Open access peer-reviewed chapter

Energy Cost of Walking and Running

Written By

Vaclav Bunc

Submitted: 12 October 2021 Reviewed: 19 January 2022 Published: 16 May 2022

DOI: 10.5772/intechopen.102773

From the Edited Volume

Exercise Physiology

Edited by Ricardo Ferraz, Henrique Neiva, Daniel A. Marinho, José E. Teixeira, Pedro Forte and Luís Branquinho

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Abstract

Walking and running are the basic means of influencing an individual’s condition, his or her health and fitness. Due to the fact that various forms of physical load are used in movement training, the cause must be described by a single number, which reflects the volume, intensity, and form of physical load. One of the possibilities is to determine the energy cost (EC) of the applied physical activities. Possibilities of evaluation of EC in laboratory and field conditions using the speed of movement allow to streamline movement training. To achieve the desired lasting effect, it is necessary that the total EC exceeds the so-called stimulus threshold, that is, the subject of physical training must reach a certain minimum level of total EC of applied physical training. The total energy content of exercise allows you to design individual exercise programs. In the paper, we present the relationships between energy and speed of movement for the most commonly used physical activities to increase fitness in people without regular physical training–walking and running in different age groups and for men and women and the principles of design of movement interventions using this parameter, as well as the implemented programs and their effect.

Keywords

  • walking
  • running
  • energy
  • intensity of exercise
  • energy cost of movement
  • movement economy

1. Introduction

Determining the energy intensity of physical activity is a basic problem in evaluating the impact of this activity on the human body, either in terms of certain civilization diseases prevention, or in terms of increasing the functional (physical) fitness of people, or to assess the body’s response to a given type of load.

The goal of all non-pharmacological intervention programs – movement programs in primary and secondary prevention is to determine the minimum amount of exercise load that will cause the necessary persistent changes in the state of the organism [1, 2, 3].

In general, the amount of energy that an organism consumes in a given physical activity is directly proportional to the intensity of that activity [1, 4, 5]. Throughout the range of movement load intensities–movement speed v, the energy E required to provide locomotor activity is proportional to the power of the velocity of movement. This relationship is generally nonlinear over the entire range of load intensities v and can be described by the following Equation [2, 6, 7].

E=cvnE1

Where c is the energy cost of movement, n for human movement ranges from 1 to 3 and expresses the density of the environment in which the movement is realized. The higher the density of the environment, the higher the value of n. The coefficient c characterizes the economics of motion and it is true that the lower its value, the better the economics of movement and the better its technique [2, 8].

The energy demands for submaximal movement intensity (i.e. movement economy) can be quantified by calculating the steady-state V̇O2, expressed with respect to body mass and time, for a standardized, submaximal movement intensity [1, 9]. Because this variable represents an aerobic need for physical activity, ATP resynthesis from ADP must be paid exclusively from substrates stored in the body and oxygen obtained from pulmonary ventilation and not from substantial protein catabolism. In untrained individuals, research has shown that at low to moderate speeds, steady state oxygen consumption is reached in approximately 3 minutes. Trained individuals reach a steady state earlier than untrained individuals. Although the existence of steady state is limited by a number of methodological limitations, this steady state can also be demonstrated by a non-increasing accumulation of lactate in the blood and RER lower than 1.00. All this is significantly influenced by diet, where in the case of predominant protein intake, the RER is less than one [1].

With a constant speed or running, at submaximal exercise intensities, the relationship between E and speed of running v is linear Energy necessary to proceed at a given running speed can be regarded as the product of c coefficient times the speed itself

E=cvE2

Where c is in J.kg−1.m−1 and running speed in m.s−1, thus yielding energy E in W.kg−1. the range of linearity depends not only on the actual training state, but also on the metabolic state, age, sex, and speed potential of the subjects studied [6, 10].

The linearity of this dependence depends on the subject’s training state. For running, it is in the range of 20–80% of the maximum intensity of movement in the untrained, and in the range of 10–90% of the maximum intensity in the trained [10].

Direct measurement of energy during real physical activity is relatively complicated. For practical reasons, we have often expressed E as oxygen uptake for the activity. In these cases, it has been convenient to express c in J.kg−1.m−1 and running speed in m.min−1, to obtain E in more customary units ml.kg−1.m−1. Thus the last equation may be rearranged as follows:

VO2=cvE3

Under aerobic conditions, since the energy E can be identified with VO2max, and in the submaximal range of intensities v, the last equation becomes:

fVO2max=cvE4

where f is the fraction of VO2max which may be utilized over a prolonged period of time [2, 11]. The duration of the competition and thus also the performance in training is obviously a decisive factor in determining the magnitude off. It is larger the longer the duration of the competitive performance [2, 6, 12].

The movement speed thus may be calculated as follows

v=fVO2maxc1E5

It follows from the above relationship that the better the economy of movement - the lower c, the higher the speed the individual can move.

