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European Journal of Sport Science logoLink to European Journal of Sport Science
. 2026 Feb 25;26(3):e70148. doi: 10.1002/ejsc.70148

Fatigue‐Induced Neuromuscular Responses Differ Between Endurance‐ and Strength‐Trained Males

Rui Wu 1,2, Madeleine M Lowery 1,2, Giuseppe De Vito 3, Jiaping Jiang 4, Che Shao 4, Wen Luo 4, Xuanbin Chen 4, HongWei Hao 5, Dan Wang 4,✉
PMCID: PMC12935569  PMID: 41741397

ABSTRACT

This study aimed to investigate the effect of training background on neuromuscular responses to submaximal isometric fatiguing contraction. Eleven strength‐trained (ST) and 11 endurance‐trained (ET) males performed maximal voluntary isometric knee extension contractions (MVIC) and a torque steadiness task at 20% MVIC before and immediately after a submaximal isometric contraction at 30% MVIC performed until task failure. High‐density surface EMG was recorded from the vastus lateralis muscle. The EMG root mean square (RMS) amplitude, median frequency (MDF) and muscle fibre conduction velocity (MFCV) were estimated. There was no significant difference in time‐to‐failure (88.2 ± 34.3 vs. 157.6 ± 126.1 s) or post‐fatigue MVIC reduction (19.4% vs. 22.5%) between the ST and ET groups. However, post‐fatigue EMG MDF decreased by 10.8% in ST (p < 0.05), but remained unchanged in ET. Furthermore, only the ET group showed a significant decrease in torque steadiness (p < 0.05) and an increase in physiological tremor (8–12 Hz; p < 0.01) during the post‐fatigue steadiness task. Throughout the fatiguing task, the mixed effects model revealed decreases in MFCV (p < 0.01) and MDF% (p < 0.001) in ST, with no differences observed in the ET group. EMG RMS amplitude increased similarly in both groups during the fatiguing contraction (p < 0.001). Fatiguing exercise induced comparable reductions in MVIC in ST and ET males. However, fatigue differentially affected the neuromuscular strategies adopted by each group when generating maximal strength and maintaining submaximal torque output, possibly reflecting differences in muscle fibre distribution between the two groups.

Keywords: distance runners, knee extensors, muscle fibre conduction velocity, neuromuscular fatigue, torque steadiness

Highlights

  • Fatigue‐induced reductions in maximal knee extensor strength were similar between endurance‐ and strength‐trained males.

  • Endurance‐ and strength‐trained males may adopt distinct neuromuscular strategies during sustained submaximal isometric contractions.

  • Strength‐trained males demonstrated a better ability maintain force steadiness following the isometric fatiguing contraction task.

1. Introduction

Neuromuscular fatigue can be defined as an exercise‐induced reduction in the force‐generating capacity of muscle (Gandevia 2001). It is a complex phenomenon involving a range of neural and muscular mechanisms (Gandevia 2001). This phenomenon is task dependent (Enoka and Stuart 1992), with evidence indicating that different types (Millet and Lepers 2004) or contraction modes (Beelen and Sargeant 1991; Pasquet et al. 2000) of fatiguing exercise can lead to distinct neuromuscular responses. For example, it has been shown that prolonged cycling exercise generally caused lower strength losses compared to prolonged running, even at similar exercise durations and intensities (Millet and Lepers 2004).

Individuals with different training backgrounds also exhibit distinct responses to neuromuscular fatigue. Cross‐sectional studies have reported that power‐trained (Garrandes et al. 2007; Mileva et al. 2009) and untrained (Bontemps et al. 2019) males were more fatigable than endurance‐trained males. This difference may be attributed to central regulation involving metabolically and mechanically sensitive group III and IV muscle afferents, which can reduce motor unit (MU) firing through inhibition of the motoneuron pool (Gandevia 2001), as well as to peripheral differences in muscle cross‐sectional area (CSA) (Häkkinen and Keskinen 1989) and type II/I fibre CSA ratio (Sleivert et al. 1995; Tesch and Karlsson 1985). Indeed, longitudinal studies have shown that training can lead to neuromuscular adaptations and structural modifications (Gabriel et al. 2006), such as changes in muscle fibre type and muscle CSA (Kyröläinen et al. 2005). Previous research has shown that 10 weeks of resistance training, but not endurance training, can increase the fascicle angle and muscle fibre CSA of the vastus lateralis (VL) and the CSA of the knee extensor (KE) muscles (Farup et al. 2012).

Neuromuscular fatigue during sustained submaximal isometric contractions is an important limiting factor in the development of performance in many sports (Pääsuke et al. 1999). However, previous studies have predominantly examined the effect of fatigue on maximal torque generation capacity, with the associated neuromuscular changes during a sustained isometric contraction in individuals with different training backgrounds remaining poorly understood. Characterising these neuromuscular responses throughout the fatigue task is critical for designing targeted training and recovery interventions. Surface electromyography (EMG) provides a non‐invasive means of tracking fatigue‐related neuromuscular changes during sustained contractions (Farina et al. 2004). In particular, multichannel surface EMG recordings acquired with linear electrode arrays allows for the estimation of muscle fibre conduction velocity (MFCV), an aggregate measure reflecting the propagation velocities of the MU action potentials contributing to the EMG signal (Casolo, Farina, et al. 2020). MFCV is closely related to the diameter of the recruited muscle fibres, thereby, it is sensitive to changes in the recruited fibres and membrane excitability as fatigue progresses (Blijham et al. 2006; Sejersted and Sjøgaard 2000). However, the time course of MFCV during the development of fatigue and how this behaviour differs across training backgrounds remains to be fully elucidated.

