As noted above, most models of athletic performance focus on distance running and endurance cycling. First, there are excellent records and standard events. Second, there is comprehensive physiological data on a large number of elite athletes. Third, it is possible, using treadmills and cycle ergometers, to reasonably simulate in a laboratory what is happening during actual competition. We should also note that for the purposes of this review we assume that environmental conditions are ideal and do not add any additional challenges to physiological regulation (most notably the challenges associated with high altitude and/or high environmental temperatures).
Several well-accepted concepts (Joyner, 1991, 1993; Coyle, 1995; Bassett & Howley, 2000) have emerged related to endurance exercise performance velocity and the first component issue is the level of aerobic metabolism that can be maintained during a race (i.e. performance ; Fig. 2). The upper limit for this is ‘maximal’ oxygen uptake. This is usually achieved during relatively large muscle mass exercise and represents the integrative ability of the heart to generate a high cardiac output, total body haemoglobin, high muscle blood flow and muscle oxygen extraction, and in some cases the ability of the lungs to oxygenate the blood (Mitchell et al. 1958; Kanstrup & Ekblom, 1984; Rowell, 1986; Dempsey, 1986; Saltin & Strange, 1992; Bassett & Howley, 2000). By the 1930s very high values for in athletes were observed and identified as a marker of elite performance (Robinson et al. 1937). Champion endurance athletes have values of between 70 and 85 ml kg−1 min−1, with values in women typically averaging about 10% lower due to lower haemoglobin concentrations and higher levels of body fat (Saltin & Astrand, 1967; Pollock, 1977; Durstine et al. 1987; Pate et al. 1987).
In summary, values 50–100% greater than those seen in normally active healthy young subjects are seen in champion endurance athletes and the most striking adaptations to training that contribute to these high values include increased cardiac stroke volume, increased blood volume, increased capillary density and mitochondrial density in the trained muscles (Costill et al. 1976). Of these, the most dominant factor is a high stroke volume (Ekblom & Hermansen, 1968; Coyle et al. 1984; Martin et al. 1986).
Figure 3. Plot or blood lactic acid concentration versus race distance (Costill, 1970) This figure is an example of the diminishing contribution of so-called ‘anaerobic’ energy sources as race distance increases. This paper also set the stage for a number of later investigations related to the fraction of (e.g. performance ) that could be sustained in competition.
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In this context, as running speed or power output on a cycle ergometer increases in untrained subjects there is typically no sustained rise in blood lactate concentration until about 60% of is reached. In trained subjects this value can be 75–90% of (Fig. 4). There is a long history of investigation about what causes this rise in blood lactate levels and also how lactate (and/or hydrogen ion) does or does not contribute to fatigue. For this review the important summary points include: (1) the initial appearance of blood lactate is not synonymous with hypoxia in the skeletal muscle, and (2) the lactate molecule per se does not ‘cause’ muscle fatigue (Holloszy et al. 1977; Holloszy & Coyle, 1984; Robergs et al. 2004).
What appears to be occurring is that the maximum rate of fat oxidation is inadequate to meet the ATP demands of muscles contracting at moderate and high intensities. This causes intracellular signalling events to occur which stimulate glycogenolysis and glycolysis and ultimately the rate of pyruvate delivery to the mitochondria progressively exceeds the ability of the mitochondria to oxidize pyruvate and this leads to accelerated generation of lactic acid (Holloszy et al. 1977; Holloszy & Coyle, 1984; Robergs et al. 2004). The associated hydrogen ion is then a likely culprit in muscle fatigue and also activates group III and IV skeletal muscle afferents that evoke important cardiovascular and autonomic reflexes (Pryor et al. 1990).
While the physiological determinants of the lactate threshold are extremely complex, they are determined mainly by the oxidative capacity of the skeletal muscle (Holloszy et al. 1977; Davies et al. 1982; Holloszy & Coyle, 1984; Gregg et al. 1989a,b). This capacity is highly plastic and can essentially increase more than twofold in the trained skeletal muscle of humans or animals who engage in 20–120 min of training at a requisite intensity (Holloszy et al. 1977; Dudley et al. 1982; Holloszy & Coyle, 1984). This more than doubling of oxidative capacity is one of the factors that is linked to the high ‘lactate threshold’ values seen in elite endurance athletes (Fig. 2). As noted above, these elite athletes have values that are 50–100% above those seen in normally active sedentary young people and their lactate threshold occurs at a higher percentage of their . This means that in elite athletes the absolute oxygen consumptions (power output and/or speed) that can be generated for long periods of time before reaching the lactate threshold is essentially doubled allowing sustained running speeds of 20 km h−1 or cycling power outputs of 400 W.
Other key factors that reduce muscle fatigability and lactate production during exercise at 85–90%, when only a fraction of the total limb muscle mass is simultaneously recruited, is the quantity of muscle mass that the athlete can recruit to share in sustaining power production (Fig. 2). Elite cyclists appear capable of rotating power production through 20–25% more muscle mass throughout a 1 h bout of cycling, thus reducing the relative power production and stress on a given fibre (Coyle et al. 1988; Coyle, 1995). Additionally, this ‘power sharing’ among fibres would also reduce the glycolytic stress and lactate production per fibre due to more total mitochondrial sharing for a given rate of aerobic metabolism. These factors should operate in a complementary way that reduces the stress per mitochondria and muscle fibre.