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2. Energy cost of movement

Coefficient of movement energy cost during running (expressed in J.kg−1.m−1), and indicates how much energy is needed to transfer the body mass of 1 kg to the distance of 1 m. It holds that the better the economy of movement, the lower the values of c we find. In our older study, we found the following values of the coefficient c for different sports, gender, and age. The value of this coefficient ranges from 3.5 for highly trained runners on middle distances to values of about 4.2 for untrained people. For example for men and women respectively in adult middle distance runners C = 3.57 +/− 0.15 and 3.65 +/− 0.20, in adult long-distance runners C = 3.63 +/− 0.18 and 3.70 +/− 0.21, in adult canoeists C = 3.82 +/− 0.34 and 3.80 +/− 0.24, in young middle-distance runners C = 3.84 +/− 0.18 and 3.78 +/− 0.26 and in young long-distance runners C = 3.85 +/− 0.12 and 3.80 +/− 0.24 [2]. This similarity may be explained by the similar training states of both sexes, resulting from the intense training which did not differ in its relative intensity and frequency between the groups of men and women. Bunc and Heller [2] found a negative relationship was found between the energy cost of running and maximal oxygen uptake (VO2max) expressed relative to body mass (for men r = −0.471, p < 0.001; for women r = −0.589, p < 0.001). Thus, the better the adaptation to a given movement load, the higher the values of the maximum consumption an individual can achieve and the lower the density of the coefficient c, and the better the technique of movement.

The energy demands of higher stride frequency at a given speed are frequently cited as the most plausible explanation for the higher energy cost of movement, for the higher coefficient c. This concept is based on the assumption that the energy required to move body mass should directly reflect the muscle tension created by each stride [13].

If we assume that differences in stored energy are not significant between different subjects [1, 2] then we may conclude that in the non-trained subjects with increasing body fat percentage, and generally with increasing body mass, the coefficient of the energy cost of movement increases, and the energy cost of movement decreases when training state decreases. The prerequisites for using stored chemical energy for moving are reduced and thus the moving capacity and movement economy decline. For improving of predispositions for moving (transfer of body mass), it is necessary to reduce body mass to the subject’s “optimal” body mass [4].

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3. Energy cost of running

Movement economy c, which has traditionally been measured as the oxygen cost of running at a given velocity, has been accepted as the physiological criterion for ‘efficient’ performance and has been identified as a critical element of overall distance running performance [4, 6, 7, 11, 14]. It follows from the above that there is a relationship between the mechanics of running and its energy intensity, but previous research does not allow to determine a clear biomechanical profile of a runner with a high economy of movement - the high technique of movement. Through movement training, individuals seem to be able to adapt to achieve movement as economically as possible and minimize energy degradation [15]. Information in the literature suggests that biomechanical factors are likely to contribute to a better economy in any runners [16].

Atropometric parameters can significantly affect the biomechanics of movement, movement technique, and its energy intensity. These include height, ponderal index, and ectomorphic or ectomesomorphic figure; body fat percentage; foot morphology, pelvic size, foot size, and shape [13].

The economy of running can be influenced by movement patterns of running, their kinematics. These factors include the length of the step, which is freely chosen depending on the current fatigue; vertical oscillation of the center of gravity of the body; knee angle during the swing; range of motion, angular velocity of plantar flexion during toe-off; arm movement with smaller amplitude; peak ground reaction forces; rotation of the arms in the transverse plane; angular deflection of the hips and shoulders about the polar axis in the transverse plane; and efficient use of stored elastic energy [7, 17, 18]. Other factors that can significantly affect the running economy are: the shoes mainly their weight and the elasticity of the sole; higher share of higher and high training intensities of training history; and medium flexibility base. This information can be crucial in identifying talents for medium and long-distance running. At higher levels of training, it is likely that “natural selection” tends to eliminate athletes who have either failed to inherit or develop traits that support the economy of movement [19, 20].

It turns out that intra-individual variations in running economics range between 2% and 11% for a particular speed. Most of these variations are probably due to biological measurement error [21]. While the sources do not support gender differences in movement economics, data from some studies suggest that men may have better movement economics than women due to more muscle mass and less body fat. The economics of running change depending on age, depending on the amount of physical training completed. Pre-adolescent children have a worse economy than older children and adults, while older adults show the same trend compared to younger counterparts [9]. Air resistance at higher speeds fundamentally affects the economy of movement. Running on a treadmill at speeds higher than 13 km.h−1 due to air resistance significantly underestimates the cost of energy intensity compared to running speeds at the same speed in the field [9]. Oxygen consumption increases as a result of the “Q 10 effect” [22, 23]. There is also no consensus on the impact of different types and intensities of training on running economics, and significant differences in economics between long-distance runners who undergo the same load (eg track) suggest that non-training factors may also affect the running economy, such as the amount and type of muscle fibers [13, 20, 24].

From a study by Black et al. [19] show that anthropometric parameters and body composition are important predictors of running economics. Relative slenderness indices, especially segment perimeters, have been commonly associated with running economy, suggesting that a slimmer individual can be expected to expend less energy and thus be more economical at any given speed [25]. It should be noted that the amount of energy available for physical activity stored in the body per kg of free fat mass is practically the same for virtually all persons of the same sex. The importance of running economics in medium and long-distance running, we recommend to trainers, applied practitioners, and athletes to evaluate anthropometric parameters and body composition as part of the evaluation of training. This is especially important in identifying talented athletes and preparing top athletes to achieve maximum individual performance [26].

Studies comparing different groups of runners with different training and focus have shown that the maximum differences in energy intensity between runners are around 20%. Factors influencing the value of coefficient c include body dimensions: body height and weight, the architecture of the lower calves, mostly the length of the calcaneal tuberosity, which are responsible for 60–80% variability of this coefficient. Children have higher c values ​​than adults. This can be explained by their higher resting metabolism, lower running technique, and lower leg/leg ratio [9, 27]. The storage of elastic energy and its reuse also contributes to the variability of c. The coefficient c increases with the increasing speed of movement due to the increase in mechanical work is blunted to speed of 6–7 m.s−1 by increasing the vertical stiffness and shortening the contact time with the ground. Fatigue caused by prolonged or intense running is associated with up to a 10% increase in c; the influence of metabolic and biomechanical factors on the energy intensity of running remains unclear. Women show c similar to men of similar body weight, despite differences in running technique. The higher performance of black African endurance athletes is probably related to their leg architecture and better elastic storage and reuse of elastic energy [20].

