Entries in Maximal Oxygen Uptake (2)

Tuesday
Dec062011

Exercise Science and Coaching: Correcting Common Misunderstandings About Endurance Exercise

By: Andrew N. Bosch, PhD
UCT/ MRC Research Unit for Exercise Science and Sports Medicine.
Department of Human Biology, University of Cape Town and Sports Science Institute of South Africa
From: International Journal of Sports Science & Coaching Volume 1 • Number 1 • 2006


Many coaches who work with endurance athletes still believe in old concepts that can no longer be considered correct. Prime amongst these are the understanding, or misunderstanding, of the concepts of maximal oxygen uptake (VO2 max), lactate threshold, training heart rate, and dehydration and fluid requirements during prolonged exercise.

Knowing the VO2 max of an athlete is not particularly useful to the coach, and the exact VO2 max value of any particular athlete can vary considerably as fitness changes. Race performance is a more useful measure on which to base training schedules.

Lactic acid production, far from being an undesirable event, is of great importance and is actually beneficial to the athlete. The lactate concentration during exercise, and the lactate turnpoint, are both widely measured. The nature of the information that these measures can provide about training and training status, however, is still based on information from the 1980s, although more current information is available and many of the original concepts have been modified.

Heart rate is often used to prescribe training intensity, but it is important to understand the limitations inherent in its use. If used correctly, it is a useful tool for the coach.
Similarly, many athletes and coaches still believe that it is necessary to maintain a high fluid intake to avoid dehydration and prevent associated collapse. These beliefs are incorrect, but modern exercise science has been able to advance the knowledge in this area and provide more accurate information.
Exercise science continues to progress and can offer much to the coach willing to accept new and changing ideas.

Key words: Anaerobic threshold; Endurance training; hydration; lactic acid; maximal oxygen uptake; training heart rate

Introduction

Sports science knowledge has progressed tremendously in the last 20 years in terms of the understanding of many of the underlying concepts in exercise physiology and human performance. Many coaches, however, have failed to take cognisance of the new information and still believe in old and out-dated concepts, many of which frankly are wrong. And it is this incorrect understanding that is then applied to the coaching of athletes. In the following article, some of these misconceptions related to endurance performance are highlighted, specifically those around maximal oxygen uptake (VO2 max), lactate threshold, training heart rate, dehydration, and fluid requirements during prolonged exercise. Although runners are most often referred to in the discussion that follows, the principles are applicable to all genre of endurance athlete.

VO2 Max

Every so often a request is received from the coach of a runner or cyclist wanting to know ifit would be possible to measure the VO2 max of one of their athletes. I explain that it is, indeed, possible, but then go on to ask why they want to have the VO2 max of the athlete measured? There is usually one of two replies. Firstly, I am told, by knowing his or her VO2 max the runner will know the esoteric time that he or she is ultimately capable of running for some particular race distance, and therefore their ultimate potential as a runner. Secondly, once their VO2 max is known it will be possible to prescribe the ultimate personalised training schedule. Unfortunately, knowing the VO2 max of a runner does not answer either question.

It is widely believed by those involved in endurance sports that the VO2 max is genetically determined and never changes, and that an individual is born with either a high or low VO2 max. Generally, someone with a high VO2 max value is considered to have a cardiovascular system capable of delivering large amounts of oxygen to the working muscle and is able to exercise at a maximum aerobic work output that is determined by the exercise intensity that can be sustained by this supply of oxygen [1]. In this paradigm it does not appear to matter whether a runner or cyclist is unfit or superbly fit, the VO2 max value obtained in a test is theoretically the same. However, it is intuitively obvious that when fit, the athlete can run much faster on the treadmill than when unfit. Thus, since VO2 max is genetically determined and does not change, VO2 max would be reached at a relatively slow running speed when a runner is unfit compared to when very fit, when a much higher speed or workload can be reached. This means that in a totally unfit world class runner we would measure a high VO2 max (for example, 75 ml/kg/min or higher, a reasonable VO2 max value for an elite runner) at a speed of maybe 17 km/hr in a testing protocol in which the treadmill remains flat during the test. When very fit the same athlete will reach the same VO2 max, but now the speed reached on the treadmill will be around 24 km/hr (a reasonable speed for an elite athlete in such a test). The problem is that such a high VO2 max is never measured at a speed of just 17 km/hr, or thereabout. This would be almost impossibly inefficient [2]. The theory of a genetically set and unchanging VO2 max, regardless of work output therefore begins to appear a little shaky.

