Neuromuscular Strategies in Novice and Advanced Taekwondo Athletes During Consecutive Roundhouse Kicks

Abstract

Background: This study investigates differences in muscle co-contraction and peak electromyography (EMG) activity between novice and advanced Taekwondo athletes during consecutive roundhouse (bandal chagui) kicks, examining the influence of body composition and experience level. Methods: Sixteen Taekwondo athletes (12 males, 4 females; mean age: 20.5 ± 4.3 years) were divided into novice (n = 8) and advanced (n = 8) groups. Muscle co-contraction indices and peak EMG activity across 15 consecutive kicks were assessed in key lower limb muscles, including the biceps femoris (BF), lateral gastrocnemius (LG), rectus femoris (RF), soleus (SO), semitendinosus (ST), tibialis anterior (TA), vastus lateralis (VL), and vastus medialis (VM). Results: Advanced athletes exhibited significantly higher co-contraction indices in BF–RF, VM–BF, and SO–TA pairs (p < 0.05) and increased peak EMG trends in the BF and LG (p < 0.05). Novice athletes showed significantly reduced peak EMG increases in the RF, VM, and VL. EMG trends were influenced by body composition, with principal component analysis indicating that higher fat mass and lower muscle mass were associated with greater variations in muscle activation. Conclusions: These findings suggest that advanced athletes refine motor control through increased co-contraction, improving stability and efficiency, while novices exhibit less optimized coordination patterns. This study provides insights into the neuromechanical adaptations associated with expertise development in Taekwondo.

1. Introduction

Originating in Korea, Taekwondo has grown into one of the most widely practiced martial arts worldwide and gained official Olympic status at the Sydney 2000 Games after being showcased as a demonstration sport in Seoul 1988 and Barcelona 1992 [1,2]. There are more than fifty thousand taekwondo practitioners spread over 206 nations, being mostly practiced by men (60%) [2]. In competitive settings, kicking actions are fundamental for scoring points and achieving overall sports performance [3]. The effectiveness of these techniques is influenced by multiple factors, including muscle activation, lower limb segment velocity, core stability, and fatigue resistance [4,5]. Specifically, performance relies on the rapid, phase-specific activation of key muscles (such as the quadriceps, hamstrings, and core stabilizers) alongside the selective relaxation of antagonists like the gluteus maximus, enhancing segmental velocity, accuracy, and impact force [6]. Additionally, cognitive processes, such as motor inhibition, further contribute to performance outcomes [7,8,9].

Biomechanical analyses have been conducted to elucidate differences in performance based on the competitive level of Taekwondo athletes. This scope explores differences between sub-elite and elite athletes [10,11] and between medalists and non-medalists [12]. Previous studies have demonstrated a medium effect of Taekwondo athletes’ experience level (novice vs. advanced) on muscle activity (d = 0.54; p < 0.05) [11], as well as in execution time and impact force between novice and advanced Taekwondo athletes [13]. These findings underscore the importance of such analyses for understanding the mechanisms that distinguish successful athletes. However, limited understanding remains of how these differences manifest during the early and advanced stages of technical skill development.

Regarding biomechanical analysis and, more specifically, muscle activity, the current literature has documented the use of surface electromyography (sEMG) to examine muscle activation patterns during Taekwondo techniques. This approach provides a more profound understanding of muscle function across different phases of movement [8,11,14,15]. In this context, co-contraction represents a crucial mechanism for joint stabilization during high-impact actions [16] and for the precise modulation of movement accuracy [17]. Increased co-contraction reflects greater antagonist resistance to the intended motion during each type of kick performed.

Previous studies on Taekwondo athletes have demonstrated that co-contraction indices (a measure of simultaneous muscle activation) during roundhouse (bandal chagui) and Dollyo Chagui kicks vary significantly depending on the athletes’ experience level or competitive category [6,11]. Therefore, muscle co-contraction is expected to vary according to experience level, as novice athletes tend to exhibit higher levels of co-contraction as a compensatory strategy to enhance joint stability. In contrast, experienced athletes modulate co-contraction more selectively, optimizing movement efficiency and performance during fast and dynamic tasks such as kicking in Taekwondo [6,11,18]. Specifically at the hip joint, a large effect size has been reported for the difference in co-contraction between the gluteus maximus and the tensor fasciae in sub-elite athletes (d = 1.07; p < 0.05) [16]. These findings may explain their reduced hip internal rotation velocity and, consequently, lower performance during kick execution. In contrast, a large effect size has been reported for the co-contraction indices of the muscles responsible for knee extension, with elite athletes showing higher values (d = 1.02; p < 0.05) [11]. Increased co-activation of the rectus femoris and biceps femoris is considered a protective mechanism for knee stability [6,18]. Concerning the ankle, no studies have been identified that specifically examine co-contraction in Taekwondo athletes according to their experience level.