Neuromuscular fatigue not only reduces maximal force production but may also impair the ability to control submaximal forces, resulting in greater magnitude and altered temporal structure of force fluctuations (Missenard et al. 2009; Wu et al. 2019). During submaximal voluntary contractions, the force produced is not constant but rather fluctuates around an average level (Enoka et al. 2003). Such variability of force has been shown to be associated with motor function and movement control (Enoka and Farina 2021). Previous studies have shown that force control can be improved by training programs (Martinez‐Valdes et al. 2017; Vila‐Chã and Falla 2016). Vila‐Chã and Falla (2016) compared 6‐week strength and endurance training protocols and showed that strength training, but not endurance training can enhance the force steadiness and reduce the variability of MU discharge rate during submaximal contractions at 20% and 30% maximal voluntary isometric contraction (MVIC). However, the effect of fatigue on force steadiness in individuals with different training backgrounds (i.e., long‐term training) has not been assessed. Understanding these effects could elucidate the neuromuscular mechanisms that control force output, thusly, informing the development of injury prevention strategies for everyday training.

Therefore, the primary objective of the present study was to investigate fatigue‐induced neuromuscular responses in strength‐trained (ST) and endurance‐trained (ET) males to submaximal isometric fatiguing contraction. A secondary objective was to analyse the effect of fatiguing exercise on submaximal torque steadiness in both ST and ET groups. It was hypothesized that (1) fatiguing exercise would result in a lower reduction in MVIC of the KE and less pronounced neuromuscular changes in ET males when compared to their ST counterparts; and (2) ET males would have a greater loss of torque steadiness following the fatiguing exercise.

2. Materials and Methods

2.1. Subjects

A total of twenty‐two young males aged 18–28 years were recruited from Shanghai University of Sport to participate in this study. Eleven ET distance runners reported training regularly for at least 1 year, with a minimum of 3 sessions per week (range 3–10 sessions) and a weekly training distance of at least 50‐km per week (range 50–200 km). Eleven ST males have been reported strength training at least 3 times per week for at least 1 year before the study. All the above information concerning training histories was based on self‐reported questionnaires. All subjects gave their informed written consent to participate in the study. This study was approved by the Human Research Ethics Committee of Shanghai University of Sport (No: 102772025RT030) and was conducted in accordance with the Declaration of Helsinki.

2.2. Experimental Design

Each subject attended one laboratory testing session. After recording the physical characteristics and a 5‐min warm‐up on a cycle ergometer at 70–80 rpm with low resistance, subjects performed several maximal and submaximal knee extension contractions with visual feedback to familiarise themselves with the testing procedures. Following a 5 min rest, an electrode grid was placed above the KE muscles, and subjects completed 3–5 s contractions at 30% (× 2), 50% (× 2), 70 (× 2) and 90% (× 1) of their perceived MVIC to further familiar themselves with the protocol. After 3‐min of rest, the following tasks were performed before (pre‐fatigue) and immediately after the fatiguing task (post‐fatigue): (1) MVIC of the KE and (2) a torque steadiness task.

2.2.1. MVIC Task

Each subject performed a minimum of two MVICs of the KE to assess baseline (pre‐fatigue) MVIC with an at least 1 min rest between trials. At each attempt, subjects were asked to generate ‘as strongly and as quickly as possible’ for approximately 3–4 s, whilst verbal encouragement and online visual feedback were provided to facilitate exertion of maximal effort. Additional MVIC attempts were performed if the torque difference between the best two attempts was greater than 5%. To verify the effect of the fatiguing task, an additional knee extension MVIC was performed immediately after the fatiguing exercise (post‐fatigue).

2.2.2. Torque Steadiness Task

A submaximal KE isometric contraction corresponding to pre‐fatigue 20% MVIC was performed before the fatiguing task and immediately after the post‐fatigue MVIC tests. A 2‐min of rest was provided following the completion of the pre‐fatigue MVICs, and no rest were given after the post‐fatigue MVIC. A horizontal target line with a width corresponding to ± 5% of the target value band was displayed on the computer monitor. Subjects were instructed to match the target value and maintain it ‘as steady as possible’ for approximately 10 s. Subject received verbal instructions and visual feedback throughout all testing procedures.

2.2.3. Fatiguing Task

After an at least 2‐min rest and once the subject's rating of perceived exertion (RPE) fell below 2, the isometric fatiguing task was performed at a target force of 30% pre‐fatigue MVIC. The subject was required to match the horizontal target line with a width corresponding to ± 5% of the target value band displayed on the monitor and was verbally encouraged to sustain their force for as long as possible. The contraction was terminated when the subject deviated from the target line by 5% for > 2 s, despite strong verbal encouragement to maintain the task. When this occurred, the subject was deemed to have reached exhaustion, and the duration of the contraction was considered as the time to task failure (TTF).

2.2.4. Rating of Perceived Exertion

RPE, was assessed using the modified Bourg 10‐point scale (Borg 1982) at pre‐ and post‐fatigue (i.e., at the task failure).

2.3. Mechanical and Electrical Recordings

2.3.1. Force Signal Recording

Maximal and submaximal isometric contractions of the KE were assessed (right leg) with the knee flexed at 90° and the hip fixed at 100° (where 0° represents full extension) using a knee dynamometer with a load cell (AF1, GICAM, Gravedona ed Uniti, Como, Italy, range 0–250 kg, output 2 mV/V) attached above the ankle. Adjustable straps (chest, pelvis, ankle and proximal thigh) were used to secure subjects to the dynamometer and avoid unnecessary movements. The force signal was amplified (× 100) and sampled at 2048 Hz.