This concept of VO2 max evolved originally from misinterpretation of the data of early experimental work [1; 3-5]. It was believed that as an athlete ran faster and faster during a treadmill test, an increasing volume of oxygen was needed by the muscles, a process which continued until the supply of oxygen became limiting, or the ability of the muscle to utilise oxygen was exceeded. At this point there would be no further increase in oxygen uptake, despite further increases in running speed [1]. The plateau in oxygen utilisation was regarded as the VO2 max of the runner. If high, then the athlete had great genetic potential. This has been termed the cardiovascular/ anaerobic model by Noakes [1] and needs revision, although others adhere to the concept [6]. However, 30% of all runners and cyclists tested in exercise laboratories never show a plateau in their oxygen uptake [4; 7]. Instead, the oxygen uptake is still increasing when the athlete cannot continue the test. The conventional view of VO2 max now appears to be even more suspect.

Consider a different possibility. The muscles of a runner require a certain amount of oxygen to sustain contraction at a given speed. When the speed is increased, the muscles have to work harder and there is therefore a corresponding increase in the volume of oxygen needed to run at the higher speed. As the runner runs faster and faster, it follows that there is a concomitant increase in the oxygen required, until ultimately something other than oxygen supply to the muscle prevents the muscle from being able to work harder and to sustain a further increase in running speed.

The brain may be the ultimate subconscious controller, by sensing a pending limitation in the maximum capacity of the coronary blood flow to supply oxygen to the heart as exercise intensity increases, and then preventing a further increase in muscle contractility to prevent damage to the heart during maximal exercise [1].

The volume of oxygen being used by the muscle when maximum running speed has been reached is termed the VO2 max. With this theory, the increase in oxygen requirement merely tracks the increase in running speed, until a peak running speed and therefore peak oxygen requirement (VO2 max) is attained. It is easy to see why the VO2 max value will change (which it does) as a runner gets fitter and becomes capable of running faster. Within this framework, the genetically determined limit of VO2 max is actually determined by the highest running speed that the contractility of the muscles can sustain [8] before the brain limits performance to protect the heart, as described above [1]. Of practical importance, is that the exercise scientist and coach cannot use the VO2 max test as a predictor of future performance in someone who still has the capacity to improve their running by utilising a scientifically designed training programme. A great training-induced increase in running speed will result in a substantial change in VO2 max. Only when widely disparate groups of athletes are tested can a VO2 max value be used to distinguish between athletes (i.e. very fast and very slow [2; 9; 10]). In a group of athletes with similar ability, the VO2 max value cannot distinguish between the faster and slower runners i.e. their race performance. Neither is the knowledge of a VO2 max value going to assist in the construction of a training programme, other than by indirectly giving an indication of the time in which a race may currently be completed, by the use of various tables that are available [11]. Indeed, current race performance provides the most useful information for the coach on which to base training prescription [11].

There are, however, some potential uses of a VO2 max test for a coach. When constructing a training programme for someone who has not run any races and who therefore has no race times from which to determine current ability, a VO2 max test will help by giving an indication of the current capability of the athlete on which to base training schedules. If done at regular intervals, the test can provide information about the efficacy of a training programme [11] as laboratory conditions are very reproducible with regard to temperature and absence of wind. However, the peak speed attained in the test is probably the best indicator of current ability [1; 12-14] and not the actual VO2 max value. Race times remain the most useful measure on which to prescribe running speed in training schedules [11].

Lactic Acid

Most athletes and coaches still believe that lactic acid is released during hard or unaccustomed exercise and that this is what limits performance, as well as being the cause of stiffness. Neither is correct. Furthermore, the terminology “lactic acid,” is not correct.