From what has been presented in the previous paragraphs, it is evident that very little literature exists on co-contraction in Taekwondo athletes as a function of experience level or competitive category [6,11], with no studies existing on the ankle in the antagonist pair of the soleus and tibialis anterior. This joint, along with the foot, plays a fundamental role in executing kicks in Taekwondo, serving as the primary point of contact with the opponent and enabling efficient force transfer generated by the leg muscles [16]. Furthermore, additional considerations must be addressed. Firstly, analyzing combat sports requires specific methodological considerations due to their explosive nature. In this regard, careful attention must be paid to sEMG challenges, such as movement artifacts, signal processing accuracy, and factors like skin impedance, adipose tissue, and body composition, all of which can impact signal reliability and quality [19,20]. Low-fat-mass percentages, adequate muscle mass, and the relationship between body composition and inter-limb asymmetry are significantly associated with performance in male and female Taekwondo athletes [21,22]; therefore, these variables should be considered in sEMG analyses.

Another important aspect is that most previous studies focused on reducing motor gesture variability. Aggeloussis et al. [23] suggested that more than 10 repetitions of kicks should be assessed to obtain reliable data on muscle function. Ervilha et al. [8] analyzed 20 consecutive kicks but reconstructed the sEMG signal by averaging them. Similarly, Valdés-Badilla et al. [14] used 15 kicks and reconstructed the signal by interpolation based on the movement cycle. These studies often relied on averaged or reconstructed sEMG signals from consecutive kicks without thoroughly examining trends in peak EMG activity [8,14]. Quinzi et al. [18] were the first to study the effect of repeated kicking actions on the performance of the roundhouse kick in karate. No studies on sEMG have been found that seek to identify changes in peak EMG trends during consecutive kicks in Taekwondo athletes.

In this context and given the lack of research on changes in peak EMG activity during consecutive kicks in Taekwondo athletes, the present study evaluated differences in muscle co-contraction and peak EMG activity between novice and advanced Taekwondo athletes during consecutive roundhouse (bandal chagui) kicks. It also aimed to explore the influence of body composition and experience level on these parameters.

2. Materials and Methods

2.1. Study Design

A comparative, cross-sectional design with a quantitative approach and single-blind evaluation (evaluators) was employed. This design compares two groups (novice vs. advanced Taekwondo athletes) to measure differences in muscle co-contraction and peak EMG activity during consecutive roundhouse (bandal chagui) kicks in Taekwondo. Additionally, it examined the impact of variables such as body composition and experience level on these measurements. Assessments were conducted in an 80 min session, which all participants completed.

2.2. Participants

Sixteen Taekwondo athletes (twelve male and four female), aged between 18 and 28 years (mean age: 21.50 ± 3.28 years), participated in the study. The sample was conveniently recruited from the University Taekwondo group. All participants were active competitors and met the following inclusion criteria: (i) a minimum of one year of Taekwondo practice; (ii) training at least three times per week; (iii) participation in national tournaments organized by the National Taekwondo Sports Federation (FEDENAT, Santiago, Chile), a body recognized by World Taekwondo; (iv) enrollment in a club affiliated with FEDENAT. Athletes were excluded if they had any acute or rehabilitation-phase injury or illness that could affect their physical performance or if there were issues with sEMG signal capture. Based on their years of practice, participants were categorized into novice (six male and two female) and advanced (six male and two female) groups. Novice athletes were defined as those with fewer than three years of Taekwondo practice, while advanced athletes had three or more years of experience.

All participants were required to comply with the criteria regarding the use and management of data by signing an informed consent document, thereby providing permission for their information to be used for scientific purposes. The research protocol was approved by the Research Ethics Committee of the Universidad Autónoma de Chile (N°080-16) and was conducted in accordance with the principles outlined in the Declaration of Helsinki for research involving human subjects.