2.3.2. High‐Density Surface EMG Recordings

High‐density surface EMG (HDsEMG) was recorded from the vastus lateralis (VL) muscle with one semi‐disposal adhesive grid of 64 equally spaced electrode (13 rows × 5 columns with an interelectrode distance (IED) of 8 mm; GR08MM1305, OT Bioelettronica, Torino, Italy). The electrode was positioned and oriented as previously described (Valli et al. 2024) with a 16‐eletrode dry array (silver bars; 1 mm thick with an IED of 5 mm; OT Bioelettronica) used to identify the innervation zone located in the distal region of the VL muscle (Botter et al. 2011). The anatomical direction of the muscle fibres was then estimated through visual inspection by an experienced investigator. The skin was shaved, lightly abraded and cleaned with ethyl alcohol. The electrode grid was attached between the proximal and distal tendon regions of VL aligned longitudinally with respect to the muscle fibre orientation, as previously described (Valli et al. 2024). The HDsEMG signals were recorded in monopolar configuration and A/D converted by means of a 16‐bit multichannel amplifier (EMG‐Quattrocento, OT Bioelettronica, Torino, Ital; 3 dB, bandwidth 10–500 Hz) with a sampling rate of 2048 Hz. Force and HDsEMG data were synchronized within the same data acquisition system, and all data were stored off‐line for further analysis. Analysis of force and HDsEMG signals were performed using custom‐written scripts (MATLAB R2023b, MathWorks, Natick, MA, USA).

2.4. Data Analysis

2.4.1. Force Signal Processing

In the off‐line analysis, the force signal was converted to Newtons and multiplied by the respective lever arm to obtain torque (Nm). Then the torque signals were filtered with a low‐pass 4th order Butterworth filter with a cutoff frequency of 30 Hz. For the MVIC task, the maximal torque values were estimated as the highest value, averaged over 500 ms, reached within any single torque recording. The MVIC values obtained after fatiguing contraction (post‐fatigue) were normalized to pre‐fatigue MVIC values for further analysis. To examine torque steadiness at 20% MVIC, the middle 6‐s epoch of the plateau was selected to estimate the mean and coefficient of variation (CV) of the torque. The power spectrum of the force signal was estimated using Welch's periodogram method with a hamming window of 500 ms (no overlap). The distribution of power across all frequencies was examined and the percentage of total power in the alpha band (8–12 Hz) was calculated.

2.4.2. HDsEMG Processing

HDsEMG signals were digitally band‐pass filtered with a 20–500 Hz Butterworth filter. Single differential signals were obtained from the monopolar recordings along the fibre direction (columns of the grid). For the MVIC task, the median frequency (MDF) and root mean square (RMS) amplitudes were estimated over a period of 500 ms corresponding to the occurrence of the highest MVIC torque and were averaged over the 59 differential channels. For the fatiguing contraction task, the EMG RMS and MDF were estimated over 500 ms windows with a 50% overlap until task failure. EMG RMS and MDF values were then normalized to their pre‐fatigue MVIC values (RMS% & MDF%). The estimates of MFCV were similarly obtained using a window of 500 ms with a 50% overlap, using a multichannel maximum‐likelihood algorithm (Farina et al. 2004). The monopolar HDsEMG signals were first band‐pass filtered between 20 and 500 Hz (2nd order Butterworth filter). Subsequently, double differential HDsEMG signals were computed and visually inspected by an experienced investigator (Figure 1). To estimate MFCV, the 4–6 channels with the highest coefficient of correlation (CC ≥ 0.8) were manually selected and clear MU action potential propagation without shape changes from the nearest innervation zone to the distal tendon were chosen for the analysis (Del Vecchio et al. 2018). MFCVs exceeding the physiological range (2–6 m/s) were excluded (Del Vecchio, Negro, et al. 2018). RMS%, MDF% and MFCV were examined at intervals corresponding to 10% of the contraction time for statistical analysis.

FIGURE 1.

FIGURE 1

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Estimation of muscle fibre conduction velocity. (Top‐left) One high‐density surface EMG grid of 64 electrodes. (Top‐right) Double differential surface EMG signals (blue) from one column and the torque (red) signals during the fatiguing task. (Bottom) The innervation zone and several muscle fibre action potential propagations propagating in the distal direction.

2.5. Statistical Analysis

Statistical analysis was performed using RStudio (Version 2024.12.1). The normality of the distribution was assessed using the Shapiro‐wilk test and the equality of variance was tested using Levene's test. For the variables that met the assumptions of normality, independent samples t‐tests were used to compare anthropometric and bassline measures (i.e., pre‐fatigue) between ST and DR group. Age, stature and TTF were non‐normally distributed and were therefore analysed using the non‐parametric Mann‐Whitney U test. A mixed effects model (MEM) was used to analyse the effect of training background (group effect: ST vs. ET) and fatigue (i.e., pre‐fatigue vs. post‐fatigue), whilst accounting for repeated measures within subjects [i.e., var ∼ group × fatigue + (1|subject)]. When an interaction effect between training background and fatigue was observed, post hoc analysis was performed with Tukey's adjustment to account for the multiple comparisons. Furthermore, to examine the effect of training background on MFCV, MDF% and RMS% across time during fatiguing exercise, a MEM was used including group (as a factor) and time (as a continuous variable) and their interaction as fixed effects, and a random intercept and random slope accounting for the time within each subject [i.e., var ∼ group × time + (time|subject)]. Models were fitted using the lme4 (Bates et al. 2015) and lmerTest package (Kuznetsova et al. 2017). To quantify the effect size, Cohen's f 2 was computed, which is considered values of 0.02, 0.15 and 0.35 represent small, medium and large effects respectively (Cohen 1988). Data are reported as mean ± standard deviation (SD). MFCV was normalized to its initial value (i.e., 0%TTF) in the figures for the purpose of presentation. Statistical significance was set at p < 0.05.