Speed and movement techniques are considered to be the main sources of changes in the energy intensity of running in individuals with different body masses. The linear dependence of energy on running speed is approximately up to a speed of 3.6 m.s−1. In the case of higher speeds, this dependence is nonlinear. At6speeds higher than 3.6 m.s−1, runners are less likely to achieve aerobic performance - steady state oxygen consumption [28].

Walking and running are the basic means of influencing an individual’s condition, his health and fitness. Possibilities of evaluation of energy intensity in laboratory and field conditions using the speed of movement, allows to streamline movement training. Energy intensity allows you to design individual exercise programs, for example, for the needs of primary and secondary prevention of obesity, cardiovascular disease, reducing the impact of current lifestyles, etc. [3, 4, 29].

Human locomotion is characterized by two principal gaits, walking and running. This makes it possible to move either at a slow speed for long periods of time or at over 10 m.s−1 during a sprint [30]. The basic features of both locomotion modes are the same: each step represents one posture phase and one swing phase, but then they differ because the foot controls have two separate operating modes for walking and running. The timing of each phase of the movement is different. The frequency of steps is usually lower when walking than when running, so the contact time with the surface of each foot is longer when walking and shorter when running, while the swing shows the opposite trend. When walking, there is always at least one foot on the ground, while running there is a flight phase where both feet are above the ground and the amplitudes of the contractions of the flexor and extensor muscles during the two phases of the step are different [31, 32].

Studies examining the interaction between stride length, energy absorption, and impact attenuation have only been performed on level ground. Stepped running places unique demands on the musculoskeletal system compared to running on a plane, resulting in differences in physiological requirements and the kinematics and kinetics of the run [5]. Downhill running is associated with greater impact magnitudes and increased energy absorption when compared to level running [5]. The increased eccentric muscular work required to absorb more energy during downhill running may also be associated with muscle damage and delayed onset muscle soreness (DOMS), which negatively affects running performance. In contrast, uphill running is more energetically costly than level or downhill running [32] but is associated with lower impact magnitudes and reduced lower extremity energy absorption, especially when compared to downhill running [32]. Step length and frequency are also known to change during graded running [5], and step length manipulation may aid in understanding the injury and performance implications of these natural changes to preferred step length [18].

The evaluation of the energy intensity of running is a suitable criterion for examining the efficiency of mechanical work, evaluation of movement technique, and analysis of endurance performance during endurance running [24, 33].

Once energy cost values (V̇O2 and caloric expenditure) are standardized using bodyweight, the primary determinant of energy cost was the speed of movement [1, 33]. The derived generalized models make it possible to determine both V̇O2 (ml.kg−1.min−1) and the energy intensity (kcal.kg−1.min−1) of walking and running. The relationship between walking speed or number of steps and the energy intensity of walking is parabolic, while the relationship between running speed and energy intensity of running is in the range of about 20–80% for untrained and 20–90% for trained line runners is linear [34]. Neither age nor body height significantly improved the prediction of the energy cost of movement from its speed.

In practice, results of spiroergometric surveys often need to be checked using the relationship between oxygen consumption and movement speed. The relationship between energy and speed of running may be used as a linear form as follows

VO2.kg1ml.kg1.min1=avkm.h1+bE6

Where a and b are constants that depend generally on the training status, sex, age and speed, and strength predisposition.

Many equations can be found in the literature for predicting energy expenditure during walking or running. Not only can the amount of energy that was “burned” during a training unit be determined, but often these relationships are implemented in miniaturized electronic devices that provide the user with relevant data on the energy intensity of the physical activity performed. At the same time, it should be noted that the energy estimation error from walking or running speed is around 10% and these relationships can be used for so-called biological testing of spiroergometric analyzers. We include in the text those prediction equations which are currently the most frequently used and which provide relevant information for a given population. We have chosen the following tables and equations because they have often been cited in the literature. The ACSM Equation [35] was used because most exercise physiologists know the ACSM guidelines. McArdle’s [33] walking and running tables have been used because they are found in commonly used exercise physiology textbooks and are often used by researchers in the field to estimate energy expenditure. Other equations were chosen because they were cited in the literature and provided additional estimates of walking and running. The prediction formulas that were used are listed below:

ACSM [34]:

Running.̇VO2mL·kg1·min1=0.2m·s1+0.9m·s1fractional grade+3.5E7
Walking.̇VO2mL·kg1·min1=0.1m·s1+1.8m·s1fractional grade+3.5E8

Bunc & Heller [14]

Running men

VO2.kg1mL·kg1·min1=3.749vkm.h12.133E9

Bunc et al. [12]

Running women

VO2.kg1mL·kg1·min1=3.549vkm.h1+3.008E10

Bunc & Dlouhá [34]

Walkimg

VO2.kg1mL·kg1·min1=3.207vkm.h11.777E11
VO2.kg1mL·kg1·min1=0.108vkm.h1+0.379v2km.h1+4.503E12

McArdle [33]:

McArdle’s tables are available in the referenced text.

Van der Walt and Wyndham [36]:

Walking

̇VO2L·min1=0.00599M+0.000366MV2E13

Running.