2.3. Surface Electromyography (sEMG) Acquisition

sEMG signals were recorded from the biceps femoris (BF), semitendinosus (ST), rectus femoris (RF), vastus medialis (VM), vastus lateralis (VL), lateral gastrocnemius (LG), soleus (SO), and tibialis anterior (TA) muscles using a 16-channel Bagnoli surface electromyograph (Delsys Inc., Boston, MA, USA). Before electrode attachment, the skin was prepared by shaving and cleaning with alcohol. Bipolar electrodes (Ag/AgCl) were placed on the dominant leg following the guidelines outlined by SENIAM [24]. The measurements were taken as follows: BF, 50% of the line between the ischial tuberosity and lateral femoral epicondyle; ST, 50% of the line connecting the medial tibial epicondyle and ischial tuberosity; RF, 50% of the line between the ASIS and superior patella; VM, 4 cm medial and superior to the patella’s medial border; VL, 2/3 of the line between the ASIS and lateral patella; LG, 1/3 of the line between the fibular head and Achilles tendon on the lateral belly; SO, midway between the fibular head and Achilles tendon, medial to the leg; and TA, 1/3 of the line between the tibial tuberosity and medial malleolus lateral to the tibia (Figure 1). Signal acquisition was performed using the EMGworks software version 4.0.13 (Delsys Inc., Boston, USA) at a sampling frequency of 4000 Hz and a gain of 1000. The sEMG assessment involved performing the maximum voluntary isometric contraction (MVIC), as recommended by SENIAM, with 5 s of isometric contraction, three repetitions, and 30 s of rest.

2.4. Body Composition Measurement

Body composition was assessed following the guidelines of the International Society for the Advancement of Kinanthropometry (ISAK) by a certified Level II anthropometrist (technical measurement error: 0.8% across all variables) in accordance with ISAK standards [25]. Body weight was measured using an electronic scale (Scale-tronix, Skaneateles Falls, NY, USA; accuracy of 0.1 kg), and standing height was recorded with a stadiometer (Seca 220, GmbH, Hamburg, Germany; accuracy of 0.1 cm). Skinfold thickness was assessed using a skinfold caliper (Harpenden, UK; accuracy of 0.2 mm), body circumferences were measured with a tape measure (Seca 201, GmbH, Hamburg, Germany; accuracy of 0.1 cm), and bone diameters were determined using anthropometers (Rosscraft, Surrey, BC, Canada; accuracy of 0.1 mm). Body mass index (BMI) was calculated as body weight divided by height squared (kg/m²). Fat mass and muscle mass were estimated using the indirect pentacompartmental fractionation method [26].

2.5. Procedure

Each Taekwondo athlete was individually evaluated at the Movement Analysis Laboratory of the Sports and Health Centre at the Universidad Autónoma de Chile. The process began with an individual interview, followed by anthropometric measurements and an sEMG assessment. The warm-up, led by a high-ranking Taekwondo instructor and former national coach, included joint mobility, dynamic flexibility exercises with directional movements, technical drills without knee extension, and assisted attack and defense simulations. It concluded with three sets of 12 front kicks and roundhouse (bandal chagui) kicks on an impact shield. After a five-minute active rest (self-directed flexibility exercises), the athletes performed 15 maximum-intensity roundhouse (bandal chagui) kicks, striking an impact shield held by an expert who provided verbal encouragement to ensure optimal performance. Kicks were executed at a self-paced rhythm, with short pauses between each attempt to allow athletes to reset their stance and ensure proper execution.

The acquired sEMG signal was processed digitally using an algorithm implemented in Matlab R2016a software (MathWorks Inc., MA, USA). Initially, the signal was corrected by offset subtraction using a detrending function. The signal then underwent filtering with fourth-order low-pass and high-pass Butterworth filters, with cut-off frequencies set at 10 Hz and 400 Hz, following methodologies used in previous studies with Taekwondo athletes [14,19]. The root mean square (RMS) of the EMG signal was calculated using a sliding window method, with a window width of 20 samples and a one-sample overlap. The amplitude was normalized relative to the MVIC, and each electromyographic burst was segmented to determine the peak amplitude for each muscle analyzed. The co-contraction index was calculated as follows [27]:

$$Co\text{-}contraction Index={\displaystyle \frac{{\sum }_{i=1}^N\mathrm{m}\mathrm{i}\mathrm{n}({EMG}_{agonist}\left(i\right),{EMG}_{antagonist}\left(i\right))}{{\sum }_{i=1}^N({EMG}_{agonist}\left(i\right)+{EMG}_{antagonist}\left(i\right))}}$$

The EMG_agonist(i) and EMG_antagonist(i) represent a specific repetition of the kick, denoted as i. N is the total number of repetitions considered in the analysis. A higher co-contraction index indicates greater simultaneous muscle activation.