Lactic acid does not exist as such in the body - it exists as lactate at physiological pH [15], and it is this that is actually measured in the blood when “lactic acid” concentration is measured, as is done when a “lactate threshold” is determined in an athlete. This distinction is important not only for the sake of correctness, but more importantly, because lactate and lactic acid would have different physiological effects.

The first misconception is that lactic acid is the cause of the stiffness felt after an event such as a marathon. Stiffness is due to damage to the muscle [1], and not an accumulation of lactic acid crystals in the muscle [1; 16], as is commonly believed.

The second misconception is that lactate is responsible for acidifying the blood, thereby causing fatigue. To the contrary, the production of lactate is actually important for two reasons. Firstly, when lactate is produced from pyruvate in the muscle, a hydrogen ion is “consumed” in the process [15]. Consequently the production of lactate actually reduces the acidity in the muscle cells and is thus a beneficial process. Secondly, lactate is an important fuel that is used by the muscles during prolonged exercise [17; 18]. It can be produced in one muscle cell and utilized as a fuel in another, or it can be released from the muscle and converted in the liver to glucose, which is then used as an energy source. So rather than cause fatigue, lactate production actually helps to delay fatigue [19].

Anaerobic Threshold

Closely allied to the thinking that lactate production is bad for performance, is the concept of measuring the blood lactate concentration to determine the so-called “anaerobic threshold” or “lactate threshold.”. The origin of this belief can probably be traced to the early studies of Fletcher and Hopkins [20]. Thus we see photographs of athletes at the track or at the side of the pool having a blood sample taken, with an accompanying caption indicating that the workout is being monitored by measuring “lactic acid.” The supposed rationale is that as speed is increased, a point is reached at which there is insufficient oxygen available to the muscle and energy sources that do not require oxygen (oxygen independent pathways, previously termed the anaerobic energy system) then contribute to the energy that is needed. This supposedly results in a disproportionate increase in the blood lactate concentration, a point identified as the “anaerobic” or “oxygen deficient” threshold. This is also known as the lactate threshold or lactate turnpoint.

There are two problems with this concept. First of all, the muscle never becomes anaerobic; there are other reasons for the increase that is measured in blood lactate concentration [21]. Secondly, the so-called disproportionate increase causing a turnpoint is not correct, in that the increase is actually exponential [22-24]. This is seen when many samples are taken, as in the exercise laboratory, where a blood sample can be drawn every 30 seconds as an athlete runs faster and faster.

Although a graph showing a “breakpoint” in lactate concentration as speed increases cannot be drawn as the breakpoint does not exist, a graph can nevertheless be drawn depicting the curvilinear increase in blood lactate concentration as running speed or exercise intensity increases. This curve changes in shape (shifts to the right) as fitness level changes. Particularly, the fitter a runner gets, the more the curve shifts to the right on the graph, meaning that at any given lactate concentration the running speed or work output is higher than before. A shift in the lactate curve to a higher workload or percentage VO2 max occurs due to a reduced rate of lactate production by the muscles and an increased ability of the body to clear the lactate produced [25; 26]. Often, the running speed at a lactate concentration of 4mmol/l is used as a standard for comparison. It is sometimes suggested that this can be used as a guide for training speed (i.e. a runner could do some runs each week at the speed corresponding to the 4mmol/l lactate concentration, some runs above this speed, and recovery runs at a lower speed). Of course, as fitness changes and the curve shifts, these speeds will change, and so a new curve will have to be determined. In concept this works well, but the problem is that neither exercise scientists nor coaches know how much running should be done below, at, and above the 4mmol/l concentration. The 4 mmol/l concentration referred to is a somewhat arbitrarily chosen concentration. It could just as well have been 3.5 mmol/l or 4.5 mmol/l, which would result in different training speeds for the athlete utilizing this system. Indeed, Borch et al [27] suggest 3 mmol/l as the lactate concentration representing an average steady state value. Measuring the maximal steady state lactate concentration may be useful, but requires 4-5 laboratory visits. Thus the real value in determining a ‘lactate curve’ is to monitor how it shifts with training. The desirable shift is one in which a faster running speed is achieved at the same lactate concentration. This regular testing can be done in the laboratory, with the athlete running on a treadmill or on a track, in which running speed can be carefully controlled, such as by means of pace lights.

