I The Scientific Bases of Training and Performance

1

Physiological Determinants of Middle- and Long-Distance Running

Philip R. Hayes and Daniel A. Gordon

Highlights

• • Middle- and long-distance running events are determined by a complex mix of factors.
• • The highest rate of energy production that can be maintained for the race duration and the ability to convert that into movement determine middle- and long-distance performance.
• • The aerobic system is the main energy system used in middle- and long-distance running.
• • The anaerobic system is important for middle-distance races and coping with changes of pace within a race.
• • Appropriate training can improve all of the factors that determine middle- and longdistance performance.

Introduction

Running is one of the most popular global sporting and leisure-time activities (Hulteen et al., 2017). The popularity of running has grown in recent years with the creation of events such as the parkrun, a weekly, free-to-enter 5-km run, which began with 13 runners back in 2004 and now has 5 million registered runners worldwide. Around the world, runners not only run for the social and health benefits, both physiological and psychological, but also to challenge themselves, run new personal bests and, at the highest level, win global titles and break world records. Whatever stage they are at, the performances of all these runners are achieved through the same physiological processes. This chapter covers these processes. It begins with the link between running performance and energy supply, before presenting a model of factors that determine running performance, each of which are subsequently explained.

Bioenergetic Pathways

First identified nearly 100 years ago (Hill and Lupton, 1923), a hyperbolic relationship exists between race duration and the running speed that can be sustained for a given distance (Figure 1.1a). This is often referred to as the power-duration model, or, in the case of running,

4 Philip R. Hayes and Daniel A. Gordon the speed-duration model. This relationship reflects the upper limit of human performance, but it is important to remember that it is generated from different runners, each of whom will have their own unique individual profile (Figure 1.1b). Individual profiles are perhaps a more realistic depiction of running capability. The speed-duration model, sometimes referred to as a bioenergetic model, can be explained by the rate at which the different cellular metabolic pathways produce energy'. Superficially, this is true, but a complex series of co-ordinated, repeated, muscle actions are required to generate the force needed to attain, and maintain, a given running speed. These muscular contractions, fuelled by cellular metabolic processes, which, in the case of the aerobic system, require integrating with the cardiovascular and pulmonary' systems to supply the necessary oxygen.

The faster the running speed, the greater the rate at which the active muscles must produce force, and by extension the metabolic systems produce the required energy. The ‘currency’ for the generation of force in the muscle is adenosine tri-phosphate (ATP) which when hydrolysed releases the energy' used in cross-bridge cycling. The amount of ATP stored in skeletal muscle is very low (~20-25 mM-kg ¹ muscle); however, a series of regulatory mechanisms, generally referred to as energy' systems or metabolic pathways, prevent its catastrophic decline (Joyner, 2016) and complete degradation. These three distinct, y'et interconnected metabolic pathways operate concurrently but at different rates. Collectively, they' operate to meet the instantaneous energy' demands of the muscle (Sahlin et al., 1998; Kang et ah, 2014). It is the different rates and durations over which these pathways produce ATP that give rise to the speed-duration relationship.

The first pathway (ATP-PCr) involves the regeneration of ATP through the splitting of the high-energy phosphate, phosphocreatine (PCr). This immediate source of energy has a rapid

FIGURE 1.1a Men’s and women’s world record running speeds

FIGURE 1.1b Comparison of running speeds of a middle-distance (MD) and long-distance (LD) runner

rate of ATP re-synthesis (~9 mM-kg^-,-dm^_1-s^-1) and during maximal exertion is almost completely depleted in approximately 10 sec (Hirvonen et al., 1987). The second pathway (glycolytic) involves the breakdown of stored carbohydrates, mainly in the form of muscle glycogen but also blood glucose, under ‘anaerobic’ conditions, resulting in ATP re-synthesis and the formation of pyruvate and then lactate. The rate of energy' release from glycolysis is, however, slightly slower than that from PCr degradation, with a peak turnover rate of approximately 6—9 mM-kg^-l-dm^_l-s^-1. Compared to the ATP-PCr pathway, glycolysis has a greater capacity for ATP re-synthesis, with glycogen stores of around 250 mM-kg^-1. Both the PCr and glycolytic pathways are anaerobic, meaning that oxygen, although present in the muscle, is not used in the chemical reactions resulting in ATP re-synthesis. The final metabolic pathway is aerobic (mitochondrial) respiration, which by contrast does require the use of oxygen to re-synthesise ATP from the breakdown of carbohydrates, fats and, under some extreme circumstances, proteins. There is a greater ATP yield from the aerobic pathway; the breakdown of one molecule of glycogen through glycolysis results in a net gain of 3 ATP, compared to a further 34 ATP produced from mitochondrial respiration. This increased energy' yield comes at the price of a considerably reduced rate of ATP production at ~1.32 mM kg '-dirT'-s^-1. If the rates of ATP release were plotted graphically, a hyperbolic relationship would be observed that largely explains the hyperbolic relationship between average race speed and time.