3. Results

3.1. Baseline Measures (Pre‐Fatigue)

Participants and baseline characteristics are summarized in Table 1. ST and ET subjects were similar in stature, however, the ST group had a greater body mass (p < 0.05), and BMI (p < 0.01) compared to the ET group. As expected, ST were stronger than ET (p < 0.01), but both EMG RMS amplitude and MDF were comparable between the two groups at MVIC (Table 1). During the torque steadiness task (i.e., 20% MVIC), no difference in CV of the torque or alpha band power was observed between the two groups.

TABLE 1.

Participants and baseline (pre‐fatigue) characteristics.

ST (n = 11) ET (n = 11) p values
Age (year) 21.5 ± 2.9 19.8 ± 2.4 0.095
Stature (cm) 179.5 ± 8.5 176.1 ± 4.32 0.373
Body Mass (kg) 80.2 ± 16.2 65.3 ± 4.6 0.012
BMI (kg/m2) 24.7 ± 3.5 21.0 ± 1.2 0.007
TTF (s) 88.2 ± 34.3 157.6 ± 126.1 0.100
MVIC
MVIC (Nm) 394.7 ± 114.2 281.3 ± 66.1 0.009
EMG MDF (Hz) 75.2 ± 9.8 71.7 ± 8.4 0.353
EMG RMS (mV) 0.179 ± 0.072 0.160 ± 0.096 0.590
Torque steadiness (pre‐fatigue 20%MVIC)
CV Of the torque (%) 1.854 ± 0.653 1.611 ± 0.336 0.286
Alpha‐band power (%total power) 6.3481 ± 0.0024 6.3481 ± 0.0036 0.895
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Note: Bold values mean the significant difference between ST and ET.

3.2. Fatiguing Contraction Task

There was no significant difference in TTF between two groups (Table 1). However, an interaction effect between group and time was observed for both MFCV (p = 0.009, f 2  = 0.37) and MDF% (p = 0.031, f 2  = 0.24). A significant decrease in both MFCV (p = 0.003) and MDF% (p < 0.001) was observed in the ST group during the sustained fatiguing contraction, whereas both remained unchanged in the ET group (MFCV: p = 0.466 & MDF%: p = 0.451) throughout the contraction (Figure 2a and 2b). Moreover, the ET group had a significantly lower MFCV than the ST group (p = 0.023) at the beginning of the task (i.e., 0%TTF) (Figure 2a). An effect of time was also observed for RMS% (p < 0.001, f 2  = 2.61), showing a similar increase in RMS% for both groups (Figure 2c).

FIGURE 2.

FIGURE 2

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Surface EMG MFCV% (a), MDF% (b) and RMS% (c) at the beginning of the fatiguing task (i.e., 0%TTF) and during fatiguing contraction. † < 0.05 compared with ET at 0%FFT; **p < 0.01, ***p < 0.001 significant decline in ST (time by group interaction); ### significant linear increase in both ST and ET (main effect for time).

3.3. Effect of Fatigue on MVIC and Torque Steadiness

3.3.1. MVIC Task

The fatiguing task resulted in similar reductions in MVIC in the ST (↓19.4% ± 20.1%) and ET groups (↓22.5% ± 22.0%) (time effect: p < 0.001, f 2  = 0.91) (Figure 3a). However, ST subjects demonstrated a significant decrease in MDF% following the fatiguing contraction task (↓10.8%; p = 0.041), whereas the MDF% in the ET group remained unchanged (group by fatigue interaction: p = 0.013, f 2  = 0.17) (Figure 3b). No difference in RMS% was observed in either group during the post‐fatigue MVIC compared with the pre‐fatigue MVIC (p = 0.387, f 2  = 0.02) (Figure 3c).

FIGURE 3.

FIGURE 3

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Fatigue‐induced changes in MVIC, MDF and RMS. ###p < 0.001 pre‐fatigue versus. post‐fatigue (fatigue effect); *p < 0.05, significant post‐fatigue reduction in ST (fatigue by group interaction: p = 0.013).

3.3.2. Torque Steadiness

A fatigue by group interaction was observed for torque steadiness (CV of the torque: p = 0.019, f 2  = 0.29) and alpha band power (p = 0.03, f 2  = 0.24) (Figure 4). Fatiguing exercise resulted in a significant decrease in torque steadiness (↑CV of the torque: p = 0.036) associated with an increase in alpha band power (p = 0.002) in ET subjects, with no change observed in the in the ST group (p = 1.0) (Figure 4).

FIGURE 4.

FIGURE 4

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Torque steadiness (CV of the torque) (a) and alpha band power %total power (physiological tremor) (b) at pre‐ and post‐fatigue torque steadiness task. *p < 0.05, **p < 0.01 significant increase from pre‐to post‐fatigue in ET (group by fatigue interaction).

3.3.3. RPE

A significant effect of fatigue on RPE was observed (p < 0.001, f 2  = 26.6), with an increase reported post‐fatigue RPE in both groups (ST: from 1.1 ± 0.3 to 8.0 ± 1.0 & ET: from 0.9 ± 0.3 to 7.7 ± 1.6).

4. Discussion

The present study investigated differences in neuromuscular performance between ET distance runners and ST males during and immediately after a sustained submaximal fatiguing contraction of the KE muscles. The primary findings were that: (1) both ET and ST groups exhibited a comparable fatigability in terms of force generating capacity, as indicated by similar TTF with similar reductions in post‐fatigue MVIC; (2) only ST subjects showed a post‐fatigue reduction in EMG MDF; (3) during submaximal isometric fatiguing exercise, there was a pronounced decline in MDF% and MFCV in ST subjects, whereas no change was observed in ET subjects; and (4) a loss of post‐fatigue torque steadiness with increased physiological tremor (alpha band power) was observed in the ET, but not the ST group.

