Introduction

Optimal upper body muscle mechanical properties (MMP) [velocity, force, and power ] are critical for performance in various ball sports, particularly those involving throwing1,2. Assessing these properties is vital for improving performance, preventing injuries, and facilitating rehabilitation3,4,5. Typically, MMP are evaluated using protocols requiring maximum voluntary exertion6,7,8. Maximum voluntary exertion (MVE) refers to the greatest level of force an individual can voluntarily produce during a given movement, independent of external loading9. Unlike maximal or submaximal external loads (e.g., 100% or 80% 1RM), MVE reflects the neural drive to the muscles, as evidenced by electromyographic activity9,10. Various factors have been shown to affect the subject’s ability to exhibit MVE during testing, including pain11,12, fatigue13, previous injury14, impaired proprioception15, discomfort16, lack of motor skills17, or inaccurate instructions18,19. Consequently, these factors can compromise the validity and reliability of motor testing results18,20.

In our study, reduced effort refers to a voluntary decrease in muscle exertion during motor tasks, such as the seated medicine ball throw (MBT). This phenomenon is associated with a lower neural drive, which can influence exerted velocity, force, and power compared to an individual’s maximum capability10. Unlike external load reductions (e.g., submaximal percentage of 1-RM), voluntary effort modulation can directly affect neuromuscular activation patterns10,21. Previous studies have demonstrated that reduced effort can alter the coordination of multi-joint exercises by redistributing muscle activity, leading to disproportionate changes in observed MMP20,21. Specifically, voluntary reduced effort has been shown to decrease and modify exerted work, force, and power in leg muscles20,21. It is suspected that these changes occur due to alterations in both intramuscular and intermuscular coordination, which are key regulators of movement efficiency20,21. Słomka et al.20 and Salles et al.21 demonstrated that reduced effort alters intermuscular coordination by redistributing muscle activation across synergistic and antagonist muscles, leading to inefficient force transfer and compromised jump performance. Their findings emphasize the role of global neuromuscular adaptations in effort-related performance declines. In contrast, Del Vecchio et al.10 focused on intramuscular coordination, highlighting that reduced effort predominantly affects motor unit recruitment and discharge rate, limiting the activation of high-threshold motor units essential for maximal force and power generation. While both perspectives agree on the negative impact of reduced effort on movement efficiency, Salles et al.21 argue that intermuscular coordination is the primary limiting factor in complex motor tasks, whereas Del Vecchio et al.10 attributes these impairments to suboptimal motor unit behavior at the muscular level. This divergence suggests that the relative contribution of intra- versus intermuscular coordination to reduced effort effects may depend on task complexity20,21, muscle group involvement10, and the degree of voluntary effort reduction22,23.

Moreover, findings in this area remain inconsistent, particularly regarding which MMP best reflects the sense of effort during various motor tasks. For instance, Salles et al.21 reported reductions in maximal jump height, ground reaction force, maximum joint torques, and peak power under conditions of reduced effort in countermovement jumps. Conversely, Słomka, et al.20 found relatively small changes in maximum force and power at 50% reduced effort, though they noted significant reductions in jump height and performed work. Despite these insights, research on the effects of reduced effort on upper body MMP remains limited. Further investigation is needed to clarify how reduced effort influences MMP in upper-body tasks and to determine the extent to which findings from lower-body studies can be generalized to upper-body performance.

To the best of the authors’ knowledge, only a few studies have explored the relationship between effort and upper-body performance. Melugin, et al.24 demonstrated that fatigue-induced effort reduction led to decreases in elbow torque and ball velocity during one-handed throws. However, they found that the reductions in elbow torque and ball velocity were not proportional to the rate of perceived effort (RPE)24. Similarly, Simim et al.25 reported elevated RPE in amputee football players following a set of physical tests after matches. This was accompanied by a significant decrease in 3-kg medicine ball throw distance (– 8%), vertical jump height (– 5%), and the number of performed push-ups (– 17%), with an estimated effort increase ranging from 67 to 82%.

Notably, prior studies examining effort and upper-body performance have primarily focused on the relationship between RPE and performance variables after intense physical activity that incorporates other sensory factors. However, no research to date has investigated the effects of voluntary reduced effort on arm MMP during throwing tasks under controlled laboratory conditions. It is important to emphasize that individuals can distinguish the feeling of effort from other sensory feelings such as pain, discomfort, weight, muscle tension, and movement26. Therefore, the results should be interpreted with caution when evaluating the effects of reduced effort on MMP outcomes23,26. Misinterpretations may arise if physical effort is substituted by other types of effort or if proprioceptive sensations are inaccurately assessed27. The perception of effort, also referred to as perceived exertion or sense of effort, is a cognitive feeling of work associated with voluntary motor actions23.