̇VO2l·min1=0.419+0.03257M+0.000117MV2E14

Pandolf, Givoni & Goldman [37]:

WJ·s1=1.5M+2.0M+LL/M2+nM+L1.5v2+0.35vGE15

M = body mass (kg), L = load carried, v = velocity (m·s−1), G = grade, and n is the terrain factor. For unloaded, level walking on a track or treadmill, the following formula is used:

W1J·s1=1.5W+1.5v2WE16

Léger & Mercier [38]:

̇VO2ml.kg1.min1=2.209+3.1633vkm.h1E17

Epstein, Stroschein & Pandolf [39]:

Mr.=Mw0.510.01LMw15L850E18

Mr. = metabolic cost of running, Mw = metabolic cost of walking, L = clothing weight

With the maximal error of estimation in the range of running speeds 8–16 km.h.−1 about 10%.

For walking in the range of intensities the oxygen consumption inaccuracy at the speeds from 4 to 10 km.h−1 is around 12% [12, 34].

Running has a greater energy cost than walking on both the track and treadmill. For running, the Léger equation, ACSM [35], and Bunc [12] prediction model appear to be the most suitable for the prediction of running energy expenditure. The ACSM [35], Pandolf, Givoni & Goldman [37], and Bunc [12] linear prediction equation also closely predict walking energy expenditure, whereas McArdle’s [32] table or the equations by Epstein and van der Walt were not as strong predictors of energy expenditure.

For movement speeds lower than 7 km.h−1, the energy cost of running is higher than walking For movement speeds higher than 7 km.h−1, the energy cost of walking is higher and increases exponentially with increasing movement intensity [34] that ACSM [35], Bunc [12] and Léger [38] predictive energy performance models for running straight are more accurate in a young healthy population. For horizontal walking, the ACSM [34, 35], and Pandolfova [37] reduction models also appear to be more accurate than other prediction equations.

The energy intensity of both running and walking represents the total energy consumption using many different mechanisms in the body, including muscle dynamics, blood circulation, and aerobic processes of energy release. In both running and walking energy-intensive experiments in humans, this is usually determined from oxygen consumption and carbon dioxide production values minus the basal metabolic rate at rest to achieve net metabolic performance. The energy intensity of exercise is commonly expressed in two different ways: energy consumed per unit time (metabolic rate or power) or energy consumed per unit distance [40].

The negative relationship between maximal oxygen consumption expressed relative to body mass and coefficient energy cost of running c means that athletes with higher aerobic capacity, higher VO2max have lower values of c, i.e. better running economy [2, 41]. These findings may bet the results of the prolonged duration of the competitions and, thus, of the training performance of these athletes when they are forced to turn out a highly economical performance over a prolonged period of time, and it may also bet the result of a high degree of adaptation to running [25].

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4. Energy cost of walking

An energetic economy has been shown to have a large influence on human walking behavior. For example, at a given speed, humans tend to walk with a preferred step length that coincides with minimum metabolic cost [40]. Despite the complexity of the relationship between walking biomechanics and its energy expenditure, relevant studies have shown significant contributors to overall walking energy intensity, such as step-by-step work to redirect the center of gravity and energy in generating muscle strength to support weight transfer and swing. Feet.

The biomechanics of complex movements, such as those that occur when walking and running, which involve a large number of cooperating body segments, can be better understood by considering the energy counterpart, ie the energy expended on muscle contraction, which must work continuously to drive the body forward. Running and especially walking are basic physical activities to which a person is maximally adapted. This adaptation has evolved over many generations in order to minimize the energy requirements of a given physical activity. Walking is an energetically beneficial physical activity, its energy intensity is only about 50% higher than the basal metabolic rate (at speed of 0.6 ms−1 it is about 2.44 W.kg−1) [16], and this has in the past allowed populations to expand their ecological niches. Conversely, running can be very challenging and can be continued without slowing down for untrained individuals for less than an hour and sprinting for a much shorter time; but while the energy intensity of walking varies with the speed of movement, when running the same distance, the energy expended, although higher overall, is independent of movement speed [32].

Our older study [34] tries to answer the question of the energy cost of walking (VO2) could be accurately predicted with the simple models which analyze the relationship oxygen uptake-speed of walking. Employing the new modification of this model from 1986 [42] to analyze VO2 - speed of walking relationship leads to the elaboration of a simple linear model, two-compartment linear model, a polynomial model of second-order and monoexponential model of the metabolic cost of treadmill walking. To verify and compare these models 87 males, age ranged from 19 to 62 years, were evaluated on a motor-driven treadmill. They walked at 0% slope on a treadmill at various velocities ranged from 3 to 12 km.h−1.

The linear model has in range of intensities 3–12 km.h−1 a form of VO2.kg−1 (ml.kg−1.min−1) = 5.228*v (km.h−1)-11.158, r = 0.812, SEE = 4.16 ml.kg−1.min−1. The two-compartment linear model has in range of intensities of 3–7 km.h−1 a form of VO2.kg−1 = 3.207*v(km.h−1)-1.777, r = 0.932, and SEE = 1.5 ml.kg−1.min−1. In the range of 7.1–12 km. VO2.kg−1 = 7.120*v-29.168, r = 0.901, SEE = 3.78 ml.kg−1.min−1. In the range of intensities from 3 to 12 km.h−1 a polynomial model was found in the form VO2.kg−1 = 4.501–0.108*v + 0.379*v2, r = 0.891, SEE = 4.43 ml.kg−1.min−1, and the exponential model had a form VO2.kg−1 = 4.360*exp.(0.223*v), r = 0.861, SEE = 6.84 ml.kg−1.min−1. All these correlation coefficients were highly significant (p < 0.001 in all cases) [34].