4.1. Baseline Comparison

Not surprisingly, the ST group demonstrated greater knee extension MVIC than the ET group, which is well‐documented in previous studies (Häkkinen and Keskinen 1989; Methenitis et al. 2016; Mileva et al. 2009). No differences were observed in pre‐fatigue EMG RMS amplitude and MDF between groups (Table 1). The differences in maximal KE strength is likely due to well‐established muscular adaptations in individuals with different training backgrounds, such as alterations in muscle (Häkkinen and Keskinen 1989) and muscle fibre (Methenitis et al. 2016; Sleivert et al. 1995) CSA. For example, Methenitis et al. (2016) have shown that male distance runners have a smaller %CSA of type II and type IIa fibres in the VL muscle than strength‐trained males. Furthermore, endurance‐trained athletes also demonstrated a smaller quadriceps CSA compared to strength‐trained athletes (Häkkinen and Keskinen 1989).

4.2. Fatigue Comparison in ST and ET Groups

In the present study, the ST and ET groups exhibited comparable fatigability, as evidenced by similar reductions observed in post‐fatigue knee extension MVIC (Figure 3a). Furthermore, there were no significant difference in TTF or post‐fatigue RPE. This finding conflicts with our initial study hypothesis. For instance, a greater reduction in post‐fatigue MVIC has been observed in ET when compared to power‐trained males using either isokinetic (Garrandes et al. 2007) or intermittent (Morana and Perrey 2009) knee extension fatiguing exercise. Individuals specialising in different sports may have contributed to this discrepancy: Garrandes et al. (2007) recruited power‐trained males with martial arts training (Garrandes et al. 2007), whereas Morana and Perrey (2009) included four professional rugby players in the power‐trained group (Morana and Perrey 2009). Moreover, these differences in findings may also be due to the distinct fatigue protocols adopted. Previous research has indicated that the effects of fatiguing exercise are task dependent (Barry and Enoka 2007; Millet and Lepers 2004).

Despite the similar performance characteristics observed, the neuromuscular responses to fatiguing exercise differed between ST and ET groups. The ST group exhibited a significant reduction in EMG MDF% during the post‐fatigue MVIC, with a more pronounced decline in MDF% and MFCV during the fatiguing exercise, whereas MDF% and MFCV remained unchanged in the ET group. This finding confirmed our hypothesis that the ET subjects would have less changes in response to fatigue, which could be primarily explained by differences in muscle fibre type distribution between the groups. Previous research has reported that long‐distance runners typically have a higher percentage of slow‐twitch (type I) fibres in the VL muscle compared to controls, wrestlers, kayakers or weightlifters and powerlifters (Tesch and Karlsson 1985). Methenitis et al. (2016) also reported that, in comparison with the strength‐trained VL muscle, ET distance runners not only had a higher proportion and %CSA of type I fibres and a lower proportion and %CSA of type II fibres, but also a slower MFCV (Methenitis et al. 2016). This is consistent with our results, showing that ST subjects have a higher MFCV at the beginning of the fatiguing exercise when compared to their ET counterparts (Figure 2a). MFCV represents the average propagation velocity of MU action potentials along the sarcolemma (Casolo, Nuccio, et al. 2020). Estimated MFCV can also be effected by differences in MU recruitment, as muscle fibres innervated by high‐threshold MUs have larger diameters than those innervated by low‐threshold MUs, and consequently have higher MFCV (Del Vecchio, Negro, et al. 2018; Masuda and De Luca 1991). Therefore, our results suggest that a greater proportion of type II fibres are recruited at the onset of the contraction in the ST than ET males resulting in a higher estimated MFCV.

Fast‐twitch muscle fibres have a higher dependency on glycolytic metabolism. During fatiguing exercise, fast‐twitch fibres rapidly accumulate metabolic by‐products (e.g., P i & H+), and experience disturbances in ion homoeostasis (including increased extracellular potassium), leading to a loss of membrane excitability (Green 2004; Sejersted and Sjøgaard 2000). This fatigue‐induced reduction in membrane excitability results in a slowing of action potential propagation, observed as a reduction in MFCV, which in turn causes a compression of the EMG MDF towards lower frequencies (Fortune and Lowery 2009). The lower type II/I fibre CSA ratio observed in ET males suggests an adaptation to endurance training that facilitates the high oxidative capacity associated with type I fibres (Martinez‐Valdes et al. 2017). This may help to maintain stable membrane excitability even under fatiguing conditions, thereby preserving MFCV and EMG MDF. These results suggest that whilst isometric fatiguing exercise results in a similar reduction in post‐fatigue MVIC in both groups, they may adapt differently to fatigue at the muscle fibre level (Figure 3).

4.3. Effect of Fatigue on Torque Steadiness

Fatigue can impair force steadiness by increasing force fluctuations during submaximal contractions (Missenard et al. 2009). In the present study, although both groups had similar torque steadiness at baseline (pre‐fatigue), the ET subjects, but not the ST subjects, exhibited a loss of steadiness and a rise in physiological tremor after the fatiguing task (Figure 4). These findings support our hypothesis that subjects with strength training better maintain force steadiness following fatigue exercise. This reflects training‐specific differences in fatigue‐induced alterations in common synaptic drive to the agonist‐antagonist pairs. Hamstring antagonist coactivation plays a protective role in stabilizing the knee joint, and is regulated via Ia presynaptic inhibition (Baratta et al. 1988). A greater hamstring antagonist coactivation has been observed in power‐trained athletes when compared to endurance‐trained athletes (Garrandes et al. 2007). This could be explained by greater presynaptic inhibition of the Ia fibres observed in endurance‐trained athletes with respect to power‐trained athletes (Koceja et al. 2004).