Until now these energy pathways have been defined separately, but as previously highlighted, they operate concurrently'. For example, during a middle-distance track event, the athlete would be using energy' derived from all three of the metabolic pathways. As race distance increases, so too does the relative contribution from the aerobic system (see Table 1.1).

TABLE 1.1 Relative energy system contribution by time and race distance

Note: This will depend on race duration.

Source: 'Sandford and Stellingwerf (2019). ^bGastin (2001).

It is important to note, however, that the duration of the race, not the distance, determines the relative contributions. For example in male and female 1500-m runners of a similar relative standard, the aerobic-to-anaerobic contributions were 75:25% and 83:17% respectively (Hill, 1999). One further point to note is that all of the middle- and long-distance events are predominantly aerobic, even the 800-m race, and can therefore be considered as ‘endurance’ events.

Models of Performance

Several models have been developed to explain the principal determinants of endurance running (e.g. Joyner, 1991; Bassett and Howley, 2000). These models generally focus on three main components, namely P0_2max, running economy and the fractional utilisation of F0_2max or a measure of maximum metabolic steady state. These three factors explain approximately 70-80% of middle- (Blagrove et al., 2019) and long-distance running performance (McLaughlin et al., 2010). Models such as this can be considered metabolic models. Paavolainen et al. (1999a) added neuromuscular factors, which included neural control, muscle force and elasticity, and running mechanics, to the traditional metabolic factors. The model proposed in this chapter (see Figure 1.2) is an amalgamation of these models to account for recent literature. In this model, race pace is dependent upon two key factors: the maximum rate of ATP (metabolic energy) production that can be sustained for the duration of the race and the running economy, which can be thought of as the ability to convert the metabolic energy' into movement. Both running economy and the maximum sustainable rate of ATP production are multifactorial. The maximum sustainable rate of ATP production depends upon the exercise duration; the shorter the duration, the greater the rate that can be sustained. At intensities above the maximum metabolic steady state, the aerobic system cannot produce ATP fast enough to meet the demands of exercise. In this situation, the shortfall, or deficit, is offset by utilising the anaerobic capacity; however, this is not sustainable because when the anaerobic capacity is depleted, fatigue occurs and the athlete has to slow down (see Burnley and Jones, 2018 for a detailed discussion of fatigue at different exercise intensities). In the middle-distance events, running speeds considerably above maximal metabolic steady state can be sustained for the race duration; therefore, the aerobic system cannot produce ATP fast enough to fuel muscular contractions, and anaerobic ATP production becomes an important factor. As race distance increases, the running speed sustained reduces, becoming closer to

FIGURE 1.2 Deterministic model of middle- and long-distance running performance

An. = anaerobic, ATP = adenosine triphosphate, Av. = average. Cap. = capacity, MTU = muscle-tendon unit, prod". = production, RE = running economy, V0_2lmx — maximal oxygen uptake the speed at maximal metabolic steady state, thereby increasing the importance of maximal metabolic steady state, while the reverse is true of anaerobic ATP production.

Maximal Oxygen Uptake (V02max)

F0_2imx is often defined as the maximum rate at which oxygen can be taken in and used by the body (Bassett and Howley, 1997), but it can also be thought of as the maximum rate of ATP production via the aerobic pathway (Medbo et al., 1988). In heterogeneous groups, there is a strong negative relationship between F0_2max and race performance (Costill et ah, 1973), i.e. runners with a bigger F0_2max have faster times. By contrast, in homogeneous groups of well-trained runners, F0_2max is a poor predictor of performance (Conley and Krahenbuhl, 1980). This seeming contradiction can be explained by considering that to run a 2 h 15 min marathon, assuming ‘average’ running economy, a runner would be required to sustain a FO, of approximately 60 mL-kg^_l-min ¹ for the entire race (Bassett and Howley, 2000). This value is higher than the F0_2max of many recreational runners. A high F0_2max is therefore a pre-requisite to be an elite runner but not necessarily a determining factor in elite runners.

The extraction of oxygen from that atmosphere through to its use within the mitochondria is a multi-stage process. Oxygen enters the lungs and diffuses into the bloodstream, where it binds with haemoglobin, the quantity of oxygen transported will depend upon the quantity of haemoglobin. The rate at which oxygen is transported around the body depends upon the maximum cardiac output (£?_max), with the final step being the extraction and use of oxygen by the working muscle. Each of these stages could potentially limit FD_2max and will be considered briefly (see reviews: Levine, 2008; Lundby et al., 2017). The ability of the working muscle to extract and use oxygen is considered to be a ‘peripheral’ limitation, while all of the others are considered as ‘central’ limitations.