2.6. Statistical Analysis

The analysis was performed using RStudio (version 2023.06.2). The Shapiro–Wilk test of normality was applied to assess the normal distribution of the dependent variables. The homogeneity of variances was verified using Levene’s test. Independent samples t-tests were initially performed to compare continuous variables between the groups, including weekly training hours, years of practice, mean peak EMG values, and body composition metrics. To examine the trends in peak EMG activity over a series of 15 continuous roundhouse (bandal chagui) kicks, linear regression analyses were conducted for each muscle. This approach allowed for the estimation of the slope and intercept for the EMG data. A mixed-effects model was employed to account for both fixed and random effects. Fixed effects included experience level, number of kicks, and a body composition component derived through principal component analysis (PCA), which combined fat mass and muscle mass into a single continuous variable. Given the strong inverse correlation between these two metrics, PCA was employed to reduce dimensionality and avoid multicollinearity. The resulting component scores (representing the relative balance between fat and muscle mass) were used as predictors in the mixed-effects models to examine their influence on EMG activity across different muscles. Random effects were incorporated to control for variability between individual participants and enhance the model’s robustness. The explained variance was calculated for the fixed effects alone and for the complete model, which combined both fixed and random effects. Marginal R² m and conditional R² c were estimated to quantify the proportion of variance explained by the fixed effects and by both fixed and random effects, respectively. Statistical significance was defined as p < 0.05.

3. Results

The advanced Taekwondo athlete group trained significantly more hours per week (12.3 ± 3.6) compared to the novice group (5.3 ± 0.8, p < 0.001; 95% CI: 4.58 to 15.27). Additionally, significant differences were noted in favor of the advanced group for the variable years of practice, with novice athletes showing 2.2 ± 0.3 years compared to 8.1 ± 2.9 years in advanced athletes (p < 0.001; 95% CI: 1.89 to 10.57). However, no significant differences were found between groups in mean peak EMG or body composition metrics.

Table 1. Independent samples t-tests between novice and advanced athletes.

Variable		Experience Level		95% CI Lower	95% CI Upper	t	p-Value
		Novice Mean ± SD	Advanced Mean ± SD
Mean peak EMG from 15 kicks	BF (%)	71.28 ± 21.59	85.80 ± 28.11	−36.25	7.22	−1.309	0.217
	ST (%)	75.06 ± 21.52	77.74 ± 13.26	−15.87	10.51	−0.399	0.697
	RF (%)	79.61 ± 29.41	63.61 ± 14.55	−7.69	39.68	1.324	0.225
	VM (%)	88.95 ± 17.01	74.29 ± 14.15	−13.74	43.06	1.012	0.341
	VL (%)	80.60 ± 23.26	90.93 ± 28.66	−40.42	19.75	−0.673	0.513
	LG (%)	82.92 ± 23.43	77.60 ± 18.60	−20.47	31.11	0.404	0.692
	SO (%)	101.38 ± 35.24	102.59 ± 30.67	−32.00	29.57	−0.077	0.939
	TA (%)	72.40 ± 25.05	70.58 ± 22.77	−12.43	16.07	0.250	0.806
Body composition	Weight (kg)	65.76 ± 168.31	68.53 ± 168.84	−13.48	7.96	−0.505	0.623
	Height (m)	23.24 ± 26.58	23.84 ± 29.75	−10.28	9.23	−0.105	0.918
	BMI (kg/m2)	44.82 ± 8.36	42.98 ± 13.02	−3.00	1.80	−0.489	0.633
	Adipose mass (%)	11.15 ± 2.30	8.59 ± 2.60	−9.02	2.68	−1.063	0.307
	Muscle mass (%)	5.12 ± 4.16	6.71 ± 5.71	−3.06	6.73	0.735	0.476

BF: biceps femoris; BMI: body mass index; ST: semitendinosus; RF: rectus femoris; LG: lateral gastrocnemius; SO: soleus; TA: tibialis anterior; VL: vastus lateralis; VM: vastus medialis.

provides descriptive statistics of the mean peak EMG across 15 roundhouse (bandal chagui) kicks for each muscle analyzed and body composition metrics for novice and advanced athletes. The results of independent samples t-tests for EMG data and body composition metrics are also presented.