It has been suggested that supraspinal influence reduces the Ia presynaptic inhibition of afferents that convey reciprocal inhibition to antagonists during agonist fatigue, thusly increasing coactivation (Gardiner 2011). Together, these findings may suggest that ST subjects experience a reduced reciprocal inhibition after fatigue. Indeed, following isokinetic knee extension fatiguing exercise, only power‐trained athletes showed a fatigue‐related increase in hamstring antagonist coactivation, whereas endurance‐trained cyclists and triathletes showed no change (Garrandes et al. 2007). The increased coactivation may increase joint stiffness, in turn, attenuating the emerging oscillations, and sustaining steadiness under fatigue. On the other hand, ET athletes have demonstrated greater sensitivity to changes in mechanical stimuli with superior recovery of the monosynaptic reflexes compared to their power‐trained counterparts (Kyröläinen and Komi 1994), indicating heightened spindle sensitivity. It has been proposed that muscle receptors may be preferentially existed in muscles containing a higher percentage of oxidative muscle fibres, which are innervated by motor neurons with lower reflex thresholds and slower axonal conduction velocity (Bawa et al. 1984). This would be consistent with the greater post‐fatigue alpha band (8–12 Hz) component of torque spectrum in ET subjects, indicative of stronger Ia afferent oscillatory feedback, thusly elevating physiological tremor.

5. Conclusion

In summary, comparable levels of muscle fatigue were induced by submaximal isometric fatiguing exercise in ET and ST males. However, neuromuscular responses during the fatiguing exercise differed between groups, with ET males more resistant to neuromuscular fatigue and ST males adopting a preventative strategy against and/or stabilize unilateral forces by reducing Ia presynaptic inhibition of afferents. Overall, our findings provide insight into differences in how the neuromuscular system responds to sustained isometric fatiguing exercise between individuals with endurance and strength training backgrounds. This information could potentially be used in designing more effective personalised training programs that cater to the specific physiological characteristics of each group, thereby optimising performance enhancement whilst effectively managing fatigue, recovery and injury risk.

Funding

The authors have nothing to report.

Ethics Statement

Ethical approval was granted for this study from the Human Research Ethics Committee of Shanghai University of Sport (No: 102772025RT030) and is detailed in the methods section.

Conflicts of Interest

The authors declare no conflicts of interest.

Acknowledgements

This publication has emanated from research supported under the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska‐Curie grant agreement No 101034252. Dan Wang was supported by programme for the Shanghai Key Lab of Human Performance (Shanghai University of Sport) (No 11DZ2261100).