Except for chronic respiratory diseases, at sea level the lung is not generally considered to be a limiting factor in exercise, even maximal exercise (Dempsey et al., 1984). A minor exception to this is some elite runners, who have exceptionally high cardiac outputs. These athletes have such high rates of pulmonary capillary blood flow that there is insufficient time for haemoglobin to be fully re-saturated, causing arterial desaturation (Dempsey et al., 1984). Altitude reduces the atmospheric partial pressure of oxygen (P0₂), decreasing the diffusion gradient and thereby reducing haemoglobin saturation (Schoene, 2001). The oxygen carrying capacity of blood (Ca0₂) is dependent upon the quantity and saturation of haemoglobin. Manipulating the haemoglobin content of blood by venepuncture with or without subsequent reinfusion has been shown to reduce (Gordon et al., 2014) or increase (Buick et al., 1980) F0_2max respectively. This is the basis of the illegal (under the sporting laws) practices of blood doping or taking erythropoietin (EPO). 0_max is the product of maximum heart rate (HR_max) and maximum stroke volume. A near perfect linear relationship exists between 0_max and F0_2max. No differences in HR_max exist between elite runners, recreational runners and untrained individuals, therefore the difference in Q_lmx is due to the larger stroke volumes of runners (Levine, 2008). In whole body exercise, the ability of the heart to supply blood to the working muscle, is thought to be the limiting factor in maximum oxygen consumption (Richardson et al., 1999). Even at F0_2max, the mitochondrial capacity for oxidative phosphorylation exceeds the ability to supply oxygen to the working muscle (Lundby et al., 2017). In middle- and long-distance running, it is maximum cardiac output, specifically maximum stroke volume, that is considered the main limiting factor in K0_2lmx.

Given that F0_2max sets the upper limit for endurance performance (Bassett and Howley, 2000), it is unsurprising that a great deal of attention has focused on how to increase it with training. Middle- and long-distance runners use varying quantities of both interval training and continuous running as training methods. In untrained and lesser-trained individuals, low intensity exercise is sufficient to improve P0_2max (Midgley et al., 2006a), suggesting that both continuous and interval training are effective. By contrast in well-trained individuals, interval training is more effective (Milanovic et al., 2015), with intensities between 95% and 100% PG>_2max recommended (Midgley et al., 2006a). In the last decade, short (<30 sec) high- intensity intervals have become a popular method of training to improve P0_2mlx; however, their effectiveness in well-trained athletes remains equivocal (Weston et al., 2014). Longer interval training repetitions (3-5 min) generate the largest increases in P0_2max (Bacon et al., 2013), with a recent meta-analysis showing that only relatively large volumes (>15 min) of long interval training repetitions (>2 min) were more effective than continuous training (Wen et al., 2019). It is important to note that most of these findings are from studies on, at best, moderately trained individuals. Very few studies exist on well-trained runners, but they do corroborate the general findings oflarge volumes of longer repetitions, with the added caveat of the need for greater intensities (Midgley et al., 2007). Increases in F0_2max arising from interval training are due mainly to an increased O_max, resulting from a greater stroke volume (Montero et al., 2015).

The general consensus is that F0_2max can be improved with training but reaches a genetic limit, with longitudinal studies on highly trained runners showing no change in F0_2mlx, despite improvements in performance (Daniels et al., 1978; Legaz Arrese et al., 2005). Despite this consensus, there is evidence of elite runners improving their K0_2mix, For example, a former US record holder for 1500 m and the mile improved his F0_2max from 74.4 mL kg^_l-min^_1 at the start of the preparation phase to 80.1 mL kg^_l min ¹ during the competition phase (Conley et al., 1984). Equally noteworthy is Michael East, a former Commonwealth Games 1500 m champion with a personal best of 3m38.9. Following a two-year intervention to polarise his training intensity distribution, his F0_2max increased from 72 mL-kg '-min ¹ to 79 mL-kg^_l-min^_l and his 1500 m time improved to 3m32.4 (Ingham et al., 2011).

Maximum Metabolic Steady State

The maximum metabolic steady state is a critical threshold in endurance physiology. Below this point, a metabolic steady state is achieved; above it, the aerobic system cannot produce ATP fast enough to meet the energetic demands of skeletal muscle, necessitating the anaerobic system to meet the shortfall. Running just above, or just below, the maximum metabolic steady state therefore generates very different metabolic, cardiorespiratory and fatigue responses (for a detailed review, see Jones et al., 2019). At intensities above the maximum metabolic steady state, the finite anaerobic capacity begins to be utilised, with exercise duration limited by its availability. Confusingly, the maximum metabolic steady state has been referred to by a variety of different terms, the most commonly used being onset of blood lactate accumulation (OBLA), lactate threshold, lactate turnpoint, maximum lactate steady state, critical speed, gas exchange threshold or anaerobic threshold. These terms are largely a reflection of the measurement technique employed (Jones et al., 2019). There is however, a broad agreement that the maximum metabolic steady state is dependent upon the ability of the mitochondria to oxidised pyruvate (Holloszy and Coyle, 1984). Mitochondrial density and oxidative enzyme content, or respirator)' capacity, therefore become key determinants (Granata et al., 2018).

