 2 W kg-1 body mass until voluntary exhaustion.

The load at LB (WLB) was defined in each individual as the load preceding an increase in BLC of >1.0 mmol․L-1 (Davis et al., 1983). The load at a BLC of 4.0 mmol․L-1 was defined as the OBLA load (WOBLA) (Sjödin & Jacobs, 1981).

Within a week after the incremental test, the MLSS testing was started. The first 30 min constant load test had a load ~12% below WOBLA. This load would in most cases be below the MLSS power (WMLSS), but so close that only two or three constant load tests would be required to reach the WMLSS. Blood samples were taken at 0, 5, 10, 20 and 30 min of exercise. According to the development of the BLC, the load was either raised or lowered for the next constant load test by 10 W, continuing until a steady increase in BLC was obtained during the test or the BLC did not increase more than the MLSS definition allowed. The highest load that resulted in a BLC increase less than 1 mmol․L-1 during the last 20 min was considered the WMLSS (Harnish et al., 2001). The heart rate of the MLSS test (HRMLSS) was the mean HR from the 15th to the 20th min of exercise. Oxygen consumption (VO2MLSS) was the mean VO2 from the 15th to the 20th min. This sampling interval was chosen to avoid possible drift in HR and VO2.

BLC was measured with a Lactate Pro LP1710® (Arkray Inc, Kyoto, Japan). It is a small size analyser (84x55x14.5 mm, weight 50g) that uses lactate oxidase and K ferricyanide to measure the lactate content of whole blood. Values are displayed as haemolysed values. Only a small amount of blood, 5 μL, is necessary. The manufacturer claims a coefficient of variation (CV) of ~3%.

The cyclists used their own bikes mounted on a Computrainer PC1 electromagnetic roller (RacerMate Inc, Seattle, USA). As load is independent of cadence, they were free to choose pedalling frequency. For one person, a Monark mechanical ergometer cycle (Monark Exercise AB, Varberg, Sweden) was used for all testing, as his own bike was not available. In his case, a cadence of 75 RPM was used throughout. Oxygen consumption was measured with a MetaMax CBS metabolic cart (Cortex Biophysik, Leipzig, Germany) at 10 s intervals throughout the test. Heart rate was assessed with a Polar heart rate monitor (Polar Electro OY, Kempele, Finland) every 5th second 

Statistical analysis

Results are presented as mean (SD) unless otherwise stated. An ANOVA for repeated measurements was used to compare power, heart rate, oxygen uptake and percent of HRmax and VO2max between the three definitions. When a significant difference was found, a Holm-Sidak post hoc test was performed. Data normality was investigated both with Kolmogorov-Smirnov (Lilliefors modification) and Shapiro-Wilk's tests due to the low number of subjects. If any of the normality tests failed, a Kruskal-Walis ANOVA on ranks was performed with Tukey post hoc test.

Pearson's r was used for correlations. Level of statistical significance was set to p<0.05. Statistical software: SigmaPlot 10/SigmaStat 3.5 (Systat Software Inc, San Jose, CA, USA), Winks 4.80 (TexaSoft, Cedar Hill, TX, USA) and Mystat 12 (Systat software, Inc, Chicago, IL, USA). 


The power that equalled the WMLSS at the incremental test corresponded to a BLC of 2.7(0.6) mmol․L-1. When exercising at WMLSS at the constant load test, the BLC was 3.4(0.7) mmol․L-1. The mean resting BLC was 1.0(0.2) mmol․L-1. A low BLC on the first load of the incremental test (1.1(0.3) mmol․L-1) indicated that the starting load was suitable for the subjects.

An ANOVA on repeated measurements showed significant differences for BLC (F2,8=58.8, p=0.001), W (F2,8=51.1, p=0.001), HR (F2,8=30.1, p=0.001) and VO2 (F2,8=28.3, p= 0.001) between the three definitions. The LB definition gave the lowest results; the MLSS results were intermediate and the OBLA results highest (Table 2).


The LB threshold occurred after the second to fourth load, and had an average BLC of 1.8(0.5) mmol․L-1. The 4.0 mmol․L-1 BLCOBLA was reached after the third to sixth load. LB results were 91-93% of the MLSS values, whereas OBLA values were 103-111% of them (see Figure 1). The different conditions correlated significantly for absolute values, but not for relative values, as expected. The three conditions did correlate highly with VO2max, but not with %VO2max. See Table 3a and b.

Figure 1. Box-plot of threshold results. Boxes represents 25 to 75 percentile, whiskers are 5th and 95th percentile. Horizontal line is median. LB=lactate breakpoint, OBLA=onset of blood lactate accumulation, W=watt, HR=heart rate, VO2 =oxygen uptake.




Our main finding is that the load corresponding to WMLSS can be estimated from the results of an incremental test with the LP1710 by using a fixed BLC of 2.7(0.6) mmol․L-1 or alternatively, using a delta value of 1.7(0.6) to the resting BLC.

When using the threshold definitions of LB and OBLA, the load has to be heightened by 8% (WLB) or lowered by 11% (WOBLA) to equal WMLSS. Equally, HRLB and HROBLA, were 8% and 3% to low/high respectively compared with the MLSS results.

From incremental tests the MLSS results can be found by adding or subtracting from LB or OBLA data. The workload, heart rate and oxygen uptake from the LB definition had values of 91% to 93% of the MLSS values. As LB is a threshold definition that uses a relative BLC change, the results are probably less dependent of the analyser used (Buckley et al., 2003). Such a threshold definition is on the other hand sensitive to sampling site (Feliu et al., 1999), so finger sampling must be used for comparison with our data. The OBLA W, HR and VO2 were from 3% to 11% higher than MLSS values. OBLA is thought to equal MLSS (Heck et al., 1985), but the finding that OBLA exceeds MLSS is not unique. In an investigation by Laursen et al. (Laursen et al., 2002) on ultra-endurance athletes, the power output during a 5 h triathlon was 69% of the secondary ventilatory threshold (VT2). This threshold is regarded as being close to the OBLA threshold (Lucia et al., 1999), indicating that OBLA would over-estimate ultra-marathon performance.

Welde and co-workers (Welde et al., 2003) used a LP1710 for OBLA determination in running and skiing, and found that well-trained female skiers had an average of 95% of VO2OBLA during a six km simulated ski competition lasting less than 25 min, indicating that the OBLA values found by the LP1710 would over-estimate the athlete. Higher OBLA results compared with MLSS results were also found by Stegmann (Stegmann & Kindermann, 1982) in rowers, and Aunola, using cycling as the form of exercise (Aunola & Rusko, 1992). HROBLA was 89% of HRmax. That value is significantly lower than what Impellizzeri (Impellizzeri & Marcora, 2007) found (p=0.009) using highly trained MTB cyclists. They reported a HROBLA of 93% of HRmax. It is known that highly trained endurance athletes can utilise a larger proportion of their aerobic power over time than less trained individuals (Wilmore & Costill, 2004), so training status may explain the difference we see in our data. This is further highlighted by the findings of Chicharro et al. who compared professional and amateur cyclists on HR, VO2 and W at OBLA and at a heart rate of 175 (HR175), (Chicharro et al., 1999). For professional cyclists OBLA results were significantly higher than at HR175, p>0.01, but no difference was found for the lesser trained amateur cyclists, documenting the effect of training status on threshold performance as HRmax did not differ significantly between the groups.

Foxdal, compared the BLC of 50 min continuous runs at the OBLA speed (4 mmol․L-1) determined by steps protocols of different durations (4 to 8 min step duration) (Foxdal et al., 1996). They found that the continuous runs gave higher BLC than the incremental tests if step duration was shorter than eight minutes and warned that OBLA results from incremental tests with step durations shorter than 8 min would over-estimate MLSS performance. This is compatible with our finding; the BLC during the MLSS test was 26% higher than the BLC from the incremental test that we found could reproduce the WMLSS. This difference may be due to how the lactate is distributed in blood during exercise (Medbø & Toska, 2001).

Our subjects must be classified as fit with a mean VO2max of nearly 60 ml․kg-1․min-1 and Bentley found that well-trained cyclists had different “low BLC threshold” (Lactate Threshold; first increment above resting BLC) in incremental tests of 3 and 8 min step duration (Bentley et al., 2001). Recreational cyclists, on the other hand, performed equally on both tests. Dividing our group into “high level” (VO2max >64 ml․kg-1․min-1) and ”low level” fitness (VO2max <65 ml․kg-1․min-1), no difference was found in LB or OBLA results. This discrepancy with the results of (Bentley et al., 2001) may be caused by a higher fitness level of their trained subjects, or may be due to the fact that the duration of our incremental test, five minutes, was long enough to eradicate any differences in response. It’s also important to note that the two threshold definitions are not equal; the BLC from the LT definition is probably lower than our LB definition, thereby making exact comparison difficult.

Given the large variability of resting BLC in the small sample of the current study (range 0.8 mmol․L-1), and the fact that a threshold definition based on relative changes in BLC are less analyser sensitive, an approach that uses a fixed level of BLC does not seem to be preferable. By adding 1.7 ± 0.6 mmol․L-1 to resting BLC, the power corresponded to WMLSS. It is important to note that the spread of this delta value was large in our group, from 0.8 to 3.0 mmol․L-1, so the use of a fixed delta value will therefore lead to underestimation of some, and overestimation of others.

All conditions correlated highly with aerobic power, (table 3b, p<0.02). The highest correlation was seen in the WOBLA, r=0.89, which has the highest BLC. WLB had r=0.78 and the lowest BLC. Relating power with %VO2max did change the situation, and none of the relations were statistically significant (r<0.35). Our results are in line with Tokmakidis and co-workers (Tokmakidis et al., 1998) who were unable to find a unique BLC that had a superior correlation with performance compared to other levels of BLC, indicating that it is the profile of the whole curve that matters (Bentley et al., 2007).

Despite several attempts to develop a simple method to estimate the MLSS power (Billat et al., 1994; Harnish et al., 2001; Van et al., 2004), if the coach and athlete need high accuracy in their testing, the time consuming MLSS test protocol has to be applied.


It is possible to estimate the WMLSS from an incremental test with the LP1710 analyser. A fixed BLC of 2.7(0.6) mmol․L-1 or a delta value of 1.7(0.6) mmol․L-1 added to the resting BLC gives the WMLSS. These results are valid for incremental tests with step durations of five minutes, and finger sampling. MLSS power can also be derived from LB and OBLA test results. The values here found are analyser specific and may induce errors in the diagnostics of athletes if applied to other brands of analysers.


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7 x 4 min step test protocol

Information for Athletes and NTC Scientists

(To be used from October 2006 - Updated 27/12/08)
Compiled by: Tony Rice
Dept of Physiology: Australian Institute of Sport and Australian Rowing
Office: (02) 6214-7891


The laboratory test protocol adopted by Rowing Australia aims to provide detailed physiological information of the rower’s submaximal capacity and efficiency and to measure maximal performance parameters in a time efficient manner. To do this a 7 x 4 min protocol has been implemented across the country since 2006. For the 2009-2012 Olympic cycle the standard protocol will change to reflect new information that has been presented by Ivan Hooper and others on the use of sliders and lower drag factors. The main aims of altering the protocol are to 1. more accurately reflect the stroke rate and drive:recovery ratio of on-water rowing and 2. minimise the risk of injury that may result from considerable time being spent on the ergometer with significant lower back load. Implementation of the new drag factor settings and sliders will be mandatory from Nov 2009.

For the 2008-2009 season each SIS/SAS will be able to asked to do complete the laboratory test in one of two ways:
1. If sliders are available then it is asked the laboratory completes the test protocol using sliders and the updated drag factor settings
2. If sliders are not available then it is asked the laboratory completes the test protocol using the old drag factor settings as per the previous Olympic cycle

The standard laboratory test will be completed at least two times within each season (all dates to be communicated early 2009 and confirmed at the beginning of each season). Rowing Australia and the
National Rowing Centre of Excellence (NRCE) require a standard summary of data on all SIS/SAS athletes who are aspiring for National Selection in the current season to be returned to the Sports
Science Coordinator shortly after the completion of each testing period.

NB Testing on Concept IID ergometers - there are no significant differences in the physiological responses to either the IIC or IID ergo and thus tests can be completed on either one with the proviso that all tests for an individual athlete are carried out on the same ergometer throughout the rowing season and every is made to continue using the same ergometer for all subsequent seasons.
The following information is designed as a detailed guide to the testing methods.

7 x 4 min Step Test Protocol Laboratory Environment and Subject Preparation


The athlete must not train at all in the 12 hours preceding the test. On the day before the test, the afternoon training session should consist of no more than 12 km on the water, and should be of low intensity (T3-T2 range). There should be no heavy weight training, or exercise to which the athlete is not accustomed. It is suggested that the athlete replicate as closely as possible similar training loads in the 24 hours leading into each testing block.


A normal meal (incorporating a high carbohydrate component) should be eaten on the evening preceding the test and, if scheduling allows, also on the day of the test. No alcohol should be consumed in the 24 hours preceding the test. The athlete should give special attention to ensuring good hydration in the lead-up to the test.

Test Preparation

Each laboratory may have information and consent forms that may need to be provided prior to the

Equipment Checklist
• Concept IIC or IID rowing ergometer
• Heart rate monitoring system
• Expired gas analysis system (as per general recommendations)
• Stopwatch
• Lactate analyser (Lactate Pro recommended) and blood gas analyser if possible.
Blood sampling equipment:
• Finalgon ointment or cream
• Autolet, lancets and platforms
• Sterile alcohol swab
• Tissues
• Heparinised capillary tubes
• Pipette (if required)
• Disposable rubber gloves
• Sharps container
• Biohazard bag.

Ergometer Settings
Table 1: Old Ergometer Drag Factor Settings
Category Drag Factor
Junior Female 110
Lightweight Female 110
Heavyweight Female 120
Junior Male 120
Lightweight Male 120
Heavyweight Male 130

New Ergometer Drag Factor Settings
(only to be used if tested on sliders – see Introduction)
Category Drag Factor
Junior Female 95
Lightweight Female 95
Heavyweight Female 105
Junior Male 105
Lightweight Male 105
Heavyweight Male 115

Step - Test Administration:

Athletes will start the 7 step test protocol with a work load based and increment based purely on their previous year’s best 2000m time. The range of times for the 2000m ergometer tests have been
divided on 10 sec increments with the fastest 2000m times having the highest starting work load and increment (see Table 2).


See resource list below for a print copy of table 2.  

In order to ensure that individual athlete’s complete the identical amount of work prior to beginning the 7th step (4 min at maximal pace) every 7 x 4 min step test undertaken by the athlete for that seasonal year must use the same starting work load and increment. In other words there will be no increment of starting work load during the season as has been in previous years. The only way an athlete will be able to change their starting work load or increment will be to perform a 2000m test that has a time that places them into a different time bracket.

The workloads and increments have been designed such that the 6th step (i.e. the step immediately preceding the maximal step) produces a blood lactate value in the range of 5-8 mmol/L. Obviously this will change depending on the time of year and the athlete’s current training status but importantly in a single season the athlete will complete an identical amount of work leading into the maximal performance component of the test.

The scientist in charge is given flexibility in choosing if moving to the next 10 sec increment is valuable or not. An example would be that when an athlete has 4 years of data all starting at the same work load and increment but finally betters their 2000m time from 5:50.6 to 5:49.8. Taking the protocol to its exact description would mean the athlete would change their starting work load and increment. However the gain in doing this is far outweighed by the inability to compare the athlete across the 4 years of data using the previous starting work load and increment. In cases such as this it is left to the discretion of the coach and scientist to decide what should be the appropriate starting work load and increment foir that athlete. Scientists can contact either the Head Coaches, HPD or Tony Rice for additional consultation if required.

The 2000m category times used in Table 2 are based on the athlete’s best 2000m time from the previous year and not their all-time personal best 2000m test. If the athlete had a medical exemption for the previous year or Thus it is possible that work loads would change slightly from year to year for a given individual. If this is the case, comparison between years for the same individual, or in the same year between individuals, must only be done using variables such as heart rate, blood lactate, VO2 and perceived exertion at LT1, LT2 and the maximal step.

Distance covered in the final maximal step can only be used as a comparison between athletes when those athletes have completed the same amount of work prior to the maximal step i.e. identical starting work load and increment.

7 x 4 min step test procedure:

1. Complete and give scientist your Informed Consent forms.
2. The scientist will measure the following physical parameters: height; weight; sitting height; arm span; sum of 7 skinfolds (only sum of 7 skinfolds is a requirement of RA but it is good practice if labs have the available resources to complete the additional measurements)
3. Attach a heart rate monitor and ensure it is working correctly.
4. The scientist will place a small quantity of Finalgon on one of your earlobes, or on some fingertips to ensure that the capillary blood is arterialised prior to sampling.
5. The scientist will adjust the ergometer drag factor to that appropriate to your competition category (see Table 1) and provide you with the work loads for your 6 increments (see Table 2).
6. The scientist will position the gas collection apparatus (respiratory valve etc) and ensure that the athlete is as comfortable as possible. Take several light strokes and the scientist will make any necessary adjustment to respiratory hoses or other apparatus to ensure that the hose is not pulling on the breathing apparatus at any stage during the stroke.
7. The scientist will collect a pre-exercise blood sample from the earlobe or fingertip using a Lactate Pro analyser.
8. The scientist set the ergometer output display to show Watts for each stroke as well as set the work load time and rest interval.
9. The scientist will attach the nose clip and prepare to start the test if the test requires gas analysis.
10. Start rowing when instructed.
11. Blood is collected from your earlobe or fingertip during each rest period and analysed. During the 1 min rest period, you are permitted to remove the gas collection apparatus to have a drink. However, it is important to ensure that the breathing apparatus is back in position well before the start of the next work bout (approximately 10–15 s).
12. You are required to complete 6 submaximal work loads prior to beginning the maximal step.
Your starting work load is based on your best 2000m time from the previous year and the work loads for all rowing categories are contained in Table 2.
13. There is only the standard 1 min break between the end of the final submaximal step (6th step) and beginning the 4 min maximal step.
14. Begin the test when instructed. Remember the aim of the test is to cover as many meters as possible in the 4 minutes. You should be exhausted at the completion of the 4 minutes of rowing. If possible try to even split the 4 minutes rather than starting conservatively and then coming home strong. You should be able to hold or better your average 2000m split for the entire 4 minutes. Coincident with the maximal performance assessment will be the attainment of maximal heart rate, blood lactate concentration and oxygen consumption.
15. At the end of the test, the scientist will help you remove the breathing apparatus as rapidly as possible.
16. An earlobe or fingertip blood sample should be collected and analysed at the completion and 4min post completion of the final maximal step. The highest value of these two readings should be used as the peak blood lactate.

Analysis of Test Results: Blood Lactate Profiles

Submaximal oxygen uptakes are calculated by averaging the readings recorded during the final 2 min of each submaximal workload. The maximum oxygen uptake is recorded as the highest value actually attained over a period of a full minute. Thus, if the gas analysis system is based on 30 s sampling periods, the maximum oxygen uptake is the sum, or if all results are expressed in L.min-1, the average of the highest two consecutive readings. If 15 s sampling periods are used, the maximum oxygen uptake is the highest value obtained on the basis of any four consecutive readings.

Submaximal heart rates are the values for the final 30 s of each submaximal workload. The maximum heart rate is the highest value recorded over a 5 s sampling period during the entire test. Computerised analysis allows for quite simple determination of the various blood lactate transition thresholds and associated measures. The ADAPT* (Automatic Data Analysis for Progressive Tests) software package is to be used to calculate these thresholds from the test data.

*(ADAPT software is available to the Australian sport science community from Sport Sciences, Australian Institute of Sport, PO Box 176, Belconnen ACT 2616.)
Data from the final maximal step are included as the final work load values (including peak lactate) and used in combination with values from the submaximal workloads for calculation of the blood lactate thresholds and related measures in ADAPT.


Gore CJ. (Editor) (2000) Physiological Tests For Elite Athletes / Australian Sports Commission. Human Kinetics Champaign IL. Chapters 2, 3, 8 and 22.
Medbø JI, Mohn A-C, Tabata I et al. (1988) Anaerobic capacity determined by maximal accumulated O2 deficit. Journal of Applied Physiology 64: 50–60.


Resources to conduct test:

For a print copy, see 7x4min step test protocol.