As exercise extends beyond about 2 h the problem becomes one of fuel availability as (Hill predicted) the glycogen content in skeletal muscle becomes depleted and the modest ability of active muscle to take up glucose from blood (via either the liver or from feeding) can limit the rate of oxidative ATP generation and thus the pace that can be sustained. In some (but not all) subjects the associated reductions in blood glucose evoke frank symptoms of hypoglycaemia that limit the ability of the individual to continue exercising (Christensen, 1939; Coyle et al. 1983, 1986). Other highly trained subjects show remarkable resistance to hypoglycaemia and for these athletes muscle glycogen depletion is probably more important. In response to these events, a number of pre-competition dietary strategies and during-exercise energy replacement regimens and products have been developed (Murray, 1998). When these are used in an optimal manner muscle glycogen stores can be augmented by 40% before exercise, and hypoglycaemia can be avoided with the net effect being that the duration of exercise at about the lactate threshold can be extended by about one-third (from 2 to 3 h to 4 h) (Coyle et al. 1983, 1986; Sherman & Costill, 1984).
Performance and anaerobic metabolism Without practical direct calorimetric methods to measure instantaneous rates of heat and work production during endurance exercise (Webb et al. 1988; Scott, 2000), the best practical estimation of the rates of actual metabolic energy production and ATP turnover is obtained from measures of oxygen consumption (i.e. indirect calorimetry) during an endurance performance bout. During marathon running the relative amount of anaerobic metabolism is small yet in events lasting 13–30 min (i.e. 5 and 10 km running), it will be significant, contributing perhaps 10–20% of total ATP turnover. This anaerobic contribution to ATP turnover during endurance performance bouts is noted in Fig. 2 and has classically been estimated from measures of post-exercise oxygen consumption and may equal the energy provided by 50–80 ml kg−1 of oxygen uptake (Fig. 2) (Bangsbo et al. 1993). However, the rate at which this energy might be generated and consumed is difficult to estimate in a definitive way.
Figure 2 also makes the point that the rate of total ATP turnover during endurance performance reflects the interplay of aerobic and anaerobic metabolism with lactate generation serving to maintain the NAD+ needed for continued glycolysis and generation of pyruvate. An example of this interplay appears to be the influence of high skeletal muscle capillary density, serving to remove or recycle within muscle fatiguing metabolites (e.g. hydrogen ions). As shown in Fig. 5, exercise time to fatigue at 88% in a population of cyclists (n= 14, individually numbered) possessing the same (i.e. 4.9 l min−1), as expected, was related to the percentage of at the blood lactate threshold. However, some subjects (see upper line in Fig. 5) were able to exercise longer than normal (see lower line in Fig. 5) even when accounting for their lactate threshold (i.e. subjects 1, 2, 7 and 8 in Fig. 4). For the most part, these individuals (i.e. 1, 2, 7 and 8) possessed an unusually high muscle capillary density which may have allowed their exercising muscles to better tolerate anaerobic metabolism and lactic acid production. For this reason, Fig. 2 indicates that ‘Performance , might be directly influenced by muscle capillary density, independent of its important role in delivering oxygen and reducing diffusion gradients, but also by removing waste products and limiting acidosis in the contracting muscles.
Figure 5. Time to fatigue during exercise at 88% ofplotted against lactate threshold (LT) in 14 highly trained cyclists and triathletes (data plotted from Coyle et al. 1988; Coyle, 1995) These athletes all had similar values and uniformly high muscle oxidative enzymes. A subgroup of 4 athletes (subjects 1, 2, 7 and 8) with exceptionally high capillary density seemed to ‘overachieve’ in comparison with their lactate threshold values compared with other members of the group.
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An additional point from Fig. 5 is that much remains to be learned about subtle factors that delay or accelerate fatigue during events performed at intensities above 80–90% of . Small increases in total energy expenditure or reductions in oxygen delivery will have disproportionate effects and accelerate fatigue (Mortensen et al. 2005) during very intense exercise. At this time it remains unclear if laboratory tests can detect the subtle adaptations in the very best performers who seem to be able manage their metabolism in a way that permits maximum efficient energy use.
Efficiency The next factor that makes an important contribution in endurance exercise performance velocity has been termed ‘economy’ or ‘efficiency.’ In the above sections we outlined how and the lactate threshold operate to determine ‘Performance , (Fig. 2). The next question then is how much speed or power can be generated for that level of oxygen consumption? The oxygen cost of endurance running (ml kg min−1) at a given speed can vary about 30–40% among individuals (Farrell et al. 1979; Conley & Krahenbuhl, 1980; Joyner, 1991), as shown in Fig. 6. When cycling at a given power output, the oxygen cost and thus gross mechanical efficiency also varies from one person to another, but by a somewhat lesser amount compared with running (i.e. 20–30%) (Coyle, 1995).