To address the issues outlined above, this study aimed to investigate how voluntary reductions in effort affect absolute and relative changes in velocity, force, and power during seated MBT and to assess whether these changes are consistent across varying load conditions. In our study, we specifically define reduced effort as the intentional execution of MBTs with submaximal effort, allowing us to investigate how voluntary modulation of exertion impacts upper-body muscle function in throwing movement. Based on previous findings, we hypothesized that: (a) reductions in velocity, force, and power would differ based on both effort and load conditions; (b) power would exhibit the greatest sensitivity to effort modulation across all loads; and (c) changes in velocity would be most pronounced at lower loads, whereas changes in force and power would be more prominent at higher loads.

The results of this study are anticipated to further illuminate the effects of reduced effort on the MMP involved in throwing performance. Additionally, this research seeks to establish a theoretical framework for interpreting and standardizing a non-invasive assessment method for arm and shoulder muscles, particularly for tracking recovery in individuals unable to perform at maximum capacity. Finally, this study aims to contribute to the optimization of submaximal training protocols for MBT, providing practical guidelines for athletes transitioning back to full performance following injury. Understanding these effects is essential for interpreting performance outcomes in sports and rehabilitation settings22.

Methods

Participants

Twelve male physical education students (age: 21.0 ± 1.2 years, body mass: 81.4 ± 8.6 kg, height: 1.84 ± 0.06 m) voluntarily participated in this study. All participants were free from neurological conditions or recent injuries that could affect the outcomes. Based on the International Physical Activity Questionnaire28, participants were classified into three physical activity levels: moderate (n = 4), high (n = 4), and very high (n = 4). Prior to testing, each participant received a comprehensive explanation of the study protocol and was advised to avoid any intense physical activity for 48 h. Informed consent was obtained from all participants before the study began. The research followed the ethical principles outlined in the Declaration of Helsinki and was approved by the Institutional Review Board (Approval No. IO175038).

Experimental protocol

A cross-sectional design was employed to investigate the effects of reduced effort on arm MMP during the MBT under varying loading conditions. The study involved two sessions, spaced 72 h apart. In the first session, participants underwent anthropometric measurements and were familiarized with the MBT protocol. The second session was dedicated to MBT performance assessments, incorporating different combinations of voluntary effort and loading levels. Both sessions were conducted at the same time of day (± 1 h) for each participant, under consistent environmental conditions (~ 22 ºC, ~ 60% humidity).

Testing procedures

At the start of the first session, body height (measured using a Seca 202 stadiometer, Seca Ltd., Hamburg, Germany) and body mass (assessed with an InBody 720 Bioelectrical Impedance Analyzer, USA) were recorded. Each session began with a standardized warm-up consisting of 5 min of cycling followed by 5 min of joint mobility exercises. After the warm-up, participants were familiarized with the MBT protocol by throwing medicine balls of varying masses until consistent technique was achieved. Effort levels were introduced verbally, with only one execution trial performed to minimize learning effects.

The MBT was conducted in a seated position, with legs extended and the back supported against a firm, immovable surface. Participants held the medicine ball with both hands at chest level, arms abducted, and elbows at shoulder height. They were instructed to remain still and to throw the ball horizontally using only their arms. To calibrate effort perception, participants first performed maximal-effort throws (100%) using the instruction: “Throw the ball as powerfully as you can! Ready, go!” This provided a clear reference for their maximum capacity. Subsequently, they performed two submaximal trials at 50% and 75% of maximum effort, receiving standardized verbal cues (e.g., “Throw the ball at 50% of your maximum effort”). After each submaximal throw, participants were asked to reflect on whether their perceived exertion matched the instructed intensity. Verbal encouragement was provided consistently during 100% trials, while submaximal efforts were accompanied by calm, neutral delivery. Trials were repeated if participants deviated from proper form (e.g., lifting the back or failing to throw horizontally).

In the second session, the effects of reduced effort were assessed using the same warm-up procedure. Participants completed 24 total trials (2 trials × 3 effort levels [100%, 75%, 50%] × 4 load conditions [0.43 kg, 2 kg, 4 kg, and 6 kg]). Trials were presented in random order, with a 60-second rest between each. Effort-specific verbal instructions were given prior to each trial (e.g., “Throw horizontally at 75% of maximum effort”).