It was concluded that when applied to adult population, the models provide a reasonable estimate of the actual requirement for treadmill walking provided the subjects in an oxygen uptake steady-state [43]. From the above, an important conclusion for practice follows: with adequate accuracy of about 10%, a linear model of the dependence of oxygen consumption and walking speed can be used in the range of walking speeds of 4–12 km.h−1.

As with other researches for VO2.step−1 or speed of movement, we have found U-shaped curves of the coefficient energy cost of walking (see Figure 1). The minimum was at a speed of about 4 km.h−1. This finding supports the speculation that does exists the “optimal” speed of moving which reflects the minimal energy expenditure during walking [34].

Figure 1.

Dependence of energy cost coefficient on speed of movement in walking and running.

The energy coefficient of walking varies depending on the increasing speed of walking. In contrast, the coefficient c for running is practically constant in the range of running speeds 6–15 km.h−1(see Figure 1) [7, 44].

In general, the dependence on walking speed or number of steps has a nonlinear parabolic course with a clearly defined minimum of around 4 km.h−1 see Figure [34, 42, 45, 46] over the entire range of walking load intensity intensities. From a speed of about 4.5 km.h−1, the value of the coefficient c when walking increases exponentially. For practical use, on the basis of the above, a linear description of the dependence between the energy or oxygen consumption and the speed of movement can be used in practice to determine the energy intensity of the movement.

For movement speeds lower than approx. 7 km.h−1, the coefficient of energy intensity of walking is lower than for running [34]. For practice, this means that in the case of mainly patients, walking at speeds lower than 7 km.s−1 is more energetically advantageous than running at the same speed.

During treadmill running most well-trained runners run at step frequencies that minimize their energy expenditure. However, outdoor running, with air resistance and wind, is different from treadmill running [23, 47].

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5. Air resistance by movement

The resistance of the environment, in our case walking and running air, is characterized by forces that act against the movement of an object that moves in space. These resistive forces act in the opposite direction to the speed of the approaching flow, thus slowing down the object. Unlike other resistance forces, resistance depends directly on speed, because it is a component of the net aerodynamic force acting against the direction of movement, on the front profile of the moving individual, and on the air density. Therefore, world records on sprinters have often been broken at higher altitudes, where the air density is lower [48, 49].

Air resistance, or drag, can be put into one of three categories; lift induced, parasitic, and wave. Each of these types of air resistance affects an object’s ability to stay up and the power it will need to keep it there [49].

Lift-induced air resistance happens as the result of the creation of lift on a three-dimensional lifting body (wing or fuselage).

Parasitic drag happens when a solid object moves through a fluid. This type of air resistance is made up of lots of components like “form drag” and “skin friction drag”.

Wave drag is made when an object moves at a high speed through a compressible fluid.

Air resistance is usually calculated using the “drag equation”, which determines the force experienced by an object moving through a fluid or gas at a relatively large velocity. This can be expressed mathematically as [49, 50]:

FD=0.5ρv2CDAE19

In this equation, FD represents the resistance force, ρ is the air density, v is the velocity of the object relative to the speed of sound, A is the cross-sectional area, and CD is the coefficient of resistance. The result is what is called “quadratic resistance.” For movement in an air environment, these constants can be determined as follows.

A=0.2660.2025height0.725body mass0.42521,CD=0.951E20

The energy required to overcome air resistance was estimated at 2% for running outdoors at 5 m.s on a calm windless day [48]. Jones and Doust showed that HR was about 3–4 beats higher when running outdoors (quiet day) compared to running on a treadmill [52]. Pugh found an increase in VO2 of about 14% as the rate of the ventilator in the laboratory increased from 0 to 10 m.s−1 for a subject running on a treadmill at 3.75 m.s−1 [23]. This would correspond to the difference between a treadmill running at 3.5 m.s−1 and running at the same speed at a headwind of 6.5 m.s-1. Based on our study of trained runners, there is a significant difference in running energy intensity and at running speeds higher than 13 km.h−1 [10, 12]. In another study, 6 runners ran in the headwind at a speed of about 6.5 ms−1. The HR value was 4–8 bpm.min−1 higher in the headwind compared to the windless, which illustrates the significant effects that wind can have on the energy intensity of the run [53].2151

The technique of movement changes depending on the increasing resistance of the air at higher speeds. Therefore, in order to maintain the correct movement technique even at speeds above 13 km.h−1 running behind the car is often used in the training of runners, which reduces the direct impact of air resistance on the runners.

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6. Movement programs based on walking and/or running

Walking and running are very often used in intervention programs for cultivating fitness or for body mass reduction [3, 29, 54, 55]. Exercise intervention with a mean weekly energy intensity of 20.40 ± 4.51 kcal.kg−1.week−1 or with a mean energy cost per day of 5.4 kcal.kg−1.day−1 which are applied for at least 7 weeks, will cause significant changes in functional and morphological parameters. These changes are independent of age and gender. In the case of weight, the total energy intensity of physical activities increases with increasing body mass [3, 27].

Physical intervention based on walking or running with an intensity corresponding to 80–90% SFmax, at least 80% of the total load must consist of running or walking for at least 8 weeks will cause changes in aerobic capacity expressed by changes in VO2peak are on average around 16% of the initial value. We find the same relative change in the speed of movement at which the load on the treadmill is terminated due to subjective exhaustion. The weight reduction is around 14% of the initial weight and the average improvement of the kinetic load assumptions as measured by the ECM / BCM coefficient is around 15% [3, 54].