Regardless of the terminology, or protocol used, a strong relationship exists between purported measures of maximum metabolic steady state and middle- and long-distance running performance (Faude et al., 2009). Many studies looking at the physiological determinants of endurance running performance have found maximum metabolic steady state to be a stronger predictor of performance than K0_2lmx (e.g. Farrell et al., 1979). In relation to absolute running speeds, athletes with faster times over the same distance exhibit a faster speed at maximum metabolic steady state. For example, in a group of marathon runners with finishing times between 2h 30m and 3h 00m, between 3h 30m and 4h 00m and over 4h 30 m, the speed at lactate turnpoint was 15.5 ± 0.7 km-h^-1, 13.1 ± 1.6 km lf¹ and 10.9 ± 1.2 km-h'¹ respectively (Gordon et al., 2017). Long-distance runners generally have a higher maximum metabolic steady state than middle-distance runners (Svedenhag and Sjodin, 1984), with it being as high as 85% F0_2nux in some long-distance runners (Joyner et al., 2020). A high relative maximum metabolic steady state (i.e. occurring at high % T0_2lmx) was the best predictor (r = 0.89) of how long moderately well-trained runners could run at the speed at F0_2max (Midgley et al., 2006b, 2006c).

Data collected over a 10-year period on the former marathon world record holder Paula Radcliffe showed that her speed at lactate threshold increased from 14.0 km-h ¹ to 18.0 km h '. During this time, her personal best performances from 3000 m up to the marathon concurrently improved (Jones, 2006). These longitudinal gains in maximum steady state, along with proportionately greater gains in muscle oxidative capacity compared to F0_2lmx, have led to the view that maximum metabolic steady state is more ‘trainable’ than F0_2lmx. Despite this apparent ‘trainability’ and strong correlation with performance, relatively few studies have focused upon the optimum training approach. In a review of 56 studies, most based on cycling in untrained or moderately active individuals, training volume was more important than training intensity in increasing mitochondrial content (Bishop et al., 2019).

Furthermore, mitochondrial training gains do, however, appear to be rapidly lost when training ceases (Granata et al., 2018). The view that volume is more important than intensity has been contested (Maclnnis et al., 2019) but parallels the high volumes and training intensity distributions of elite athletes (see Chapter 8) and training data from recreational marathon runners (Gordon et al., 2017). Anecdotally, a wide range of training approaches have been used by athletes including ‘tempo runs’ at just below maximum metabolic steady state, and both continuous and interval training at just above maximum metabolic steady state (Tjelta, 2016; Joyner et al., 2020). The inclusion of a weekly 20 min run at the speed eliciting a blood lactate of 4 mmolL ¹ (a surrogate measure for maximum metabolic steady state) for 14 weeks in highly trained runners resulted in an increase in this speed (Sjodin et al., 1982).

Anaerobic Capacity

Defined as the maximal amount of ATP re-synthesised from anaerobic metabolism by the whole organism (Green, 1994), anaerobic capacity can play a pivotal role in the performance outcome of middle-distance runners in particular. The proportion of energy supplied by the aerobic system increases with increasing race distance; therefore, the contribution from anaerobic capacity is greater in shorter races (Gastin, 2001). This does not, however, discount its relevance in longer races where sudden changes in energy demand, due to changes in pace or terrain, need to be met instantaneously, principally through the anaerobic system. High peak blood lactate values (18.6 ± 2.1 ad 21.9 ± mmolL ¹ in females and males respectively) have been found in elite 800 m runners post-race, compared to typical values of approximately 3-5 mmol L ¹ at maximum metabolic steady state. Unsurprisingly, the anaerobic capacity of middle-distance runners is approximately 30% larger than that of long-distance runners (Medbo and Burgers, 1990).

Due to the difficulty in measuring anaerobic capacity and lower contribution of anaerobic metabolism to total energy production, this physiological determinant has received little research attention. The findings of this research are somewhat varied; weak relationships (r < 0.23) have been found between a variety of anaerobic capacity measures and 800 m performance (Craig and Morgan, 1998; Bosquet et al., 2007a) in club-level runners, while a moderate correlation (r = —0.61) was found between maximum accumulated oxygen deficit and 800 m time (Ramsbottom et al., 1994). Over 1500 m, in national standard runners, a strong relationship exists between peak blood lactate after a run to exhaustion and 1500 m speed (Ferri et al., 2012). Both long (2 min) and short (20 sec) intervals performed at intensities above F0_2max improve anaerobic capacity by around 10% in untrained individuals. Scientific evidence regarding the optimum approach to training anaerobic capacity in well-trained middle- and long-distance runners is currently lacking. Improving performance in high- intensity exercise can also be achieved through non-metabolic means with improved muscle membrane potential regulation (Iaia and Bangsbo, 2010) and strength training (Blagrove et al., 2018a).

