The Influence of Elite Race Walkers’ Year-Long Training on Changes in Total Energy and Energy Cost While Walking at Different Speeds

Abstract

The aim of the study was to assess the influence of year-long training of race walkers on physiological cost and total energy center of mass (CoM). The assessment performed was based on indicating the differences between the resulting energy cost in a group of elite race walkers walking at technical, threshold, and racing speeds calculated by physiological and biomechanical methods before beginning and after finishing a year-long training cycle. The study involved 12 competitive race walkers who had achieved champion or international champion level. Their aerobic endurance was determined by means of a direct method, applying an incremental exercise test on the treadmill. The gait of the participants was recorded using the 3D Vicon analysis system. Changes in mechanical energy amounted to the value of the total external work of the muscles needed to accelerate and lift the center of mass during a normalized gait cycle. The highest influence on the total external work increase for increasing speeds of gait in both examinations was attributed to the changes in the kinetic energy resulting from the center of mass movement. A statistically significant decrease of the mean value of total external work for racing speed was observed in the second examination (p < 0.001). An approx. 8% decrease (NS) of EE energy cost, standardized by body mass and distance covered, was found between the first and second examinations. The energy cost and total external work were significantly differentiated by the walkers’ gait speeds (p < 0.05–0.001). The energy cost significantly differed from the total external work at p < 0.001.

1. Introduction

The economics of movement involves the transformation of physiological energy produced by muscles into mechanical energy that powers the motor system. This concept includes two main components: efficiency, which refers to the conversion of chemical energy into mechanical energy within muscles, and effectiveness, which relates to the application of mechanical energy to achieve motion. This motion is observed as the displacement of the center of mass (CoM) within the gravitational field. To enhance clarity, it is essential to simplify and break down complex sentences, focusing on conveying each idea more concisely [1]. Efficiency is measured by physiological methods, whereas effectiveness is measured using biomechanical methods for individual movement techniques. As stressed by Yamada et al. [2], during a race walking competition, the final effect, i.e., race result, depends not only on maintaining a high level of gait speed but also on following the individual technique strategy despite growing fatigue. It is a very difficult task. As indicated by Brisswalter and Fougeron [3], race walkers are able to follow their individual walking scheme only if energy cost is kept at a similar level. That is why the necessity of developing a high level of efficiency and effective individual movement technique, as well as resistance to organism fatigue, as sports success factors is frequently highlighted in works concerning the analysis of race walking [4,5,6,7]. The forerunners in measuring the energetics of race walking from a biomechanical perspective were Cavagna and Franzetti [8] as well as Marchetti et al. [9]. On an experimental basis, the authors calculated changes in the mechanical energy of the CoM in a gait cycle for different movement speeds. However, as indicated by Pavei et al. [10] in their survey work, there exist few reliable studies concerning the issue of gait energetics. Although many authors [4,5,6] stress that analysis of gait energetics, on an equal level with assessment of the mechanical power of the lower limbs and their efficiency, is the key element of gait technique analysis which can influence coaching in race walking, the level of studies differs, and they include many drawbacks affecting their value. Pavei et al. [10] point to some key deficiencies in current studies. They comprise testing athletes at training speeds, much lower than racing ones, and an insufficient number of cases and steps (often only 1 step was analyzed). Preatoni et al. [11] suggest that only an analysis of at least 15 steps allows a reliable description of the biomechanical variables of race walking. However, there are no works that truly illustrate the influence of long-term race walking training directed at the optimization of individual technique and maintenance of a high level of organism efficiency on energy expenditure during walking. A reaction to the lack of knowledge in this research field was an experiment whose initial results were presented by Chwała et al. [7]. The current study extends the presentation of the findings from the referenced work and includes a comparison of the outcomes from the first and second phases of research following a year of training designed to enhance individual techniques of race walking. The authors of the work assessed the level of energy expenditure in elite race walkers moving at technical, threshold, and racing speeds. Next, after identifying individual technical errors, the principles of walker movement technique correction were elaborated. After the year-long implementation of individual guidelines of technique optimization, energy expenditure was assessed again using physiological and biomechanical methods. The outcome of the research was to obtain the comparative material demonstrated in the present work.