Figure 6. Regression lines for high, average and low running economy (efficiency) in elite endurance athletes based on values gleaned from a number of sources (Joyner, 1991) Since there has been little systematic data collected above ∼18 km h−1 the filled triangles in the figure are individual data from a limited number of champions with exceptional running economy. This figure emphasizes the importance of efficiency among groups of elite performers with relatively uniform and lactate threshold values. It is also of note that the physiological determinants of efficiency (especially for running) are poorly understood.
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Gross mechanical efficiency when endurance-trained cyclists generate 300 W can vary from 18.5 to 23.5% and it appears that more than one-half of this variability is related to the percentage of type I (slow twitch) muscle fibres of the vastus lateralis muscle (Coyle et al. 1992). The efficiency with which the chemical energy of ATP hydrolysis is converted to physical work depends greatly on the velocity of sarcomere and muscle fibre shortening. Type I (slow twitch) fibres display greater mechanical efficiency when cycling at cadences of 60–120 r.p.m. Therefore, it is not surprising that elite endurance cyclists typically possess a higher percentage of type I muscle fibres, given that they are more efficient. Although type I muscle fibres in untrained humans possess higher mitochondrial density compared with type II fibres (fast twitch), it is important to note that with intense interval training, mitochondrial activity can be increased to equally high levels in both fibre types (Chi et al. 1983). Thus, with intense endurance training over years, the main functional advantage of type I fibres appears to be efficiency when cycling rather than total oxidative ability, although type I fibre seem to retain a greater ability to oxidize fat.
It is also of note that many champion cyclists chose pedal cadences of around 90 r.p.m. This is a cadence that may actually increase whole body oxygen consumption slightly for a given total body power output from the minimum which usually occurs at 50–60 r.p.m. In a comprehensive engineering/physiology analysis of this problem Hansen et al. (2002) noted that subjects with higher levels of myosin heavy chain I (MHC I, the predominant myosin in type I fibres) self-selected higher pedal rates and these rates closely matched the rate of peak mechanical efficiency. In this context, they speculated that motor control patterns in these subjects might favour a faster cadence so that relatively low total muscle forces (probably from fatigue-resistant motor units) per pedal stroke could generate the needed power so that the higher force (and more fatigable) motor units could be conserved. On a speculative note, with lower force per contraction there might be less compression of the microcirculation in the active muscle and better distribution of blood flow in a way that is consistent with the concepts presented in Fig. 5.
Running is a more complicated movement than cycling in that it elicits more stretch on the muscle prior to contraction and there is more potential to capture mechanical energy in the elastic elements of tissue. However, although there has been long-standing interest in identifying the biomechanical and anatomical factors that allow one person compared with another to expend 30–40% less energy per kilogram of body to move at a given velocity, the aetiology of differences in running economy generally remain a mystery, and biomechanical descriptions of running are not good predictors of running economy (Kyröläinen et al. 2001; Williams, 2007).
Elite endurance runners typically possess a predominance of type I muscle fibres and it would seem logical that they are more mechanically efficient at the velocities of distance running (Costill et al. 1976; Fink et al. 1977; Bosco et al. 1987). However, running and walking economy has not often been highly correlated with a person's percentage of type I muscle fibres (Morgan & Craib, 1992; Hunter et al. 2005). This agrees with the idea that running economy reflects the interaction of numerous factors including muscle morphology, elastic elements and joint mechanics in the efficient transfer of ATP to running speed.
The extent to which cycling efficiency or running economy can be improved with training has also been of long-standing interest. Until recently, it was generally believed that cycling efficiency and running economy did not improve much with training (Moseley et al. 2004). At best, running economy might sometimes increase slightly over the course of 2 months when explosive-type weight training is added to an endurance training program (Paavolainen et al. 1999; Millet et al. 2002). However, the conclusion that efficiency does not change with training was based on cross-sectional comparisons of relatively small numbers of endurance athletes (Moseley et al. 2004).
In this context, there are no comprehensive longitudinal data on groups of endurance athletes followed over several years to determine the trainability of cycling efficiency or running economy. However, there are at least two cases reporting that running economy can be improved over years of training in elite athletes (Conley et al. 1984; Jones, 2006). In fact, the current world record holder for the women's marathon displayed a remarkable 14% improvement in running economy over the course of 5 years of training (Jones, 2006). Furthermore, cycling efficiency was observed to increase 8% over the course of 7 years in an elite endurance cyclist (Coyle, 2005). In general, these case reports suggest that muscular efficiency and running economy might indeed improve with continued endurance training at a rate of approximately 1–3% per year. One possible contributing factor is that at least some of the fast myosin in endurance-trained muscle shifts to a different and perhaps more efficient isoform (Green et al. 1984). Additionally, in some models of extreme muscle use there can be a complete conversion of fast twitch to slow twitch muscle fibres, whether this occurs in elite athletes who train for two or more hours per day for many years is not known and it is further not known if such a shift would explain any improvements in efficiency that might occur with years of training (Pette, 2001).