Sensitivity of the load-velocity relationship variables to discriminate the level of fatigue induced by multiple sets of the smith-machine squat exercise

Sensitivity of the load-velocity relationship variables to discriminate the level of fatigue induced by multiple sets of the smith-machine squat exercise | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Sensitivity of the load-velocity relationship variables to discriminate the level of fatigue induced by multiple sets of the smith-machine squat exercise deniz şentürk, Aliasker Kumak, Danica Janicijevic This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8079121/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Feb, 2026 Read the published version in BMC Sports Science, Medicine and Rehabilitation → Version 1 posted 12 You are reading this latest preprint version Abstract Background The aim of the research was to examine the sensitivity of load-velocity (L-V) relationship parameters ( L 0 , v 0 , and A line (area under the L–V line; Aline = L 0 × v 0 / 2) ) in detecting fatigue after different fatigue protocols as well as their correlation with changes in 1-repetition maximum (1RM). Methods After a familiarization and preliminary testing session which was used for 1RM smith-machine squat (SMS) determination and performing a set of repetition to failure with 70%1RM load, 28 resistance-trained men randomly performed three fatigue protocols. All fatigue protocols were carried out between two incremental loading tests, conducted at the beginning (pre-session) and end (post-session) of the training session. The characteristics of the fatigue protocols were as follows: (i) control protocol: no training, (ii) moderate-fatigue protocol: 5 sets of the SMS exercise at 70%1RM performing half the maximum possible number of repetitions, and (iii) high-fatigue protocol: 5 sets of the SMS exercise performed to failure against the 70%1RM. Results Post-session declines in 1RM ( p < 0.001), L 0 ( p = 0.001) and A line ( p < 0.001) were the greatest after the high fatigue protocol, followed by the moderate fatigue protocol and finally the control protocol. Changes in v ₀ did not differentiate between the fatigue protocols ( p = 0.325). The post-session percentage change in 1RM was significantly correlated with the percentage change in A line ( r = 0.832) and L 0 ( r = 0.764), but not with the percentage change in v 0 ( r = -0.012). Conclusions These results suggest that L-V relationship variables offer a highly sensitive and practical solution for fatigue monitoring. Trial registration: Trial registration: ClinicalTrials.gov, NCT07307963 (First posted: 19 December 2025; retrospectively registered). fatigue resistance training strength testing velocity-based training Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Muscle fatigue can be defined as the loss of the ability of a specific muscle or muscle group to produce strength and power due to exercise-induced stress [ 1 ]. It is acknowledged that muscle fatigue can result from multiple mechanisms with the key contributing factors including (a) disruptions in ionic balance and neural stimulation, (b) accumulation of metabolic byproducts, and (c) reduced motor unit activation [ 2 ]. The level of fatigue induced during resistance training (RT) significantly influences how the neuromuscular system adapts in response to RT stimuli [ 3 ]. Therefore, the periodic assessment of RT-induced fatigue is paramount in optimizing strength adaptations and enhancing training efficacy [ 4 ]. Countermovement jump (CMJ) height [ 5 ], mean velocity against a fixed absolute load [ 6 ], and isometric maximal strength [ 7 ] are among the most commonly used metrics for monitoring fatigue. However, assessing fatigue based on a single mechanical variable is problematic, as it does not indicate whether the reduction in maximal power (P max ) [ 8 ] is due to impaired force ( F 0 ) or velocity ( v ₀) capabilities [ 9 ]. This situation underscores the necessity of a more comprehensive evaluation of the fatigue process and its impact on performance variables. A potential solution to this problem is the modeling of the force-velocity (F-V) relationship and the computation of the F-V parameters ( F 0 , v ₀ and P max ) under different fatigue conditions. For example, Garcia-Ramos et al. [ 10 ] found that a reduction in v ₀ was the primary cause of the decrease in P max following a light-load fatigue protocol involving bench press performed to failure at 60% of the one-repetition maximum (1RM). Conversely, the decline in P max during the non-failure high-load traditional (HLT) protocol involving bench press sets at 80% 1RM was mainly due to a decrease in F 0 , with no significant change in v ₀. Another study by Li et al. [ 11 ] examined the sensitivity of the F-v parameters in detecting fatigue under a HLT protocol consisting of five squat repetitions at 80% 1RM and a light-load ballistic (LLB) protocol comprising five squat jump repetitions at 30% 1RM. Even though both RT protocols resulted in a decline in P max (10.1% and 12.2% for HLT and LLB, respectively), F 0 and v ₀ contributed to this reduction to varying extents. In the LLB squat protocol, the decline in v ₀ (9.7%) was more pronounced than the minimal decrease in F 0 (0.4%), whereas during the HLT squat protocol, F 0 showed a greater reduction (8.4%) compared to v ₀ (4.1%). The findings of both studies consistently indicate that, in low-load, high-velocity exercises, the decline in P max is predominantly attributed to a reduction in v ₀. Conversely, in high-load, low-velocity exercises, the decrease in P max is attributed to a decrease in F 0 . These observations substantiate the hypothesis that monitoring fatigue through a sole mechanical parameter may lack adequate sensitivity. Although F–V modeling provides a comprehensive framework to characterize neuromuscular mechanical capacities [ 12 ] some applications show substantial variability in v 0 , which may limit its usefulness for certain exercises [ 13 ]. For example, González-Hernández et al. [ 14 ] explored the sensitivity of F-V parameters to selectively detect fatigue before and after three RT involving full squats and bench presses. Each session consisted of five repetitions at 10 RM, with varying inter-set rest durations in each session (1, 3, or 5 minutes). Contrary to predictions, the v ₀ significantly increased after RT sessions. For example, in the CMJ test conducted following a one-minute recovery period, v ₀ rose from a pre-training baseline of 3.46 ± 0.93 m·s⁻¹ to 3.61 ± 0.77 m·s⁻¹ post-training. Similar increase was observed in the bench press throw test, where v ₀ rose from 2.51 ± 0.34 m·s⁻¹ to 2.60 ± 0.27 m·s⁻¹ after a 3-minute rest period. The high variability in v ₀ observed in this and similar studies when performing exercises against gravity is likely explained by the large extrapolation needed from the experimental point representing the lowest load (e.g., unloaded jump) to the velocity-axis [ 15 ]. To address the issue of high v 0 variability, load-velocity (L-V) modelling may offer a more suitable alternative to traditional F-V modelling, when it comes fatigue assessment as previous studies have established validity and reliability of the L-V modelling in exercises such as bench press throw [ 16 ] and squat [ 17 ]. The parameters related to the L-V modelling include theoretical maximal load ( L 0 : load at 0 m·s⁻¹), theoretical maximal velocity ( v 0 : velocity at 0 kg), and the area under the L-V relationship line (A line = L 0 × v 0 /2). Recent studies have reported significant reductions in v 0 and A line following the fatigue protocol, whereas L 0 remained unchanged [ 18 , 19 ]. However, a different study observed decreases in L 0 and A line , but not v 0 [ 20 ]. Overall, these results suggest that there is currently no clear consensus in the literature on how L-V relationship variables respond to fatigue. Therefore, to address the shortcomings of the previous studies, the objective of this study was (i) to compare how L-V relationship parameters (L 0 , v 0 , and A line ) change after two fatigue protocols involving several sets of smith-machine squat (SMS) exercises. One protocol aimed to induce moderate fatigue (performing half the maximum number of repetitions), and the other aimed to induce high fatigue (performing sets to exhaustion), and (ii) to determine whether changes in L 0 , v 0 , and A line following the fatigue protocols significantly correlate with the SMS 1RM test, a traditional measure of maximal strength. We hypothesized that (i) all L–V parameters would decrease after the fatigue protocols, with larger decreases in L 0 and A line than in v 0 , particularly after the high-fatigue protocol; and (ii) percentage changes in 1RM would correlate strongly with changes in L 0 and A line , but not with v 0 . Materials and Methods Subjects Twenty-eight physically trained males, with an average age of 23.3 years (standard deviation [SD]: 3.0 years; range: 20–36 years), willingly enrolled in this research endeavor. The participants exhibited a mean body mass of 78.1 kg (SD: 9.3 kg), a body height of 177.2 cm (SD: 3.3 cm), and a one-repetition maximum (1RM) for