Data collection and processing

Data acquisition, including acceleration and velocity, was performed using Qualisys 3D cameras (Qualisys Pro Reflex MCU120 Motion Capture System, Sweden). A single reflective marker was attached to the subject’s right hand at the styloid process of the ulna. The cameras tracked the marker’s position at a sampling rate of 200 Hz. Velocity and acceleration were derived from the first and second derivatives of the position-time curve, respectively. Simultaneously, force values were calculated by multiplying the measured acceleration by the actual mass of the medicine ball. Peak power was then computed as the product of the calculated force and the marker’s velocity. All variables were processed and analyzed using MATLAB software (MATLAB and Statistics Toolbox Release 2013a, The MathWorks, Inc., Natick, Massachusetts, USA). The trial with the higher force value at 100% effort of each loading level was used for further data analyses, while the average of two trials with the same loading levels were calculated for the 50% and 75% efforts. The normalized MMP variables (MMP%) of the reduced effort of 75% [MMP75%: Velocity at 75% effort (V75%), Force at 75% effort (F75%), and Power at 75% effort (P75%)], 50% effort [MMP50%: velocity at 50% effort (V50%), force at 50% effort (F50%), and power at 50% effort (P50%)] compared to 100% effort, and the reduced effort of 50% compared to 75% [MMP75 − 50%: velocity between 75% and 50% effort (V75 − 50%), force between 75% and 50% effort (F75 − 50%), and power between (P75 − 50%)] at each loading level was computed using Eq. 1.

$$X_{{(MMP\% )}} = {\text{ 1}}00\,{-}\,\left[ {\varvec{X1abs}_{{(75\% \,or\,50\% \,effort)}} /\varvec{X2abs}_{{(100\% \,or\,75\% \,effort)}} *{\text{ 1}}00} \right]$$
(1)

where X represents the relative decrease in one of the absolute MMPs, X1abs represents the absolute value of one of the absolute MMP at 75% or 50% effort, and X2abs represents the absolute value of velocity, force, or power at 100% or 75% effort.

$$Example: P_{75\%} = 100-\left(P_{(75\%{\text{ effort}}} )/P_{(100\% \text{ effort})} *100 \right)$$

Statistical analysis

Descriptive statistics are presented as means and standard deviations. Normality was evaluated using the Shapiro-Wilk test, revealing that most variables adhered to normal distribution, except for three normalized variables (V100-50% at 6 kg, P100-75% at 2 kg, and P75-50% at 2 kg), which were log-transformed. Levene’s test was employed to assess homogeneity, confirming equal variances for absolute variables [Velocity: (0.38 ≤ p ≤ 0.91); Force (0.43 ≤ p ≤ 0.95); and Power: (0.17 ≤ p ≤ 0.77)], and normalized variables [MMP75%: (0.18 ≤ p ≤ 0.21); MMP50%: (0.43 ≤ p ≤ 0.75); MMP75–50%: (0.57 ≤ p ≤ 0.62)].

Two-way repeated measures ANOVA was conducted to examine the effects of effort levels (100%, 75%, and 50%) and loading levels (0.43 kg, 2 kg, 4 kg, and 6 kg) on absolute and normalized values of velocity, force, and power. Post-hoc analyses with Holm correction was performed to identify specific differences between effort and loading conditions. Effect sizes for main effects and interactions were reported using eta squared (η²), interpreted according to established thresholds (small = 0.01, medium = 0.06, large = 0.14 29). For pairwise comparisons, Hedges’ g was used to account for small sample bias and is recommended for repeated-measures designs; thresholds for interpretation were set as small = 0.2, medium = 0.5, and large = 0.8. In addition to the primary ANOVA approach, generalized linear mixed-effects models were conducted as supplementary analyses to account for within-subject variability and further verify the robustness of the findings. All statistical procedures were performed using JASP (version 0.16.1.0), with significance set at p < 0.05.

Results

A significant main effect of effort level was found across all performance outcomes: velocity, force, and power, indicating that reductions in voluntary effort (100%, 75%, 50%) led to systematically lower outputs at each measured load (0.43 to 6 kg). Repeated-measures ANOVA revealed strong effects for effort on velocity, F(1.563, 68.76) = 318.77, p < 0.001, η² = 0.410; on force, F(1.566, 68.91) = 276.97, p < 0.001, η² = 0.080, ω² = 0.519; and on power, F(1.464, 64.42) = 299.69, p < 0.001, η² = 0.194. Due to violations of sphericity (Mauchly’s test, p < 0.05), the Greenhouse–Geisser correction was applied in all cases.