The aim of this work was to determine the influence of a year’s training directed at optimization of individual technique and maintenance of high efficiency of movement on the level of energy expenditure assessed by physiological and biomechanical methods in elite race walkers. Assessment of the influence of gait biomechanical correction on the amount of physiological cost and total energy was analyzed based on race technique gait at threshold, racing, and technical speeds.

A reduction in the amount of energy expended on work directly decreases the muscles’ demand for oxygen, which results in a decreased proportion of anaerobic metabolism without affecting the intensity of the exercise. Thus, the athlete can move faster consuming the same volume of oxygen [7]. It was assumed that correction of movement technique aiming at elimination of identified errors and its optimization regarding somatic build would have a positive effect on lowering the exercise’s physiological cost.

2. Materials and Methods

2.1. Characteristics of the Participants

The study involved 12 elite race walkers who had achieved either championship or international level. The average age of the participants was 24.9 years in the first study and 25.9 years in the second study. The participants had an average height of 1.80 m and a body weight of 69 kg in the first study, and 1.81 m and 68.9 kg in the second study, respectively. Detailed characteristics, including BMI, can be found in

Table 1. Characteristics of the subjects.

N = 12	Age [Years]	Body Height [m]	Body Mass [kg]	BMI [kgm−2]
I study	24.9 ± 4.10	1.80 ± 0.68	69 ± 7.06	21.29 ± 1.81
II study	25.9 ± 4.10	1.81 ± 0.76	68.9 ± 0.8	21.03 ± 1.26

. The training experience of the participants ranged from 6 to 20 years. Some of the participants were finalists in the Olympic Games and World Championships, and one was a European Championship medalist. Inclusion criteria included being an elite-level race walker with a minimum of 6 years of training experience and having competed at the national or international level. Exclusion criteria were any current injuries or medical conditions that could affect performance or participation in the study and failure to complete the full year of prescribed training.

2.2. Experimental Procedure

The research was conducted twice: once in the initial period and again after a year of training aimed at optimizing individual race walking technique. Before the first main stage of the research, the aerobic capacity of each participant was determined, specifically maximum oxygen uptake (VO₂max) and anaerobic threshold, using a direct method involving an incremental exercise test on a treadmill. The same procedure was repeated after a year of targeted training to assess the impact of the training on the physiological cost and total external work. The research method for both stages of the experiment and the results of the first stage were precisely described in the work of Chwała et al. [7]. The results of the second stage of the experiment are presented in this paper.

2.3. Measurement Methods

The energy cost for the race walkers was determined at three different speeds: technical speed (vt), threshold speed (vp), and racing speed (vs). Before the 8-min test, participants performed a warm-up session including gradual speed progression to adapt to the test conditions. Data were collected during the entire 8-min walking period at each speed. The energy cost was determined using an indirect method based on the net oxygen uptake for each of the analyzed walking speeds and immediately after their completion until the total oxygen debt was repaid. The caloric equivalent used was selected based on the current ratio of the volume of exhaled carbon dioxide to the volume of oxygen uptake (RER).

Gait analysis was recorded using the Vicon Oxford Metrics system (Vicon, Oxford Metrics Ltd., Oxford, UK) with Nexus software, version 2.10.3. The system operated at a frequency of 120 frames per second, utilizing passive reflective markers placed on the participants’ skin according to the Golem model, allowing for precise 3D motion capture and biomechanical analysis. Changes in mechanical energy corresponding to the total external energy were calculated using the methods described by Cavagna et al. [12], Minetti et al. [13], and Schepens et al. [14]. Average energy changes were standardized per kilogram of body weight and per meter of distance traveled.

In the next step, the average changes energy were standardized by kilogram of body mass and one meter of distance covered.

Body mass was determined with the electronic scale “Tanita” Body Composition Analyzer (Tanita Corporation, Tokyo, Japan), and body height was measured using an anthropometer with an accuracy of 0.01 m. All exercise tests were performed on the treadmill, “Cardionics” model 2113 (Cardionics Inc., Webster, TX, USA). The respiratory system parameters were recorded by a portable ergospirometer Start-2000-M made by MES (Kraków, Poland). The participants’ heart rate was recorded by a recording device connected to the ergospirometer.