Training Heart Rate

In recent years the concept of using heart rate while training as an indicator of the correct training intensity, has gained in popularity. Specifically, various heart rate “training zones” have been suggested, and ways to calculate these proposed. This approach has been described in many articles written for coaches and runners [27-29] and does have potential for being a precise way to regulate running intensity in training, particularly for novice runners. However, at present there are no scientific data to support an ideal specific heart rate for different types of training, and much of what is written is based on anecdotal experiences. There is no doubt that future studies will refine this area, making the prescription of training heart rate a more exact science.

Probably the greatest value in heart rate in training is for the coach to use it as a way of ensuring that an athlete does not train too hard on those days when nothing more than an easy training session is prescribed. The use of heart rate for more absolute prescription has the risk of the athlete training at the wrong intensity as a result of the large daily variability in heart rate due to influence of diurnal variation, temperature differences, sleep patterns, and stress [30]. All these can result in the prescribed training heart rate being either too high or too low on a given day. Thus the athlete may train too easily on one day, but on another day when more recovery is needed from a prior hard training session, the training intensity may be too high. The coach is probably better able to assess the most suitable intensity for the athlete. Nevertheless, training intensity based on heart rate may have some value [28].

The use of heart rate as a monitoring tool during training, as opposed to being used to dictate training intensity is a useful aid that coaches can use to assess the training response of an athlete. A progressive decline (over weeks) in heart rate for a given training session would indicate appropriate adaptive response by the athlete; a progressive increase in normal training heart rate would indicate a failure in the adaptive process and that the training load should be adjusted. Similarly, an abnormally high heart rate for a given training session may indicate approaching illness or failure to adapt to the training load and impending overtraining. The athlete would then be well-advised to train only lightly, or to rest [30].

Post-Run Stiffness

In the section on lactic acid, it was stated that the stiffness and muscle pain felt after a marathon or hard workout is not caused by lactic acid. While this was believed to be the case some decades ago, it is now known that lactic acid is not the cause of muscle stiffness, but is the result of damage to the muscle cells, connective tissue and contractile proteins [31-33].

Although the precise cause of delayed onset muscle soreness remains unknown, all runners and coaches are aware that the degree of pain depends on the intensity, duration and type of workout. For example, there is more muscle pain after a long or hard downhill run than after running over flat terrain (i.e. if eccentric muscle actions are emphasised [34]). In fact, it is this phenomenon that begins to exclude a build-up of lactic acid as a cause of the pain. In downhill running, the concentration of lactate in the blood and muscle will be low compared to running at the same speed on the flat. Thus, the most painful post-race stiffness can occur when the lactate concentration is lowest.

If a blood sample is taken from a runner the day after a marathon, especially an ultra- marathon, the concentration of an enzyme, creatine kinase, will be high [35-37]. This is an indication that muscle damage has occurred, as this particular enzyme “leaks” from damaged muscle. The “damage” referred to is minute tears or ruptures of the muscle fibres [38; 39]. This trauma to the muscle can be visualised if a sample of the muscle is examined microscopically. However, it is not just the muscle that is damaged. By measuring the amino acid hydroxyproline, it is possible to show that the connective tissue in and around the muscles is also disrupted [40]. What this shows is that stiffness results from muscle damage and breakdown of connective tissue.

Running fast or running downhill places greater strain on the muscle fibres and connective tissue compared with running on the flat. Downhill running is particularly damaging because of the greater eccentric muscle actions that occur. It is this simultaneous contracting of the muscle while being forced biomechanically to lengthen that is most damaging to muscle fibres.