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

References

  1. Baratta, R. , Solomonow M., Zhou B. H., Letson D., Chuinard R., and D'Ambrosia R.. 1988. “Muscular Coactivation: The Role of the Antagonist Musculature in Maintaining Knee Stability.” American Journal of Sports Medicine 16, no. 2: 113–122. 10.1177/036354658801600205. [DOI] [PubMed] [Google Scholar]
  2. Barry, B. K. , and Enoka R. M.. 2007. “The Neurobiology of Muscle Fatigue: 15 Years Later.” Integrative and Comparative Biology 47, no. 4: 465–473. 10.1093/icb/icm047. [DOI] [PubMed] [Google Scholar]
  3. Bates, D. , Mächler M., Bolker B., and Walker S.. 2015. “Fitting Linear Mixed‐Effects Models Using lme4.” Journal of Statistical Software 67: 1–48. 10.18637/jss.v067.i01. [DOI] [Google Scholar]
  4. Bawa, P. , Binder M. D., Ruenzel P., and Henneman E.. 1984. “Recruitment Order of Motoneurons in Stretch Reflexes is Highly Correlated With Their Axonal Conduction Velocity.” Journal of Neurophysiology 52, no. 3: 410–420. 10.1152/jn.1984.52.3.410. [DOI] [PubMed] [Google Scholar]
  5. Beelen, A. , and Sargeant A. J.. 1991. “Effect of Fatigue on Maximal Power Output at Different Contraction Velocities in Humans.” Journal of Applied Physiology 71, no. 6: 2332–2337. 10.1152/jappl.1991.71.6.2332. [DOI] [PubMed] [Google Scholar]
  6. Blijham, P. J. , ter Laak H. J., Schelhaas H. J., van Engelen B. G., Stegeman D. F., and Zwarts M. J.. 2006. “Relation Between Muscle Fiber Conduction Velocity and Fiber Size in Neuromuscular Disorders.” Journal of Applied Physiology 100, no. 6: 1837–1841. 10.1152/japplphysiol.01009.2005. [DOI] [PubMed] [Google Scholar]
  7. Bontemps, B. , Piponnier E., Chalchat E., et al. 2019. “Children Exhibit a More Comparable Neuromuscular Fatigue Profile to Endurance Athletes Than Untrained Adults.” Frontiers in Physiology 10: 119. 10.3389/fphys.2019.00119. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Borg, G. A. 1982. “Psychophysical Bases of Perceived Exertion.” Medicine & Science in Sports & Exercise 14, no. 5: 377–381. 10.1249/00005768-198205000-00012. [DOI] [PubMed] [Google Scholar]
  9. Botter, A. , Oprandi G., Lanfranco F., Allasia S., Maffiuletti N. A., and Minetto M. A.. 2011. “Atlas of the Muscle Motor Points for the Lower Limb: Implications for Electrical Stimulation Procedures and Electrode Positioning.” European Journal of Applied Physiology 111, no. 10: 2461–2471. 10.1007/s00421-011-2093-y. [DOI] [PubMed] [Google Scholar]
  10. Casolo, A. , Farina D., Falla D., Bazzucchi I., Felici F., and Del Vecchio A.. 2020. “Strength Training Increases Conduction Velocity of High‐Threshold Motor Units.” Medicine & Science in Sports & Exercise 52, no. 4: 955–967. 10.1249/mss.0000000000002196. [DOI] [PubMed] [Google Scholar]
  11. Casolo, A. , Nuccio S., Bazzucchi I., Felici F., and Del Vecchio A.. 2020. “Reproducibility of Muscle Fibre Conduction Velocity During Linearly Increasing Force Contractions.” Journal of Electromyography and Kinesiology 53: 102439. 10.1016/j.jelekin.2020.102439. [DOI] [PubMed] [Google Scholar]
  12. Cohen, J. 1988. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. Lawrence Erlbaum Associates. [Google Scholar]
  13. Del Vecchio, A. , Bazzucchi I., and Felici F.. 2018. “Variability of Estimates of Muscle Fiber Conduction Velocity and Surface EMG Amplitude Across Subjects and Processing Intervals.” Journal of Electromyography and Kinesiology 40: 102–109. 10.1016/j.jelekin.2018.04.010. [DOI] [PubMed] [Google Scholar]
  14. Del Vecchio, A. , Negro F., Felici F., and Farina D.. 2018. “Distribution of Muscle Fibre Conduction Velocity for Representative Samples of Motor Units in the Full Recruitment Range of the Tibialis Anterior Muscle.” Acta Physiologica 222, no. 2: e12930. 10.1111/apha.12930. [DOI] [PubMed] [Google Scholar]
  15. Enoka, R. M. , Christou E. A., Hunter S. K., et al. 2003. “Mechanisms That Contribute to Differences in Motor Performance Between Young and Old Adults.” Journal of Electromyography and Kinesiology 13, no. 1: 1–12. 10.1016/S1050-6411(02)00084-6. [DOI] [PubMed] [Google Scholar]
  16. Enoka, R. M. , and Farina D.. 2021. “Force Steadiness: From Motor Units to Voluntary Actions.” Physiology 36, no. 2: 114–130. 10.1152/physiol.00027.2020. [DOI] [PubMed] [Google Scholar]
  17. Enoka, R. M. , and Stuart D. G.. 1992. “Neurobiology of Muscle Fatigue.” Journal of Applied Physiology 72, no. 5: 1631–1648. 10.1152/jappl.1992.72.5.1631. [DOI] [PubMed] [Google Scholar]
  18. Farina, D. , Pozzo M., Merlo E., Bottin A., and Merletti R.. 2004. “Assessment of Average Muscle Fiber Conduction Velocity From Surface EMG Signals During Fatiguing Dynamic Contractions.” IEEE Transactions on Biomedical Engineering 51, no. 8: 1383–1393. 10.1109/tbme.2004.827556. [DOI] [PubMed] [Google Scholar]
  19. Farup, J. , Kjølhede T., Sørensen H., et al. 2012. “Muscle Morphological and Strength Adaptations to Endurance vs. Resistance Training.” Journal of Strength & Conditioning Research 26, no. 2: 398–407. 10.1519/JSC.0b013e318225a26f. [DOI] [PubMed] [Google Scholar]
  20. Fortune, E. , and Lowery M. M.. 2009. “Effect of Extracellular Potassium Accumulation on Muscle Fiber Conduction Velocity: A Simulation Study.” Annals of Biomedical Engineering 37, no. 10: 2105–2117. 10.1007/s10439-009-9756-4. [DOI] [PubMed] [Google Scholar]
  21. Gabriel, D. A. , Kamen G., and Frost G.. 2006. “Neural Adaptations to Resistive Exercise: Mechanisms and Recommendations for Training Practices.” Sports Medicine 36, no. 2: 133–149. 10.2165/00007256-200636020-00004. [DOI] [PubMed] [Google Scholar]
  22. Gandevia, S. C. 2001. “Spinal and Supraspinal Factors in Human Muscle Fatigue.” Physiological Reviews 81, no. 4: 1725–1789. 10.1152/physrev.2001.81.4.1725. [DOI] [PubMed] [Google Scholar]
  23. Gardiner, P. 2011. Advanced Neuromuscular Exercise Physiology. Human Kinetics. [Google Scholar]