2.4. Training Description

The training conducted over the year was typical for the preparatory period, characterized by a high volume of aerobic and mixed work, with participants covering 140–180 km per week. After the first stage of the study, based on analyses of exhaled gases and 3D movement technique, individual schemes of race walking were elaborated for individual competitors as well as physiological EE and biomechanical (total energy) ΔEc costs adequate for them. During the analyzed annual cycle, the athletes completed a weekly training amount ranging from 80 to 180 km, depending on their performance level and training period. The amount of training was primarily aimed at improving walking economy, while mixed-intensity methods (aerobic–anaerobic) were used to increase the threshold speed in walking. The obtained information was then analyzed and used to detect errors in competitors’ individual technique. Next, a model of changes in technique was elaborated for each competitor that was to change the movement pattern through optimization of step length ratio (following the index suggested by Murray et al. [15]) and frequency of steps in relation to actual walking speed at accepted values of vertical and side oscillations of the center of mass and duration of the swing phase. An individual competitor model was directed at saving energy and the symmetrical work of both lower and upper limbs as well as the pelvis.

Additionally, the training focused on strengthening the extensors of the hip joint, whose importance was highlighted by Hoga et al. [5], and knee joint flexors, whose activity in the initial stage of the support phase controls extension of the knee joint, as indicated by Hanley and Bissas [6]. Subsequent muscles included in the strengthening work and coordination exercises in the year-long training were plantar and dorsal flexors, which control the support phase of the limb. Their significant role in shaping a proper scheme of race walking was underlined by Hoga et al. [5]. An important aspect of the training was the wide range of exercises coordinating the work of all the joints in the lower limbs with the work of pelvis, trunk, and upper limbs in order to obtain the best effects of energy transfer between body segments and to shape the driving force properly.

After the year’s training, identical control measurements were taken using the same measurement tools.

2.5. Statistical Analysis

To detect statistically significant differences between the energy cost and total external work of the muscles at different walking speeds, a repeated measures ANOVA and Tukey’s post hoc test were applied. The differences between the average values of energy cost and total external work were tested for significance using one-way analysis of variance, with walking speed as the dependent variable. Tukey’s post hoc test was used to identify statistically significant variable pairs in both analyses.

3. Results

The results of the study are presented in

Table 2. Average values of physiological parameters during maximal exercise.

VO2max mL·kg−1·min−1	VO2max L·min−1	VEmax L·min−1	HRmax sk·min−1
67.4 ± 7.25	4.7 ± 0.81	139.7 ± 18.8	186 ± 10.7

VO₂max—maximum oxygen uptake. VEmax—maximal lung ventilation. HRmax—maximal heart rate.

and

Table 3. The average energy cost of walking at technical (v_t), threshold (v_p), and racing (v_s) speeds.

V m·s−1	Study	Energy Cost of EE Effort
		kJ·min−1	J·m−1	J·m−1·kg−1
vt = 3.1 ± 0.19	I	51.2 ± 10.05	267 ± 52.3	3.86 ± 0.55
	II	48.38 ± 6.04	252 ± 31.5	3.68 ± 0.42
vp = 3.7 ± 0.13	I	69.2 ± 11.04	312 ± 49.7	4.51 ± 0.51
	II	65.72 ± 7.90 vt − vp ***	296 ± 35.6 vt − vp ***	4.30 ± 0.21 vt − vp ***
vs = 4.0 ± 0.14	I	78.3 ± 13.01	326 ± 54.2	4.74 ± 0.62
	II	74.42 ± 6.34 vt − vs ****	310 ± 26.4 vt − vs ****	4.37 ± 0.27 vt − vs **** I − II p = 0.058

*** p < 0.005. **** p < 0.001.

, as well as in Figure 1, Figure 2 and Figure 3, which detail the changes in energy cost and total external work across different walking speeds before and after the year of training. In the incremental exercise test, participants achieved an average maximum oxygen uptake (VO₂max) of 67.4 ± 7.25 mL·kg⁻¹·min⁻¹, a value typical for elite endurance athletes capable of competing at the international level. The corresponding maximal lung ventilation and heart rate were 139.7 ± 18.8 L·min⁻¹ and 186 ± 10.7 beats per minute, respectively. These physiological parameters reflect the high aerobic capacity of the participants, which is crucial for sustained performance in race walking.

Table 2 presents the average physiological parameters during maximal exercise, illustrating the VO₂max, maximal lung ventilation (VEmax), and maximal heart rate (HRmax) achieved by the participants during the incremental exercise test.