Table 2: Determination of Test Protocol.


Recovery nutrition?

Written by the AIS Sports Nutrition, last updated July 2009. © Australian Sports Commission

Website link: Australian Rowing

What are the priorities for recovery nutrition?

Recovery is a challenge for athletes who are undertaking two or more sessions each day, training for prolonged periods, or competing in a program that involves multiple events. Between each work-out, the body needs to adapt to the physiological stress. In the training situation, with correct planning of the workload and the recovery time, adaptation allows the body to become fitter, stronger and faster. In the competition scenario, however, there may be less control over the work-to-recovery ratio. A simpler but more realistic goal may be to start all events in the best shape possible.

Recovery encompasses a complex range of processes that include;
 • refueling the muscle and liver glycogen (carbohydrate) stores
 • replacing the fluid and electrolytes lost in sweat
 • manufacturing new muscle protein, red blood cells and other cellular components as part of the repair and adaptation process
 • allowing the immune system to handle the damage and challenges caused by the exercise bout

The emphasis an athlete needs to place on each of these broad goals will vary according to the demands of the exercise session.  Key questions that need to be answered include - How much fuel was utilised?  What was the extent of muscle damage and sweat losses incurred?  Was a stimulus presented to increase muscle protein?

A proactive recovery means providing the body with all the nutrients it needs, in a speedy and practical manner, to optimise the desired processes following each session.  State-of-the-art guidelines for each of the following issues are presented below.


Muscle glycogen is the main fuel used by the body during moderate and high intensity exercise. Inability to adequately replace glycogen stores used up during a workout will compromise performance in subsequent sessions.

The major dietary factor in postexercise refueling is the amount of carbohydrate consumed. Depending on the fuel cost of the training schedule or the need to fuel up to race, a serious athlete may need to consume between 7-12 g of carbohydrate per kg body weight each day (350-840 g per day for a 70kg athlete) to ensure adequate glycogen stores. As an overemphasis on other nutrients, such as protein and fat, can easily replace carbohydrate foods within the athlete’s energy requirements, careful planning of meals and snacks throughout the day is needed achieve the required level of intake (for more information on carbohydrate requirements for athletes, refer to the “Carbohydrate”  Fact Sheet). 

In the immediate post exercise period, athletes are encouraged to consume a carbohydrate rich snack or meal that provides 1-1.2 g of carbohydrate per kg body weight within the first hour of finishing, as this is when rates of glycogen synthesis are greatest. This is especially important if the time between prolonged training sessions is less than 8 hrs. The type and form (meal or snack) of carbohydrate that is suitable will depend on a number of factors, including the athletes overall daily carbohydrate and energy requirements, gastric tolerance, access and availability of suitable food options and the length of time before the next training session. Table 1 gives examples snacks providing at least 50g of carbohydrate.


The majority of athletes will finish training or competition sessions with some level of fluid deficit.  Research suggests that many athletes fail to adequately drink sufficient volumes of fluid to restore fluid balance. As a fluid deficit incurred during one session has the potential to negatively impact on performance during subsequent training sessions, athletes need to incorporate strategies to restore fluid balance, especially in situations where there is a limited amount of time before their next training session.

Athletes should aim to consume 125-150% of their estimated fluid losses in the 4-6 hours after exercise (Refer to the “How much do athletes sweat?” Fact Sheet for advice on how to monitor fluid losses during exercise). The recommendation to consume a volume of fluid greater than that lost in sweat takes into account the continued loss of fluid from the body through sweating and obligatory urine losses.

Fluid replacement alone will not guarantee re-hydration after exercise. Unless there is simultaneous replacement of electrolytes lost in sweat, especially sodium, consumption of a large volume of fluid may simply result in large urine losses. The addition of sodium, either in the drink or the food consumed with the fluid, will reduce urine losses and thereby enhance fluid balance in the post exercise period.  Further, sodium will also preserve thirst, enhancing voluntary intake. As the amount of sodium considered optimal for re-hydration (50-80 mmol/L) is in excess of that found in most commercially available sports drinks, athletes may be best advised to consume fluids after exercise with everyday foods containing sodium.

In considering the type of fluids needed to achieve their re-hydration goals, athletes should also consider the length of time before their next session, the degree of the fluid deficit incurred, taste preferences, daily energy budget, as well as their other recovery goals. With the latter, athletes can simultaneously meet their refueling, repair and contribute to their re-hydration goals by consuming fluids that also provide a source of carbohydrate and protein e.g. flavoured milk, liquid meal supplement.

Muscle Repair and Building

Prolonged and high-intensity exercise causes a substantial breakdown of muscle protein. During the recovery phase there is a reduction in catabolic (breakdown) processes and a gradual increase in anabolic (building) processes, which continues for at least 24 hours after exercise. Recent research has shown that early intake after exercise (within the first hour) of essential amino acids from good quality protein foods helps to promote the increase in protein rebuilding. Consuming food sources of protein in meals and snacks after this “window of opportunity” will further promote protein synthesis, though rate at which it occurs is less.

Though research is continuing into the optimal type (e.g. casein Vs whey), timing and amount of protein needed to maximise the desired adaptation from the training stimulus, most agree that both resistance and endurance athletes will benefit from consuming 15-25g of high quality protein in the first hour after exercise. Adding a source of carbohydrate to this post exercise snack will further enhance the training adaptation by reducing the degree of muscle protein breakdown.  Table 2 provides a list of carbohydrate rich snacks that also provide at least 10g of protein, while Table 3 lists a number of everyday foods that provide ~10g of protein.

Immune System

In general, the immune system is suppressed by intensive training, with many parameters being reduced or disturbed during the hours following a work-out.  This may place athletes at risk of succumbing to an infectious illness during this time. Many nutrients or dietary factors have been proposed as an aid to the immune system - for example, vitamins C and E, glutamine, zinc and most recently probiotics - but none of these have proved to provide universal protection.  The most recent evidence points to carbohydrate as one of the most promising nutritional immune protectors.  Ensuring adequate carbohydrate stores before exercise and consuming carbohydrate during and/or after a prolonged or high-intensity work-out has been shown to reduce the disturbance to immune system markers.  The carbohydrate reduces the stress hormone response to exercise, thus minimising its effect on the immune system, as well as also supplying glucose to fuel the activity of many of the immune system white cells.

How does recovery eating fit into the big picture of nutrition goals?

To optimise recovery from a training session, meals (which generally supply all the nutrients needed for recovery) must either be timetabled so that they can be eaten straight after the work-out, or special recovery snacks must be slotted in to cover nutrient needs until the next meal can be eaten.

For athletes who have high-energy needs, these snacks make a useful contribution towards their daily kilojoule requirement.  When there is a large energy budget to play with, it may not matter too much if the snacks only look after the key recovery nutrients - for example carbohydrate e.g. sports drink.  On the other hand, for those athletes with a low energy budget, recovery snacks will also need to contribute towards meeting daily requirement for vitamins, minerals and other nutrients.  Snacks that can supply special needs for calcium, iron or other nutrients may double up as suitable recovery snacks. e.g. yoghurt

Real food Vs supplements

Many athletes fall into the trap of becoming reliant on sports food supplements, believing this to be the only and/or best way to meet their recovery goals. This often results in athletes “doubling up” with their recovery, consuming a sports food supplement that meets certain recovery goals e.g. liquid meal supplement, then following this up soon afterwards with a meal that would help them meet the same recovery goal e.g. bowl of cereal with fresh fruit.  Unless constrained by poor availability or lack of time, athletes are best advised to favour real food/fluid options that allow them to meet recovery and other dietary goals simultaneously. This is especially important for athletes on a low energy budget.

What are some other the practical considerations for recovery eating?

Some athletes finish sessions with a good appetite, so most foods are appealing to eat. On the other hand, a fatigued athlete may only feel like eating something that is compact and easy to chew. When snacks need to be kept or eaten at the training venue itself, foods and drinks that require minimal storage and preparation are useful. At other times, valuable features of recovery foods include being portable and able to travel interstate or overseas. Situations and challenges in sport change from day to day, and between athletes - so recovery snacks need to be carefully chosen to meet these needs.

Table 1- Carbohydrate-rich recovery snacks (50g CHO portions)
 • 700-800ml sports drink
 • 2 sports gels
 • 500ml fruit juice or soft drink
 • 300ml carbohydrate loader drink
 • 2 slices toast/bread with jam or honey or banana topping
 • 2 cereal bars
 • 1 cup thick vegetable soup + large bread roll
 • 115g (1 large or 2 small) cake style muffins, fruit buns or scones
 • 300g (large) baked potato with salsa filling
 • 100g pancakes (2 stack) + 30g syrup

Table 2- Nutritious carbohydrate-protein recovery snacks (contain 50g CHO + valuable source of protein and micronutrients)
 • 250-300ml liquid meal supplement
 • 300g creamed rice
 • 250-300ml milk shake or fruit smoothie
 • 600ml low fat flavoured milk
 • 1-2 sports bars (check labels for carbohydrate and protein content)
 • 1 large bowl (2 cups) breakfast cereal with milk
 • 1 large or 2 small cereal bars + 200g carton fruit-flavoured yoghurt
 • 220g baked beans on 2 slices of toast
 • 1 bread roll with cheese/meat filling + large banana
 • 300g (bowl) fruit salad with 200g fruit-flavoured yoghurt
 • 2 crumpets with thick spread peanut butter + 250ml glass of milk
 • 300g (large) baked potato + cottage cheese filling + glass of milk

Table 3 - Foods providing approximately 10g of protein.

Animal foods
 • 40g of cooked lean beef/pork/lamb
 • 40g skinless cooked chicken
 • 50g of canned tuna/salmon or cooked fish
 • 300 ml of milk/glass of Milo
 • 200g tub of yoghurt
 • 300ml flavoured milk
 • 1.5 slices (30g) of cheese
 • 2 eggs

Plant based foods
 • 120g of tofu
 • 4 slices of bread
 • 200g of baked beans
 • 60g of nuts
 • 2 cups of pasta/3 cups of rice
 • .75 cup cooked lentils/kidney beans



Arterial compliance of rowers: implications for combined aerobic and strength training on arterial elasticity

By: Jill N. Cook, Allison E. DeVan, Jessica L. Schleifer, Maria M. Anton, Miriam Y. Cortez-Cooper and Hirofumi Tanaka.
Am J Physiol Heart Circ Physiol 290:H1596-H1600, 2006. First published 11 November 2005; doi:10.1152/ajpheart.01054.2005


Cook, Jill N., Allison E. DeVan, Jessica L. Schleifer, Maria M. Anton, Miriam Y. Cortez-Cooper, and Hirofumi Tanaka. Arterial compliance of rowers: implications for combined aerobic and strength training on arterial elasticity. Am J Physiol Heart Circ Physiol 290: H1596–H1600, 2006. First published November 11, 2005; doi:10.1152/ajpheart.01054.2005.—Regular endurance exercise increases central arterial compliance, whereas resistance training decreases it. It is not known how the vasculature adapts to a combination of endurance and resistance training. Rowing is unique, because its training encompasses endurance- and strength-training components. We used a cross-sectional study design to determine arterial compliance of 15 healthy, habitual rowers [50 é 9 (SD) yr, 11 men and 4 women] and 15 sedentary controls (52 é 8 yr, 10 men and 5 women). Rowers had been training 5.4 é 1.2 days/wk for 5.7 é 4.0 yr. The two groups were matched for age, body composition, blood pressure, and metabolic risk factors. Central arterial compliance (simultaneous ultrasound and applanation tonom-etry on the common carotid artery) was higher (P § 0.001) and carotid -stiffness index was lower (P § 0.001) in rowers than in sedentary controls. There were no group differences for measures of peripheral (femoral) arterial stiffness. The higher central arterial compliance in rowers was associated with a greater cardiovagal baroreflex sensitivity, as estimated during a Valsalva maneuver (r 0.54, P § 0.005). In conclusion, regular rowing exercise in middle-aged and older adults is associated with a favorable effect on the elastic properties of the central arteries. Our results suggest that simultaneously performed endurance training may negate the stiffen-ing effects of strength training.   

Arterial compliance of rowers: implications for combined aerobic and strength training on arterial elasticity

THE AORTA AND CENTRAL ARTERIES are not simply tubes or conduits; rather, they are highly complex components of the vascular tree that buffer oscillations in blood pressure and blood flow. Reductions in this cushioning function result in increased left ventricular afterload, increased myocardial oxy-gen demand, and decreased coronary blood flow and eventu-ally lead to coronary ischemia (19, 22). Furthermore, because the vascular structure of the carotid sinus determines the deformation of and strain on the arterial baroreceptor endings during changes in arterial blood pressure, decreased arterial compliance is associated with impaired arterial baroreflex reg-ulation of heart rate (17). Thus, through these mechanisms, stiffening of the central arteries exerts a combined effect on the heart, the arteries, and the autonomic nervous system in older humans.

Regular aerobic exercise and strength training are recom-mended for the prevention and treatment of cardiovascular disease and frailty associated with aging. Regular aerobic exercise is beneficial for reversing arterial stiffening in middle-aged and older adults (18, 26) and attenuates the age-related decline in cardiovagal baroreflex sensitivity (BRS) (16). In contrast to the beneficial effects of aerobic exercise, resistance training in middle-aged adults is associated with lower, rather than higher, central arterial compliance (14). Therefore, regular aerobic exercise and resistance exercise seem to exert opposite effects on the elastic properties of the arterial wall. It is not known how the elastic properties of the arterial wall will behave when one performs endurance training and strength training simultaneously.

In this regard, rowing exercise is unique, as it includes components of aerobic endurance and muscular strength (23). Rowers require large muscle strength to accelerate the boat at the start of the race and high endurance capacity to maintain this speed during the race (24). Similarly, rowers perform a combination of endurance and strength training during their usual training regimen, as demonstrated by their large maximal aerobic capacity and muscle strength (13, 23, 28, 29). Because more time may be required for development of vascular wall adaptations, a cross-sectional study analyzing arterial compli-ance in rowers may shed light on this clinically important question.

Accordingly, the primary aim of this study was to determine whether central and peripheral arterial compliance is higher in middle-aged and older rowers than in age-matched sedentary controls. We hypothesized that habitual rowers would demon-strate greater central arterial compliance than sedentary con-trols. Moreover, we hypothesized that compliance of peripheral (more muscular) arteries would be similar between the two groups, because exercise training has been shown to have no impact on these vascular beds (14, 25, 26). Because a reduction in arterial BRS is one of the important sequela of arterial stiffening (17), we also determined whether the hypothesized higher arterial compliance in rowers would be accompanied by greater BRS.


Subjects. A total of 30 healthy middle-aged and older adults (37–71 yr) were studied. They were either rowers (11 men and 4 women) or age-matched sedentary controls (10 men and 5 women). All of the subjects were healthy, nonobese, nonsmoking, normotensive (§140/90 mmHg), normolipidemic, and free of overt cardiovascular and other chronic diseases as assessed by medical history question- naire. None of the subjects were taking cardiovascular-acting medi-cations, including hormone replacement therapy. Physical activity was documented by a modified Godin physical activity questionnaire

(4). Rowers had been training 5.4 é 1.2 (SD) times/wk, 73 é 14 min/session for 5.7 é 4.0 yr, and rowing was their primary form of regular exercise. Approximately 65% of their training sessions were devoted to high-intensity workouts, and 87 é 8% of rowing was performed on water. Sedentary participants had not exercised for Ž12 mo. All procedures were approved by the Institutional Review Board at the University of Texas at Austin, and written informed consent was obtained from each individual before participation.

Procedures. All laboratory procedures were performed at rest under comfortable laboratory conditions. Subjects abstained from food, alcohol, and caffeine for Ž4 h before laboratory procedures. An overnight 12-h fast was required before the measurements of meta-bolic risk factors. Premenopausal women were tested during the early follicular phase of the menstrual cycle.

Body composition. Body composition was measured using dual-energy X-ray absorptiometry (Lunar DPX, GE Medical Systems, Fairfield, CT).

Dietary intake analysis. A 3-day diet record was obtained and analyzed by a registered dietitian. Carbohydrate, fat, protein, and alcohol intakes were presented as percentage of the total caloric intake.

Handgrip strength. Handgrip strength was measured using an electrical handgrip dynamometer (model HDM-915, Lode Instru-ments, Groningen, The Netherlands).

Arterial blood pressure and heart rate at rest. Brachial and ankle blood pressure and heart rate were measured by an automated oscil-lometric device (model VP-2000, Colin Medical Instruments, San Antonio, TX) after Ž15 min of rest in the supine position (4). Ankle-brachial pressure index was calculated as ankle systolic blood pressure divided by brachial systolic blood pressure and was used to screen for peripheral artery disease.

Blood samples. A blood sample was collected from the antecubital vein after an overnight fast. Plasma concentrations of glucose, lipids, and lipoproteins were determined enzymatically using a Vitros DT60 analyzer (Ortho-Clinical Diagnostics, Raritan, NJ). Plasma norepi-nephrine concentrations were analyzed by enzyme immunoassay (Labor Diagnostika Nord, Nordhorn, Germany). Hematocrit was measured using a microcapillary reader (Damon/IEC Division, Need-ham, MA).

Arterial compliance. A combination of ultrasound imaging with simultaneous applanation of tonometrically obtained arterial pressures from the contralateral artery permitted noninvasive determinations of arterial compliance and -stiffness index (14, 26). The common carotid artery was imaged using B-mode ultrasound (model HDI 5000CV, Philips, Bothel, WA) equipped with a high-resolution linear-array transducer. Ultrasound images were transferred to digital view-ing software (Access Point 2000, Freeland, Westfield, IN). Diameters were measured from the intima of the far wall to the media-adventitia of the near wall. Pulsatile changes in the common carotid artery and common femoral artery diameters were analyzed 1–2 cm proximal to the bifurcation. Blood pressure waveforms were obtained from the contralateral artery using arterial applanation tonometry (model TCB-500, Millar Instruments, Houston, TX) (14, 26) and analyzed by waveform browsing software (WinDaq 2000, Dataq Instruments, Akron, OH). To eliminate interinvestigator variability, one investiga-tor analyzed all ultrasound images and blood pressure waveforms.

Cardiovagal BRS. Cardiovagal BRS was determined using the Valsalva maneuver (16, 17, 20). Briefly, subjects were seated in an upright position and familiarized with the procedure. Subjects per-formed a Valsalva maneuver and maintained an expiratory mouth pressure of 40 mmHg for 10 s. R-R interval (ECG) and blood pressure (Pilot 9200, Colin Medical, San Antonio, TX) were measured contin-uously. Subjects performed three Valsalva maneuvers Ž5 min apart to allow heart rate and blood pressure to return to baseline.

Data for cardiovagal BRS were recorded and analyzed by wave-form browsing software (WinDaq 2000) during the phase IV over-shoot. Systolic blood pressure values were linearly regressed against corresponding (lag 1) R-R intervals from the point where the R-R intervals began to lengthen to the point of maximal systolic blood pressure elevation (16, 17).

Carotid artery intima-media thickness. Carotid artery intima-media thickness was measured from images derived from an ultrasound machine equipped with a high-resolution linear-array transducer (model HDI-5000, Philips) (27). Images were analyzed by use of computerized software (QLab, Philips).

Statistics. One-way ANOVA and analysis of covariance were used for statistical analysis to determine significant group differences. Statistical significance was set a priori at P § 0.05 for all compari-sons. Values are means é SD, except in Figs. 1 and 2, where means é SE are reported. Initially, univariate correlation and regression anal-ysis were used to assess the strength of the relation between carotid arterial compliance and cardiovagal BRS. Partial correlation analysis and forward stepwise multiple regression analysis were then used to determine an independent association between cardiovagal BRS and arterial compliance.


Table 1: Selected subject characteristics, dietry intake, and metabolic risk factors.  

There were no group differences in age, height, body mass, body mass index, body composition, or waist circumference (Table 1).

As expected, physical activity scores assessed by the modified Godin questionnaire and handgrip strength were higher (both P § 0.02) in rowers than in sedentary controls. There were no group differences for total caloric intakes, percent carbohydrate, percent fat, percent alcohol, or sodium intakes. Daily protein intake was higher (P § 0.05) in rowers than in sedentary controls. Fasting plasma glucose, lipid, and lipoprotein concentrations were not different between groups.