What does this mean for the runner and coach? From a training and racing point of view, after the muscles have recovered from the damage that caused the stiffness and the adaptive process is complete, the muscle is more resistant to damage from subsequent exercise for up to six weeks [41]. From a coaching point of view, hard training sessions should be withheld when there is muscle pain, as further damage could result. It would be better to allow the appropriate physiological adaptations to take place before resuming hard training sessions. Weight training to increase the strength of the muscle [1] may be beneficial. It has been suggested that vitamin E may help to reduce muscle soreness, but there is little evidence to support this idea [42]. Vitamin E is thought to act as an antioxidant that may blunt the damaging action of free radicals, which attack the cell membrane of the muscle fibre.

It has also been suggested that stretching the painful muscle or muscles may be beneficial, but this has not consistently been shown to alleviate delayed onset muscle soreness. Neither is there any evidence that massage or ultrasound speed up recovery [43]. Similarly, an easy “loosening up” run “to flush out the lactic acid” is unlikely to speed up recovery, although it is also unlikely to result in further damage.

The real cause of muscle stiffness after a hard run is clearly not due to lactic acid in the muscle. Coaches will be in a better position to manage the return to normal training of their athletes after training or racing that has induced muscle soreness, if they understand the effects of type, intensity, and volume of training on muscle stiffness after exercise.

Dehydration, Heat Exhaustion and Heat Stroke

Historically, the understanding has been that runners collapse (most often at the end of races) due to dehydration. This is popularly thought to be more likely when the environmental temperature is high and dehydration more severe. “Heat exhaustion” has been incorrectly thought to be associated with dehydration, yet there is no evidence to support this [1]. “Heat stroke” is an entirely different condition, associated with an increase in body temperature.

There are a number of critical errors in the traditional thinking on the issue of dehydration. Firstly, and possibly most importantly, rectal temperatures are not abnormally elevated in collapsed runners suffering from “dehydration” [44; 45]. Secondly, there is no published evidence that runners with dehydration/ heat exhaustion will develop heat stroke if left untreated [46; 47]. And thirdly, the question must be asked why these runners nearly always collapse at the finish of the race and not during the race. Thus we must look for another explanation as to why the runners collapse.

The explanation is found in a condition called postural hypotension [44; 47-50]. While running, the high heart rate and rhythmic contraction of the leg muscles maintain blood pressure and aids in the return of blood from the legs. When running ceases, the pump action of the leg muscles stops and the heart rate drops rapidly. This results in pooling of the blood in the veins of the lower limb, which in turn causes blood pressure to decrease. It is the lowered blood pressure that results in collapse. Secondly, there is an increase in peripheral blood flow to regulate body temperature. This is more pronounced in hot conditions, and results in a reduction in the pressure of blood filling the heart [51]. Treatment is therefore very simple: If the runner lies down with the legs elevated, the return of blood from the legs is aided, blood pressure is restored and after a short while the runner will have recovered. Cooling the legs may be beneficial. As a preventive measure, it is a good idea to continue to walk after the finish line has been crossed. A second possibility is to lie down as soon as possible and elevate the legs slightly, with cooling of the legs as an additional option.

Heat exhaustion as a result of dehydration does not, therefore, exist and is not a condition that coaches need to be concerned about. This contrasts with heat stroke, in which the body temperature becomes very high (rectal temperature above 41°C) and is a potentially dangerous condition. Even after the athlete has stopped, either voluntarily or because of collapse, the temperature remains elevated because of physiological and biochemical abnormalities in the muscles. Thus the athlete must be cooled as quickly as possible, using methods such as fans, to bring the body temperature down to below 38 degrees Celsius.
Heat stroke develops as a result of a combination of a number of factors. Particularly, a high environmental temperature (>28°C) is more likely to result in the problem than when conditions are cooler. If the humidity is also high, there is an additional heat load on the runner because the sweating mechanism of the body is rendered ineffective. Sweat running off the body, as it does when the humidity is high, does not result in cooling. To cool the surface temperature of the skin, the sweat must evaporate. In addition, and also very importantly, the metabolic heat produced by the runner must be high. Thus, it is the faster athletes who are at risk, who are exercising at a high intensity. It is also, therefore, in races shorter than the marathon in which there is a high likelihood of heat stroke because of the much higher running intensity in shorter races such as cross country (on a hot day) or a 10 km race [46; 47; 52]. Thirdly, it appears that some runners are more susceptible to the development of heat stroke than others [53].