Table 3 presents the average energy cost at the three studied walking speeds: technical speed (vt), threshold speed (vp), and racing speed (vs). It can be observed that the energy cost decreased at all speeds after the year of targeted training. Specifically, at technical speed the energy cost decreased by 5.9%, and at racing speed there was a notable 8% reduction, which approached statistical significance (p = 0.058). These reductions suggest improved efficiency in the participants’ movement following the training period.

Table 3 provides detailed data on the energy cost of walking at technical, threshold, and racing speeds, highlighting the reductions observed after the year of training.

Table 4. Average values of ΔEp, ΔEk, and total external work ΔEc (the biomechanical cost of walking) at technical (vt), threshold (vp), and racing (vs) speeds.

V m·s−1	Measurement	Biomechanical Cost of Exercise “Total Energy”
		ΔEp kJ·min−1	ΔEk kJ·min−1	ΔEc kJ·min−1	ΔEp_std J·m−1	ΔEk_std J·m−1	ΔEc_std J·m−1	ΔEp_std J·m−1·kg−1	ΔEk_std J·m−1·kg−1	ΔEc_std J·m−1·kg−1
vt = 3.1 ± 0.19	I	4.25 ± 0.39	8.91 ± 1.08	13.16 ± 1.14	22.1 ± 2.24	46.4 ± 5.18	68.6 ± 7.01	0.32 ± 0.04	0.67 ± 0.07	0.99 ± 0.11
	II	4.28 ± 0.37	9.18 ± 0.88 vt − vp *** vt − vs ****	13.46 ± 1.15 vt − vp * vt − vs ****	22.9 ± 2.38	47.9 ± 4.69 vt − vp *** vt − vs ****	70.9 ± 6.23 vt − vp * vt − vs ****	0.33 ± 0.04	0.69 ± 0.08 vt − vp *** vt − vs ****	1.02 ± 0.09 vt − vp * vt − vs ****
vp = 3.7 ± 0.13	I	4.93 ± 0.48	13.05 ± 1.52	18.00 ± 1.79	22.3 ± 2.11	58.8 ± 6.89	81.1 ± 7.47	0.32 ± 0.04	0.85 ± 0.12	1.17 ± 0.10
	II	4.48 ± 0.49	11.85 ± 1.42 vp − vs ***	16.33 ± 1.79	20.2 ± 2.06	53.4 ± 6.12 vp − vs ***	73.6 ± 7.85	0.29 ± 0.04	0.77 ± 0.09 vp − vs ***	1.06 ± 0.10
vs = 4.0 ± 0.14	I	5.14 ± 0.48	16.19 ± 1.57	21.33 ± 2.01	21.4 ± 1.99	67.4 ± 6.32	88.9 ± 8.18	0.31 ± 0.03	0.98 ± 0.12	1.29 ± 0.13
	II	4.50 ± 0.32 I − II *	13.39 ± 1.61 I − II *	17.69 ± 1.98 I − II *	17.9 ± 2.06 I − II *	55.8 ± 622 I − II *	73.7 ± 7.69 I − II **	0.26 ± 0.04 I − II *	0.81 ± 0.09 I − II *	1.07 ± 0.10 I − II *

* p < 0.05. ** p < 0.01. *** p < 0.005. **** p < 0.001.

and Figure 1, Figure 2 and Figure 3 provide a detailed breakdown of the biomechanical costs associated with walking at different speeds. The biomechanical cost, represented as total external work (ΔEc), also showed a significant decrease, particularly at racing speed. The reduction in total external work was largely driven by changes in kinetic energy (ΔEk), which decreased significantly (p < 0.05) after the training, as illustrated in Figure 2 and Figure 3. This indicates that the training led to more efficient energy transfer during the gait cycle, particularly during the faster, more demanding phases of race walking.

Table 4 details the biomechanical costs of exercise, including potential energy (ΔEp), kinetic energy (ΔEk), and total external work (ΔEc), for each walking speed. This table provides a comprehensive overview of how these variables were affected by the year of training.

Figure 1, Figure 2 and Figure 3 visually depict the changes in potential energy (ΔEp), kinetic energy (ΔEk), and total external work (ΔEc) across the different walking speeds. The most significant improvements were observed at racing speed, where the reduction in kinetic energy changes indicates more effective control during the contact phase, leading to a smoother and more energy-efficient gait.