Plasma norepinephrine concentrations were higher (P § 0.05) in rowers than in sedentary controls. Heart rate at rest was lower (P § 0.05) in rowers than in sedentary controls (Table 2). Brachial blood pressure, carotid blood pressure, carotid artery intima-media thickness, and ankle-brachial pressure in-dex were not different between the groups.

Table 2: Selected physiological variables at rest.  

Carotid arterial compliance was higher (P § 0.001) and -stiffness index was lower (P § 0.001) in rowers than in sedentary controls (Fig. 1).

Because of the significant group difference in heart rate at rest, analysis of covariance was performed with heart rate as the covariate. The group differ-ence in carotid arterial compliance remained statistically sig-nificant (P 0.01). Femoral arterial compliance and -stiff-ness index were not different between rowers and sedentary controls. Cardiovagal BRS was greater (P § 0.01) in rowers than in sedentary controls (Fig. 2) and was positively associ-ated with carotid arterial compliance (r 0.54, P § 0.005).

Stepwise regression analysis revealed that, among the variables correlated with cardiovagal BRS (arterial compliance, diastolic blood pressure, and heart rate), carotid arterial compliance was the strongest independent physiological correlate of cardiova-gal BRS, inasmuch as it explained 36% of the variance (P § 0.01).

Additionally, when the influence of other variables (e.g., diastolic blood pressure and heart rate) was accounted for using a partial correlation analysis, the relation between cardiovagal BRS and carotid arterial compliance remained significant (r 0.45, P § 0.05).


The primary findings of the study are as follows:
1) Central arterial compliance was higher and -stiffness index was lower in habitual rowers than in age-matched sedentary controls who were matched for age, body mass, metabolic risk factors, blood pressure, and sodium intake.
2) Measures of peripheral arterial stiffness were not different between the groups.
3) Cardiovagal BRS was higher in rowers than in sedentary controls and was positively related to carotid arterial compliance. These results indicate that regular rowing exercise in middle-aged and older adults is associated with favorable effects on the elastic prop-erties of the central arteries. 
Because vascular adaptations may be a long-term process requiring a prolonged follow-up or intervention periods to induce appreciable changes, we used a cross-sectional study design. To minimize the weaknesses of this study design and to isolate the influence of rowing as much as possible, both groups were carefully matched for age, body composition, blood lipids, plasma glucose, blood pressure, and dietary so-dium intake. Additionally, to isolate the effect of rowing, we excluded individuals for whom rowing was not their primary form of exercise. Rowers were also excluded if more than two training days per week were exclusively nonrowing exercise, such as running, cycling, or weightlifting. Many rowers were competitive and followed similar training schedules. The ma-jority ( 65%) of their training sessions were devoted to high-intensity workouts. We found that central arterial com-pliance was higher and -stiffness index was lower in habitual rowers. Therefore, the results of the present study suggest that chronic rowing exercise is associated with a greater central arterial compliance.
Because of the contrasting effects of endurance and resis-tance training on the elastic properties of arteries, it is of particular interest to determine how the arteries adapt to a combination of these training modes. To gain insight into this issue, we studied a group of highly trained rowers. Rowing is unique for examination of training adaptations, because it includes the components of endurance training and resistance training (13, 23). Rowers exhibit markedly enlarged left ven-tricular dimensions as well as left ventricular wall thickness (13, 21). This is thought to be due to a combination of extreme volume load (as seen in endurance training) and extreme pressure overload (as seen in resistance training) during rowing (21). Rowing uses the upper and lower body and utilizes both limbs simultaneously to generate powerful force, causing large fluctuations in blood pressure and pulse pressure (2, 23). As shown in the present study, in regard to the impact of the overall rowing training on the vasculature, the endurance-training component appears to outweigh the resistance-training component, producing a higher arterial compliance in rowers. These results suggest that stiffening of the large arteries may be avoided if endurance training is incorporated into an exercise program that has a strength-training component. Intervention studies are necessary to draw more definite conclusions on this issue.
Endurance training does not influence the compliance of peripheral arteries (25, 26). Similarly, peripheral arterial com-pliance is not different between sedentary and resistance-trained individuals (14). Consistent with these observations, we found that femoral arterial compliance was not different be-tween groups. A lack of association between exercise training and peripheral arterial compliance is attributed to the fact that the arterial wall components of the femoral artery, which, in contrast to the central elastic arteries, do not act to buffer large fluctuations in blood pressure and blood flow.
The sympathetic nervous system exerts a tonic restraint on the compliance of the common carotid artery (11), and the removal of that restraint produces an immediate increase in its compliance (11). We measured plasma concentrations of nor-epinephrine, a rough index of sympathetic nervous system activity, in an attempt to gain insight into the physiological mechanisms underlying the effects of rowing training on arte-rial compliance. Although carotid arterial compliance was greater in rowers than in sedentary controls, plasma norepi-nephrine levels were also higher in rowers. These results are not consistent with the idea that decreased sympathetic vaso-constrictor activity is responsible for the greater arterial com-pliance in rowers. A more likely explanation for the greater arterial compliance in rowers is increased nitric oxide bioavail-ability. Arterial compliance is modulated significantly by en-dothelial function (7), and regular aerobic exercise improves this important function (5). Other possibilities include in-creases in vasa vasorum flow (1), decreases in collagen cross-linking (8), and/or decreases in local endothelin-1 action (12). Given that the influence of exercise training manifests only in the central elastic arteries, where beat-by-beat arterial disten-sion is greater, there may be an interaction between these physiological mechanisms and mechanical factors that are inherent in the central arterial wall.
The vascular structure of the carotid sinus determines the deformation of the arterial baroreceptor endings during changes in arterial pressure. A compliant artery acts to aug-ment stimulus transduction and afferent responsiveness of baroreceptors. Endurance training is associated with enhanced cardiovagal BRS (17). However, it is unclear whether resis-tance training has the same effect (3, 9). Given the lower arterial compliance in strength-trained individuals and the close association between arterial compliance and arterial BRS, it is reasonable to hypothesize that strength training is associated with lower cardiovagal BRS. The higher cardiova-gal BRS in rowers was positively and independently associated with carotid arterial compliance. Thus regular rowing exercise appears to enhance arterial BRS arguably via its effects on arterial compliance. Alternatively, rowing exercise itself causes large blood pressure changes that mimic the Valsalva maneuver at the catch of the stroke (23). Therefore, in contrast to sedentary individuals, rowers may have developed a greater capacity to adjust disturbances in blood pressure because of frequent exposure to this stimulus.
In addition to the use of a cross-sectional study design, the present study has other important limitations. Because of the risks associated with the testing and a lack of specific testing procedure, we did not measure maximal aerobic capacity and muscle strength to confirm that rowers were endurance trained as well as strength trained. As alternatives, we used the Godin physical activity questionnaire and handgrip strength. Even though these are indirect measures, the magnitude of the differences in these results between the sedentary controls and rowers clearly shows that rowers in the present study demon-strated greater aerobic fitness and muscular strength.

In conclusion, habitual rowers demonstrate a greater central arterial compliance and higher cardiovagal BRS than sedentary controls who are matched for many potentially confounding factors. Our findings suggest that concurrently performed en-durance training may negate the stiffening effects of resistance training on arterial compliance.   


We thank Rhea Montemayor, Phil Stanforth, and Jill Tanaka for assistance.


This study was supported by National Institute on Aging Grant AG-20966. M. M. Anton was supported by a fellowship award from the Ministerio de Educacio´n y Ciencia (Spain) and M. Y. Cortez-Cooper and A. E. DeVan by National Institutes of Health Grants HL-072729 and DA-018431, respectively.


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2. Clifford PS, Hanel B, and Secher NH. Arterial blood pressure response to rowing. Med Sci Sports Exerc 26: 715–719, 1994.
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4. Cortez-Cooper MY, Supak JA, and Tanaka H. A new device for automatic measurements of arterial stiffness and ankle-brachial index. Am J Cardiol 91: 1519 –1522, 2003.
5. DeSouza CA, Shapiro LF, Clevenger CM, Dinenno FA, Monahan KD, Tanaka H, and Seals DR. Regular aerobic exercise prevents and restores age-related declines in endothelium-dependent vasodilation in healthy men. Circulation 102: 1351–1357, 2000.
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7. Joannides R, Richard V, Haefeli WE, Benoist A, Linder L, Luscher TF, and Thuillez C. Role of nitric oxide in the regulation of the mechanical properties of peripheral conduit arteries in humans. Hyperten-sion 30: 1465–1470, 1997.
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Basic Principles for Improving Sports Performance 

By David R. Lamb, Ph.D. Exercise Physiology Laboratory, Sport and Exercise Science Faculty, The Ohio State University, Columbus, OH , Chairman,
Gatorade Sports Science Institute. Print copy: Basic Principles for Improving Sports Performance

Key points

1. For most sports, the top competitor is generally the one who can appropriately sustain the greatest power output to overcome resistance or drag.
2. It is not sufficient for championship performance to simply have the ability to produce great power. The champion must be able to sustain power output in an efficient and skillful manner for the duration of the competition.
3. During maximal exercise lasting a few seconds, the anaerobic breakdown of phosphocreatine and glycogen in muscles can provide energy at rates many times greater than can be supplied by the aerobic breakdown of carbohydrate and fat. However, this high rate of anaerobic energy production cannot be sustained for more than about 20 seconds.
4. For exercise lasting more than a few minutes, an athlete who has a high lactate threshold, that is, one who can produce a large amount of energy aerobically without a major accumulation of lactic acid in the blood, will be better able to sustain a higher rate of energy expenditure than will a competitor who has a lower lactate threshold.
5. A high level of mechanical efficiency, which is the ratio of the mechanical power output to the total energy expended to produce that power, is vital if an athlete is to make the most of his or her sustainable rate of energy expenditure. Mechanical efficiency depends upon the extent to which the athlete can recruit slow-twitch muscle fibers, which are more efficient at converting chemical energy into muscle contraction than are fast-twitch fibers.
6. Neuromuscular skill is also critical to mechanical efficiency because the more skillful athlete will activate only those muscle fibers required to produce the appropriate movements. Extraneous muscle contractions require more energy expenditure but do not contribute to effective power output.


The criterion for success in many sports, including those involving running, swimming, bicycling, speed skating, rowing, and cross-country skiing, is simply the time required to propel the athlete's body (and essential equipment such as a bicycle, rowing shell, or skis) for a given distance. In the case of Olympic weightlifting and power lifting, success is determined by how much weight can be lifted in the appropriate movements, whereas a wrestler is judged by the degree of physical control over the opponent. These sports are quite different in terms of the patterns of muscle recruitment, the force and power produced, and the equipment used; nevertheless, success in all of these seemingly diverse sports depends on a complicated application of a simple principle--the champion is the athlete best able to reduce the resistance or drag that must be overcome in competition and best able to sustain an efficient power output to overcome that resistance or drag (Figure 1)(Coyle et al., 1994). This review provides an analysis of the major factors that contribute to an athlete's ability to produce power appropriately to overcome resistance or drag and a number of important applied principles designed to help trainers, coaches, physiologists, and others assist athletes in achieving their goals in sport.

Table 1: mode of the interrelationship of major factors determining sport performance. Performance is determined by how effectively the athlete can sustain sufficient power output to overcome various types of resistance or drag, depending on the sport event. Sustainable power output depends on the rate of energy expenditure that can be sustained throughout the event and the efficiency with which that energy can be converted into mechanical power. Depending on the sport event, sustainable energy expenditure will be a function of the ability to sustain the production of energy by anaerobic and/or aerobic means. Mechanical efficiency is dependent on muscle efficiency, i.e., the efficiency with which muscles convert the energy stored in carbohydrate and fat into muscle shortening, and the neuromuscular skill with which the athlete performs the event, i.e., the degree to which the athlete has learned to recruit only those motor units required to produce maximal power output in a skillful way.

Resistance and drag: Examples in Sport

Examples of resistance in sport include the mass of a barbell in Olympic lifting or power lifting, the muscular efforts of an opponent in wrestling or judo that are used to offset the movements of a competitor, and the effect of gravity on resisting a marathon runner's ability to move up a hill. A lifter who can sustain adequate power output long enough to correctly lift a greater weight than a competitor will beat that competitor. Likewise, a competitor in wrestling or judo who can sustain power sufficient to overcome the resistance provided by the opponent throughout the match will be the winner.

Drag is a special case of resistance in which the friction of air or water around a competitor retards forward motion. Obvious examples of drag are the adverse effects of a headwind on the forward velocity of a competitive cyclist and the retarding effects of water drag on the efforts of a swimmer to move quickly ahead. In cycling on a flat course at speeds greater than 13 km/h (8 mph), most of the resistance to the power generated by a bicyclist is created by the air through which the cyclist's body moves; relatively little bicycling power is lost to friction of the moving components of the bicycle or to the rolling resistance of the contact between the tire and road (Kale, 1991). It is also important to realize that the air drag increases as the square of the velocity of the moving object, i.e., if speed is doubled, the drag increases by four-fold (Kale, 1991).

Air drag offers great resistance in any sport requiring the athlete to move at relatively high velocities; such sports include speed skating--30-40 km/h (19-25 mph) at distances of 0.5-10 km (3-6 mi)--and sprint running--25-35 km/h (15-22 mph) at distances of 100-400 m. In fact, the air creates so much resistance in speed skating that the skaters must assume a tightly crouched posture to reduce their frontal areas exposed to air. Although this posture reduces leg power, it reduces air drag to an even greater extent and thus produces higher skating velocities. Swimmers move at relatively low velocities because they encounter large drag forces from the water as well as from the turbulence at the surface of the water. This drag encountered by a swimmer is not simply a function of body mass, but also of the geometry of the body as it moves through the water.

It is obvious that in events such as bicycling, speed skating, and possibly sprint running, each of which requires the athlete to move through the air at high speeds, the ultimate race time will be determined by the power generated relative to the air resistance. The same is true for the swimmer who must overcome the drag of the water at lower speeds. The main point is that the race velocity in these sports is a function of power production relative to the drag encountered at racing speeds. Therefore, velocity (performance) can be increased by improving power output and/or by reducing drag.

Reducing resistance and drag

In some sports, such as Olympic lifting, power lifting, and the shot put, the very nature of the competition makes it impossible to reduce resistance. If a competitive lifter chooses a low resistance--a lightweight barbell, that athlete is unlikely to win the competition. Likewise, the rules do not allow a shot putter to choose a lightweight shot. However, there are methods that can be used in many sports to reduce resistance or drag. Here are a few examples:
Use Skillful Technique. Competitors in wrestling, judo, rugby, American football, and other "contact" sports can reduce the resistance applied by opponents by skillful misdirection movements that trick the opponents into resisting in the wrong direction. These techniques are learned through many years of practice under the instruction of skillful coaches.

Use Aerodynamic and Hydrodynamic Equipment and Body Postures. In some sports, effective techniques have been employed to reduce resistance and drag in air and water. The designs of golf balls and javelins have become more aerodynamic over the years, and the resulting reductions in air drag have improved the flight characteristics of both. In cycling, riders wear aerodynamic helmets and skintight clothing and assume crouch positions over the handle bars ("aero bars") to minimize wind resistance. In swimming, body position in the water and stroke mechanics are optimized by careful study of underwater videos so that the swimmer reduces water drag as much as possible. Also, engineers have successfully modified the designs of rowing shells, canoes, kayaks, sailboats, oars, and paddles to minimize water drag in competitive events.

Reduce Body Mass. Athletes should carefully consider whether they can effectively reduce resistance or drag by reducing body weight. For pole vaulters, high jumpers, long jumpers, and triple jumpers, gravity is the principal resistance that must be overcome, and body weight is responsible for nearly all of this effect of gravity. Therefore, if these athletes can reduce their body weights without equivalent reductions in their abilities to skillfully generate muscular power, their performances should improve. Of course, if the body weight loss leads to a serious loss of muscular power, performance may well be worsened, not improved. Competing at an effectively low body weight is also critical for distance runners, endurance cyclists, and cross-country skiers. In these sports, the resistance of gravity is a crucial factor in determining performance; in addition, at the higher velocities of cycling, air drag is a major type of resistance that must be overcome, and a smaller frontal body surface area can reduce that resistance.

Weight reduction is not so much of an issue in swimming because the body mass is buoyed up by being immersed in water. However, to the extent that reductions in body weight help reduce water drag, weight loss could be of benefit in swimming, too. Differences in swimmers' individual body builds could play a significant role in determining whether or not weight loss improves swim performance. For example, weight loss may be quite ineffective in a swimmer who already presents a small frontal area and who tends to lose weight mostly in the thighs. However, if a swimmer has exceptionally large shoulders and a large chest, and if the mass of these areas can be reduced effectively through a weight loss program, such an approach could shave time off that swimmer's personal records.

Providing efficient sustained power output to overcome resistance and drag

Power is the ability to apply force through a distance quickly. In other words, power can be thought of as a combination of strength and speed. Interestingly, the sport of power lifting is misnamed because only strength, not speed, is required to be successful; as long as the barbell is moved appropriately, time is of no importance. On the other hand, a person could have exceptionally strong leg muscles and be a pitiful high jumper, sprinter, or long jumper if that strength could not be brought to bear quickly.

Unfortunately, absolute maximal muscular power can be sustained for only a fraction of a second. Thus, assuming equal resistance or drag, the champion in a sport event will not necessarily be the competitor who can produce the greatest maximal power, but instead will be the one who can sustain the greatest power output to overcome the resistance or drag for the duration of the event. This duration may be only a second or two, such as in power lifting, or many hours, such as in an Ironman triathlon.

The ability to sustain a high power output to efficiently overcome resistance or drag involves two major factors--the ability to sustain energy production by the muscles and the ability to apply that muscular energy efficiently to overcome resistance or drag.

Sustaining energy production by the muscles

When energy requirements are extremely high, such as during a sprint in track or swimming or during an Olympic weightlifting event, most of the muscular energy is supplied by two fuels, phosphocreatine (PCr) and glycogen, that are stored in small amounts in the muscles. Because these two fuels can be broken down for energy without the use of oxygen, this is known as anaerobic (without air) energy production. Aerobic energy production occurs at a much slower rate as fats and carbohydrates are broken down with the aid of oxygen in the muscles.

Sustainable Energy Expenditure in Brief, High-Power Events

Brief, high-power activities such as weightlifting and sprinting rely largely on the anaerobic breakdown of PCr and muscle glycogen for energy. When estimates of anaerobic energy production are coupled with simultaneous measurements of aerobic energy production, the approximate relative contributions of these two energy sources during various phases of exercise lasting from 0-180 s are as shown in Table 1. It is clear from the table that the percentage anaerobic contribution to energy production falls off rapidly as the exercise duration increases.

Both PCr degradation and anaerobic glycolysis are activated instantaneously at the onset of high-intensity exercise. Measurements of PCr and lactate from muscle biopsies taken following as little as 1-10 s of electrical stimulation (Hultman & Sjoholm, 1983) and after sprint cycling (Boobis et al., 1982; Gaitanos et al., 1993; Jacobs et al., 1983) confirm the rapid breakdown of PCr and rapid accumulation of lactate. At the onset of less intense exercise, a similar instantaneous activation of both PCr degradation and anaerobic glycolysis occurs but at a much slower rate because the mismatch between energy demand and aerobic supply is reduced during submaximal exertion.

Rate of Anaerobic Energy Production During Exercise

The rate of anaerobic energy provision is critical to success in sports that require the development and short-term maintenance of high power outputs. World-class power lifters and weightlifters can produce power outputs that are 10-20 times that required to elicit the maximal rate of aerobic energy provision, which is estimated by the maximal rate at which the athlete can consume oxygen (VO2max). However, such high power outputs can be maintained for only a fraction of a second. Sprinters can achieve power outputs that are 3-5 times the power output that elicits VO2max, but they can sustain that power output for only about 10 s. However, power output over a 30-40 s sprint can still be sustained at twice the power output at VO2max. Estimates of the rates of anaerobic provision of energy have been calculated from biochemical changes in muscles following intense exercise lasting from 1.3 to 200 s (Spriet, 1994). These studies used non-elite athletes who performed sprint cycling, sprint running, or repeated knee extensions or who underwent electrical stimulation of their muscles. The highest measured rates for energy production from PCr and anaerobic glycolysis during various types of exercise lasting from 1.3-10 s were each approximately 250-500% of the estimated maximal rate of energy provision from aerobic metabolism. In other studies of sprint cycling for 6-10 s, energy production rates from PCr and anaerobic glycolysis combined were about 400-750% of that during maximal aerobic metabolism (Boobis et al., 1982; Jacobs et al., 1983).