smith-machine squat (SMS) exercise of 150.9 kg (SD: 12.5 kg). All subjects possessed prior RT experience, averaging 5.0 years (SD: 2.6 years), and demonstrated proficiency in executing the SMS exercise during the familiarization session. Note that all athletes were using the SMS exercise in their RT programs. None of the participants exhibited any physical limitations or neuromuscular injuries that could impede their safe participation in the study. Participants were explicitly instructed to refrain from engaging in strenuous lower-body exercises throughout the study and were required to arrive at each testing session in a rested state. Prior to commencement, all subjects received a comprehensive verbal explanation of the testing procedures and subsequently provided informed consent by signing a consent form, acknowledging their willingness to participate in the study. The research protocol strictly adhered to the principles outlined in the Declaration of Helsinki and was approved by the Ethics Committee of Istanbul Gelisim University (Approval no: IGU2024/04/67). The study was retrospectively registered at ClinicalTrials.gov (Identifier: NCT07307963; First posted: 19 December 2025). Study design A crossover study design was applied to investigate the sensitivity of L-V relationship variables in distinguishing the extent of fatigue caused by multiple sets of SMS exercise. Subjects were required to visit the laboratory on five separate occasions. There was 72 hours of rest after the familiarization session and 96 hours of rest after each testing sessions. The initial session aimed to familiarize subjects with the SMS exercise, during which they performed the exercise at their maximal intended velocity while dealing with varying external loads. In the second session, participants underwent an incremental loading test to establish their SMS 1RM, after which participants rested 5 min and then performed one set to failure with 70% of their 1RM. The subsequent three experimental sessions were conducted in a randomized sequence. A consistent element across these experimental sessions was that, at the beginning and end of each experimental testing session, subjects underwent a complete incremental loading test using the SMS exercise, with the loads ranging from 30% of the 1RM determined in the second session to the actual 1RM. This test aimed to determine both the 1RM and L-V relationship parameters ( L 0 , v 0 , and A line ). The difference among the experimental sessions lay in the nature of the activity undertaken by subjects during the 30-minute interval that separated the two incremental loading tests. These activities included control, moderate-fatigue, and high-fatigue protocols. All sessions were meticulously conducted at the university research laboratory, supervised by the same researcher (DS), and maintained at approximately 22–24°C with humidity of approximately 60%. Familiarization session (session 1) Following a general warm-up that included jogging and joint mobilization exercises, participants performed a series of the SMS exercise against four different loads (30%, 50%, 70%, and 80% of their self-estimated 1RM) for three repetitions and one repetition against a load corresponding to 90% and 100% of their self-estimated 1RM. The participants initiated the exercise from a fully extended posture, maintaining a shoulder-width stance with the barbell positioned across the back at the acromion level, commonly referred to as the "high-bar position." Instructed to execute a continuous descent, they were guided to lower themselves until their thighs were parallel to the ground. Upon reaching this bottom position, participants were required to swiftly return to the initial position as fast as possible [ 22 ]. To ensure the adherence to prescribed technique, an iPad 10th generation (Apple Inc., Cupertino, CA, USA) was thoughtfully positioned on a tripod with a height of 0.5 meters, located 1.5 meters away from the subjects, diagonally behind them. This configuration facilitated the continuous observation of hip movements during all repetitions, which were diligently captured in slow-motion recordings. Any repetitions deviating from the desired technique were promptly identified and repeated for accuracy. The standardized technique requirements and instructions were consistently applied across all sessions throughout the course of this study. Emphasis was placed on maintaining constant downward pressure on the barbell throughout the entirety of the movement, and subjects were explicitly prohibited from utilizing any jumping motions. After a 