4. Discussion

Biomechanical analyses of gait energetics have so far developed in two directions. The first, that relies on applying the method of inverted dynamics, was used in their research methodology by Hoga et al. [4,5]. Although the obtained results seem to be interesting, especially in the context of intersegmental energy flow description, employment of 2D analysis decreases the value of the presented observations due to its low precision.

On the basis of the value of muscle force moments, Hanley and Bissas [6] estimated the net level of energy produced by muscles in joints and determined directions of intersegmental energy flows. According to their assessment, the lower limbs generated approx. 16 J of net energy in a gait cycle. A drawback of the elaboration seems to be a lack of standardization of the obtained results, which eliminates the influence of body build on the obtained results. It led to a significant dispersion of individual results around the mean values of variables.

Another direction of studies that originates from the description of physiological gait [12,13] and which was subsequently developed in research on physiological and race walking by Chwała [16], has been applied in the methodology of the present work. It comprises assessment of the total external work needed to accelerate and lift the center of mass in a gait cycle on the basis of momentary values of changes in CoM mechanical energy components. The analysis included a 14-element kinematic body model [17], and data were collected based on 3D analysis with the use of the Vicon Oxford Metrics system. The research method was precisely characterized by Chwała et al. [7], describing results of the initial stage of the research.

Effective use of the whole body mechanical energy is based on generating mechanical energy by muscles working in joints and its recovery in the form of forced energy, mutual two-direction transformation of kinetic energy into potential energy, as well as mechanical energy flow between body segments in joints and between limbs [5,16,18]. As indicated by Chwała [16], due to a slight time displacement of the curves of translational motion kinetic energy and CoM potential energy changes, energy recovery compared to physiological gait reaches a low level of approx. 10–15%, depending on race walking velocity. Therefore, the standardized value of the changes in total energy used to lift, accelerate, and slow down the CoM movements in a normalized gait cycle seems to be a reliable measure of the efficiency of race technique realized in interaction with the external force of gravity.

The energy expenditure calculated using physiological methods additionally takes into account energy demand resulting from the necessity to maintain a vertical posture, muscle co-contractions, etc. [19] Application of both methods simultaneously in the assessment of race walking efficiency provides clear benefits like a deep analysis of energy processes that can significantly add to competition success. As was demonstrated, one of the important factors affecting the level of EE is efficiency of individual gait technique realized at racing speed [7]. This can be improved by long-term training that increases the level of motor abilities and is directed at eliminating technical errors and optimizing technique considering the athlete’s build. An energy-consuming gait can result from technical imperfections, such as excessive oscillation of the center of gravity and high asymmetry in limb work, manifested by a lack of step length optimization relative to the athlete’s somatic build. The reduction in energy cost and total external work results from improved metabolic and biomechanical efficiency, including optimizing gait technique, reducing center of mass oscillations, and better utilization of kinetic and potential energy, leading to more economical movement [20,21]. Technique improvement, aimed at minimizing limb asymmetry and reducing unnecessary vertical movements, directly contributes to lowering energy costs, which is crucial for achieving better athletic performance [22].

The one year of training implemented in a group of 12 elite race walkers resulted in lowering the level of total external work, particularly visible during walking at racing speed. However, it is important to note that each athlete followed a slightly different training regimen, tailored to their individual needs and technical errors identified in the initial analysis. The most relevant technical changes observed were a reduction in center of mass oscillations and improved limb symmetry. While all athletes received corrections, the extent of errors varied, influencing the magnitude of the improvements [7].

The inner group variance analysis performed to determine the significance of statistical differences of energy cost (EE) in the second test during walking at different speeds indicated in the post hoc analysis that there were pairs of variables with statistically significant differences. The differences were registered between mean values of energy cost for vt and vp (p < 0.005), indicating a 36% growth in energy cost when accelerating the body from technical to threshold speeds and a 54% growth when accelerating from vt to vs (p < 0.001). However, mean values of EE did not differ significantly between vp and vs (p < 0.05).