The anaerobic energy provision rates decrease when averaged over longer periods of time. In studies that examined intense exercise for 30 s, the average energy provision rate from PCr was about 70-100% of that from maximal aerobic metabolism; anaerobic glycolysis provided energy at a rate estimated to be 220-330% of that from maximal aerobic metabolism (Spriet, 1994). The large decrease in energy produced from PCr when averaged over 30 s, as compared to less than 10 s, indicates that the PCr store becomes depleted between 10 and 30 s of intense exercise. Thus, for maximal exertion lasting longer than about 30 s, it appears that only glycolysis can provide for further anaerobic energy production.

Anaerobic Energy Production During Intermittent High-Power Exercise

Many athletes repeatedly engage in bursts of high-intensity exercise with varying amounts of recovery time between exercise bouts. Examples include a wide receiver in American football, a basketball player in repeated fast break situations, or a swimmer or track athlete during interval training. Most of the energy for short bouts of high-intensity exercise is derived from anaerobic sources; therefore, the ability to recover during rest periods is essential for success in this type of activity. Many studies have examined the performance effects of intermittent high intensity exercise, but few have examined the anaerobic metabolism associated with this type of metabolic stress. Examples of the exercise models that have been studied and provided some conclusions include: 10 bouts of sprint cycling, each lasting 6 s with rest periods of 30 s; four bouts of sprint cycling for 30 s with 4-min rest periods; and two bouts of knee extension exercise to exhaustion in 3 min with 10-60 min of recovery (Bangsbo et al., 1992; Gaitanos et al., 1993; McCartney et al., 1986). Muscle biopsy measurements demonstrated that PCr was decreased by approximately 50% after 6 s and by 75-80% during longer sprints. The PCr is quickly resynthesized during recovery, reaching 50% of rest values by 30-60 s and about 80% by 2-4 min. With repeated sprinting, energy production from anaerobic glycolysis is progressively more difficult to achieve. Presumably, the accumulation of lactic acid in the active muscles plays a major role in the inability to continue producing energy by anaerobic glycolysis. Therefore, after repeated bursts of exercise, PCr is the only potential anaerobic energy source that can be relied upon. However, as described above, it is essential that adequate rest be provided in between intermittent exercise bouts to allow PCr stores to be replenished in the muscles.

Sustained Aerobic Energy Production

The maximal rate of aerobic energy production (VO2max) can be sustained for only about 8-10 min by elite athletes. In events lasting longer than 8-10 min, the best competitor among those with similar values for VO2max is usually the one who can sustain aerobic energy production at the greatest proportion of his or her maximal rate, that is, at the greatest percentage of the VO2max. This in turn is largely dependent on the extent to which the athlete can produce energy aerobically at a high rate without accumulating a large amount of lactic acid in the blood. In other words, the athlete who produces a large amount of lactic acid at a given speed of running, swimming, or cycling cannot continue to perform at that pace for as long as the athlete who does not accumulate as much lactic acid. An athlete who has the ability to exercise at a high intensity before blood lactic acid begins to accumulate is said to have a high lactate threshold (Coyle et al., 1988; Holloszy & Coyle, 1984). An athlete's lactate threshold seems to be a better indicator of endurance performance lasting 30 min to 4 h than does the VO2max (Coyle et al., 1988, 1991).

This is because the lactate threshold is a better index of the athlete's ability to sustain a high rate of energy expenditure for the duration of the competition.

Role of Nutrition in Determining Sustainable Energy Production

Two nutrients, carbohydrate and water, are the dietary constituents that have repeatedly been shown to be most important for optimizing endurance performance. Muscles obviously cannot produce energy without fuels derived from nutrients obtained in the diet, and carbohydrate is an obligatory fuel for high-caliber sport performance. It is well established that dietary carbohydrate consumption before, during, and after exercise can make an important contribution to performance. Carbohydrate consumption acts primarily by increasing the body's stores of glycogen in muscles and in the liver before exercise and by increasing the availability of glucose for use by the muscles during exercise (Coggan & Swanson, 1992; Costill & Hargreaves, 1992; Coyle, 1991; Williams, 1993). Fluid intake during prolonged exercise is also required to counteract the debilitating effects of exercise and heat on cardiovascular function and on body temperature regulation. When dehydration reduces blood volume, oxygen delivery to the muscles by the blood can be compromised, and this reduces the ability of the muscles to produce energy aerobically. Dehydration also compromises the ability of the body to regulate its temperature, resulting in eventual lethargy and potential heat illness, both of which adversely affect the athlete's ability to sustain a high rate of energy production. Carbohydrate-electrolyte beverages are advocated as the most effective way to supply both carbohydrate and fluid to the body during exercise (Coggan & Swanson, 1992; Gisolfi & Duchman, 1992).

Improving the ability to sustain energy production at a high rate

Here are some ways that athletes may be able to improve their abilities to sustain high rates of energy production so they can sustain greater power output to overcome resistance and drag:
At the onset of a training season, the athlete should establish a solid aerobic training foundation by training at relatively low intensities for long durations. This will help develop a greater blood volume, an improved ability of the heart to pump blood, and better networks of capillaries in the trained muscles. These cardiovascular adaptations will lead to an improved delivery of oxygen to the muscles and an enhanced ability of the muscles to sustain high rates of aerobic energy production.

For the bulk of the athlete's training, the specific muscle groups involved in the competitive event should be overloaded, and the athlete should train at a pace or intensity similar to that used in competition (Hickson, 1977, 1985). Such training can lead to improved stores of glycogen and PCr in the trained muscles so that greater energy reserves will be present in the muscles before competition begins. Furthermore, metabolic adaptations to this type of training are likely to enhance the ability of the muscles to utilize fat for energy and to spare muscle glycogen, resulting in less lactic acid production and less accumulation of lactic acid in the blood at a given pace or intensity (Holloszy & Coyle, 1984). This means that the athlete's lactate threshold will be increased so that aerobic energy production can be sustained longer at a greater rate than was possible before training.

During high intensity, anaerobic interval training, the duration of recovery intervals should be sufficient--usually between 30 s and 4 min--to allow the muscles to replenish most of the PCr depleted in the previous exercise interval. If these recovery intervals are too brief, the supply of PCr in the exercising muscles will be inadequate to provide energy anaerobically at a high rate (Gaitanos et al., 1993; McCartney et al., 1986). This means that the athlete will be forced to exercise at a lower intensity (slower pace) and that inappropriate muscle groups may be recruited to accomplish subsequent exercise intervals. If these events occur, the athlete will be learning incorrect movement patterns during training that may adversely affect competitive performance.

The athlete should receive adequate rest--approximately 24 h--between exhaustive training sessions to allow for total replenishment of depleted glycogen stores in the muscles prior to the next training session (Coyle, 1991). Otherwise, the quality of the next training session may be compromised because the athlete's muscles will be easily depleted of one of their main fuels. In addition, training intensity and duration should be gradually reduced during the week before a competitive event so that the athlete's energy reserves are fully loaded before competition.

The athlete should drink plenty of fluids before, during, and after exercise to avoid becoming dehydrated. Dehydration can lead to a diminished ability to deliver oxygen to the muscles, heat cramps, heat exhaustion, and even heat stroke, all of which can impair muscular energy production.

On a daily basis, the athlete should consume a diet high in carbohydrate, about 8 g of carbohydrate per kilogram of body weight (4 g/lb). This will ensure that the muscles can store extra glycogen and may help sustain energy production during competition.

Preliminary evidence suggests that dietary creatine supplementation may increase PCr stores in muscles (Dalsom et al., 1995) and perhaps improve performance in events such as fastbreak basketball that require repeated brief exertions. The extent to which creatine supplementation proves to be useful in actual sport settings remains to be seen.

During prolonged exercise, the athlete should consume carbohydrate-electrolyte drinks containing approximately 6% carbohydrate (glucose, sucrose, or maltodextrins) and a small amount of sodium to help maintain an adequate carbohydrate energy supply to the muscles and to minimize dehydration. Volumes of 150-250 mL (5-8 oz) should be consumed every 15-20 min to replace most, if not all, of the sweat lost by the athlete during exercise (Montain & Coyle, 1992).

Mechanical efficiency: A major determinant of effective power output

Mechanical efficiency for a sporting event is the ratio of the mechanical power output to the total energy expended to produce that power. Typically, both power output and energy expenditure are expressed in watts (W), and the ratio is expressed as a percentage. For example, if a cyclist expends energy at the rate equivalent to 5 L of oxygen per minute (1745 W) to produce 400 W of power on a bicycle ergometer, the mechanical efficiency would be (400/1745) 100 = 23%. Two of the principal factors that determine the mechanical efficiency of an athlete in a sport event are 1) the efficiency with which the active muscles convert the chemical energy stored in carbohydrate and fat to the mechanical energy required to shorten the contractile elements in the muscles, and 2) the neuromuscular skill with which the athlete performs the event.

Role of Muscle Efficiency in Determining Mechanical Efficiency

Muscle efficiency has two components, the first of which is the efficiency with which chemical energy from carbohydrate and fat is converted to adenosine triphosphate (ATP), the only form of chemical energy that can power muscle contraction. The process of ATP synthesis is about 40% efficient, i.e., 40% of the metabolic energy in carbohydrate and fat is transferred into ATP synthesis, whereas 60% of the energy is lost as heat (Kushmerick, 1983; Kushmerick & Davies, 1969). This efficiency of ATP synthesis is fairly constant among individuals.

The second component of muscle efficiency, i.e., the efficiency with which the energy released during ATP hydrolysis is converted to muscle fiber shortening, is more variable than is the efficiency of converting stored fuels to ATP. The efficiency of ATP hydrolysis is dependent on the velocities of muscle contraction (Goldspink, 1978; Kushmerick & Davies, 1969). A peak efficiency of approximately 60% or more can be elicited from myofilaments contracting at one- third of maximal velocity; i.e., the velocity of peak efficiency (Kushmerick, 1983; Kushmerick & Davies, 1969). Thus, slow-twitch muscle fibers obviously have slower velocities of peak efficiency than do fast-twitch fibers (Fitts et al., 1989).

Mechanical efficiency when cycling at 80 rpm is directly related to the percentage of slow- twitch muscle fibers in the vastus lateralis muscles (Coyle et al., 1992). It seems that when cycling at this cadence, the velocity of muscle fiber shortening in the vastus lateralis is close to one-third maximal velocity of the slow-twitch fibers (Coyle et al., 1992). This makes slow-twitch muscle fibers substantially more efficient than fast-twitch muscle fibers at converting ATP into muscular power when cycling at 80 rpm (Coyle et al., 1992; Goldspink, 1978).

Muscle fiber type has a large effect on mechanical efficiency, which in turn has a large influence on sustainable power output as measured during a 60-min bout of cycling in a homogeneous group of cyclists (Horowitz et al., 1994). The cyclists in this study were paired and divided into two groups based upon the percentage (i.e., above or below 56%) of slow-twitch muscle fibers in their vastus lateralis muscles. One group possessed a normal distribution of fiber types, with an average of 48% slow twitch fibers. The other group had 72% slow-twitch fibers on average. These two groups were identical in VO2 max as well as in the VO2 maintained during the ride. Therefore, they possessed the same aerobic energy expenditure potential for this type of task. However, the cyclists with a high percentage of slow-twitch fibers displayed significantly higher mechanical efficiencies and were therefore able to sustain a 9% greater power output (342 W vs. 315 W) during the 60-min ride. Clearly, endurance cycling performance is heavily influenced by mechanical efficiency, which in turn appears to be dependent on the rider's muscle fiber type profile and the efficiency of ATP hydrolysis by the muscle.

Role of Neuromuscular Skill in Determining Mechanical Efficiency

No matter how efficiently one can transform chemical energy into mechanical energy in a given muscle fiber, the overall mechanical efficiency in a sports event will be poor if the athlete is poorly skilled. A good example of the importance of skill is the contrast in the freestyle swimming performances of novice and elite swimmers. The novice may produce a great deal of power, but because the swimmer is so unskillful, the power output is misdirected so that lots of thrashing about occurs with little forward velocity. The elite swimmer, on the other hand, has learned to swim rapidly and gracefully, using only those muscle fibers required to execute the stroke effectively. Neuromuscular skill obviously plays a greater role in determining the mechanical efficiency for some sports, e.g., swimming and wrestling, than it does for others, e.g., running and power lifting, but even small differences in skill can have a major impact on performance in any sport at the elite level.

Improving the athlete's ability to provide power output in an efficient manner

There is little that the athlete can do to improve muscle efficiency because the chemical efficiency of converting fuels to ATP and the proportion of slow-twitch fibers involved in various movements are largely determined by heredity. An exception may be that athletes over many months of training may learn to recruit more of the efficient slow-twitch muscle fibers and fewer of the less efficient fast-twitch fibers. In addition, there are three important steps that can be taken to improve the skill with which power output is applied.

The athlete should obtain the technical advice of competent coaches who can explain how movement patterns should be altered to become more skillful. Often the coach can rely upon personal experience and observation to make critical improvements in an athlete's technique.

Video analysis of the athlete's performance can provide clues about changes in movement patterns that can be made to improve efficiency. The assistance of a sport biomechanist or a coach well-educated in biomechanics could be important in this phase of the athlete's preparation.

The athlete must repeat the appropriate movement patterns in a skillful manner many thousands of times during practice so the nervous system learns to perform the movement correctly every time throughout the entire duration of competition. There is no substitute for skillful repetition of muscular activities to ensure that such activities are likely to remain skillful in the heat of competition.


For most competitive sports, improving the performance of an athlete can be accomplished by reducing the resistance or drag that must be overcome or by increasing the athlete's ability to sustain a high power output to overcome that resistance or drag. Reducing air resistance or water drag typically involves improving body position in the air or water by minimizing the frontal surface area of the athlete that is exposed to the air or water. Sometimes the apparel or equipment used in the sport, e.g., helmets, swimwear, bicycles, and rowing shells, can be made more aerodynamic or hydrodynamic to reduce resistance or drag.

Increasing sustainable power output requires that the athlete undergo a carefully designed training program that will improve the athlete's abilities to: 1) produce metabolic energy by both aerobic and anaerobic means, 2) sustain aerobic energy production at high levels before lactic acid accumulates excessively in the blood, 3) recruit more of the efficient slow-twitch muscle fibers at exercise intensities used in competition, and 4) become more skillful by recruiting fewer non- essential muscle fibers during competition. Careful attention to maintaining a sufficient intake of fluids and carbohydrate before, during, and after strenuous competition and training sessions is also important.

Although it is apparent that some uniquely gifted athletes are able to win consistently even when their approaches to training are obviously not optimal for reducing resistance or drag and for enhancing their sustainable power outputs, it is clear that such athletes cannot achieve their full potentials in sport without addressing these two basic principles.

* This article was adapted from "Introduction to Physiology and Nutrition for Competitive Sport," by E.F. Coyle, L. Spriet, S. Gregg, and P. Clarkson, which appeared in D.R. Lamb, H.G. Knuttgen, and R. Murray (eds.), Perspectives in Exercise Science and Sports Medicine, Vol. 7: Physiology and Nutrition for Competitive Sport. Carmel, IN: Cooper Publishing Group, 1994, pp. xv-xxxix. The author is especially grateful to Edward Coyle, Ph.D. and Lawrence Spriet, Ph.D. who contributed much of the text for this article.


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• Jacobs, I., P. Tesch, O. Bar-Or, J. Karlsson, and R. Dotan (1983). Lactate in human skeletal muscle after 10 and 30 s of supramaximal exercise. J. Appl. Physiol. 55:365-367.
• Kushmerick, M.J. (1983). Energetics of muscle contraction. In: L.E. Peachey, R.H. Adrian, and S.R. Geiger (eds.) Handbook of Physiology, Section 10: Skeletal Muscle. Bethesda, MD: American Physiological Society, pp. 189-236.
• Kushmerick, M.J., and R.E. Davies (1969). The chemical energetics of muscle contraction II. The chemistry, efficiency, and power of maximally working sartorius muscle. Proc. R. Soc., Ser. B. 1174:315-353.
• Kyle, C.R. (1991). Ergogenics of bicycling. In: D.R. Lamb and M.H. Williams (eds.) Perspectives in Exercise Science and Sports Medicine, Vol 4: Ergogenics--Enhancement of Performance in Exercise and Sport. Carmel, IN: Brown & Benchmark, pp. 373-413.
• McCartney, N., L.L. Spriet, G.J.F. Heigenhauser, J.M. Kowalchuk, J.R. Sutton, and N.L. Jones (1986). Muscle power and metabolism in maximal intermit-tent exercise. J. Appl. Physiol. 60:1164-1169.
• Montain, S.J., and E.F. Coyle (1992). The influence of graded dehydration on hyperthermia and cardiovascular drift during exercise. J. Appl. Physiol. 73:1340-1350.
• Spriet, L.L. (1994). Anaerobic metabolism during high-intensity exercise (Chapter 1). In: M. Hargreaves (ed.) Exercise Metabolism. Champaign, IL: Human Kinetics (In press).
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The Gatorade Sports Science Institute® was created to provide current information on developments in exercise science, sports nutrition, and sports medicine and to support the advancement of sports science.


Heart Rate Variability (HRV), Recovery Index (RI) and Heart Rate Variability Index (HRVI)

By: Eddie Fletcher, Fletcher Sport Science Ltd 2007
A briefing note written by Sports Physiologist and Coach Eddie Fletcher
Accurate tools for assessing Psychological Stress, Physiological Workload and Recovery in Athletes

General Introduction

There are a number of factors which influence training and race performance, ranging from daily living (work and family), diet and hydration, cold, heat and humidity through to the lack of adequate rest and recovery. It is important to understand how stressful a normal training day is and to know the extent of overnight recovery.

The human heart is a wonderful barometer of the overall psychological stress and physical workload experienced by the body. The heart is a muscle, it gets tired and like any other muscle requires time to recover if optimum training and race performance is to be maintained.

The heart responds automatically and immediately to any increase or decrease in stress level. This heart rate response can be used to manage and mitigate the risk of over training, under recovery, illness or injury, to the body.

By monitoring the influence of psychological stress and physiological workload it is possible to use an analysis of heart rate to monitor overnight recovery and to moderate the duration and intensity of training to match the extent of recovery.

The consequences of getting it wrong should not be under estimated. Unless ‘listening to your heart’’ is normal practice deterioration in performance can occur almost unseen.

What are the benefits of measuring daily stress?

• Maximize recovery between training sessions
• Know how travelling, jetlag, high altitude and other stressors influence stress and recovery
• Learn how different daily routines enable and limit recovery
• Measure recovery between training sessions when training in high altitude
• Assess how travelling and jetlag influences recovery after a competition
• Check for social and psychological stressors that influence recovery
• Check athlete's daily routines for arrangements that could be done better to minimize stress during the day
• Interpret results together with athlete to detect stressors that influence recovery and to plan things that could be done differently in the future
• Repeat the daily stress recordings and observe how changes in daily routines influence stress and recovery

What are the benefits of measuring recovery?

• Detect early signs of overtraining or illness
• Optimize training load by finding the balance between training load and recovery
• Evidence based support for critical coaching decisions
• Record individual reference values e.g. during off-season when the body is recovered
• Check the recovery status during hard training periods
• Check recovery status when subjective feelings and fitness level indicates poor recovery
• Make sure that the body is recovered sufficiently before a new hard training period

How does it work?

Tracking daily stress and overnight recovery needs only one physiological signal – beat-by-beat heart rate data (the R-R interval). This measurement may be carried out during normal daily routines, whilst training and whilst sleeping. Although the data collection procedure is simple, the analysis methodology produces accurate recovery information.

Under resting conditions, healthy athletes show a periodic variation in the R-R interval. This rhythmic fluctuation is caused by breathing. Heart rate increases whilst breathing in and decreases when breathing out.

By accurately measuring the time interval between heartbeats (known as Heart Rate Variability HRV) it is possible to use the detected variation in time to measure the psychological and physiological stress and fatigue on the body. Generally speaking the more relaxed and free from fatigue the body is, the more variable the time between heartbeats. Increased Heart Rate Variability is linked to good health; decreased Heart Rate Variability is linked to stress or fatigue.