Significant effort and load interaction effects were also observed: velocity (F(4.688, 68.76) = 7.29, p < 0.001, η² = 0.028), force (F(4.698, 68.91) = 24.53, p < 0.001, η² = 0.021), and power (F(4.392, 64.42) = 19.18, p < 0.001, η² = 0.037). Post hoc comparisons showed a consistent, stepwise reduction across all effort levels at each load (p < 0.001, Holm-adjusted). As depicted in Fig. 1, velocity decreased most sharply at lighter loads (e.g., 100% vs. 50% at 0.43 kg, g = 2.63), whereas the difference in force and power was more pronounced at heavier loads (e.g., force at 6 kg: 100% vs. 50%, g = 2.46; power at 6 kg: g = 3.87; see Figs. 2 and 3). At each load, all pairwise comparisons between effort levels (100% vs. 75%, 100% vs. 50%, and 75% vs. 50%) were statistically significant with large effect sizes (Hedges’ g ranging from 0.77 to 3.87).

Fig. 1
figure 1

Mean velocity (± standard deviation) across increasing loads (0.43–6 kg) during medicine ball throws at three effort levels (100%, 75%, and 50%). Significant differences between effort levels at each load are annotated with asterisks (p < 0.001) and corresponding Hedges’ g effect sizes.

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Fig. 2
figure 2

Mean force (± standard deviation) across increasing loads (0.43–6 kg) during medicine ball throws performed at three effort levels (100%, 75%, and 50%). Significant differences between effort levels at each load are annotated with asterisks (p < 0.001) and.

corresponding Hedges’ g effect sizes.

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Fig. 3
figure 3

Mean power output (± standard deviation) across increasing loads (0.43–6 kg) during medicine ball throws performed at three effort levels (100%, 75%, and 50%). Significant differences between effort levels at each load are annotated with asterisks (p < 0.001) and corresponding Hedges’ g effect sizes.

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Independent of effort, load alone also exerted a significant main effect. For velocity, increased load produced stepwise reductions (e.g., 0.43 vs. 6 kg: Δ = 0.85 m/s, p < 0.001, g = 2.41), reflecting a typical inverse relationship between load and movement velocity. Conversely, both force and power increased significantly with higher loads. For instance, force rose from 20.85 N to 165.90 N at 100% effort (0.43 to 6 kg; Fig. 2), and power increased from 92.21 W to 533.53 W (Fig. 3). All load-step comparisons were statistically significant (p < 0.001), with large to very large effect sizes (e.g., force: 2 vs. 4 kg, g = 3.11; power: 1 vs. 4 kg, g = 5.24). Notably, while velocity differences diminished at higher loads, the increases in force and power intensified, indicating a load-dependent divergence across outcome domains.

To assess the robustness of our findings, supplementary linear mixed-effects models were conducted for each outcome variable, incorporating participants as random intercepts. These models produced excellent fit indices, with marginal R² values of 0.700 for velocity, 0.641 for force, and 0.714 for power, indicating the proportion of variance explained by fixed effects alone (i.e., effort and load). When including random effects to account for inter-individual variability, conditional R² values increased substantially—reaching 0.985 for velocity, 0.961 for force, and 0.983 for power. These results confirm that both effort intensity and external load have consistent and interactive effects on evaluated variables, even when accounting for subject-level differences.

When examining normalized measures of MMP%, two-way repeated measures ANOVA revealed significant reductions in velocity, force, and power at 75% effort (Fig. 4a; F(1.432, 63.02) = 280.48, p < 0.001, η² = 0.401), 50% effort (Fig. 5a; F(1.693, 74.50) = 411.91, p < 0.001, η² = 0.516), and between 75% and 50% effort (Fig. 6a; F(1.305, 57.42) = 242.29, p < 0.001, η² = 0.345). Although no significant load and MMP% interactions were observed (Figs. 4b, 5b and 6b; all p > 0.05), some pairwise comparisons between load levels showed small to moderate effect sizes (Hedges’ g ≈ 0.3–0.7). However, these were inconsistent in direction and not statistically significant after correction, indicating that performance reductions were consistent across all external loads.