Anaerobic Speed Reserve

The anaerobic speed reserve (ASR) is the difference between maximum sprinting speed and the speed at F0_2max (Sandford et al., 2019c). Being competitive in championship middle- distance races requires more than simply being able to produce a high sustainable rate of

ATP production for the race duration. It requires the ability to produce a fast-absolute speed, particularly over 800 m (Sandford et al., 2018) but also the ability' to cope with surges in pace (Sandford et al., 2019b). The ASR is sufficiently sensitive to be able to differentiate subpopulations of elite middle-distance runners, with faster 800 m runners having a larger ASR (Sandford et al., 2019c). Theoretically', ASR can be improved by increasing maximum sprinting speed or decreasing speed at K0_2lmx. As a higher speed at F0_2max is also associated with better middle-distance performance (Blagrove et al., 2019), the only logical way to enhance ASR is therefore to improve maximal speed. ASR is an emerging concept and could yet provide further insights into successful middle- and long-distance running (a more detailed view can be found in Sandford et al., 2019c).

Running Economy

Running economy is a simple concept and has traditionally been defined as the steady state oxygen or energetic cost of running at any given sub-maximum speed. This approach has been criticised for a number of reasons: (i) it does not allow a comparison between runners at different speeds, (ii) the substrate used will affect the rate of oxygen consumption and (iii) the speeds are absolute and not relative to the individual’s maximum metabolic steady state (Fletcher et al., 2009). A more valid approach is to use energy' expenditure per unit of distance covered (Beck et al., 2018; Blagrove et al., 2019) at speeds relative to an individual’s maximum metabolic steady state (Fletcher et al., 2009; Shaw et al., 2014). This approach enables a comparison of RE at different running speeds and accounts for variations in substrate usage.

Of the key' factors that determine distance running performance in athletes of similar ability, running economy (RE) is perhaps the most variable, with inter-individual differences of as much as 30% reported (Daniels, 1985). Morgan et al. (1995) compared RE in four groups of male runners: (i) those who competed at Olympic trials (5 km, 10 km, marathon), (ii) sub-elite runners (~33 mins for 10 km), (iii) regular runners (~40 mins for 10 km) and (iv) active but untrained. Overall, faster runners had better RE, however there was considerable variation within- (~20%) and between-groups (~10%). It was particularly noteworthy that the RE of the most economic untrained runner was similar to the group mean of the Olympic trialists (182.1 vs. 181.9 mL kg^_l-min~^l). Notwithstanding this, in homogeneous groups of highly trained runners, running economy is a better predictor of long-distance race performance than K0_2lmx (Conley and Krahenbuhl, 1980; Morgan et al., 1989a). Some studies have shown an iiwerse relationship between F0_2mix and RE when runners are matched on performance (Jones, 2006). This, however, is inevitable given the study design; a low F0_2max must be compensated for by better RE in order to achieve a similar performance. Irrespective, a runner with excellent RE will produce less metabolic energy at any given running speed than one with poor economy. Fletcher and Macintosh (2017) provided an excellent illustration of the importance of running economy by' analysing the physiology' required to run a marathon in 2h 02m 57s, the world record at the time. A runner with average RE (4.38 kj-kg^_1-km^_1) would require a F0_2max of 85 mL kg '-min ', whereas someone with excellent RE (3.77 kj-kg^_l-km'¹) would ‘only’need 77.5 mL'kg^-1-min^_l.

Despite its conceptual simplicity and ease of measurement, RE is multifaceted, incorporating anthropometric, neuromuscular, cardiorespiratory and biomechanical factors (for more detail, see Saunders et al., 2004; Barnes and Kilding, 2015b) plus the ability to maintain RE when fatigued (Figure 1.2). Moreover, RE is often incorrectly referred to as ‘efficiency’.

Efficiency is the ratio of work done to energy expended. Work is done when the point of force application moves through a distance (Winter and Fowler, 2009). In running, the point of force application is obviously the foot, but it remains stationary while applying force and therefore the net work done is zero. Economy, by contrast, is the measurement of energy' expenditure (Saunders et al., 2004), therefore during running it is possible to measure economy, but not efficiency. In essence, running economy is the ability to convert metabolic energy' into movement.