Recent cross-sectional studies have demonstrated the ability to economize movement, either alone or in combination with V02max, a crucial factor that may explain a substantial portion of performance variations between trained long-distance runners and untrained subjects of comparable levels of exercise and fitness. Limited data from short-term and long-term longitudinal research also suggest that the success of endurance running is related to training and improving the economics of movement leading to a reduction in the energy intensity of movement [53].

In practice, this leads to the clear conclusion that the first step in any endurance training is to improve the economics of running - running techniques leading to reduced energy consumption and delayed fatigue due to depletion of energy resources stored in the body.

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7. Discussion

Walking and running are the basic locomotor activities of a person. They are not demanding on the environment and are implemented in practically any weather and in almost all environments - on flat and changing surfaces, movement on the plane, and downhill or uphill. We adopt very well to these forms of physical activity, which results from their long-term use for livelihood and the implementation of work and leisure activities. The energy intensity coefficient of walking depends on the speed of movement and reaches a minimum at a speed of about 4 km.h-1 [34]. On the contrary, the coefficient of energy intensity of the run is practically independent of the running speed. The minimum dependence of the coefficient of walking energy intensity on its speed is probably due to the optimal use of the recovery of elastic forces at this speed of movement.

This minimum of energy intensity is often used in the rehabilitation of cardiac patients because the changes caused by the speed corresponding to this minimum are the smallest [56, 57].

Evaluation of the degree of adaptation to running, with the help of c coefficient as an additional characteristic during laboratory tests, enables us to ascertain, along with other parameters, not only the effectiveness of training procedures, but also helps in the evaluation of the technique of the movement performed. This is essential in sports events where training is started at an early age and enables us to determine the energy cost of the training stimulus used [1, 2, 24].

Movement economization in the case of long-term exercise loads is associated with the maximum use of automated movements, which are less energy-intensive than non-automated movements. In practice, this makes key recommendations. At the beginning of each movement intervention, it is always necessary to focus on economizing movement, improving technique \ movement, and only then concentrating on managing the required volume of exercise loads [41].

To master the necessary movement techniques in the case of deepening fatigue, ie in the case of deepening acidosis and reducing the amount of energy substrates is possible only as a result of long-term intensive training [4, 32]. Running economics - the energy intensity of running is primarily dependent on completed training, but the genetic disposition of the runner also plays a role here, i.e. its current level is given by the intersection of genetic preconditions and completed running training [58].

Based on the energy cost of walking or running for a particular individual, it is possible to design a movement intervention that allows you to optimize the effect of this intervention and mainly minimize the time required for this intervention [59].

Evidence suggests that several internal (sex, running biomechanics, anatomy) and external factors (experience, mileage, training routines) may contribute to the risk of injury in recreational runners [60]. Good exercise technique, its good economy, a good value of the energy intensity coefficient of running c, can significantly delay the onset of fatigue during long-term running training load and can act preventively against muscle injuries [61]. Therefore, special attention should be paid to cultivating running techniques in preparation for long-distance races, such as marathons and ultra-endurance races, in order to ensure the necessary condition without increasing the risk of injury from overload.

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8. Conclusions

Evaluation of energy cost of majority of physical movement activities and subsequent cultivation, used to influence fitness or in primary and secondary prevention, allows to increase the effectiveness of the applied physical intervention. At the same time, it can delay the onset of fatigue and thus reduce the incidence of muscle injuries. Assessing the energy intensity of running or walking in laboratory or field functional tests can significantly expand the information content of these surveys and should therefore be an integral part of these surveys. Models relating energy and intensity to exercise are useful for quantifying the training load of both recreational and trained runners and allow you to minimize the time devoted to endurance training.