Fig. 4
figure 4

(a) Raincloud plots with mean ± standard deviations of relative decrease in velocity (V75%, green bar), force (F75%, orange bar) and power (P75%, blue bar) at 75% effort of marginal means across variables. (b) Mean ± standard deviations of the relative decrease of velocity, force, and power at 75% effort through all loadings. Horizontal arrows indicate significant differences between variables. ***Statistical difference at p < 0.001.

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Fig. 5
figure 5

(a) Raincloud plots with mean ± standard deviations of relative decrease in velocity (V50%, green bar), force (F50%, orange bar) and power (P50%, blue bar) at 50% effort of marginal means across variables. (b) Mean ± standard deviations of the relative decrease of velocity, force, and power at 50% effort through all loadings. Horizontal arrows indicate significant differences between variables. ***Statistical difference at p < 0.001.

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Fig. 6
figure 6

(a) Raincloud plots with mean ± standard deviations of relative decrease in velocity (V75 − 50%, green bar), force (F75 − 50%, orange bar) and power (P75 − 50%, blue bar) between 75% and 50% effort of marginal means across variables. (b) Mean ± standard deviations of the relative decrease of velocity, force, and power between 75% and 50% effort through all loadings. Horizontal arrows indicate significant differences between variables. ***Statistical difference at p < 0.001.

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Post-hoc analysis on estimated marginal means revealed consistent and significant differences between velocity, force, and power across each effort contrast (all p < 0.001). Reductions in power were particularly pronounced: at 75% effort, power decreased by 32.7% (SD = ± 11.1), compared to 20.9% (± 8.5) for force and 14.5% (± 5.1) for velocity (Fig. 4a); at 50% effort, power dropped by 49.1% (± 12.4), with force decreasing 35.8% (± 10.3) and velocity 24.6% (± 7.5) (Fig. 5a); between 75% and 50%, power declined by 28.8% (± 10.9), force by 19.8% (± 8.9), and velocity by 11.8% (± 6.4) (Fig. 6a). Hedges’ g estimates confirmed large effect sizes in each comparison—power vs. velocity: 2.00–2.51; power vs. force: 0.94–1.41; and force vs. velocity: 0.78–1.10.

Discussion and implications

This study investigated how voluntarily reduced effort affects MMP during a medicine ball throw and assessed the consistency of these changes across varying loading conditions. The results revealed consistent reductions in velocity across all loads, with the decline being most pronounced at lower loads. In contrast, reductions in force and power became more significant at 50% and 75% effort levels as load increased, highlighting the interplay between loading and effort in shaping the magnitude of MMP alterations. In contrast, reductions in force and power became more significant at 50% and 75% effort levels as load increased, highlighting a load-dependent interaction for absolute values, but not for normalized outcomes.

Interestingly, relative reductions in velocity and force were less pronounced than expected when transitioning from one effort level to another, while power consistently exhibited the largest declines at each comparison point. For lower loads, power exhibited slight increases or values approximating those anticipated at reduced effort levels. This suggests that at lower resistances, neuromuscular adjustments allow for a compensatory increase in movement efficiency, potentially through optimized motor unit synchronization and recruitment strategies9. Such adaptations could reflect enhanced coordination of low-threshold motor units that improve timing precision and reduce variability in ballistic tasks, supporting more efficient control under submaximal effort30. Additionally, percentage reductions in MMP demonstrated a diminishing trend as load increased, with the most significant effects observed at the lowest loads. This diminishing trend further supports the notion that effort modulation affects neuromuscular coordination differently across resistance levels, as higher loads demand greater stabilization and force production31. Moreover, the lack of significant effort and load interactions in normalized outcomes implies that the central nervous system likely scales down voluntary effort consistently across all resistance conditions. This supports the idea that submaximal effort is regulated by a stable internal model, possibly relying on central motor commands and afferent feedback mechanisms to proportionally modulate output, regardless of the external load. These interpretations align with recent models of motor control which suggest that voluntary scaling of effort operates under supraspinal coordination mechanisms optimized for stability and task goals32,33.

These findings underscore the importance of task specificity in the force-velocity relationship, as it is highlighted in previous studies34,35,36. In MBT tasks, low-mass balls shift the focus toward velocity dominant properties, making velocity highly sensitive to muscle mechanical potential and effort modulations at minimal loads. Conversely, at higher loads, force and power become more responsive to effort variations, reflecting the unique demands of throwing movements at varying intensities37,38. These results align with the hypothesis that MMP alterations under reduced effort depend not only on the level of muscle activation but also on the contextual characteristics of the motor task39. The morphological and physiological properties of the arms and shoulder girdle play a critical role in executing these complex tasks, particularly when higher masses demand greater coordination38,40,41.