An independent samples t-test revealed significant mean differences in muscle co-contraction for specific muscle pairs between novice and advanced Taekwondo athletes (Figure 2). The advanced group demonstrated greater co-contraction across the different muscle pairs studied. The BF–RF pair showed a mean co-contraction index of 0.046 ± 0.011 for the advanced group and 0.037 ± 0.010 for the novice group, with a t = 3.660, p = 0.001 (95% CI for advanced: 0.042 to 0.050; for novices: 0.034 to 0.039), and d = 0.907 (large effect). The VM–BF pair also showed a significant difference, with mean values of 0.042 ± 0.010 for the advanced group, 0.035 ± 0.009 for the novice group, t = 2.744, p = 0.012 (95% CI for advanced: 0.026 to 0.033; for novices: 0.025 to 0.030), and d = 0.727 (medium effect). The SO–TA pair exhibited a significant difference as well, with mean values of 0.037 ± 0.012 for the advanced group, 0.028 ± 0.010 for the novice group, t = 2.407, p = 0.025 (95% CI for advanced: 0.039 to 0.046; for novices: 0.033–0.038), and d = 0.813 (large effect). The VL–ST pair demonstrated a p-value of 0.384, while the LG–TA pair showed a p-value of 0.645, indicating no statistically significant differences between the groups.

Figure 3 presents the linear regression analysis of peak EMG during a series of 15 continuous roundhouse (bandal chagui) kicks, which demonstrated significant trends in specific muscles: BF (slope = 2.082, intercept = 61.890, R² = 0.661, p < 0.001), LG (slope = 0.866, intercept = 73.331, R² = 0.288, p = 0.039), and TA (slope = −1.192, intercept = 81.024, R² = 0.457, p = 0.006). The BF and LG exhibited upward trends, while the TA showed a downward trend. The other muscles did not present significant changes in peak EMG trends (p > 0.05).

Fixed effects explain a range of variance between 0.8% and 11.6%, while the complete models (fixed and random effects) explain 50.4% to 55.8% of the variance. The PCA component of fat mass (loading coefficient = 0.707) and muscle mass (loading coefficient = −0.707) showed a significant and positive effect in the BF, RF, VL, SO, and TA muscles, indicating that higher fat mass and lower muscle mass are significantly associated with an increase in the trend of peak EMG change. The experience level parameter presented a negative and significant impact in the RF, VM, and VL muscles, showing that being a novice is significantly associated with a lower tendency to increase the quadriceps peak EMG. Additionally, the number of kicks was significant in the BF and TA muscles, suggesting that EMG activity increases with successive roundhouse (bandal chagui) kicks in the BF, while at the same time, it decreases in the TA. The results of the mixed-effects model are presented in

Table 2. Mixed-effects model.