Contrary to popular belief, dehydration is not a major cause of the development of heat stroke. Although adequate fluid replacement during racing in the heat may reduce the risk of heat injury, it is not the only factor and may not even be the most important factor [46; 47; 54; 55]. Arunner can develop heat stroke without being dehydrated. Conversely, a runner can be dehydrated, but not develop heat stroke. If the recommended guidelines for fluid ingestion are followed (~600 ml/ hour), it is very unlikely that fluid deficit will play a role in the development of heat stroke.

Fluid Intake During Exercise

During events such as marathon running, one often reads recommendations suggesting that more than 1L of fluid should be ingested every hour. Ingestion of such a high volume is unnecessary, however, and probably impossible for faster runners to adhere to.

The rate at which fluid ingested during exercise empties from the stomach before being absorbed in the small intestine is influenced by a number of factors. These include the temperature of the fluid, the volume of fluid ingested, and the concentration of any carbohydrate such as glucose, fructose, sucrose or glucose polymer in the water. Thus it is important that athletes follow the correct regimen to ensure optimal fluid and carbohydrate replacement. This consists of ingesting 500-600 ml per hour of a fluid containing 7-10% carbohydrate [56]. This serves two purposes. It supplies a source of carbohydrate to maintain blood glucose concentration, as well as all the fluid replacement that is necessary during prolonged exercise, except possibly under extreme environmental conditions.

Ingestion of too much water during prolonged exercise is not only unnecessary, but can be harmful. In some susceptible people, ingesting large volumes of water can result in a condition called “water intoxication” or hyponatraemia. This occurs when the body’s normal sodium concentration becomes significantly diluted, because the amount of water or sports drink ingested is far in excess of what is needed during exercise to maintain hydration [46; 54; 57; 58]. As with heat stroke, in extreme cases this condition can be life threatening, in this case due to cerebral oedema.

Conclusions

VO2 max testing can be of some limited use to a coach when constructing a training programme for someone who has not run any races. If done regularly, the test can provide information about the efficacy of a training programme. However, the peak speed attained in the test is probably the best indicator of current ability, but race times are the most useful measure on which to prescribe running speed in training schedules.

Rather than cause fatigue, the process of lactate production helps to delay fatigue. In addition, it is important as a fuel substrate. The real value in determining a ‘lactate curve’ is to monitor how it shifts with training, the desirable shift being one in which a faster running speed is achieved at the same lactate concentration.

Heart rate during training is a useful monitoring tool, but should not be used to dictate training intensity. Rather, training heart rate information, together with knowledge of current race speeds and training-induced fatigue, should be used by the coach to determine training intensity. Ultimately, the influence of various parameters that effect heart rate response will be well researched and the coach will then be able to prescribe a specific heart rate for different types of training.

Muscle stiffness after a hard run is not due to lactic acid in the muscle. Return to normal training should be prescribed based on the knowledge that soreness is due to muscle damage.

Dehydration is not a major cause of the development of heat stroke. Heat stroke can occur without dehydration, and conversely, dehydration can occur without heat stroke. If the recommended guidelines for fluid ingestion are followed, it is very unlikely that fluid deficit will play a role in the development of heat stroke.

Ingestion of too much water during prolonged exercise is not only unnecessary, but can be harmful. In some susceptible people, ingesting large volumes of water can result in a condition called “water intoxication” or hyponatraemia.

Despite the exponential increase in knowledge in exercise physiology in the last two decades, the process of exercise physiologists and coaches changing old ideas and concepts and accepting new ones has been slow. Both should examine the new information available and use it to proceed with the next series of research studies and coaching concepts, respectively.

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Thursday
Sep292011

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

Sportscience - sportsci.org
Perspectives / Training

Stephen Seiler1 and Espen Tønnessen2
Sportscience 13, 32-53, 2009 (sportsci.org/2009/ss.htm)
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.


Abstract:

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.

Content:

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
Conclusions
References

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 ml.kg-1min-1. 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.

Conclusions

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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