Heart Rate Variability also distributes as a function of Frequency.

Because of the characteristics of the increase (high frequency HF) and decrease (very low and low frequency LF) of the heart beat, changes in this frequency distribution can be used to monitor overall daily stress and overnight recovery.

Recovery is strongly associated with high frequency reactions and stress with low frequency reactions. These values are highly individual and the most sensitive markers for monitoring stress and recovery status. By looking at the difference from athlete specific baseline values the status of stress and recovery can be monitored and a

Recovery Index or Heart Rate Variability Index created.

The  intensity  of  stress/recovery  is  calculated  from  the  HF,  LF,  Respiration rate and HR.

How easy is it to collect the data?

Very easy, simply wear a Suunto t6 or Suunto Memory Belt during training sessions and overnight. The log is downloaded into Suunto Training manager software and Firstbeat SPORTS or Firstbeat PRO for detailed analysis.

What is a Recovery Index?

The Recovery Index is the relationship between the total duration of the Stress (low frequency) and Recovery (high frequency) reactions during an overnight measurement. The index is generally calculated from the first 4 hours of sleeping time as this time period is the most sensitive time for detecting recovery status. Average values provide information for both stress and recovery reactions during the selected time period indicating the relative strength of the reactions.

The intensity of the Stress/Recovery is calculated from the high and low Heart Rate Frequency mix, Respiration Rate and Heart Rate. The Recovery Index is represented by two numbers i.e. 60/100. The left number represents Stress reactions with the right number representing Recovery reactions.

Athletes need to measure their own individual baseline values at rest and compare subsequent values against the baseline figures.

What is a Heart Rate Variability Index?

Another useful tool for detecting recovery is the Heart Rate Variability Index

This is a single number and reflects the slowing down of the heart. The index can be used to detect recovery from an overnight recording. A high index figure represents increased recovery and a low value poor recovery.

During the day the value should be at least 15 but normally over 25. During the night the value should be at least 50 % higher (20-30) although athletes can have a value of several hundred (athlete above is 100 +). These limits are just guidelines; medication, heritage and training status also influence HRV level. Research indicates that these limits may be associated with burn-out.

As with the Recovery Index an individual baseline Heart Rate Variability Index value would need to be established for comparison purposes.


The ratio for this athlete is 42/100 and represents full recovery. For this athlete normal 100% recovery is 40-110

During a period of high stress for a different athlete a ratio of 117/74 represents under recovery. For this athlete normal 100% recovery range is 60-100

Tracking the Recovery Index

There are some endurance athletes whose heart rate level is so low during the night that despite the changes in HF and LF levels the night recording appears to show mainly recovery reactions.

The overall index may indicate 100% recovery when the underlying values show under recovery. It is important to get a reference level by measuring athlete specific baseline values in a rested state and comparing future results to the baseline figures.

In the example below note 100% recovery during the period 6/11/2007 to 18/11/2007.

Baseline resting values for this athlete 50 (stress)/115 (recovery)

By looking at the individual figures for stress and recovery the true extent of stress or recovery can be determined and compared against baseline level.

The intensity of the stress reactions

The intensity of the recovery reactions.

Normally when recovery increases, stress level decreases and vice versa. It will be noted that although the overall index shows 100% recovery for the 16/11/2007 the Recovery Index is approximately 85/100 which when compared against baseline 50/115.

Am I fully recovered?

More precise answers are obtainable with a long measurement history.

In this example the days when the athlete is recovered are marked on both the Stress and Recovery follow-up charts.

Stress reactions:

Recovery reactions:

Am I tired but training can continue? Am I tired and must rest.

These are the too hardest questions to answer and this is where the experience of the athlete and coach in using the Recovery Index is important. When the goal is to train hard and upset the body’s homeostasis the stress level should increase and recovery decrease.

In the charts above the hard training period was 18/10/07 – 25/10/07 (8 days). Based on the rate of recovery (recovery occurred within two days - see Recovery index 27.10.07) the overreaching period was successful.

The chart below is another athlete training at high altitude 12/10-07 – 27/10/07. The last measurement was 25/10/07. The recovery level was below baseline value all the time and the athlete reported subjective feelings of “big fatigue”. This 15 days hard training period without any easy days may have been too long. Time to reach baseline values after the training period took 10 days (recovery occurred 07/11/07).

When will I know I can train again?

After ending the last hard training period, the recovery level should be measured daily to see when the baseline values are reached again. In the example above, the new training period could be started on 07/11/07 or later.


Measuring recovery is a vital component of any training programme if an athlete is to maintain optimum training and race performance. ‘Listening to your heart’ must become normal practice to avoid deterioration in performance, illness or injury.

More information

Coaches and Athletes are referred to the following articles by Eddie Fletcher for more detailed information

Peak Performance Issues:

• 237 Heart rate variability – what is it and how can it be used to enhance athletic performance
• 246 Using HRV to optimize rest and recovery
• 253 Duration-intensity-recovery: a new training concept

Also see for further reference articles.

Eddie Fletcher can be contacted by email

Note: Some sections of this briefing guide are based upon copyrighted materials owned by Firstbeat Technologies Ltd. They are reproduced with the permission.


Intervals, Thresholds, and Long Slow Distance: the Role of Intensity and Duration in Endurance Training

Sportscience -
Perspectives / Training

Stephen Seiler1 and Espen Tønnessen2
Sportscience 13, 32-53, 2009 (
1 University of Agder, Faculty of Health and Sport, Kristiansand 4604, Norway.  
2 Norwegian Olympic and Paralympic Committee National Training Center, Oslo,  Norway.
Reviewers: Iñigo Mujika, Araba Sport Clinic, Vitoria, Spain; Stephen Ingham, English Institute of Sport, Loughborough University, Leicestershire, LE11 3TU, UK.


Endurance training involves manipulation of intensity, duration, and frequency of training sessions.   The relative impact of short, high-intensity training versus longer, slower distance training has been studied and debated for decades among athletes, coaches, and scientists.  Currently, the popularity pendulum has swung towards high-intensity interval training.  Many fitness experts, as well as some scientists, now argue that brief, high-intensity interval work is the only form of training necessary for performance optimization.   Research on the impact of interval and continuous training with untrained to moderately trained subjects does not support the current interval craze, but the evidence does suggest that short intense training bouts and longer continuous exercise sessions should both be a part of effective endurance training.  Elite endurance athletes perform 80 % or more of their training at intensities clearly below their lactate threshold and use high-intensity training surprisingly spar-ingly.  Studies involving intensification of training in already well-trained ath-letes have shown equivocal results at best.  The available evidence suggests that combining large volumes of low-intensity training with careful use of high-intensity interval training throughout the annual training cycle is the best-practice model for development of endurance performance. KEYWORDS: lactate threshold, maximal oxygen uptake, VO2max, periodization.


Interval Training: A long History
Exercise Intensity Zones
Training Plans and Cellular Signaling
Training Intensities of Elite Endurance Athletes
Units for Trainong Intensity
The 80-20 Rule for Intensity
Training Volume of Elite Athletes
Intensified-Training Studies
Intensity for Recreational Athletes:
- Case 1-From Soccer Pro to Elite Cyclist
- Case 2 From Modern Pentathete to Runner
Valid Comparisons of Training Interventions

The evening before the start of the 2009 European College of Sport Science Congress in Oslo, the two of us were sitting at a doctoral dissertation defense dinner that is part of the time honored tradition of the “doctoral disputas” in Scandinavia. One of us was the relieved disputant (Tønnessen) who had successfully defended his dissertation. The other had played the adversarial role of “førsteopponent.” Tønnessen’s research on the talent development process included extensive empirical analyses of the training characteristics of selected world champion female endurance athletes. His career case-study series systematized training diary logs of over 15,000 training sessions from three World and/or Olympic champions in three sports: distance running, cross-country skiing, and orienteering. Common for all three champions was that over their long, successful careers, about 85 % of their training sessions were performed as continuous efforts at low to moderate intensity (blood lactate ?2 mM). Among the 40 guests sat coaches, scientists, and former athletes who had been directly or indirectly involved in winning more endurance sport Olympic gold medals and world championships than we could count. One guest, Dag Kaas, had coached 12 individual world champions in four different sports. In his toast to the candidate he remarked,  ”My experience as a coach tells me that to become world champion in endurance disciplines, you have to train SMART, AND you have to train a LOT. One without the other is insufficient.”

So what is smart endurance training? The question is timely: research and popular interest in interval training for fitness, rehabilitation, and performance has skyrocketed in recent years on the back of new research studies and even more marketing by various players in the health and fitness industry. Some recent investigations on untrained or moderately trained subjects have suggested that 2-8 wk of 2-3 times weekly intense interval training can induce rapid and substantial metabolic and cardiovascular performance improvements (Daussin et al., 2007; Helgerud et al., 2007; Talanian et al., 2007). Some popular media articles have interpreted these findings to mean that long, steady distance sessions are a waste of time. Whether well founded or not, this interpretation raises reasonable questions about the importance and quantity of high- (and low-) intensity training in the overall training process of the endurance athlete. Our goal with this article is to discuss this issue in a way that integrates research and practice.

In view of the recent hype and the explosion in the number of studies investigating interval training in various health, rehabilitation, and performance settings, one could be forgiven for assuming that this training form was some magic training pill scientists had devised compara-tively recently. The reality is that athletes have been using interval training for at least 60 years.  So, some discussion of interval training research is in order before we address the broader question of training intensity distribution in competitive endurance athletes.

Interval Training: a Long History

International running coach Peter Thompson wrote in Athletics Weekly that clear references to “repetition training” were seen already by the early 1900s (Thompson, 2005).  Nobel Prize winning physiologist AV Hill incorporated intermittent exercise into his studies of exercising humans already in the 1920s (Hill et al., 1924a; Hill et al., 1924b).  About this time, Swede Gosta Holmer introduced Fartlek to distance running (fart= speed and lek= play in Swedish).  The specific term interval training is attributed to German coach Waldemer Gerschler. Influenced by work physiologist Hans Reindell in the late 1930s, he was convinced that alternating periods of hard work and recovery was an effective adaptive stimulus for the heart. They apparently adopted the term because they both believed that it was the recovery interval that was vital to the training effect. Since then, the terms intermittent exercise, repetition training, and interval training have all been used to describe a broad range of training prescriptions involving alternating work and rest periods (Daniels and Scardina, 1984). In the 1960s, Swedish physiologists, led by Per Åstrand, performed groundbreaking research demonstrating how manipulation of work duration and rest duration could dramatically impact physiological responses to intermittent exercise (Åstrand et al., 1960; Åstrand I, 1960; Christensen, 1960; Christensen et al., 1960). As Daniels and Scardina (1984) concluded 25 years ago, their work laid the foundation for all interval training research to follow. In their classic chapter Physical Training in Textbook of Work Physiology, Åstrand and Rodahl (1986) wrote, “it is an important but unsolved question which type of training is most effective: to maintain a level representing 90 % of the maximal oxygen uptake for 40 min, or to tax 100 % of the oxygen uptake capacity for about 16 min.” (The same chapter from the 4th edition, published in 2003, can be read here.)  This quote serves as an appropriate background for defining high intensity aerobic interval training (HIT) as we will use it in this article: repeated bouts of exercise lasting ~1 to 8 min and eliciting an oxygen demand equal to ~90 to 100 % of VO2max, separated by rest periods of 1 to 5 min (Seiler and Sjursen, 2004; Seiler and Hetlelid, 2005). Controlled studies comparing the physiological and performance impact of continuous training (CT) below the lactate turnpoint (typically 60-75 % of VO2max for 30 min or more) and HIT began to emerge in the 1970s. Sample sizes were small and the results were mixed, with superior results for HIT (Henriksson and Reit-man, 1976; Wenger and Macnab, 1975), superior results for CT (Saltin et al., 1976), and little difference (Cunningham et al., 1979; Eddy et al., 1977; Gregory, 1979). Like most published studies comparing the two types of training, the CT and HIT interventions compared in these studies were matched for total work (iso-energetic). In the context of how athletes actually train and perceive training stress, this situation is artificial, and one we will return to.

McDougall and Sale (1981) published one of the earliest reviews comparing the effects of continuous and interval training, directed at coaches and athletes. They concluded that both forms of training were important, but for different reasons. Two physiological assumptions that are now largely disproven influenced their interpretation. First, they concluded that HIT was superior for inducing peripheral changes, because the higher work intensity induced a greater degree of skeletal muscle hypoxia. We now know that in healthy subjects, increased lactate accumulation in the blood during exercise need not be due to increased muscle hypoxia (Gladden, 2004). Second, they concluded that since stroke volume already plateaus at 40-50 %VO2max, higher exercise intensities would not enhance ventricular filling. We now know that stroke volume continues to rise at higher intensities, perhaps even to VO2max, in well trained athletes (Gledhill et al., 1994; Zhou et al., 2001). Assuming a stroke volume plateau at low exercise intensity, they concluded that the benefit of exercise on cardiac performance was derived via stimulation of high cardiac contractility, which they argued peaked at about 75 %VO2max. Thus, moderate-intensity continuous exercise over longer durations and therefore more heart beats was deemed most beneficial for enhancing cardiac performance. While newer research no longer supports  their specific conclusions, they did raise the important point that there are underlying characteristics of the physiological response to HIT and CT that should help explain any differential impact on adaptive responses.

Poole and Gaesser (1985) published a citation classic comparing 8 wk of 3 × weekly training of untrained subjects for either  55 min at 50 %VO2max, 35 min at 75 %VO2max, or 10 × 2 min at 105 %VO2max with 2-min recoveries.  They observed no differences in the magnitude of the increase in either VO2max or power at lactate threshold among the three groups. Their findings were corroborated by Bhambini and Singh (1985) in a study of similar design published the same year. Gorostiaga et al. (1991) reported findings that challenged McDougall and Sale's conclusions regarding the adaptive specificity of interval and continuous training. They had untrained subjects exercise for 30 min, three days a week either as CT at 50 % of the lowest power eliciting VO2max, or as HIT, alternating 30 s at 100 % of power at VO2max and 30 s rest, such that total work was matched. Directly counter to McDougall and Sales conclusions, they found HIT to induce greater changes in VO2max, while CT was more effective in improving peripheral oxidative capacity and the lactate profile. At the beginning of the 1990s, the available data did not support a consensus re-garding the relative efficacy of CT vs HIT in inducing peripheral or central changes related to endurance performance.
Twenty years on, research continues regarding the extent to which VO2max, fractional utilization of VO2max, and work efficiency/economy are differentially impacted by CT and HIT in healthy, initially untrained individuals. Study results continue to be mixed, with some studies showing no differences in peripheral and central adaptations to CT vs HIT (Berger et al., 2006; Edge et al., 2006; Overend et al., 1992) and others greater improvements with HIT (Daussin et al., 2008a; Daussin et al., 2008b; Helgerud et al., 2007). When differences are seen, they lean in the direction that continuous work at sub-maximal intensities promotes greater peripheral adaptations and HIT promotes greater central adaptations (Helgerud et al., 2007).

Controlled studies directly comparing CT and HIT in already well-trained subjects were essentially absent from the literature until recently. However, a few single-group design studies involving endurance athletes did emerge in the 1990s. Acevedo and Goldfarb (1989) reported improved 10-km performance and treadmill time to exhaustion at the same pace up a 2 % grade in well-trained runners who increased their training intensity to 90-95 %VO2max on three of their weekly training days. In these already well-trained athletes, VO2max was unchanged after 8 wk of training intensification, but a right shift in the blood lactate profile was observed. In 1996 -97, South African sport scientists published the results of a single group intervention involving competitive cyclists (Lindsay et al., 1996; Weston et al., 1997). They trained regionally competitive cyclists who were specifically selected for study based on the criteria that they had not undertaken any interval training in the 3-4 months prior to study initiation. When 15 % of their normal training volume was replaced with 2 d.wk-1 interval training for 3-4 wk (six training sessions of six 5-min high intensity work bouts), 40-km time trial performance, peak sustained power output (PPO), and time to fatigue at 150 %PPO were all modestly improved. Physiological measurements such as VO2max and lactate profile changes were not reported. Stepto and colleagues then addressed the question of interval-training optimization in a similar sample of non-interval trained, regional cyclists (Stepto et al., 1999). They compared interval bouts ranging from 80 to 175 % of peak aerobic power (30 s to 8 min duration, 6-32 min total work). Group sizes were small (n=3-4), but the one group that consistently improved endurance test performance (~3 %) had used 4-min intervals at 85 % PPO. These controlled training intensification studies essentially confirmed what athletes and coaches seemed to have known for decades: some high-intensity interval training should be integrated into the training program for optimal performance gains. These studies also seemed to trigger a surge in interest in the role of HIT in athlete performance development that has further grown in recent years.

If doing some HIT (1-2 bouts per week) gives a performance boost, is more even better? Billat and colleagues explored this question in a group of middle distance runners initially training six sessions per week of CT only. They found that a training intensification to four CT sessions, one HIT session, and one lactate threshold (LT) session resulted in improved running speed at VO2max (but not VO2max itself) and running economy. Further intensifi-cation to two CT sessions, three HIT sessions and one LT session each week gave no additional adaptive benefit, but did increase subjective training stress and indicators of impending overtraining (Billat et al., 1999). In fact, training intensification over periods of 2-8 wk with frequent high-intensity bouts (3-4 sessions per week) is an effective means of temporarily compromising performance and inducing overreaching and possibly overtraining symptoms in athletes (Halson and Jeukendrup, 2004).  There is likely an appropriate balance between high- and low-intensity training in the day-to-day intensity distribution of the endurance athlete. These findings bring us to two related questions: how do really good endurance athletes actually train, and is there an optimal training intensity distribution for long-term performance development?

While arguments can be made that tradition, resistance to change and even superstition may negatively influence training methods of elite endurance athletes, sports history tells us that athletes are experimental and innovative. Observing the training methods of the world's best endurance athletes represent a more valid picture of “best practice” than we can develop from short-term laboratory studies of untrained or moderately trained subjects.  In today’s per-formance environment, where promising athletes have essentially unlimited time to train, all athletes train a lot and are highly motivated to optimize the training process. Training ideas that sound good but don't work in practice will fade away. Given these conditions, we argue that any consistent pattern of training intensity distribution emerging across sport disciplines is likely to be a result of a successful self-organization (evolution) towards a “population optimum.” High performance training is an individualized process for sure, but by population optimum, we mean an approach to training organization that results in most athletes staying healthy, making good progress, and performing well in their most important events. 

Exercise Intensity Zones

To describe intensity distribution in endurance athletes we have to first agree on an intensity scale. There are different intensity zone schemes to choose from. Most national sport governing bodies employ an intensity scale based on ranges of heart rate relative to maximum and associated typical blood lactate concentration range.  Research approaches vary, but a number of recent research studies have identified intensity zones based on ventilatory thresholds.  Here we will examine an example of each of these scales.
Table 1 shows the intensity scale used by all endurance sports in Norway. A valid criticism of such a scale is that it does not account for individual variation in the relationship between heart rate and blood lactate, or activity specific variation, such as the tendency for maximal steady state concentrations for blood lactate to be higher in activities activating less muscle mass (Beneke and von Duvillard, 1996; Beneke et al., 2001).

Several recent studies examining training intensity distribution (Esteve-Lanao et al., 2005; Seiler and Kjerland, 2006; Zapico et al., 2007) or performance intensity distribution in multi-day events (Lucia et al., 1999; Lucia et al., 2003) have employed the first and second venti-latory turnpoints to demarcate three intensity zones (Figure 1). The 5-zone scale in the table above and the 3-zone scale below are reasonably super-imposable in that intensity Zone 3 in the 5-zone system coincides well with Zone 2 in the 3-zone model. While defining five “aerobic” intensity zones is likely to be informative in training practice, it is important to note that they are not based on clearly defined physiological markers. Note also that 2-3 additional zones are typically defined to accommodate very high intensity sprint, anaerobic capacity, and strength training. These zones are typically defined as “anaerobic” Zones 6, 7, and 8.