ANOVA for reproducible measurements indicated that the mean energy cost (EE) of walking at vt speed calculated per minute of work in the second test was lower by 2.9 kJ·min⁻¹ on average, which in comparison to the first test amounted to a 5.9% difference. However, mean cost values for technical speed did not differ significantly between tests (p < 0.05). Analogous values of EE standardized by meter of covered distance and body mass were lower in the second test (from 4.9 to 5.9%). Differences between mean values were clear but statistically insignificant.

The results for walking at threshold speed present similar features. Lowering of EE calculated per minute of work by 3.5 kJ·min⁻¹ on average was observed in the second test, which meant a difference at the level of 5.3%. Although the registered differences were remarkable, they turned out to be statistically insignificant (p < 0.05). Energy cost standardized by meter of distance and body mass was lower by 5.4% and 4.9%, respectively, but did not differ significantly between the first and second tests.

The lowest values of energy cost in the second test, calculated per minute of work at 3.9 kJ·min⁻¹ in relation to the first test, were registered for walking at racing speed. Energy cost per meter of covered distance was lower by 5.2% but statistically insignificant (p < 0.05), whereas EE calculated per meter of distance and kilogram of body mass decreased by 8.5% and was on the borderline of statistical significance (p = 0.058). To sum up, it should be stated that the energy cost of EE recorded in the second study was several percent lower for individual walking speeds compared to the results of the first study [7], but the observed contrasts turned out to be statistically insignificant.

Recapitulating, it should be stated that the highest, over 8%, energy savings, measured in units taking into account body build and gait kinetics, were recorded during walking at racing speed, but the obtained differences were not clearly confirmed in the statistical analysis performed.

The inner group variance analysis within the range comparing mean values of total work realized at accelerating and lifting CoM during walking at technical, threshold, and racing speeds indicated in post hoc testing the occurrence of significant contrasts. Similarly, as for physiological cost, a higher statistically significant growth (p < 0.05) was recorded for the mean value of ΔEc, expressed in kJ·min⁻¹ between technical and threshold speeds (21% difference) than between threshold and racing speeds (8% difference) (NS). Also, statistically significant differences were observed between the mean values of total external work of muscles expressed in kJ·min⁻¹ at 31% for speeds vt and vs (p < 0.001). Total external work increased the cost by approx. 4.2 kJ·min⁻¹ when the speed was increased from technical to racing. Similar observations were made from comparisons of total external work standardized by meter of covered distance and body mass of the athletes examined.

Comparing changes of ΔEp values calculated per minute of work, it should be stated that no significant contrasts were found between mean values of pairs of variables for individual gait speeds. In the case of kinetic energy (ΔEk) changes, significant differences were recorded in post hoc testing between vt and vp (p < 0.005) and vt and vs (p < 0.001) as well as vp and vs (p < 0.05). When speed value was increased from technical to racing speed, a mean growth of ΔEp of 5% was observed and of ΔEk approx. 46%. This proves a much higher influence of the changes in ΔEk than in ΔEp on the level of total external work in body acceleration. From a biomechanical perspective, key changes include reducing oscillations of the center of mass and improving energy transfer between body segments, which limits unnecessary energy losses during the deceleration and acceleration phases of gait [23].

This subsequently indicates the key importance of changes in speed of CoM displacement in the phase of foot contact with the ground. Side and vertical components of movement speed in athletes reach much lower values than the front–back component. Efficient work by a competitor during the contact phase after achieving racing speed should rely on minimizing a decrease of the speed component in the absorption phase, when the negative (opposite to the movement direction) phase of the front–back component of the ground reaction force takes place. A slight reduction in speed in the absorption phase will result in saving energy in the propulsion, bounce phase. The phase of foot contact with the ground should be smooth to ensure the lowest changes of kinetic energy. Effective utilization of kinetic and potential energy during the foot–ground contact phase reduces total external work, leading to more economical movement and less muscle fatigue [20].

The variance analysis of reproducible measurements showed statistically significant differences for ΔEc calculated only for racing speed. Mean values of ΔEc differed statistically in both tests by approx. 3.6 kJ·min⁻¹ (p < 0.01). This reflected a lowering of the cost in the second test by approx. 20% in relation to the work results [7]. Mean values of ΔEc for technical speed were very close in both tests, whereas analogous mean values for threshold speed were lower in the second test by approx. 10%, but the differences were not significant (p < 0.05). Similar dependencies were observed for variables standardized by meter of covered distance and athletes’ body mass. Also, mean values of ΔEp and ΔEk during walking at racing speed were significantly lower in the second test, respectively by approx. 0.6 kJ·min⁻¹ and 2.8 kJ·min⁻¹ (p < 0.05). This reflected a lowering of ΔEp by approx. 14% and of ΔEk by approx. 20% compared with the first test [7].