Muscle	Parameter	Estimate	Std. Error	p-Value	95% CI Lower	95% CI Upper	Rm2	Rc2
BF	Intercept	57.99	35.52	0.103	−11.63	127.61	0.116	0.558
	PCA component	10.21	1.68	<0.001 *	6.91	13.50
	Experience level	7.79	4.67	0.095	−1.36	16.94
	Kick number	2.08	0.52	<0.001 *	1.05	3.11
ST	Intercept	71.00	23.63	0.003 *	24.70	117.31	0.008	0.504
	PCA component	0.78	1.12	0.487	−1.42	2.97
	Experience level	2.17	3.10	0.484	−3.91	8.26
	Kick number	0.54	0.35	0.123	−0.15	1.22
RF	Intercept	80.38	34.47	0.020 *	12.83	147.94	0.074	0.537
	PCA component	8.14	1.63	<0.001 *	4.94	11.34
	Experience level	−21.36	4.53	<0.001 *	−30.24	−12.49
	Kick number	0.24	0.51	0.639	−0.76	1.24
VM	Intercept	86.35	32.31	0.008 *	23.03	149.67	0.030	0.515
	PCA component	1.75	1.53	0.252	−1.25	4.75
	Experience level	−15.81	4.25	<0.001 *	−24.13	−7.49
	Kick number	0.40	0.48	0.406	−0.54	1.33
VL	Intercept	73.29	45.93	0.111	−16.73	163.31	0.022	0.511
	PCA component	5.63	2.18	0.010 *	1.36	9.89
	Experience level	−14.04	6.04	0.020 *	−25.87	2.21
	Kick number	0.68	0.68	0.315	−0.65	2.01
LG	Intercept	75.90	35.74	0.034 *	5.85	145.95	0.008	0.504
	PCA component	−0.28	1.69	0.870	−3.60	3.04
	Experience level	−5.14	4.70	0.274	−14.34	4.07
	Kick number	0.87	0.53	0.101	−0.17	1.90
SO	Intercept	96.69	46.18	0.036 *	6.19	187.19	0.032	0.516
	PCA component	8.18	2.19	<0.001 *	3.89	12.47
	Experience level	−4.18	6.07	0.491	−16.07	7.72
	Kick number	0.92	0.68	0.176	−0.41	2.26
TA	Intercept	83.04	30.32	0.006 *	23.61	142.46	0.026	0.513
	PCA component	3.35	1.44	0.020 *	0.54	6.17
	Experience level	−4.03	3.98	0.312	−11.84	3.78
	Kick number	−1.19	0.45	0.008 *	−2.07	−0.31

BF: biceps femoris; LG: lateral gastrocnemius; PCA: principal component analysis of adipose mass and muscle mass; RF: rectus femoris; SO: soleus; ST: semitendinosus; TA: tibialis anterior; VL: vastus lateralis; VM: vastus medialis; *: statistically significant differences.

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4. Discussion

The present study evaluated differences in muscle co-contraction and peak EMG activity between novice and advanced Taekwondo athletes during consecutive roundhouse (bandal chagui) kicks. It also aimed to explore the influence of body composition and experience level on these parameters. The results confirm the first hypothesis proposed, indicating significant differences in co-contraction indices between novice and advanced Taekwondo athletes, with effect sizes ranging from moderate to large, particularly in the BF–RF, VM–BF, and SO–TA muscle pairs. These findings suggest that the advanced group exhibited higher co-contraction levels, which may serve as a strategy to enhance joint stabilization and improve precision during consecutive roundhouse (bandal chagui) kicks. These findings aligned with Moreira et al. [6,11], where advanced Taekwondo athletes demonstrated higher co-contraction indices than novices, specifically in muscles involved in knee extension. In other combat sports, Sbriccoli et al. [17] found that elite karateka exhibited higher levels of co-contraction in the VL and BF compared to amateur practitioners, which is also consistent with our findings. We did not find any previous studies that examined the co-contraction of the SO and TA under kicking conditions. However, Valdés-Badilla et al. [13] found significant differences in the SO peak EMG between novice and advanced Taekwondo athletes. This finding is noteworthy, as these muscles likely play a pivotal role in dynamic ankle stabilization and effective force transmission.

The linear regression analysis confirms our second hypothesis, revealing significant trends in peak EMG activity for specific muscles. BF and LG activity increased significantly with successive roundhouse (bandal chagui) kicks, possibly reflecting progressive motor unit recruitment due to fatigue [28] or the need for enhanced knee stabilization [16]. Sun et al. [15] observed significantly increased activation of the BF and LG when comparing the ‘hit’ and ‘miss’ actions of roundhouse kicks. These muscles play a role in controlling knee flexion and tibial external rotation, counteracting the actions of the VM on the tibia during knee extension [29]. In contrast, the TA reduced peak EMG activity as the number of kicks increased. This decrease could be explained by the reactive forces generated when striking the shield, which are transmitted through the leg [16]. We hypothesized that the neuromuscular system adapts to the impact with each roundhouse (bandal chagui) kick by shifting the foot into a more neutral or slightly plantarflexed position, optimizing energy transfer upon contact. This adaptation may progressively reduce the need for intense TA activation to stabilize the joint. Thibordee and Prasartwuth [16] observed that during the impact phase in elite Taekwondo athletes, individuals in the high-impact group demonstrated reduced TA muscle activity. This finding suggests that novice athletes may experience lower impact forces, resulting in reduced activation of this muscle. On the other hand, our results differ from those of Ervilha et al. [8], who reported higher TA activity in elite Taekwondo athletes compared to novice athletes. This discrepancy may be attributed to our focus on peak EMG, whereas they analyzed the integral of the entire waveform and phases. While no studies have specifically evaluated this trend under similar conditions, these findings offer new insights into the dynamic role of muscle activation during repetitive roundhouse (bandal chagui) kicks.