The ‘trainability’ of RE is unclear (Joyner et ah, 2020). Improvements in those previously untrained are unsurprising, although the results are somewhat equivocal in trained runners. Longitudinal profiling has shown mixed results in trained runners (Bragada et ah, 2010; Galbraith et ah, 2014; Kubo, Miyazaki et ah, 2015) with improvements in national (Svedenhag and Sjodin, 1985) and world-class runners (Conley et ah, 1984; Jones, 2006), adding to the anecdotal belief that good economy is a product of long-term or high-volume training. Experimental training interventions are more likely to be conducted on untrained or recreational athletes. Better runners are generally less willing to alter their training programmes, leaving a dearth of information regarding effective training interventions in high-calibre runners. Consistent with the view that higher training volumes are important, a recent metaanalysis revealed continuous training to be more effective than interval training in improving RE in recreational runners (Gonzalez-Mohino et ah, 2020). Furthermore, the meta-analysis revealed some moderating factors, with improvements in RE more likely in training programmes longer than 8 weeks and weekly volumes of more than 105 minutes of continuous running. There were also benefits from interval training, but only where the repetitions were more than 1 minute long and the cumulative weekly interval time exceeded 23.2 min (Gonzalez-Mohino et ah, 2020). Strength training is also an effective strategy for improving RE in middle- and long-distance runners (Blagrove et ah, 2018a).

Anthropometry

In adult middle-and long-distance races, competitor’s stature and mass can range by as much as 30 cm and/or 25 kg respectively (Barnes and Kilding, 2015b). All other things being equal, the absolute oxygen uptake (L-min^-1) and energy expenditure scores (kj-min^-1) increase with increasing body mass. To enable inter-individual comparison, scores are usually corrected by body mass (per kg); however, this assumes a linear relationship between either oxy'gen uptake (or energy expenditure) and body mass. The validity of this assumption has been long been challenged (Tanner, 1949) with alternative approaches such as allometric (i.e. non-linear) scaling recommended (Lolli et ah, 2017). Across a wide range of age, mass and activity levels there is a lack of linearity between oxygen uptake and body mass (Lolli et ah, 2017) however, in a large sample (N = 71 women; 101 men) of well-trained runners, correcting scores by absolute mass (kg) was better at removing the influence of mass on RE than non-linear scaling (Shaw et ah, 2014).

It is not just a runner’s mass that can affect their RE but potentially the distribution (e.g. height, limb lengths, segmental masses and volumes) and composition (e.g. bone, fat and muscle) of their mass. The effect of different distributions has been studied by attaching weights to different parts of the body. A given load attached to the foot will increase RE more than when added to the thigh or trunk (Frederick, 1984; Martin, 1985; Myers and Steudel, 1985). This has popularised the idea that a larger distal leg mass will impair economy due to the additional energy required to swing the leg (Barnes and Kilding, 2015b). Elite East African runners have better RE (Saltin et al., 1995; Lucia et al., 2006; Santos-Concejero et al., 2015) compared to other elite runners, which has been proposed to account for their dominance in distance running (Larsen, 2003; Lucia et al., 2006). Their more slender body shape and lower leg circumference compared to similar Caucasian populations (Saltin et al., 1995; Larsen et al., 2004), would, in principle, reduce the energy required to swing the leg, accounting for their better RE. Conflicting evidence exists in support of this, Lucia et al. (2006) compared elite (<13m 50s for 5 km) Eritrean and Spanish runners, finding RE at 21 km-h^-1 to be related to calf girth but not body mass or body mass index. By contrast, a comparison of elite Eritrean and European 10 km runners (~28 min) found better RE in the Eritreans but no relationship with either limb lengths or circumferences (Santos-Concejero et al., 2015). In a homogenous group of high-calibre Kenyan runners (993 IAAF points), no single limb length or circumference explained RE; rather, it was a complex interplay of anthropometric factors (Mooses et al., 2015). These studies have generally used small sample sizes, although Black et al. (2020) had similar findings in a large heterogeneous sample of runners (N = 71 women; 101 men) concluding that superior RE was associated with slimmer limbs and a slender physique.

One anthropometric factor that has consistently shown a link to RE is length of the Achilles tendon moment arm, which is the horizontal distance from the centre of the calcaneus to the Achilles tendon when the person is seated with the knee and ankle at 90°. Shorter Achilles tendon moment arms confer superior RE through a greater storage and release of elastic strain energy' (Scholz et al., 2008; Barnes et al., 2014; Mooses et al., 2015).