The statistical analysis performed also indicates that the physiological cost EE and total external work ΔEc, expressed in kJ·m⁻¹, differed significantly in mean values for all gait speeds at the level of p < 0.001.

4.1. Limitations of the Study

One of the primary limitations of this study is the relatively small sample size, consisting of only 12 elite race walkers, all of whom were male. This gender-specific sample limits the generalizability of the findings to female athletes. Future research should aim to include female race walkers to determine if similar biomechanical and physiological adaptations occur across genders. Although the participants were highly specialized athletes, the limited number of subjects may restrict the generalizability of the findings to a broader population. Additionally, the study focused exclusively on elite male race walkers, which limits the applicability of the results to female athletes or those at different levels of experience. The study also used a controlled laboratory environment, which may not fully replicate the dynamic and variable conditions experienced during actual competition. Another limitation is the reliance on indirect methods for measuring energy cost, which, while widely accepted, may not capture all aspects of physiological and biomechanical efficiency.

4.2. Directions for Future Research

Future research should aim to include a larger and more diverse sample of athletes, including female race walkers and those at various competitive levels, to enhance the generalizability of the findings. Studies should also consider longitudinal designs that track athletes over multiple training cycles to better understand the long-term effects of biomechanical and physiological optimizations. Additionally, incorporating real-world competition conditions into the research, rather than relying solely on laboratory-based assessments, could provide more ecologically valid results. Future studies might also explore the impact of different types of training interventions, such as strength training or flexibility exercises, on the energy cost and biomechanical efficiency of race walking.

5. Conclusions

• The highest decrease in energy cost influenced by a year’s training directed at optimization of individual movement technique was found at a level above 8% in a group of competitors moving at racing speed. However, it is important to consider that the study’s conclusions are drawn from a small but highly specialized group of athletes, which may not fully represent broader populations. Acknowledging this, the results highlight significant benefits of targeted technical adjustments and tailored training regimens, but further studies with diverse samples are needed. For other gait speeds, EE was lower by approx. 4–6%.
• Clear, statistically significant lowering of the total external work level was found when competitors walked at racing speed, compared to the results of the first study.
• Changes of potential energy were characterized by a slight growth influenced by increasing velocity of gait in both tests. The lower level for racing speed was recorded in the second test.
• Changes of kinetic energy turned out to be of key importance for the level of total external work. Its values increased by approx. 30% at body acceleration from technical to racing speeds. A clear, statistically significant decrease of this component energy value in the second test during walking at racing speed was registered, which indicates a distinct improvement of contact phase efficiency.
• Year-long training directed at optimization of individual gait technique resulted in notable benefits for elite race walkers, both in lowering energy cost and in total external work, particularly when moving at racing speed.

Practical Applications

The findings of this study can be directly applied to training programs to enhance performance and efficiency in race walking. Coaches and athletes can incorporate the following strategies:

• Optimization of Walking Technique: Regular motion analysis sessions using tools like the Vicon system can help identify technical errors such as excessive center of mass oscillations and limb asymmetry. Coaches should focus on correcting these errors with targeted exercises that improve stability and symmetry.
• Strengthening Key Muscle Groups: Incorporating exercises to strengthen hip extensors, knee flexors, and muscles controlling the support phase, such as plantar and dorsal flexors, should be a key component of training. These exercises will improve muscle coordination and enhance energy utilization during walking.
• Individualized Training Plans: Training programs should be personalized based on the athlete’s specific biomechanical and physiological needs, including regular testing to monitor progress and adjust the training regimen accordingly.
• Monitoring Fatigue and Recovery: Coaches should carefully monitor the intensity and volume of training, particularly during high-intensity sessions, to prevent excessive muscle fatigue. Proper rest periods and recovery protocols can help maintain optimal walking technique.
• Use of Technology in Training: Utilizing advanced tools such as 3D motion analysis, EMG monitors, and portable ergospirometers allows for precise assessment of performance and technique, enabling more tailored adjustments to training programs to improve energy efficiency.