Training Plans and Cellular Signaling

Athletes do not train at the same intensity or for the same duration every day. These va-riables are manipulated from day to day with the implicit goals to maximize physiological capacity over time, and stay healthy. Indeed, the former is quite dependent on the latter. Training frequency is also a critical variable manipulated by the athlete. This is particularly evident when comparing younger (often training 5-8 times per week) and more mature athletes at peak performance level (often training 10-13 sessions per week). Ramping up training frequency (as opposed to training longer durations each session) is responsible for most of the increase in yearly training hours observed as teenage athletes mature. Cycling might be an exception to this general rule, since cycling tradition dictates single daily sessions that often span 4-6 h among professionals. The ultimate targets of the training process are individual cells.  Changes in rates of DNA transcription, RNA translation, and ultimately, synthesis of specific proteins or protein constellations are induced via a cascade of intra-cellular signals induced by the training bout. Molecular exercise biologists are unraveling how manipulation of intensity and duration of exercise specifically modifies intracellular signaling and resulting protein synthetic rates at the cellular or whole muscle/myocardial level (Ahmetov and Rogozkin, 2009; Hoppeler et al., 2007; Joseph et al., 2006; Marcuello et al., 2005; McPhee et al., 2009; Yan, 2009). About 85 % of all publications involving gene expression and exercise are less than 10 y old, so we do not yet know enough to relate results of Western blots to the specific training of an athlete.

The signaling impact of a given exercise stress (intensity×duration) almost certainly decays with training (Hoppeler et al., 2007; Nordsborg et al., 2003).  For example, AMP activated protein kinase a2 (AMPK) activity jumps 9-fold above resting levels after 120 min of cycling at 66 %VO2max in untrained subjects.  However, after only 10 training sessions,  almost no increase in AMPK is seen after the same exercise bout (McConell et al., 2005). Manipulating exercise intensity and duration also impacts the systemic stress responses associated with training. Making this connection is further complicated by recent findings suggesting that muscle glycogen depletion can enhance and antioxidant supplementation can inhibit adaptations to training (Brigelius-Flohe, 2009; Go-mez-Cabrera et al., 2008; Hansen et al., 2005; Ristow et al., 2009; Yeo et al., 2008). It seems fair to conclude that while we suspect important differences exist, we are not yet able to relate specific training variables (e.g., 60 min vs 120 min at 70 %VO2max) to differences in cell signaling in a detailed way. Our view of the adaptive process remains limited to a larger scale. We can still identify some potential signaling factors that are associated with increased exercise intensity over a given duration (Table 2) or increased exercise duration at a given sub-maximal intensity (Table 3). Some of these are potentially adaptive and others maladaptive.  There is likely substantial overlapping of effects between extending exercise duration and increasing exercise intensity.

It may be a hard pill to swallow for some exercise physiologists, but athletes and coaches do not need to know much exercise physiology to train effectively. They do have to be sensitive to how training manipulations impact athlete health, daily training tolerance, and performance, and to make effective adjustments. Over time, a successful athlete will presumably organize their training in a way that maximizes adaptive benefit for a given perceived stress load. That is, we can assume that highly successful athletes integrate this feedback experience over time to maximize training benefit and minimize risk of negative outcomes such as  illness, injury, stagnation, or overtraining.

Training Intensities of Elite Endurance Athletes

Empirical descriptions of the actual distribution of training intensity in well-trained athletes have only recently emerged in the literature. The first time one of us (Seiler) gave a lecture on the topic was in 1999, and there were few hard data to present, but a fair share of anecdote and informed surmise. Carl Foster, Jack Daniels and Seiler published a book chapter the same year, “Perspectives on Correct Approaches to Training” that synthesized what we knew then (read chapter here via Google books). At that time, much of the discussion and research re-lated to the endurance training process focused on factors associated with overtraining (a training control disaster), with little focus on what characterized “successful training.” The empirical foundation for describing successful training intensity distribution is stronger 10 years later. 
Robinson et al. (1991) published what was according to the authors “the first attempt to quantify training intensity by use of objective, longitudinal training data.”  They studied training characteristics of 13 national class male, New Zealand runners with favorite distances ranging from 1500 m to the marathon. They used heart rate data collected during training and related it to results from standardized treadmill determinations of heart rate and running speed at 4-mM blood lactate concentration (misnamed anaerobic threshold at the time). Over a data collection period of 6-8 wk corresponding to the preparation phase, these athletes reported that only 4 % of all training sessions were interval workouts or races. For the remaining training sessions, average heart rate was only 77 % of their heart rate at 4-mM blood lactate. This heart rate translates to perhaps 60-65 % of VO2max. The authors concluded that while their physiological test results were similar to previous studies of well trained runners, the training intensity of these runners was perhaps lower than optimal, based on prevailing recommendations to perform most training at or around the lactate/anaerobic threshold.

In one of the first rigorous quantifications of training intensity distribution reported, Mujika et al. (1995) quantified the training intensity distribution of national and international class swimmers over an entire season based on five blood-lactate concentration zones. Despite specializing in 100-m and 200-m events requiring ~60 to 120 s, these athletes swam 77 % of the 1150 km completed during a season at an intensity below 2 mM lactate.  The intensity distribution of 400- and 1500-m swim specialists was not reported, but was likely even more weighted towards high-volume, low-intensity swimming.

Billat et al. (2001) performed physiological testing and collected data from training diaries of French and Portuguese marathoners. They classified training intensity in terms of three speeds: marathon, 10–km, and 3–km. During the 12 wk preceding an Olympic trials mara-thon, the athletes in this study ran 78 % of their training kilometers at below marathon speed, only 4 % at marathon race speed (likely to be near VT1), and 18 % at 10–km or 3–km speed (likely to be > VT2). This distribution of training intensity was identical in high-level (<2 h 16 min for males and <2 h 38 min for females) and top-class athletes (<2 h 11 min and <2 h 32 min). But the top-class athletes ran more total kilometers and proportionally more distance at or above 10–km speed.

Kenyan runners are often mythologized for the high intensity of their training. It is therefore interesting that with data from another study by Billat et al. (2003), we calculated that elite male and female Kenyan 5- and 10-km runners ran ~85 % of their weekly training kilometers below lactate-threshold speed.

The first study on runners to quantify training intensity using three intensity zones was that of Esteve-Lanao et al. (2005). They followed the training of eight regional- and national-class Spanish distance runners over a six-month period broken into eight, 3-wk mesocycles. Heart rate was measured for every training session to calculate the time spent in each heart-rate zone defined by treadmill testing. All told, they quantified over 1000 heart-rate recordings. On average these athletes ran 70 km.wk-1 during the six-month period, with 71 % of running time in Zone 1, 21 % in Zone 2, and 8 % in Zone 3. Mean training intensity was 64 %VO2max. They also reported that performance times in both long and short races were highly negatively correlated with total training time in Zone 1. They found no significant correlation between the amount of high-intensity training and race performance.

Rowers compete over a 2000-m distance requiring 6-7 min. Steinacker et al. (1998) reported that extensive endurance training (60- to 120-min sessions at <2 mM blood lactate) dominated the training volume of German, Danish, Dutch, and Norwegian elite rowers. Rowing at higher intensities was performed ~4-10 % of the total rowed time. The data also suggested that German rowers preparing for the world championships performed essentially no rowing at threshold intensity, but instead trained either below 2 mM blood lactate or at intensities in the 6-12 mM range.

Seiler collaborated with long time national team rower, coach, and talent development coordinator Åke Fiskerstrand to examine historical developments in training organization among international medal winning rowers from Norway (Fiskerstrand and Seiler, 2004). Using questionnaire data, athlete training diaries, and physiological testing records, they quantified training intensity distribution in 27 athletes who had won world or Olympic medals in the 1970s to 1990s.  They documented that over the three decades: training volume had increased about 20 % and become more dominated by low-intensity volume; the monthly hours of high-intensity training had dropped by one-third; very high intensity overspeed sprint training had declined dramatically in favor of longer interval training at 85-95 %VO2max; and the number of altitude camps attended by the athletes increased dramatically. Over this 30-y timeline, VO2max and rowing ergometer performance improved by ~10 % with no change in average height or body mass.  Most of the changes occurred between the 1970s and 1980s, coinciding with major adjustments in training intensity.

Most recently, Gullich et al. (2009) described the training of world class junior rowers from Germany during a 37-wk period culminating in national championships and qualification races for the world championships. These were very talented junior rowers, with 27 of 36 athletes winning medals in the junior world championships that followed the study period. Remarkably, 95 % of their rowing training was performed below 2 mM blood lactate, based on daily heart-rate monitoring and rowing ergometer threshold determinations performed at the beginning of the season.  This heavy dominance of extensive endurance training persisted across mesocycles. However, the relatively small volume of Zone 2 and Zone 3 work shifted towards higher intensities from the basic preparation phase to the competition phase. That is, the intensity distribution became more polarized. It is important to point out that time-in-zone allocation based on heart-rate cut-offs (the kind of analysis performed by software from heart watch manufacturers) underestimates the time spent performing high-intensity exercise and the impact of that work on the stress load of an exercise session (Seiler and Kjerland, 2006). Although the outcomes are biased by this problem, there was still a clear shift in the intensity distribution towards large volumes of low- to moderate-intensity training. We also evaluated retrospectively whether there were any differences in junior training characteristics between a subgroup of rowers who went on to win international medals as seniors within three years (14 of 36 athletes) and the remainder of the sample, who all continued competing at the national level. The only physical or training characteristic that distinguished the most successful rowers from their peers was a tendency to distribute their training in a more polarized fashion; that is, they performed significantly more rowing at very low aerobic intensities and at the highest intensities. We concluded that the greater polarization observed might have been due to better management of intensity (keeping hard training hard and easy training easy) among the most successful athletes. This polarization might protect against overstress.

Professional road cyclists are known for performing very high training volumes, up to 35,000 km.y-1. Zapico and colleagues (2007) used the 3-intensity zone model to track training characteristics from November to June in a group of elite Spanish under-23 riders. In addi-tion, physiological testing was performed at season start and at the end of the winter and spring mesocycles.  There was an increase in total training volume and a four-fold increase in Zone 3 training between the winter and spring mesocycles (Figure 2), but there was no further improvement in power at VT1, VT2 or at VO2max between the end of the winter and spring mesocycles (Figure 3), despite the training intensification. Anecdotally, this finding is not unusual, despite the fact that athletes feel fitter. It may be that VT2 and VO2max determi-nation using traditional methods can miss an important increase in the duration that can be maintained at the associated workloads.


Individual and team pursuit athletes in cycling compete over about 4 min. The event ap-peals to sport scientists because the performance situation is highly controlled and amenable to accurate modeling of the variables on both sides of the power balance equation. Schumacher and Mueller (2002) demonstrated the validity of this approach in predicting “gold medal standards” for physiological testing and power output in track cycling. However, less obvious from the title was the detailed description of the training program followed by the German cyclists monitored in the study, ultimately earning a gold medal in Sydney in world-record time. These athletes trained to maintain 670 W in the lead position and ~450 W when following using a training program dominated by continuous low to moderate intensity cycling on the roads (29-35,000 km.y-1). In the 200 d preceding the Olympics, the athletes performed “low-intensity, high-mileage” training at 50-60 % of VO2max on ~140 d. Stage races took up another ~40 d. Specific track cycling at near competition intensities was performed on less than 20 d between March and September.  In the ~110 d preceding the Olympic final, high-intensity interval track training was performed on only 6 d.

Units for Training Intensity

Cross country skiers have rather legendary status in exercise physiology circles for their aerobic capacity and endurance capacity in arms and legs. Seiler et al. (2006) studied 12 competitive to nationally elite male 17–y old skiers from a special skiing high school in the region. The mean VO2max for the group was 72 They were guided by coaches with national team coaching experience and were trained along similar lines to the seniors, but with substantially lower volumes of training. Like Esteve-Lanao (2005) did with runners, we used heart-rate monitoring to quantify all endurance sessions and determined three aerobic intensity zones based on ventilatory turn points. We also recorded the athletes' rating of perceived exertion (RPE) using the methods of Foster et al. (1996; 1998; 2001a) for all training bouts. Finally, we collected blood lactate during one training week to relate heart rate and perceived exertion measurements to blood lactate values.

When comparing the three different intensity quantification methods, we addressed the issue of how training intensity is best quantified. Heart-rate monitoring is clearly appealing. We can save heart rate data, download entire workouts to analysis software, and quantify the time heart rate falls within specific pre-defined intensity zones. Using this “time-in-zone” approach, we found that 91 % of all training time was spent at a heart rate below VT1 intensity, ~6 % between VT1 and VT2, and only 2.6 % of all 15-s heart rate registrations were performed above VT2. We then quantified intensity by allocating each training session to one of the three zones based on the goal of the training and heart rate analysis. We called this the “session-goal approach”. For low-intensity continuous bouts, we used average heart rate for the entire bout. For bouts designed to be threshold training we averaged heart rate during the threshold-training periods. For high-intensity interval-training sessions, we based intensity on the average peak heart rate for each interval bout. Using this approach, intensity distribution derived from heart rate responses closely matched the session RPE (Figure 4), training diary distribution based on workout description, and blood-lactate measurements. The agreement between the session-by-session heart-rate quantification and session RPE-based assignment of intensity was 92 %. In their training diaries, athletes recorded 30-41 training sessions in 32 d and described 75% of their training bouts as low intensity continuous, 5% as threshold wor-kouts, and 17% as intervals.

We have also recently observed the same time-in-zone mismatch when quantifying intensity distribution in soccer training (unpublished data). It seems clear that typical software-based heart-rate analysis methods overestimate the amount of time spent training at low intensity and underestimate the time spent at very high workloads compared to athlete perception of effort. We think this mismatch is important, because the unit of stress perceived and responded to by the athlete is the stress of the entire training session or perhaps training day, not minutes in any given heart-rate zone. 

The 80-20 Rule for Intensity

In spite of differences in the methods for quantifying training intensity, all of the above studies show remarkable consistency in the training distribution pattern selected by successful endurance athletes.  About 80 % of training sessions are performed predominantly at inten-sities under the first ventilatory turn point, or a blood-lactate concentration <=2mM. The remaining ~20 % of sessions are distributed between training at or near the traditional lactate threshold (Zone 2), and training at intensities in the 90-100 %VO2max range, generally as interval training (Zone 3). An elite athlete training 10-12 times per week is therefore likely to dedicate 1-3 sessions weekly to training at intensities at or above the maximum lactate steady state. This rule of thumb coincides well with training studies demonstrating the efficacy of adding two interval sessions per week to a training program (Billat et al., 1999; Lindsay et al., 1996; Weston et al., 1997). Seiler and Kjerland (2006) have previously gone so far as say that the optimal intensity distribution approximated a “polarized distribution” with 75-80 % of training sessions in Zone 1, 5 % in Zone 2, and 15-20 % in Zone 3. However, there is considerable variation in how athletes competing in different sports and event durations distribute their training intensity within Zones 2 and 3. 

Why has this training pattern emerged?  We do not have sufficient research to answer this question, but we can make some reasonable guesses. One group of factors would involve the potential for this distribution to best stimulate the constellation of training adaptations required for maximal endurance performance. For example, large volumes of training at low intensity might be optimal for maximizing peripheral adaptations, while relatively small volumes of high intensity training fulfill the need for optimizing signaling for enhanced cardiac function and buffer capacity. Technically, lots of low intensity training may be effective by allowing lots of repetitions to engrain correct motor patterns. On the other side of the adaptation-stress equation is the stress induced by training. Athletes may migrate towards a strategy where longer duration is substituted for higher intensity to reduce the stress reactions associated with training and facilitate rapid recovery from frequent training (Seiler et al., 2007). Interestingly, Foster and colleagues reported a very similar intensity distribution by professional cyclists during the 3 wk and 80+ racing hours of the grand tours, such as the Tour de France. Perhaps this distribution represents a form of pacing that emerges over the months of elite training (Foster et al., 2005).

”Low intensity”–between 50 %VO2max and just under the first lactate turnpoint–represents a wide intensity range in endurance athletes. There is probably considerable individual varia-tion in where within this range athletes accumulate most of their low-intensity training volume. Technique considerations may play in: athletes have to train at a high enough intensity to allow correct technique. For example, Norwegian Olympic flat-water kayak gold medalist Eric Verås Larsen explained that the reason most of his Zone 1 continuous endurance training tended to be closer to his lactate threshold than normally observed was that he could not paddle with competition technique at lower intensities (Verås Larsen, personal communication).  These qualifiers aside, we conclude that a large fraction of the training within this zone is being performed at ~60-65 %VO2max, We note that this intensity is about the intensity associated with maximal fat utilization in trained subjects (Achten and Jeukendrup, 2003), but it is unclear why optimizing fat utilization would be important for athletes competing over 3-15 min.

Training Volume of Elite Athletes

Obviously, training intensity distribution and training volume together will determine the impact of training. Elite athletes train a lot, but to be more specific requires some common metric for comparing athletes in different sports. Runners and cyclists count kilometers, swimmers count thousands of meters, and rowers and cross-country skiers count training hours.  With a few reasonable assumptions, we can convert these numbers to annual training hours. This physiological metric is appropriate, since the body is sensitive to stress duration. 

Training volume increases with age in high-level performers, mostly through increased training frequency in sports like running and cross-country skiing, but also through increases in average session duration, particularly in cycling. A talented teenage cyclist training five days a week might accumulate 10-15 h.wk-1. A professional cyclist from Italy performing a 1000-km training week will likely be on the bike between 25 and 30 h.

Cycling 30-35,000 kilometers a year at, say, ~35 km.h-1 with occasional sessions of strength training, will add up to ~1000 h.y-1.  An elite male marathoner would likely never run more than about 15 hours in a week. At an average running speed of 15 km.h-1, that would be at most 225 km.  Former world record holder in the 5 km, 10 km, and marathon, Ingrid Kristiansen trained 550 h.y-1 when she was running (Espen Tønnessen, unpublished data). At a younger age, when she competed in the Olympics for Norway as a cross country skier, she actually trained 150 more h.y-1. Bente Skari, one of the most successful female cross country skiers ever, recorded peak annual training loads of 800 h.y-1 (Espen Tønnessen, unpublished data). Annual training volume measured in hours is around 1000 among world class rowers. For example, Olaf Tufte recorded 1100 training hours in 2004, when he took his first gold medal in the single scull event (Aasen, 2008). His monthly training volume for that year is shown in Figure 5. Of these hours, about 92 % were endurance training, with the remainder being primarily strength training. An Olympic champion swimmer like Michael Phelps may record even higher annual training volumes, perhaps as much as 1300 h (a reasonable guess based on training of other swimming medalists).

The Kenyan marathoner, Italian cyclist, Norwegian rower and American swimmer are all at the top of their sport, but when we measure their training volume in hours, they seem quite different, with international success being achieved with a two-fold or larger range in hours per year (Figure 6). What can explain this difference?  One explanation is that the muscle, tendon, and joint loading stress of the different movements vary dramatically. Running imposes severe ballistic loading stress that is not present in cycling or swimming.

There seems to be a strong inverse relationship between tolerated training volume and degree of eccentric or ballistic stress of the sport. Swimming, rowing, and cross-country skiing are all highly technical events with movement patterns that do not draw on the genetically pre-programmed motor pathways of running.  Thus high volumes of training may be as important for technical mastery as for physiological adaptation in these disciplines. Rowers and speed skaters do less movement-specific training than most other athletes, but they accumulate substantial additional hours of strength training and other forms of endurance training.

Intensified-Training Studies

Is the “80-20” training intensity distribution observed for successful athletes really optimal, or would a redistribution of training intensity towards more threshold and high intensity interval training and less long slow distance training stimulate better gains and higher perfor-mance? Proponents of large volumes of interval training might invoke the famous pareto principle and propose that in keeping with this “rule” of effects vs causes, these athletes are achieving 80 % of their adaptive gains with 20 % of their training and wasting valuable training energy. In the last 10 y, several studies have been published addressing this question.

Evertsen et al. (1997; 1999; 2001) published the first of three papers from a study involving training intensification in 20 well-trained junior cross-country skiers competing at the national or international level. All of the subjects had trained and competed regularly for 4-5 years. In the two months before study initiation, 84 % of training was carried out at 60-70 %VO2max, with the remainder at 80-90 %VO2max.  They were then randomized to a moderate-intensity (MOD) or a high-intensity training group (HIGH). MOD maintained essentially the same training-intensity distribution they had used previously, but training volume was increased from 10 to 16 h.wk-1. HIGH reversed their baseline intensity distribution so that 83 % of training time was performed at 80-90 %VO2max, with only 17 % performed as low-intensity training.  This group trained 12 h.wk-1. The training intervention lasted five months. Intensity control was achieved using heart-rate monitoring and blood-lactate sampling.