References

  1. 1. Astrand PO, Rodahl K. Textbook of Work Physiology. New York, USA: McGGraw Hill; 1986
  2. 2. Bunc V, Heller J. Energy cost of running in similarly trained men and women. European Journal of Applied Physiology and Occupational Physiology. 1989;59(3):178-183. DOI: 10.1007/BF02386184
  3. 3. Bunc V. Obesity - causes and remedies. Physical Activity Review. 2016;4:50-56. DOI: 10.16926/par.2016.04.06
  4. 4. Bunc V. Energy cost of treadmill running in non-trained females differing in body fat. The Journal of Sports Medicine and Physical Fitness. 2000;40(4):290-296
  5. 5. Vernillo G, Giandolini M, Edwards WB, Morin JB, Samozino P, Harvais N, et al. Biomechanics and physiology of uphill and downhill running. Sports Medicine. 2017;47:615-629. DOI: 10.1007/s40279-016-0605-y
  6. 6. di Prampero PE, Atchou G, Brückner JC, Moia C. The energetics of endurance running. European Journal of Applied Physiology and Occupational Physiology. 1986;55(3):259-266. DOI: 10.1007/BF02343797
  7. 7. Margaria R. Biomechanics and Energetics of Muscular Exercise. Oxford, UK: Claredon; 1976
  8. 8. Zamparo P, Matteo Cortesi M, Gatta G. The energy cost of swimming and its determinants. European Journal of Applied Physiology. 2020;120(1):41-66. DOI: 10.1007/s00421-019-04270-y
  9. 9. Bunc V, Heller J. Energy cost of running in young and adult female athletes. Ergonomics. 1994;37(1):167-174. DOI: 10.1080/00140139408963635
  10. 10. Bunc V, Heller J. Exercise intensity coversion from a bicycle ergometer to a treadmill. The Journal of Sports Medicine and Physical Fitness. 1991;Fitness 31:490-493
  11. 11. di Prampero PE. The energy cost of human locomotion on land and in water. International Journal of Sports Medicine. 1986;7(2):55-72. DOI: 10.1055/s-2008-1025736
  12. 12. Bunc V, Heller J, Leso J. Energy requirements of running on a treadmill and in the field. Czech Republic Medical Journal. 1986;125:1391-1394
  13. 13. Morgan DW, Martin PE, Krahenbuhl GS. Factors affecting running economy. Sports Medicine. 1989;7:310-330
  14. 14. Bunc V, Heller J. Energy demands in women’s running on the treadmill and cross county running. Czech Republic Medical Journal. 1990;129:650-653
  15. 15. de Ruiter CJ, van Daal S, van Dieën JH. Individual optimal step frequency during outdoor running. European Journal of Sport Sciences. 2020;20(2):182-190. DOI: 10.1080/17461391.2019.1626911
  16. 16. Hall C, Figueroa A, Ferrnhall BO, Kanaley JA. Energy expenditure of walking and running: Comparison with prediction equations. Medicine & Science in Sports & Exercise. 2004;36(12):2128-2134. DOI: 10.1249/01.MSS.0000147584.87788.0E
  17. 17. Anderson T. Biomechanics and running economy. Sports Medicine. 1996;22(2):76-89. DOI: 10.2165/00007256-199622020-00003
  18. 18. Peyré-Tartaruga LA, Dewolf AH, di Prampero PE, Fábrica G, Malatesta D, Minetti AE, et al. Mechanical work as a (key) determinant of energy cost in human locomotion: Recent findings and future directions. Experimental Physiology. 2021;106(9):1897-1908. DOI: 10.1113/EP089313
  19. 19. Black MI, Allen SJ, Forrester SE, Folland JP. The anthropometry of economical running. Medicine & Science in Sports & Exercise. 2020;52(3):762-770. DOI: 10.1249/MSS.0000000000002158
  20. 20. Lacour JR, Bourdin M. Factors affecting the energy cost of level running at submaximal speed. European Journal of Applied Physiology. 2015;115(4):651-673. DOI: 10.1007/s00421-015-3115-y
  21. 21. Snyder KL, Farley CT. Energetically optimal stride frequency in running: The effects of incline and decline. The Journal of Experimental Biology. 2011;214:2089-2095
  22. 22. Arsac LM, Locatelli E. Modeling the energetics of 100-m running by using speed curves of world champions. Journal of Applied Physiology. 2002;92(5):1781-1788. Crossref. Retrieved from: https://www.ncbi.nlm.nih.gov/pubmed/11960924
  23. 23. Pugh LGCE. The influence of the wind resistence in running and walking and the mechanical efficiency of work against horizontal forces. Journal of Physiology (London). 1971;213:255-276
  24. 24. Costill DL. A scientific approach to distance running. Track Fields News. 1979;14:12-40
  25. 25. Cavagna GA, Mantovani M, Willems PA, Musch G. The resonant step frequency in human running. Pflügers Archiv - European Journal of Physiology. 1997;434:678-684
  26. 26. Arellano CJ, Kram R. Partitioning the metabolic cost of human running: A task-by-task approach. Integrative and Comparative Biology. 2014;A54(6):1084-1098. DOI: 10.1093/icb/icu033
  27. 27. Cebolla i Martí A, Álvarez-Pitti JC, Guixeres PJ, Lisón JF, Baños RR. Alternative options for prescribing physical activity among obese children and adolescents: Brisk walking supported by an exergaming platform. Nutrición Hospitalaria. 2014;31(2):841-848. DOI: 10.3305/nh.2015.31.2.7929
  28. 28. Walker JL, Murray TD, Jackson AS, Morrow JR Jr, Michaud TJ. The energy cost of horizontal walking and running in adolescents. Medicine and Science in Sports and Exercise. 1999;31(2):311-322
  29. 29. Murtagh EM, Murphy MH, Boone-Heinonen J. Walking: The first steps in cardiovascular disease prevention. Current Opinion in Cardiology. 2010;25(5):490-496. DOI: 10.1097/HCO.0b013e32833ce972
  30. 30. Cavagna GA, Saibene FP, Margaria R. Mechanical work in running. Journal of Applied Physiology. 1964;19:249-256
  31. 31. Giandolini M, Pavailler S, Samozino P, Morin P, Horvais N. Foot strike pattern and impact continuous measurements during a trail running race: Proof of concept in a world-class athlete. Footwear Science. 2015;7(2):127-137. DOI: 10.1080/19424280.2015.1026944