Cardiorespiratory Variables

The effect of cardiorespiratory variables on RE has been examined using both inter- and intra-individual comparisons. Across a large sample of habitual runners (N = 69 women; 119 men), stratified by age (20-60 years old) and training volume (10—50 miles-wk ¹ for women; 10-70 miles-wk^-1 for men), when running at 9.6 km-h^-1, only V_t and HR were significantly correlated with RE (Pate et al., 1992). Pate et al. (1992) estimated that 1-2% of RE was due to differences in myocardial work, while approximately 7-8% was due to ventilation. Given the range of participants used in this study (Pate et al., 1992), the use of an absolute intensity ignores differences in relative intensity, making inter-individual comparisons problematic (Fletcher et al., 2009). Increases in Lj, from the beginning to end of a treadmill 5 km run were the only physiological variable that correlated with the impaired RE (Thomas et al., 1999). Collectively, these data suggest that cardiorespiratory variables account for a small portion of the energy expended during running, although more evidence is required. Following 6 weeks increased training intensity in habitual recreational runners, RE improved in those undertaking either long interval training (4 min reps; 2 min recovery) or continuous running (20-30 mins @ ~93% HR_max) but not those using short intervals (15 sec reps; 15 sec recovery) (Franch et al., 1998). The improvement in RE was strongly correlated (r = 0.77) with the reduction in V_v during a standardised sub-maximal run.

Neuromuscular Determinants of RE

Running requires a repeated sequence of co-ordinated voluntary' muscular contractions, the energy' for which is provided by intra-cellular metabolic processes. RE measures the oxygen consumed and carbon dioxide produced during aerobic re-synthesis of the ATP used. The number of actin-myosin cross-bridges required to produce the necessary force to attain (and maintain) a given running speed will determine the level of muscle activation and energy production needed (Fletcher and Macintosh, 2017). This is achieved through the interaction of different neuromuscular factors, including muscle fibre recruitment strategy, the ability of the muscle to produce force and the muscle-tendon unit (MTU) to create, store and use elastic strain energy. These are briefly considered next; for a more detailed explanation, see Barnes and Kilding (2015b) and Fletcher and Macintosh (2017).

Fibre Recruitment

The muscle fibre recruitment strategy, which is a combination of the number of fibres recruited and frequency of recruitment (rate coding), is dependent upon the force-velocity and length-tension relationships (Fletcher and Macintosh, 2017). Not only does voluntary activation of muscle increase with increasing running speed (Kyrolainen et al., 2005), but also the timing of activation. EMC (muscle electrical) activity of the leg extensor muscles shows increased activation prior to, and during, the braking portion of the stance phase; this is particularly noticeable in the biarticular muscles (i.e. gastrocnemius, hamstrings, rectus femoris). Concomitant with the increase in fibre recruitment is a reduction in contraction time, thereby requiring a more rapid rate of cross-bridge cycling; together these factors will increase the rate of energy' expenditure. This illustrates the importance of measuring RE as an energy' expenditure (kJ kg ' knU¹) rather than an oxygen cost (mL kg^_1km^_1) as energy expenditure increases with running speed while oxygen cost is linear (Shaw et al., 2014; Blagrove et al., 2019c). Faster running speeds, with their shorter contraction times, require the recruitment of fast twitch fibres which are energetically more costly', further increasing the rate of energy' expenditure. Well-trained runners show a reduced stride-to-stride variation in EMG than less trained runners; this reduced variation is considered an indicator of more skilled movement (Osu et al., 2002; Chapman et al., 2008). It is unknown whether this is a cause of consequence of their higher trained status, although motor learning studies show reduced variation as individuals improve their skill (Osu et al., 2002).

Force Production

Increasing strength, usually due to an increase in muscle cross-sectional area, theoretically allows each muscle contraction to occur at a lower percentage of maximum strength. This would require fewer muscle fibres to be recruited to produce the force necessary during ground contact, thereby reducing the energy required (Fletcher and Macintosh, 2017). The endurance running community has been relatively slow to embrace resistance training due to long-standing beliefs that hypertrophic adaptations will reduce mitochondrial density (Chili- beck et al., 1999) and increase energy' expenditure (Beattie et al., 2017). In stark contrast to these beliefs, a recent review found overwhelming evidence that RE improved through resistance training (~2-8%), with no discernible gains in mass (Blagrove et al., 2018a). The ability to generate force also decreases after a run to exhaustion (Nicol et al., 1991b; Dierks et al., 2008; Bazett-Jones et al., 2013) and typical training sessions (Riazati et al., 2020), although this has not been directly linked to changes in RE.