Despite 60 % more training volume in MOD and perhaps 400 % more training at lactate threshold or above in HIGH, physiological and performance changes were modest in both groups of already well-trained athletes. Findings from the three papers are summarized in Table 4.

Gaskill et al. (1999) reported the results of a 2-y project involving 14 cross-country skiers training within the same club who were willing to have their training monitored and manipu-lated. The design was interesting and practically relevant. During the first year, athletes all trained similarly, averaging 660 training hours with 16 % at lactate threshold or higher (nominal distribution of sessions). Physiological test results and race performances during the first year were used to identify seven athletes who responded well to the training and seven who showed poor VO2max and lactate-threshold progression, and race results. In the second year, the positive responders continued using their established training program. The non-responders performed a markedly intensified training program with a slight reduction in training hours. The non-responders from Year 1 showed significant improvements with the intensified program in Year 2 (VO2max, lactate threshold, race points). The positive responders from Year 1 showed a similar development in Year 2 as in Year 1.

It is interesting in this context to point out that many elite athletes now extend the  peri-odization of their training to a 4-y Olympic cycle. The first year after an Olympics is a “recovery season”, followed by a building season, then a season of very high training volume, culminating with the Olympic season, where training volume is reduced and competition specificity is emphasized more.  Variation in the pattern of training may be important for maximal development, but these large scale rhythms of training have not been studied.

Esteve-Lanao et al. (2007) randomized 12 sub-elite distance runners to one of two training groups (Z1 and Z2) that were carefully monitored for five months. They based their training intensity distribution on the 3-zone model described earlier and determined from treadmill testing. Based on time-in-zone heart-rate monitoring, Z1 performed 81, 12, and 8 % of training in Zones 1, 2, and 3 respectively. Z2 performed more threshold training, with 67, 25, and 8 % of training performed in the three respective zones. That is, Group Z2 performed twice as much training at or near the lactate threshold. In a personal communication, the authors reported that in pilot efforts, they were unable to achieve a substantial increase in the total time spent in Zone 3, as it was too hard for the athletes. Total training load was matched between the groups. Improvement in a cross-country time-trial performed before and after the five-month period revealed that the group that had performed more Zone 1 training showed significantly greater race time improvement (-157 ± 13 vs  122 ± 7 s).

Most recently, Ingham et al. (2008) were able to randomize 18 experienced national standard male rowers from the UK into one of two training groups that were initially equiva-lent based on performance and physiological testing. All the rowers had completed a 25-d post-season training-free period just prior to baseline testing. One group performed “100 %” of all training at intensities below that eliciting 75 %VO2max (LOW). The other group per-formed 70 % training at the same low intensities as well as 30 % of training at an intensity 50 % of the way between power at lactate threshold and power at VO2max (MIX). In practice, MIX performed high intensity training on 3 d.wk-1. All training was performed on a rowing ergometer over the 12 wk. The two groups performed virtually identical volumes of training (~1140 km on the ergometer), with ±10 % individual variation allowed to accommodate for variation in athlete standard. Results of the study are summarized in Table 5.

Sixteen of 18 subjects set new personal bests for the 2000-m ergometer test at the end of the study. The authors concluded that LOW and MIX training had similar positive effects on performance and maximal oxygen consumption. LOW training appeared to induce a greater right-shift in the blood-lactate profile during sub-maximal exercise, which did not translate to a significantly greater gain in performance. If MIX training enhanced or preserved anaerobic capacity more than LOW, this may have compensated for the observed differences in blood-lactate profile.

Intensity for Recreational Athletes

Elite endurance athletes train 10-12 sessions and 15-30h each week.  Is the pattern of 80 % below and 20 % above lactate threshold appropriate for recreational athletes training 4-5 times and 6-10 hours per week?  There are almost no published data addressing this question. Recently Esteve-Lanao (personal communication) completed an interesting study on recreational runners comparing a program that was designed to reproduce the polarized training of successful endurance athletes and compare it with a program built around much more threshold training in keeping with the ACSM exercise guidelines.  The intended intensity distribution for the two groups was: Polarized 77-3-20 % and ACSM 46-35-19 % for Zones 1, 2, and 3. However, heart-rate monitoring revealed that the actual distribution was: Polarized 65-21-14 % and ACSM 31-56-13 %.

Comparing the intended and achieved distributions highlights a typical training error com-mitted by recreational athletes.  We can call it falling into a training intensity “black hole.”  It is hard to keep recreational people training 45-60 min a day 3-5 days a week from accumulating a lot of training time at their lactate threshold. Training intended to be longer and slower becomes too fast and shorter in duration, and interval training fails to reach the desired intensity. The result is that most training sessions end up being performed at the same threshold intensity. Foster et al. (2001b) also found that athletes tend to run harder on easy days and easier on hard days, compared to coaches' training plans.  Esteve Lanao did succeed in getting two groups to distribute intensity very differently. The group that trained more polarized, with more training time at lower intensity, improved their 10-km performance significantly more at 7 and 11 wk. So, recreational athletes could also benefit from keeping low- and high-intensity sessions at the intended intensity.

Interval training can be performed effectively with numerous combinations of work duration, rest duration, and intensity. We have found that when subjects self-select running speed based on a standard prescription, 4-min work duration and 2-min recovery duration combine to give the highest physiological response and maintained speed (Seiler and Sjursen, 2004; Seiler and Hetlelid, 2005). However, perceptual and physiological response differences across the typical work and recovery spectrum are fairly small and performance enhancement differences are unclear at best. Some researchers have proposed that specific interval regimes (e.g., 4 × 4 min at 95 %VO2max) may be superior for achieving adaptive gains (Helgerud et al., 2007; Wisloff et al., 2007), but other research studies and our observations of athlete practice suggest that a variety of combinations of work and rest duration are effective for long-term development. Table 6 shows typical combinations of intensity and effective duration used by elite endurance athletes for workouts in the different aerobic training zones described earlier. All the examples are taken from the training diaries of elite performers. The effective durations for the different zones are utilized by highly trained athletes. For those without the same training base, similar workouts would be performed but with less total effec-tive duration.

Case Studies of Training Manipulation

Case studies are the weakest form of scientific evidence. But, for coaches and high performance athlete support teams, each elite athlete is a case study.  So, we present here two case studies that we think are instructive in demonstrating the potential physiological impact of successfully manipulating training  volume and intensity distribution variables at the individual level. Both cases involve Norwegian athletes who were followed closely by one of the authors (Tønnessen).  Both would be considered already highly trained prior to the training reorganization.

Case 1–From Soccer Pro to Elite Cyclist

Knut Anders Fostervold was a professional soccer player in the Norwegian elite league from 1994 to 2002.  A knee injury ended his soccer career at age 30 and he decided to switch to cycling.  Knut had very high natural endurance capacity and had run 5 km in 17:24 at age 12.  After 15 y of soccer training at the elite level, he adopted a highly intensive training regime for cycling that was focused on training just under or at his lactate threshold and near VO2max; for example, 2-3 weekly training sessions of 4-5 × 4 min at 95 %VO2max.  Weekly training volume did not exceed 10 h.

After 2.5 years of this high-intensity, low-volume training, Fostervold initiated cooperation with the Norwegian Olympic Center and his training program was radically reorganized.  Weekly training volume was doubled from 8-10 h to 18-20.  Training volume in Zone 2 was reduced dramatically and replaced with a larger volume of training in Zone 1.  Training in Zone 5 was replaced with Zones 3 and 4, such that total training volume at intensities at or above lactate threshold was roughly doubled without overstressing the athlete. The typical effective duration of interval sessions increased from ~20 min to ~ 60 min (for example 8 × 8 min at 85-90 %HRmax with 2-min recoveries).  The intensity zones were initially based on heart rate but later adjusted relative to lactate and power output measurements made in the field.  Table 7 shows the training intensity distribution and volume loading for the athlete during the season before and after the change in training to a high-volume program. Table 8 shows the outcome.





The athlete responded well to the training load amplification and reorganization.  During the 2005 season, after 2.5 y performing a low-volume, high-intensity program, a season training with higher volume and lower average intensity resulted in marked physiological and performance improvement. Although the athlete’s training de-emphasized both training near his lactate threshold intensity and training at near VO2max, both of these physiological anchors improved markedly.

Fostervold won a bronze medal in the Norwegian national time-trial championships, seconds behind former world under-23 time trial champions and Tour de France stage win-ners Thor Hushovd and Kurt Asle Arvesen.  His failure to perform even better, given his exceptionally high VO2max, was attributed to poorer cycling efficiency and aerodynamics and a lower fractional utilization at lactate threshold compared to the best professionals with many years of specific training. In 2006 and 2007 he represented Norway in the world cham-pionship time trial. His absolute VO2max in 2005 was equal to the highest ever measured in a Norwegian athlete. 

Case 2–From Modern Pentathlete to Runner

Prior to 2003, Øystein Sylta was a military pentathlete (European champion in 2003).  In the Fall of 2003 he decided to focus on distance running and is now nationally competitive, with personal bests for 3000-m steeplechase, 5000-m, and 10000-m of 8:31, 14:04 and 29:12 respectively.  His case is interesting due to the dramatic change in training volume and intensity distribution he undertook from 2003 to 2004 and associated changes in physiolog-ical test results.

Prior to 2003, Sylta trained using a high-intensity, low-volume training structure.  When he agreed to try a new approach, emphasis was placed on increasing training volume with low-intensity sessions and changing his interval training.  He either trained long slow distance or long intense interval sessions. However, his total training distance at intensities above his lactate threshold was reduced and redistributed.  From 2002/2003 to 2003/2004 he increased his total running distance from 3,500 to 5,900 km.  He also reduced his strength training from 100 annual hours to 50.  Table 9 shows a typical hard training week in the Fall of 2003 and Fall of 2004, and Table 10 summarizes the running specific training.  His physiological adaption to the first year of restructured training is documented in Table 11.

From 2003 to 2009, Sylta’s threshold running speed increased from 16.9 to 19.5 km.h-1. From 2002 to 2009,  his 10-km time improved from 31:44 to 29:12, and 3000-m steeplechase from 9:11 to 8:31.  In the first five months of training reorganization, his 3000-m steeple result improved by 30 s.




Both these case studies demonstrate that even in already well trained athletes, meaningful improvements in physiological test results and performance may occur with appropriate training intensity and volume manipulation.  Both athletes showed clear improvements in physiological testing despite reductions in HIT training.  Both seemed to respond positively to an increase in total training volume and specifically, more low-intensity volume.

Valid Comparisons of Training Interventions

Matching training programs based on total work or oxygen consumption seems sensible in a laboratory.  As we noted earlier, this has been the preferred method of matching when com-paring the effects of continuous and interval training in controlled studies. Unfortunately, it is not realistic from the view of athletes pursuing maximal performance. They do not compare training sessions or adjust training time to intensity in this manner. A key issue here is the non-linear impact of exercise intensity on the manageable accumulated duration of intermittent exercise. We have exemplified this in Table 12 by comparing some typical training sessions from the training of elite athletes.

The point we want to make is that the athlete’s perception of the stress of performing 4 × 15 min at 85 %VO2max is about the same as that of performing 6 × 4 min at 95 %VO2max, even though total work performed is very different. To answer a question like, “is near VO2max interval training more effective for achieving performance gains in athletes than training at the maximal lactate steady state?”, the matching of training bouts has to be realistic from the perspective of perceived stress and how athletes train. Future studies of training intensity effects on adaptation and performance should take this issue of ecological validity into account.


Optimization of training methods is an area of great interest for scientists, athletes, and fitness enthusiasts. One challenge for sport scientists is to translate short-term training intervention study results to long-term performance development and fitness training organi-zation. Currently, there is great interest in high-intensity, short-duration interval training programs. However, careful evaluation of both available research and the training methods of successful endurance athletes suggests that we should be cautious not to over-prescribe high-intensity interval training or exhort the advantages of intensity over duration.
Here are some conclusions that seem warranted by the available data and experience from observations of elite performers:

• There is reasonable evidence that an ~80:20 ratio of low to high intensity training (HIT) gives excellent long-term results among endurance athletes training daily.
• Low intensity (typically below 2 mM blood lactate), longer duration training is effective in stimulating physiological adaptations and should not be viewed as wasted training time.
• Over a broad range, increases in total training volume correlate well with improvements in physiological variables and performance.

• HIT should be a part of the training program of all exercisers and endurance athletes. However, about two training sessions per week using this modality seems to be sufficient for achieving performance gains without inducing excessive stress.
• The effects of HIT on physiology and performance are fairly rapid, but rapid plateau effects are seen as well. To avoid premature stagnation and ensure long-term development, training volume should increase systematically as well.
• When already well-trained athletes markedly intensify training with more HIT over 12 to ~45 wk, the impact is equivocal.
• In athletes with an established endurance base and tolerance for relatively high training loads, intensification of training may yield small performance gains at acceptable risk.
• An established endurance base built from reasonably high volumes of training may be an important precondition for tolerating and responding well to a substantial increase in training intensity over the short term.
• Elite athletes achieve periodization of training with reductions in total volume, and modest increases in volume of training above the lactate threshold. An overall polarization of training intensity characterizes the transition from preparation to competition mesocycles. The basic intensity distribution remains similar throughout the year.


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Skeletal muscle: master or slave of the cardiovascular system?

By: Russell S. Richardson, Craig A. Harms, Bruno, Grassi, and Russell T. Hepple
Department of Medicine, University of California, San Diego, La Jolla, CA; Department of Kinesiology, Kansas State University, Manhatten, KS; and Istituto di Tecnologie Biomediche Avanzate, National Research Council, Milano, ITALY.

From: Skeletal muscle: master or slave of the cardiovascular system?

RUSSELL S. RICHARDSON, CRAIG A. HARMS, BRUNO, GRASSI, and RUSSELL T. HEPPLE. Skeletal muscle: master or slave of the cardiovascular system? Med. Sci. Sports Exerc., Vol. 32, No. 1, pp. 89–93, 1999.


Skeletal muscle and cardiovascular system responses to exercise are so closely entwined that it is often difficult to determine the effector from the affector. The purpose of this manuscript and its companion papers is to highlight (and perhaps assist in unraveling) the interdependency between skeletal muscle and the cardiovascular system in both chronic and acute exercise. Specifically, we elucidate four main areas: 1) how a finite cardiac output is allocated to a large and demanding mass of skeletal muscle, 2) whether maximal muscle oxygen uptake is determined peripherally or centrally, 3) whether blood flow or muscle metabolism set the kinetic response to the start of exercise, and 4) the matching of structural adaptations in muscle and the microcirculation in response to exercise. This manuscript, the product of an American College of Sports Medicine Symposium, unites the thoughts and findings of four researchers, each with different interests and perspectives, but with the common intent to better understand the interaction between oxygen supply and metabolic demand during exercise.


Although recognizing the numerous physiological systems and the many interactions during exercise, still perhaps the most significant interplay is between the cardiorespiratory system and skeletal muscle, which determines both O2 supply and demand (Fig. 1). At the beginning of exercise, the integrated response of the pulmonary, cardiovascular, and muscular systems characterize the V˙O2 on-kinetics. This kinetic response is highly sensitive to aerobic training (31) and can be measured both at the mouth and across a muscle (10). However, the role that each system plays in determining the V˙O2 on-kinetics continues to be the subject of considerable debate (4,18).

Beyond this transitional period, we encounter the issue of blood flow distribution, which is the appropriate distribution of a finite cardiac output among essential organs such as the brain, heart, intestines (48), and the metabolically very active skeletal muscle involved in the exercise (32). Which area of demand takes precedence as the metabolic requirements increase and the limits of cardiac output are approached (11)? The introduction of isolated skeletal muscle models (2,51) has highlighted this issue of skeletal muscle perfusion under conditions of maximal cardiac output versus a small muscle mass where central components are less taxed, allowing a greater level of skeletal muscle perfusion to be achieved (41,47). Additionally, these skeletal muscle models have proved fruitful in another long standing area of study: the determinants of maximal metabolic rate (V˙O2max), specifically whether V˙O2max is governed by O2 supply or O2 demand (35,43). Finally, the study of the structural interface between the cardiovascular system and skeletal muscle can be a powerful approach to elucidating the interplay between these two systems. It can be experimentally demonstrated that O2 conductance from blood to muscle cell plays an important role in determining V˙ O2max (37,52), suggestive of a passive role played by the muscle itself. However, when exposed to a repeated exercise stimulus, skeletal muscle now takes a very active role and demonstrates a remarkable plasticity (17) that positively affects exercise capacity (16).

Thus, here again the issue of who is the master and who the slave in the relationship between the cardiovascular system and skeletal muscle is open to debate.

Muscular Perfusion: Determined by Muscular Demand or Cardiovascular Supply?

The greatest demand for cardiac output during exercise is from skeletal muscle, as nearly 85% of total blood flow is directed to the working legs during maximal cycle ergometry (20,32). Several investigations have examined how different groups of skeletal muscle compete for the cardiac output during exercise and whether a “steal” phenomenon exists. Although Secher et al. (50) observed a decrease in leg blood flow when arm exercise was added to two legged cycle ergometry, more recent investigations have failed to corroborate these findings (36,44,49). However, the majority of data suggest that some degree of leg vasoconstriction or an attempt to vasoconstrict, as determined from norepinephrine spillover, occurs when arm exercise is added to leg exercise (44,49). Recently, a set of experiments have been conducted to determine whether a different group of skeletal muscles, those associated with breathing, influence cardiac output and its distribution during maximal exercise (11–13,56). These reports have demonstrated that respiratory muscles demand a significant portion of the cardiac output, primarily through stroke volume and total V˙O2, approximating 14–16% of the total (12). Additionally, it was shown that during heavy exercise, this metabolic demand from the respiratory muscles affects the distribution of cardiac output between the respiratory muscles and the legs such that leg vascular conductance and blood flow increases with respiratory muscle unloading and decreases with respiratory loading (11). Exercise performance may also be affected by the work of breathing during heavy exercise due to redistribution of blood flow between the chest wall and the locomotor muscles (56). Therefore, it appears that, in contrast to arm versus leg exercise, respiratory muscle work normally encountered during maximal exercise significantly influences cardiac output and its distribution.

V˙O2max: Governed by Oxygen Supply or Demand?

It has now been repeatedly demonstrated that an increase in O2 delivery can increase V˙O2max (1,3,5,21,30,34,38,43,55), which suggests that O2 supply limitation exists. As the isolated human quadriceps exercise does not approach the upper limits of cardiac output, this exercise paradigm has previously unveiled a skeletal muscle metabolic reserve and results in the highest mass specific V˙O2 and work rates recorded in man (37,41,46). This observation in of itself is evidence of O2 supply limitation of muscle V˙O2max. In a recent human knee-extensor study, the V˙O2max increased with an elevated O2 delivery (hyperoxia) demonstrating that in normoxic conditions even in the highly perfused isolated quadriceps, muscle V˙O2max is not limited by mitochondrial metabolic rate, but rather by O2 supply (35).

Although it is clear that in many scenarios an increase in O2 delivery can increase V˙O2max, it has also been demonstrated that this is not the sole determinant; in fact, the interaction between the convective and diffusive components of O2 transport may ultimately set the maximal metabolic rate (52). In the isolated canine gastrocnemius preparation, infusion of the allosteric modifier of hemoglobin RSR13 (Allos Therapeutics, Denver, CO) significantly increased P50, and at a constant arterial O2 delivery resulted in an increase in O2 extraction and a consequent increase in muscle V˙O2max (43). This indicates, for the first time, that the canine gastrocnemius muscle is normally O2 supply-limited, even when the animal is breathing 100% O2. In addition, the increase inV˙ O2max was proportional to the increase in venous PO2. Taken together, these findings support the concept that the diffusion of O2 between the red cell and the mitochondria plays a role in determining V˙O2max.