  32. 32. Gianluca V, Giandolini M, Edwards WB, Morin JB, Samozino P, Horvais N, et al. Biomechanics and physiology of uphill and downhill running. Sports Medicine. 2017;47:615-629. DOI: 10.1007/s40279-016-0605-y
  33. 33. McArdle W, Katch F, Katch V. Exercise Physiology: Energy Nutrition, and Human Performance. Baltimore, MD: Williams & Wilkins; 2001
  34. 34. Bunc V, Dlouhá R. Energy cost of treadmill walking. The Journal of Sports Medicine and Physical Fitness. 1997;37(2):103-109
  35. 35. ACSM. American College of Sports Medicine. Guidelines for Exercise Testing and Prescription. 6th ed. Baltimore, MD: Lippincott Williams & Wilkins; 2000. pp. 163-176
  36. 36. van der Walt W, Wyndham C. An equation for prediction of energy expenditure of walking and running. Journal of Applied Physiology. 1973;34:559-563
  37. 37. Pandolf K, Givoni B, Goldman R. Predicting energy expenditure with loads while standing or walking very slowly. Journal of Applied Physiology. 1978;43:577-581
  38. 38. Léger L, Mercier D. Gross energy cost of horizontal treadmill and track running. Sports Medicine. 1984;1:270-277
  39. 39. Epstein Y, Stroschein LA, Pandolf KB. Predicting metabolic cost of running with and without backpack loads. European Journal of Applied Physiology and Occupational Physiology. 1987;5(6):495-500
  40. 40. Faraji S, Wu AR, Ijspeert AJ. A simple model of mechanical effects to estimate metabolic cost of human walking. Scientific Reports. 2018;8(1):10998. DOI: 10.1038/s41598-018-29429-z
  41. 41. Saunders PU, Pyne DB, Telford RD, Hawley JA. Factors affecting running economy in trained distance runners. Sports Medicine. 2004;34:465-485
  42. 42. Workman JM, Armstrong BW. Metabolic cost of walking: Equation and model. Journal of Applied Physiology. 1986;61(4):1369-1374. DOI: 10.1152/jappl.1986.61.4.1369
  43. 43. Donelan JM, Kram R, Kuo AD. Mechanical work for step-to-step transitions is a major determinant of the metabolic cost of human walking. The Journal of Experimental Biology. 2002;205:3717-3727
  44. 44. Falls HB, Humphrey LD. Energy cost of running and walking in young women. Medicine and Science in Sports. 1976;8:9-13
  45. 45. Grabowski A, Farley CT, Kram R. Independent metabolic costs of supporting body weight and accelerating body mass during walking. Journal of Applied Physiology. 2005;98:579-583. DOI: 10.1152/japplphysiol.00734
  46. 46. Pivarnik JM, Sherman NW. Responses of aerobically fit men and women to uphill/downhill walking and slow jogging. Medicine and Science in Sports and Exercise. 1990;22:127-130
  47. 47. Pugh LGCE. Oxygen uptake in track and treadmill running with observationon the effect of the air resistence. Journal of Physiology (London). 1970;207:823-835
  48. 48. Davies CTM. Effect of wind assistance and resistence on the forward motion of a runner. Journal of Applied Physiology. 1980;48:702-709
  49. 49. Falkovich G. Fluid Mechanics (a Short Course for Physicists). UK: Cambridge University Press; 2011. ISBN 978-1-107-00575-4
  50. 50. Mero A, Komi PV, Gregor RJ. Biomechanics of sprint running. A review. Sports Medicine. 1992;13(6):376-392. DOI: 10.2165/00007256-199213060-00002
  51. 51. van Ingen Schenau GJ, Jacobs R, de Koning JJ. Can cycle power predict sprint running performance? European Journal of Applied Physiology and Occupational Physiology. 1991;63(3–4):255-260. Retrieved from: https://www.ncbi.nlm.nih.gov/pubmed/1761017. Crossref
  52. 52. Jones AM, Doust JH. A 1% treadmill grade most accurately reflects the energetic cost of outdoor running. Journal of Sports Sciences. 1996;14(4):321-327. DOI: 10.1080/02640419608727717
  53. 53. de Ruiter CJ, van Daal S, van Dieën JH. Individual optimal step frequency during outdoor running. European Journal of Sport Science. 2020;20(2):182-190. DOI: 10.1080/17461391.2019.1626911
  54. 54. Bunc V, Hrasky P, Skalska M. Possibilities of body composition influence by walking program in senior women. Journal of Aging and Physical Activity. 2012;20:S289-S290
  55. 55. Fanning J, Opina MT, Leng I, Lyles MF, Nicklas BJ, Rejeski WJ. Empowered with movement to prevent Obesity & Weight Regain (EMPOWER): Design and methods. Contemporary Clinical Trials. 2018;72:35-42. DOI: 10.1016/j.cct.2018.07.010
  56. 56. Girold S, Rousseau J, Le Gal M, Coudeyre E, Le Henaff J. Nordic walking versus walking without poles for rehabilitation with cardiovascular disease: Randomized controlled trial. Annals of Physical and Rehabilitation Medicine. 2017;60(4):223-229. DOI: 10.1016/j.rehab.2016.12.004
  57. 57. Han P, Zhang W, Kang L, Ma Y, Fu L, Jia L, et al. Clinical evidence of exercise benefits for stroke. Advances in Experimental Medicine and Biology. 2017;1000:131-151. DOI: 10.1007/978-981-10-4304-8_9
  58. 58. Boullosa D, Esteve-Lanao J, Casado A, Peyré-Tartaruga LA, Gomes da Rosa R, Del Coso J. Factors affecting training and physical performance in recreational endurance runners. Sports (Basel). 2020;8(3):35. DOI: 10.3390/sports8030035
  59. 59. Hausswirth C, Lehénaff D. Physiological demands of running during long distance runs and triathlons. Sports Medicine. 2001;31(9):679-689. DOI: 10.2165/00007256-200131090-00004
  60. 60. Malisoux L, Delattre N, Urhausen A, Theisen D. Shoe cushioning influences the running injury risk according to body mass: A randomized controlled trial involving 848 recreational runners. The American Journal of Sports Medicine. 2019;48:473-480
  61. 61. Pons V, Riera CX, Martorell M, Sureda A, Tur JA, Drobnic F, et al. Calorie restriction regime enhances physical performance of trained athletes. Journal of the International Society of Sports Nutrition. 2018;15:12

Written By

Vaclav Bunc

Submitted: 12 October 2021 Reviewed: 19 January 2022 Published: 16 May 2022