Muscle-Tendon Unit Elasticity

Running has been likened to a repeating series of cyclical bounds, during which the leg acts like a spring, compressing during the braking and mid-stance phases of foot strike and then recoiling during push-off (Farley et al., 1993). Within this analogy, as the leg compresses, it stores elastic energy within the series elastic component of the muscle-tendon units, which is subsequently released during the recoil phase. This release of stored elastic energy reduces the amount of force required from the subsequent concentric contraction to propel the runner compared to a concentric-only contraction, thereby reducing the energy expenditure (Ito et ah, 1983). A more compliant tendon would, theoretically, be capable of storing and returning higher levels of energy; however, the most economical runners possess stiffer tendons (Dalleau et ah, 1998; Arampatzis et ah, 2006; Dumke et ah, 2010; Rogers et ah, 2017). The stiffness of 'the spring’ is therefore an important element in both the storage and return of elastic energy and determining ground contact time. A stiffer spring enables a shorter ground contact time and greater return of the stored elastic energy (McMahon and Cheng, 1990). Unsurprisingly, as running speed increases, so too does MTU stiffness (Kyrolainen et ah, 2005) facilitating shorter ground contact times and faster rates of force production. The increase in stiffness occurs due to increased pre-activation of motor units prior to ground contact; this is thought to assist with both absorbing the impending impact force and generating the braking impulse (Kyrolainen et ah, 2005). Increased muscle activity prior to ground contact would be expected to increase energy expenditure; however, it serves to maintain more optimal length-tension and force-velocity relationships (Lutz and Rome, 1994). Optimising these relationships enables muscle fibres to produce force under (near) isometric conditions which is more energy efficient than concentric actions (Lutz and Rome, 1994; Fletcher et ah, 2013). MTU stiffness increases in response to various types of resistance training (Kubo et ah, 2001a, 2001b, 2002; Fletcher et ah, 2010) and could therefore enhance RE.

Running Mechanics

When watching any middle- or long-distance race, a range of running styles can be observed. Despite an obvious intuitive link between RE and running mechanics (Anderson, 1996) and extensive research on the topic, very little clear-cut evidence exists in support of it. A review of biomechanical factors contributing to RE concluded that it was not possible to definitively identify any variables (Moore, 2016). The majority of research on this topic has been conducted on small (N < 25), often homogeneous samples, recording a limited range of variables. The exception to this is a recent study by Folland et ah (2017), which examined 24 different variables in a heterogeneous sample of 97 runners (47 women; 50 men). They found pelvic vertical oscillation during ground contact normalised to height, minimum knee joint angle during the stance phase and minimum pelvic horizontal velocity accounted for 39% of the variation in RE. Chapter 13 on gait retraining addresses the effect of training on running mechanics.

Ability to Maintain RE

Although not explicitly stated, the three parameters of the ‘classical’ model of endurance running (F0_2max, fractional utilisation of FQ_2imx and RE) while trainable, are assumed to be relatively stable on a day-to-day basis. This, however, is not true for RE, which can deteriorate with fatigue. Increases in RE occur towards the end of a long, continuous run, ranging from 60 min to the marathon (e.g. Brueckner et al., 1991; Hunter and Smith, 2007; Garcia- Pinillos et al., 2020) over the course of a 5-km run (Thomas et al., 1999), after a 4 min bout at the speed associated with F0_2max (Hayes et al., 2011) and after interval training (James and Doust, 1998).

The multifactorial nature of RE makes it difficult to determine the underlying causal mechanism(s) for the increase in RE. To a layperson watching middle- or long-distance races, the apparent fatigue induced changes in the gait of some runners would seem the obvious cause. Kinematic changes in gait do occur towards the end of both continuous, long-distance running (e.g. Nicol et al., 1991a) see also Chapter 2) and also interval training (e.g. Riazati et al., 2020); however, they do not appear to be related to the changes in RE (e.g. Nicol et al., 1991a). This is likely due to the individual variation in kinematic changes, ranging from large to no change (Nicol et al., 1991a; Hunter and Smith, 2007). The weight of evidence points towards neuromuscular factors causing the impairment in RE with increases in muscle fibre recruitment during braking and push-off phases (Nicol et al., 1991a), a decrease in eccentricxoncentric EMC ratio - suggesting a reduction in stored elastic strain energy (Abe et al., 2007), reduction in MTU stiffness (Girard et al., 2013; Garcia-Pinillos et al., 2020) and reductions in muscular force production (Riazati et al., 2020). Furthermore, there is a negative relationship between the increase in RE following a 4-min run at speed at FD_2max and eccentric muscular endurance of the knee flexors (KF), i.e. better eccentric KF endurance resulted in a smaller increase in RE (Hayes et al., 2011). Collectively, these changes suggest a reduced ability to tolerate repeated impact forces, and a loss of stretch-shortening cycle function is most likely to explain the increase in RE with fatigue (Nicol et al., 1991a). The link between being able to maintain RE and race performance has not been investigated but could be an important determinant of middle- and long-distance performance.

Summary

Running, while a seemingly simple activity, is a complex interaction of different physiological systems. Performance in middle- and long-distance events is determined by the highest rate at which ATP can be re-synthesised for the race duration and the ability to convert this energy' into movement. All of the middle- and long-distance running events depend primarily on the aerobic system for ATP re-synthesis, and this dependence increases with increasing distance. Despite this, the anaerobic system plays an important role in race situations, allowing an athlete to cope with changes in pace, particularly in the middle-distance races. Running economy, or ability to convert this energy into movement, is a key factor that differentiates runners and is made up of a complex interaction of, mostly, neuromuscular factors. All of the factors that determine middle- and long-distance performance are, to a greater or lesser extent, trainable, enabling athletes of all levels to improve.

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