The observed relationship between effort modulation and MMP in medicine ball throws aligns with existing evidence on the force-velocity-power interplay, demonstrating that velocity reductions are more pronounced at lower loads, whereas force and power become increasingly effort-sensitive at higher resistances31,42. This supports the notion that ballistic movements, particularly those involving low-mass implements, prioritize velocity-dominant properties, making velocity the primary variable affected by submaximal effort43. Conversely, as load increases, neuromuscular adaptations shift toward force production, reinforcing findings that force output is a stronger determinant of power under greater resistances44. Given that power is the product of force and velocity, and that increases in power at higher loads are predominantly due to greater force production, it can serve as a stable marker of neuromuscular function in the MBT test. This consistency suggests that relative power measured in MBT with 2 to 6 kg loads could serve as a robust marker for neuromuscular performance, independent of loading levels. Additionally, the role of intra- and intermuscular coordination in regulating MMP at varying intensities suggests that neuromuscular adjustments to reduced effort are task-specific, influenced by the biomechanical constraints of the movement and the morphological characteristics of the involved musculature10,21,39. These findings align with evidence that muscle coordination strategies are load-dependent, as demonstrated by the significant reductions in velocity, force, and power at different effort levels. This suggests that neuromuscular control mechanisms, including motor unit recruitment and sensory feedback, adjust based on effort to optimize force transfer and movement efficiency38,45. In the context of reduced effort, these mechanisms appear to operate within a flexible but stable internal control structure, capable of maintaining proportional scaling of motor output even under varying resistance demands. Given these complexities, further research is needed to explore how effort modulation influences neuromuscular adaptations across different motor tasks, particularly regarding fatigue resistance, motor learning, and overall movement efficiency.

The practical implications of this study suggest that relative reductions in power could serve as a valuable tool for evaluating neuromuscular performance and customizing training loads. This is especially relevant for athletes returning from injury or individuals undergoing rehabilitation, where monitoring neuromuscular function is crucial for optimizing recovery and minimizing reinjury risk. Additionally, these findings contribute to understanding how intra- and intermuscular coordination regulate MMP under varying effort levels. While the accuracy of self-reported effort levels was not quantitatively verified, our research question focused on mechanical output changes rather than the precision of effort estimation. The protocol was designed in accordance with psychophysiological principles of effort perception, which posit that perceived exertion is modulated by both central motor command and afferent sensory feedback23,46. Although no established psychophysical model governs effort perception in ballistic movements, evidence from EMG studies suggests that cortical motor drive and neuromuscular coordination strongly influence perceived effort10,47. Therefore, even in the absence of real-time objective verification, we assumed that participants could reliably self-regulate their effort through internal motor control mechanisms.

Nonetheless, some methodological limitations warrant consideration. Force was estimated indirectly, rather than being directly measured using force plates or linear transducers, which may have affected the precision of mechanical output calculations. Additionally, only two trials were conducted per condition to reduce potential learning effects and simulate clinical or athletic testing environments. While this increased ecological validity, it may have introduced variability and reduced within-condition reliability. Future studies should incorporate at least three trials per condition and employ RPE scales to better triangulate effort levels. More importantly, the integration of EMG neuromuscular assessments or real-time biofeedback systems would allow for more precise validation of voluntary effort and motor drive. Future work should also expand the scope of investigation to include various upper-body ballistic tasks, consider fatigue and motivational states, and adopt kinematic and kinetic measurement systems to explore how neuromechanical responses evolve under different effort conditions. These extensions would enrich our understanding of submaximal neuromuscular function and inform the design of more personalized rehabilitation and training protocols.

Conclusions

This study concludes that voluntarily reduced effort leads to disproportional changes in velocity, force, and power during MBT. As load increases, the effects of reduced effort on velocity become less pronounced, while its influence on force and power becomes more significant. Among the MMP variables, relative power closely adhered to effort instructions, demonstrating its potential as a stable marker in treatments involving MBT loads of 2 to 6 kg. These findings emphasize the need for tailored methodological procedures and assessment protocols to optimize the use of MBT in clinical and athletic settings, particularly for submaximal training and rehabilitation.