  24. Garrandes, F. , Colson S. S., Pensini M., Seynnes O., and Legros P.. 2007. “Neuromuscular Fatigue Profile in Endurance‐Trained and Power‐Trained Athletes.” Medicine & Science in Sports & Exercise 39, no. 1: 149–158. 10.1249/01.mss.0000240322.00782.c9. [DOI] [PubMed] [Google Scholar]
  25. Green, H. J. 2004. “Membrane Excitability, Weakness, and Fatigue.” Canadian Journal of Applied Physiology 29, no. 3: 291–307. 10.1139/h04-020. [DOI] [PubMed] [Google Scholar]
  26. Häkkinen, K. , and Keskinen K. L.. 1989. “Muscle Cross‐Sectional Area and Voluntary Force Production Characteristics in Elite Strength‐ and Endurance‐Trained Athletes and Sprinters.” European Journal of Applied Physiology and Occupational Physiology 59, no. 3: 215–220. 10.1007/bf02386190. [DOI] [PubMed] [Google Scholar]
  27. Koceja, D. M. , Davison E., and Robertson C. T.. 2004. “Neuromuscular Characteristics of Endurance‐ and Power‐Trained Athletes.” Research Quarterly for Exercise & Sport 75, no. 1: 23–30. 10.1080/02701367.2004.10609130. [DOI] [PubMed] [Google Scholar]
  28. Kuznetsova, A. , Brockhoff P. B., and Christensen R. H. B.. 2017. “lmerTest Package: Tests in Linear Mixed Effects Models.” Journal of Statistical Software 82, no. 13: 1–26. 10.18637/jss.v082.i13. [DOI] [Google Scholar]
  29. Kyröläinen, H. , Avela J., McBride J. M., et al. 2005. “Effects of Power Training on Muscle Structure and Neuromuscular Performance.” Scandinavian Journal of Medicine & Science in Sports 15, no. 1: 58–64. 10.1111/j.1600-0838.2004.00390.x. [DOI] [PubMed] [Google Scholar]
  30. Kyröläinen, H. , and Komi P. V.. 1994. “Stretch Reflex Responses Following Mechanical Stimulation in Power‐ and Endurance‐Trained Athletes.” International Journal of Sports Medicine 15, no. 6: 290–294. 10.1055/s-2007-1021062. [DOI] [PubMed] [Google Scholar]
  31. Martinez‐Valdes, E. , Falla D., Negro F., Mayer F., and Farina D.. 2017. “Differential Motor Unit Changes After Endurance or High‐Intensity Interval Training.” Medicine & Science in Sports & Exercise 49, no. 6: 1126–1136. 10.1249/mss.0000000000001209. [DOI] [PubMed] [Google Scholar]
  32. Masuda, T. , and De Luca C. J.. 1991. “Recruitment Threshold and Muscle Fiber Conduction Velocity of Single Motor Units.” Journal of Electromyography and Kinesiology 1, no. 2: 116–123. 10.1016/1050-6411(91)90005-p. [DOI] [PubMed] [Google Scholar]
  33. Methenitis, S. , Karandreas N., Spengos K., Zaras N., Stasinaki A. N., and Terzis G.. 2016. “Muscle Fiber Conduction Velocity, Muscle Fiber Composition, and Power Performance.” Medicine & Science in Sports & Exercise 48, no. 9: 1761–1771. 10.1249/mss.0000000000000954. [DOI] [PubMed] [Google Scholar]
  34. Mileva, K. N. , Morgan J., and Bowtell J.. 2009. “Differentiation of Power and Endurance Athletes Based on Their Muscle Fatigability Assessed by New Spectral Electromyographic Indices.” Journal of Sports Sciences 27, no. 6: 611–623. 10.1080/02640410802707011. [DOI] [PubMed] [Google Scholar]
  35. Millet, G. Y. , and Lepers R.. 2004. “Alterations of Neuromuscular Function After Prolonged Running, Cycling and Skiing Exercises.” Sports Medicine 34, no. 2: 105–116. 10.2165/00007256-200434020-00004. [DOI] [PubMed] [Google Scholar]
  36. Missenard, O. , Mottet D., and Perrey S.. 2009. “Factors Responsible for Force Steadiness Impairment With Fatigue.” Muscle & Nerve 40, no. 6: 1019–1032. 10.1002/mus.21331. [DOI] [PubMed] [Google Scholar]
  37. Morana, C. , and Perrey S.. 2009. “Time Course of Postactivation Potentiation During Intermittent Submaximal Fatiguing Contractions in Endurance‐ and Power‐Trained Athletes.” Journal of Strength & Conditioning Research 23, no. 5: 1456–1464. 10.1519/JSC.0b013e3181a518f1. [DOI] [PubMed] [Google Scholar]
  38. Pääsuke, M. , Ereline J., and Gapeyeva H.. 1999. “Neuromuscular Fatigue During Repeated Exhaustive Submaximal Static Contractions of Knee Extensor Muscles in Endurance‐Trained, Power‐Trained and Untrained Men.” Acta Physiologica Scandinavica 166, no. 4: 319–326. 10.1046/j.1365-201x.1999.00573.x. [DOI] [PubMed] [Google Scholar]
  39. Pasquet, B. , Carpentier A., Duchateau J., and Hainaut K.. 2000. “Muscle Fatigue During Concentric and Eccentric Contractions.” Muscle & Nerve 23, no. 11: 1727–1735. 10.1002/1097-4598(200011)23:11<1727::aid-mus9>3.0.co;2-y. [DOI] [PubMed] [Google Scholar]
  40. Sejersted, O. M. , and Sjøgaard G.. 2000. “Dynamics and Consequences of Potassium Shifts in Skeletal Muscle and Heart During Exercise.” Physiological Reviews 80, no. 4: 1411–1481. 10.1152/physrev.2000.80.4.1411. [DOI] [PubMed] [Google Scholar]
  41. Sleivert, G. G. , Backus R. D., and Wenger H. A.. 1995. “Neuromuscular Differences Between Volleyball Players, Middle Distance Runners and Untrained Controls.” International Journal of Sports Medicine 16, no. 6: 390–398. 10.1055/s-2007-973026. [DOI] [PubMed] [Google Scholar]
  42. Tesch, P. A. , and Karlsson J.. 1985. “Muscle Fiber Types and Size in Trained and Untrained Muscles of Elite Athletes.” Journal of Applied Physiology 59, no. 6: 1716–1720. 10.1152/jappl.1985.59.6.1716. [DOI] [PubMed] [Google Scholar]
  43. Valli, G. , Wu R., Minnock D., et al. 2024. “Can Non‐invasive Motor Unit Analysis Reveal Distinct Neural Strategies of Force Production in Young With Uncomplicated Type 1 Diabetes?” European Journal of Applied Physiology 125: 247–259. 10.1007/s00421-024-05595-z. [DOI] [PubMed] [Google Scholar]
  44. Vila‐Chã, C. , and Falla D.. 2016. “Strength Training, But Not Endurance Training, Reduces Motor Unit Discharge Rate Variability.” Journal of Electromyography and Kinesiology 26: 88–93. 10.1016/j.jelekin.2015.10.016. [DOI] [PubMed] [Google Scholar]
  45. Wu, R. , Delahunt E., Ditroilo M., Ferri Marini C., and De Vito G.. 2019. “Torque Steadiness and Neuromuscular Responses Following Fatiguing Concentric Exercise of the Knee Extensor and Flexor Muscles in Young and Older Individuals.” Experimental Gerontology 124: 110636. 10.1016/j.exger.2019.110636. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

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