The insinuation that the production of lactate with progressively intense muscular work is evidence of inadequate intramuscular oxygenation has been long lived (15). Since then, the term “anaerobic threshold” has been used to describe the point at which lactate begins to accumulate in the blood, thought to indicate the inadequacy of O2 supply for the metabolic demand (54). Magnetic resonance spectroscopy, utilizing myoglobin as an endogenous probe of intracellular PO2 (29,53), in combination with the isolated human quadriceps model (38) has revealed that in hypoxic or normoxic exercise conditions net muscle lactate efflux is independent of intracellular PO2. The former increases whereas the latter remains constant during progressive incremental exercise (39). However, in hypoxia intracellular PO2 is systematically decreased in comparison to normoxia, whereas the changes in intracellular pH and muscle lactate efflux are accelerated. Whereas the latter observations indicate that a role for intracellular PO2 as a modulator of metabolism cannot be ruled out, arterial epinephrine levels are closely related to skeletal muscle lactate efflux in both normoxia and hypoxia and thus may be a major stimulus for the observed rise in muscle lactate efflux during progressively intense exercise and for the elevated lactate efflux in hypoxia. We would postulate that it is systemic and not intracellular PO2 that increases catecholamine responses in hypoxia and is therefore responsible for the correspondingly higher net lactate efflux (39).

Recently, evidence supporting the importance of intracellular PO2 in determining skeletal muscle V˙O2max has come to light (38). Studies of intracellular PO2 in trained human skeletal muscle with varied FIO2 suggest that in hyperoxia there is the expected rise in intracellular PO2 (due to increased mean capillary PO2), but this elevated O2 availability is now in excess of mitochondrial capacity (40). Indicating that intracellular PO2 is a determinant of V˙O2max in each FIO2 (12, 21, and 100% O2) but that in the latter case the increased intracellular PO2 results in diminishing returns with respect to an increase in V˙O2max. These observations are consistent with cellular metabolism that is moving toward a transition between O2 supply and O2 demand as a determinant of V˙O2max. It seems that further increases in intracellular PO2, beyond those recorded in hyperoxia, may have smaller effects upon V˙O2max until a plateau is reached and V˙O2max becomes invariant with intracellular PO2. From this point, intracellular PO2 may no longer be a determinant of skeletal muscle V˙O2max. This hyperbolic relationship, perhaps stemming from the origin, between intracellular O2 tension and cellular respiration is similar to data previously presented by Wilson et al. (57) in which the metabolic rate of isolated kidney cells was demonstrated to be O2 supply dependent below a certain O2 availability. Again, these myoglobin-associated PO2 data fit with the supply dependence of V˙O2max in healthy exercise trained human skeletal muscle (35,37).

V˙O2 On-Kinetics: Set by Blood Flow or Muscle Metabolism?

Upon a step transition from rest to exercise, or from a lower to higher workload, O2 uptake (V˙O2) lags behind the power output increase, following a time course usually termed V˙O2 on-kinetics. The mechanism(s) determining this kinetic response has been a matter of considerable debate between those who consider it mainly related to the rate of adjustment of O2 delivery to the exercising muscles and those supporting the concept that the V˙O2 on-kinetics is mainly set by an inertia of intramuscular oxidative metabolism.

In recent years, experiments in both exercising humans (9,10) and in the isolated in situ dog gastrocnemius preparation (7,8) have provided evidence in favor of the “metabolic inertia” hypothesis. Specifically, the transition from unloaded-to-loaded pedalling (below the “ventilatory threshold”) was studied in humans. 

Blood flow to one of the exercising limbs was determined continuously by a modified constant-infusion thermodilution technique, andV˙O2 across the limb was determined every ;5s by the Fick principle. Leg blood flow rose rapidly upon the change in work intensity, whereas arteriovenous O2 concentration difference across the limb did not increase during the first 10–15 s of the transition (10). During this type of metabolic transition, therefore, muscle O2 utilization kinetics lag behind the kinetics of bulk O2 delivery to muscle.

Heart transplant recipients show a slower V˙O2 on-kinetics compared with healthy controls. This slower V˙O2 onkinetics may be attributed to a slower adjustment of heart rate, cardiac output, and O2 delivery to muscles. In a group of heart transplant recipients, a “warm-up” exercise, performed before a rest-to-50-W transition, resulted in a slightly faster adjustment of cardiac output and more favourable conditions as far as O2 delivery to exercising muscles but did not speed up the V˙ O2 on-kinetics (9). Again, indicative of the lag in O2 uptake originating in the muscle itself.

By utilizing the isolated in situ dog gastrocnemius preparation, the metabolic transition from rest-to-electrically stimulated tetanic contractions corresponding to ;70% of V˙O2max was studied (7). The delay in the adjustment of convective O2 delivery to muscle was completely eliminated by pump-perfusing the muscle, at rest and during contractions, at a constant level of blood flow corresponding to the steady state value obtained during contractions in preliminary trials conducted with spontaneous adjustment of muscle blood flow (muscle perfused via the contralateral femoral artery). Adenosine was infused intra-arterially to prevent any vasoconstriction associated with the elevated muscle blood flow. Elimination of delay in convective O2 delivery did not affect muscle V˙O2 on-kinetics, which was not different to that observed in control conditions (7).

Finally, another study was conducted on the isolated in situ dog gastrocnemius preparation, during the same metabolic transition described above. Peripheral O2 diffusion was enhanced by having the dogs breathe a hyperoxic gas mixture and by the administration of RSR 13 (Allos Therapeutics), which right-shifts the oxy-hemoglobin dissociation curve. Mean capillary PO2 (PcapO2) was estimated by numerical integration. Hyperoxic breathing and RSR 13 significantly increased PcapO2 (i.e., the driving force for peripheral O2 diffusion) at rest and during contractions but did not affect muscle V˙O2 on-kinetics (8). Taken together, the results of this study and the previous one indicate that, in this experimental model, neither convective nor diffusive O2 delivery to muscle fibers affects muscleV˙ O2 on-kinetics, supporting the hypothesis that the latter is mainly set by an inertia of muscle oxidative metabolism. These conclusions appear in agreement with observations obtained by other authors in humans during step transitions to workloads lower than the “ventilatory threshold” (6,24). It should be noted, however, that these authors indicate that during step transitions to workloads higher than the “ventilatory threshold” the kinetics of O2 delivery to muscle appears to be a critical factor in determining the V˙O2 on-kinetics.

Plasticity of Skeletal Muscle: Microcirculatory Adaptation to Metabolic Demand?

The issue of whether skeletal muscle is master or slave of the cardiovascular system depends on frame of reference. Although acute manipulations of convective O2 delivery clearly show that O2 supply sets the upper limit of mitochondrial respiratory rate (42), interspecies comparisons (23) and study of adaptation to chronic conditions such as physical training show that capillarization (14,19) and mitochondrial development (28,45) are key components of the adaptive response in systemic V˙O2max. In addition, adaptations in the structural capacity for aerobic metabolism in skeletal muscle are closely regulated (e.g., close matching of capillary supply and fiber mitochondrial content) (26,33) and are maintained in proportion to the aerobic capacity of the whole organism (17). The study of adaptive variation in skeletal muscle structure within and between species has revealed design features that are uniform throughout muscles of widely varying metabolic demand. One of these features is that the size of the capillaryto- fiber interface rather than diffusion distance relates most closely to the structural capacity for O2 flux into muscle fibers (27). Recent studies have also shown that the size of the capillary-to-fiber interface is matched to mitochondrial volume/ fiber length with adaptation to training (33), electrical stimulation (26), and chronic hypoxia (25). These observations suggest another regulated design feature in skeletal muscle is matching the structural capacity for O2 flux to fiber metabolic demand (33).

Changes in capillarization and fiber mitochondrial content are important parts of the adaptive response to exercise training. In older humans, both high-intensity resistance training and aerobic training increase the size of the capillary-to-fiber interface (14). Furthermore, the change in V˙O2max is related to changes in the size of the capillary-to-fiber interface rather than capillary density, suggesting an increase in the structural capacity for O2 flux is an important feature of the adaptation in V˙O2max with both modes of training in this population (14).

Similarly, mitochondrial electron transport chain (ETC) capacity appears important to muscle V˙O2max. Poisoning of complex III (NADH-cytochrome c reductase) of the ETC results in a stepwise reduction in peak muscle O2 (27) and reduces peak muscle V˙O2 to pretraining levels in trained rat hindlimb muscle (45). It is noteworthy that this occurs even when muscle metabolism, blood flow, and convective O2 delivery are markedly lower than seen during maximal exercise in vivo (22).

In conclusion, there appears to be a paradox between the well-known increase in V˙O2max that occurs with increased O2 delivery and the proportional alterations in V˙O2max that accompany manipulations in mitochondrial oxidative capacity at submaximal O2 delivery and submaximal metabolic demand.

This, in conjunction with the observation that adaptation in skeletal muscle structural capacity for O2 flux (e.g., increased capillarization and fiber mitochondrial content) occurs in response to alterations in metabolic demand through exercise training and chronic hypoxia, supports an independent role of skeletal muscle in determining systemic V˙O2max.


It is clear that both on a functional and structural level the response of the cardiovascular system and skeletal muscle are closely linked. Here we have addressed the issue of which of these systems is dominant and which more submissive.

Although we offer insight to this question, perhaps the most striking observation is that a single answer would not be appropriate as the role of each system appears to be highly dependent upon a multitude of factors that together create the scenario under investigation. A change in one of these variables, for example, acute exercise becoming chronic exercise, will markedly alter the relationship between the cardiovascular system and skeletal muscle and change the answer to the question of control.

Funding was provided by NIH 17731, RR02305, and HL-15469, and Dr. Richardson and Dr. Harms were supported by Parker B. Francis Fellowships in Pulmonary Research.
Address for correspondence: Russell S. Richardson, Ph.D., Department of Medicine, University of California, San Diego, La Jolla, CA 90293-0623. E-mail:


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Elite Rowing: Maintaining Maximum Condition

By: Dr Richard Godfrey and Greg Whyte
From: Elite Rowing: Maintaining Maximum Condition

Dr Richard Godfrey is a Senior Research Lecturer at Brunel University and has previously spent 12 years working as a chief physiologist for the British Olympic Association

Greg Whyte FACSM is director of science and research at the English Institute of Sport

Life at the top – how are elite rowers tested and monitored?

Elite rowers subject their bodies to incredibly high levels of physiological stress. So what kind of testing and monitoring is needed to maintain maximum condition during rowing training without complete breakdown? Richard Godfrey and Greg Whyte explain. 

Olympic rowing events are conducted over a 2,000m course. The event lasts about 320 seconds (s) to 460s, depending upon the number of rowers in the boat and upon competition classification eg heavyweight (now more commonly referred to as ‘open weight’), lightweight, men or women, sculling or rowing. Furthermore, performance, as measured on the water, also depends on external factors, including the environmental conditions ie water temperature, wind speed and direction, and air temperature.
The advent of rowing ergometers has facilitated training by providing a controllable and repeatable tool in the assessment of rowing performance. Performance over 2,000m on a rowing ergometer is dependent upon the functional capacity of both the aerobic and anaerobic energy pathways, with the relative amount of energy derived from anaerobic metabolism being 21-30%(1).
The study of physiological characteristics of rowers has revealed that power at VO2max, VO2 at lactate threshold (LT), maximum power production and power at a blood lactate of 4mmol.L-1 are the most important predictors of 2,000m rowing ergometer performance in elite rowers(2). (The use of power output at 4mmol·L-1 blood lactate level has been used by a number of coaches and is widely agreed to be important predictor of performance.) However, of the measures listed it is generally agreed that power at VO2max is the strongest aerobic correlate of performance (a finding similar to that seen for endurance running). 

Of the short-term maximal effort tests, maximum force and power production are the strongest correlates of rowing performance. Elite rowers sustain, on average, 77% of maximum power during a 2,000m time trial(1). Thus, if all other determinants remain the same, the greater the maximum power, the greater the average power and resultant speed.
The results of ‘off-water’ ergometer studies indicate the importance of higher intensity parameters (power at VO2max and maximum power) in rowing performance. Given this fact, it is perhaps surprising to note that most international teams utilise vast volumes of low intensity training for competition preparation(3). It must be remembered however that sub-maximal economy is important in underpinning power at VO2max, and thus the importance of training that is focused on improving economy and sub-maximal parameters should not be ignored. This type of training typically consists of a number of sessions per week dedicated to lactate threshold training, which has the dual advantage of improving submaximal economy, and improving the power output that can be sustained.

Weight and gender differences

There are significant performance differences between male and female and between heavyweight and lightweight rowers. On the ergometer, researchers have shown that male rowers were on average 7.7% faster than their female counterparts(2). Results from World Championships and World Cup single scull events, suggest that this difference is increased to 10.9% on-water (there are subtle relationships between technique and power delivery which make on-water rowing harder than ergometer rowing, but why the difference is greater between ergometer and on-water rowing in women is not known).
The difference between heavyweight and lightweight rowers was 5.5% on-ergometer compared to 4% on-water. While heavyweights are faster than lightweights, research suggests that any increase in body mass should be primarily composed of functional (lean) mass to effect a change in ergometer/boat speed. This is particularly true for lightweight rowers and requires the right combination of diet, rowing-specific ergometer and on-water work, coupled with weight training, which ensures the development of an appropriate functional mass.
In describing the physiological components that are necessary for good rowing performance it must be remembered that anthropometric (ie height, limb length), technical (ie stroke length, stroke rate) and psychological factors are also crucial elements of that performance. Assessing the physiological aspects of performance is important in the profiling of athletes, as this allows the design of better training programmes, which in turn improves adaptation.
The physiological assessment of the rower should aim to test the range of physiological requirements of rowing performance, both aerobic and anaerobic. The following section outlines the range of tests employed by physiologists to assess elite rowers in laboratory and field (on-water or on ergometers in the boathouse or gym) settings. 

Laboratory testing for rowers

Rowing is a strength-endurance sport with a large aerobic component. A number of endurance sports have been proposed as the ‘most aerobic’, including cross-country skiing and running. But when scaling is used (that is a mathematical technique to allow individuals of different sizes and weights to be compared) then heavyweight rowers come out on top (4,5).
Heavyweight rowers are large individuals with an average height of 1.93m and average weight of 93kg. Although their body fat values tend to be slightly higher than their lightweight team-mates, they still carry considerable muscle mass.
Elite rowers require the ability to generate moderate to high forces and sustain efforts for six minutes (the average time to complete 2,000m in competition at World Championships or Olympic games). Physiology support in the laboratory is therefore designed to examine the current conditioned state of the individual with respect to body composition, muscle power and force, aerobic power and sustainable percentage of maximal aerobic power.
Body composition testing is particularly important for lightweight rowers because they cannot afford to be carrying excess ‘non-functional’ weight (ie body fat).
As mentioned previously, it is important to measure maximal aerobic power (VO2max) and the percentage of maximal aerobic power that can be sustained. To do this the discontinuous incremental protocol (commonly referred to as a ‘step-test to max’ and shown in figure 1) is the usual test used.
In the lab, testing occurs on a Concept 2 Model C rowing ergometer, the kind of rowing machine found in most health clubs. There is a difference however, as (unlike the standard rowers) the lab ergometer is also fitted with a special force transducer at the handle, so that the force produced by the rower can be directly and very accurately measured.
On this equipment, a test is first carried out to examine strength and power. Before the test begins the rower performs a 10-minute warm-up followed by some light stretching. A specific warm-up is then completed using hard efforts of two, three, and four strokes prior to starting the test. For the test itself, the rower is instructed to carry out seven strokes as hard as possible at a rate of 30 strokes per minute. From this test, work (in joules), mean force (in newtons), mean power (in watts), stroke rate (strokes per minute, spm) and stroke length (in metres) are reported from the last five strokes.
Elite rowers are often asked to perform 2,000m time trials on the ergometer in training, and so will have a recent 2,000m time. If a young rower visits the lab for the first time it can be difficult to know what intensity to start the step test at. However, a means of determining this has been devised.
The time for 2,000m should be converted into a 500m split time. For heavyweight men and women add 15 seconds to this time and you have the split for the third stage of the step-test. For the power output that equates to the time for stage 3, subtract 25 watts to get the power output (and split time) for stage 2 and subtract 50 watts for stage 1. For stage 4 add 25 watts and for stage 5 add 50 watts. For lightweight men and women, also add 15 seconds to the calculated 500m split time to find the split for the third stage. However, it may be more appropriate to use 15-20 watt increments (rather than a 25 watt increment) to calculate subsequent stage workloads(5).
During the step test the rower wears a heart rate monitor and a mouthpiece for collection and analysis of expired air, and every four minutes the rower stops to have an earlobe blood sample taken for blood lactate analysis.
The heart rate associated with LT can be used to determine a number of heart rate zones that can be used for training, and, after a few weeks, improvements in endurance are detected as a rightward shift of the lactate curve.
For the final stage of testing, the individual is asked to cover the furthest distance possible (at a relatively even pace) in four minutes. Traditionally, laboratory-based blood lactate measuring equipment such as Analox, Yellow Springs or Eppendorf lactate analysers have been preferred, as their validity and reliability has been tested and is well known. Although it is possible to use new ‘palm top’ lactate analysers, their validity and reliability continue to be questioned.
The data collected and calculated from the step test includes VO2max, power at VO2max, the percentage of maximum that can be sustained (ie at lactate threshold as a percentage of VO2max), power at LT and power at reference blood lactate vales of 2 and 4mmol.L-1

Field-testing for rowers

Many elite sports routinely enjoy a physiology support programme and hence, coaches and athletes have greater experience of sports science. As a result, coaches in many sports are increasingly demanding that field-based testing replace laboratory-based testing. However, coaches and athletes rarely have the training and experience of professional sports scientists and, while many physiologists are not averse to an increase in the use of field-testing, it is very difficult to justify the elimination of laboratory-based testing altogether.
Laboratory-based testing provides an objective set of data collected under standardised conditions(5). This level of standardisation and objectivity could never be achieved in the field. However, field-based data has greater sports specificity, something which is very difficult, or is impossible, to achieve in a laboratory-based simulation of the sport. Accordingly, GB elite rowers are still lab tested two to three times per year with 4-5 field-based (step-test) sessions. To supplement this, the coach also carries out some performance tests such as, 18km, 30minute, 2km or 250m rows. On some occasions blood samples can be taken (by a physiologist) at the end of such rows, or the 18km row can be broken into 3 x 6km rows with a 30-60 second rest interval for blood samples to be taken.
At field camps overseas, early morning monitoring is routinely carried out prior to daily training. This involves the measurement of urine concentration to monitor hydration status, blood urea, body mass and resting heart rate to examine how the athlete is coping with the physical stress of exposure to a new, often extreme, environment, coupled with normal training. All of these measures are viewed in combination with a psychological inventory and some discussion with the coach and athlete. As a result, the coach decides on whether any modification of training is required for certain individuals as a consequence of this plus on-water and gym-based data.

Altitude camps

Originating in Eastern Europe, the use of altitude training camps in rowing has become commonplace. Elite rowers may ascend to altitude for training camps lasting up to 3 weeks on as many as three occasions per year. Altitude results in a lower availability of oxygen to the working muscles, due to lower barometric pressures, and this reduced availability of oxygen results in an increased physiological stress both at rest and during exercise.
The primary purpose of altitude training is to capitalise on the adaptations associated with this increased physiological stress, which is suggested to increase red cell mass and haemoglobin concentration and hence, increase oxygen carrying capacity.
Unfortunately, these adaptations come at a price; altitude has a number of undesirable effects that can affect the health and performance of the rower including; sleep disturbance, dehydration, glycogen depletion, immune suppression and an increased incidence of illness including upper respiratory tract infections and gastrointestinal upsets. Altitude training can even lead to a reduction in performance due to a relative deconditioning associated with an enforced lowering of training intensity(6).
It is for these reasons that monitoring rowers at altitude is crucial to optimise the beneficial effects and reduce the adverse effects of low oxygen availability. Physiological monitoring of the rower at altitude is based upon assessing sleep quality, recovery, hydration and training intensities. Recent advances in the simulation of high altitude environments at sea level by reducing partial oxygen pressure (ie reduced O2 concentration) in chambers, tents and face masks has led to new opportunities in the use of hypoxia (low oxygen) for training and competition(6).


The functional capacities of the aerobic and anaerobic energy systems are important in 2,000m rowing, and performance and power at VO2max, VO2 at lactate threshold, power at a blood lactate of 4mmol.L-1 and maximum power production are the most important predictors of 2,000m rowing ergometer performance in elite rowers. Laboratory-based testing is centred on step and maximum power tests and body composition assessment, while field-testing includes ‘on-water’ tests such as 18km, 30minute, 2km or 250m rows and lactate measurement following set pieces.

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