The mixed-effects model showed that experience level significantly impacted quadriceps muscles (RF, VM, and VL), with novice Taekwondo athletes displaying a reduced capacity to increase peak EMG during consecutive kicks. This decline could be related to lower technical efficiency or neuromuscular capacity [6,14]. Similarly, the PCA showed that higher fat mass and lower muscle mass were significantly associated with changes in peak EMG trends in the BF, RF, VL, SO, and TA muscles, thereby confirming our third hypothesis. This PCA-derived component, representing the inverse relationship between fat and muscle mass, allowed us to capture the combined influence of these two factors in a single predictor, reducing multicollinearity and improving model interpretability. The relationship between higher fat mass and lower muscle mass with increased peak EMG might be explained by greater relative effort and reduced contractile efficiency, whereby muscles would need to recruit more motor units to compensate for the lack of strength [30], thereby increasing EMG activity during consecutive kicks. Although the influence of experience and body composition on peak EMG trends was modest, the results suggest that less experienced athletes have a diminished ability to increase maximum muscle activation in key muscles, such as the RF, VM, and VL. Furthermore, it is important to highlight that our study provides additional and well-founded evidence that aligns with previous studies [21,22] on fat control and muscle gain in the performance of Taekwondo athletes. It is important to highlight that, although the fixed effects have an influence, there is a high level of individual variability in the muscle activation data.

Among the main strengths of this study is the use of a within-subject design, which increases statistical power by reducing inter-individual variability and allowing for a more accurate assessment of neuromuscular adaptations across repeated kicks. Another important strength was the inclusion of both novice and advanced Taekwondo athletes who were comparable in age, body weight, standing height, and BMI. This homogeneity helped to minimize potential confounding variables and strengthened the internal validity of our comparisons.

Our findings present several practical applications for Taekwondo training. Coaches and practitioners should prioritize exercises targeting antagonistic muscle groups to enhance the performance and efficiency of the roundhouse (bandal chagui) kick. Novice athletes may benefit from drills designed to strengthen and improve the coordination of the quadriceps and from incorporating controlled co-contraction exercises to enhance joint stability during kicking.

The limitations of this study include the small sample size, which restricts its external validity. Nonetheless, the statistical significance and large effect sizes observed underscore the importance of the differences identified in our research. Participants performed repeated roundhouse (bandal chagui) kicks from a static position, which differs from real combat conditions. However, the study aimed to use this setup as an experimental model to simulate task-specific demands and assess performance changes with repetition. Additionally, muscle fatigue was not directly measured. Fatigue is a key factor that can distinguish between novice and expert athletes, as experts often demonstrate greater fatigue resistance and more efficient motor unit recruitment. Well-established EMG-based fatigue estimation methods, such as median frequency or zero-crossing rate analysis [31], are typically applied to sustained or cyclic contractions and may be less reliable in dynamic, high-intensity tasks like consecutive kicks. Nonetheless, future studies are encouraged to incorporate these or alternative fatigue indicators to better characterize performance dynamics and muscle function over time. Finally, the binary classification of athletes into novice and advanced groups may not fully reflect the continuous nature of skill development. Differences in neuromuscular patterns could be more closely related to training volume and cumulative experience rather than to group labels alone.

5. Conclusions

Advanced Taekwondo athletes exhibited significantly higher co-contraction indices in knee and ankle muscles, suggesting improved joint stabilization during consecutive roundhouse (bandal chagui) kicks. In contrast, novice athletes showed a significantly reduced capacity to increase peak EMG in the RF, VM, and VL muscles. Although the influence of experience level and body composition on peak EMG trends was modest, the findings suggest that higher fat mass and lower muscle mass may be linked to the observed trend in peak EMG. Additionally, novice Taekwondo athletes may face challenges in achieving maximum muscle activation, potentially limiting their performance efficiency.