5-minute rest period, subjects performed six consecutive repetitions against the 70% of their self-estimated 1RM using eccentric-concentric technique as described above. Preliminary testing session (session 2) After the completion of the identical general warm-up routine implemented in the familiarization session, which encompassed jogging and joint mobilization exercises, participants underwent a specific warm-up protocol. This entailed one set each of 8, 5, and 2 repetitions against loads corresponding to 40%, 60%, and 80% of their self-estimated 1RM, respectively. Subsequently, the load was incrementally augmented until reaching the 1RM for eccentric-concentric SMS. Load increments were determined by assessing the mean velocity (MV) of the repetitions, with increases of 20–40 kg for MV above 0.80 m·s⁻¹, 10–20 kg for MV ranging from 0.80 m·s⁻¹ to 0.40 m·s⁻¹, and 1–10 kg for MV below 0.40 m·s⁻¹. Performance involved three repetitions with light loads (MV > 0.80 m·s⁻¹), two repetitions with medium loads (MV = 0.50–0.80 m·s⁻¹), and one repetition with heavy loads (MV < 0.50 m·s⁻¹). Participants adhered to rest intervals of 2, 3, and 5 minutes when lifting light, medium, and heavy loads, respectively. Experimental testing sessions (sessions 3–5) The three experimental sessions commenced with the implementation of the identical general and specific warm-up routines delineated in the preliminary testing session. Subsequently, participants undertook the initial incremental loading test of the experimental session (pre-session L-V assessment): 3 repetitions at 30%1RM, 2 repetitions at 50%1RM and 70%1RM, and 1 repetition at 80%1RM and 90%1RM. Following the 90%1RM trial, participants executed single 1RM attempts until they were unable to complete a repetition. The maximal load lifted with proper technique was considered the 1RM. Recovery time was set at 3 minutes for submaximal lifts and 5 minutes for 1RM attempts. An identical incremental loading test was conducted at the conclusion of the experimental session (post-session L-V assessment). The three experimental sessions differed in the activity participants engaged in during the 30-minute interval that separated pre-session and post-session incremental loading tests: (i) control protocol: passive rest for 30 min; (ii) After completing the pre-session 1RM test, 5 min of rest was provided. Participants then performed 5 sets of the SMS exercise at 70% of 1RM, each set consisting of half of the maximum number of repetitions determined during Session 2, with 2-min inter-set rest. After the final set, 15 min of rest was provided before the post-session incremental loading 1RM test; and (iii) high-fatigue protocol: identical to the moderate-fatigue protocol, except that all sets of the SMS exercise at 70% of 1RM were performed to failure. It is worth noting that in the medium fatigue protocol participants executed only half of the maximum number of repetitions performed in the set up to 70% failure determined during preliminary testing session (session 2), while in the high fatigue protocol all sets were executed up to failure. Data acquisition and analysis The SMS exercise was executed utilizing a 10 kg smith-machine bar along with calibrated weight discs (Technogym; Italy, Europe) ranging from 0.5 to 25 kg. To capture the MV of all repetitions, a validated linear position transducer (GymAware RS PowerTool, Kinetic Performance Technologies, Canberra, Australia) was affixed to the right side of the barbell using a velcro strap, as detailed by [ 21 ]. The data acquired from the device were wirelessly transmitted through BluetoothTM to a tablet (iPad, Apple Inc., Cupertino, CA) via the GymAware v4.1.6 app, and subsequently, to an online cloud platform. Following this, the data were exported to Microsoft Excel (Microsoft Corporation, Redmond, WA) and prepared for subsequent analysis. The L-V relationship was established based on individual values of MV and the external load lifted (kg) across five loading conditions (30%, 50%, 70%, 80%, and 90% of 1RM). For each load, only the trial with the highest MV was considered in determining the individual L-V relationship. Utilizing a least-square linear regression model (L[v] = L 0 – sv), where L 0 represents the load at zero velocity and s is the slope of the L-V relationship. Subsequently, the theoretical maximal velocity ( v 0 ) and the area under the L-V relationship line (A line ) were calculated as v 0 = L 0 /s and A line = L 0 · v 0 /2. Hence, four dependent variables were taken into account in this study including the actual 1RM, and three variables derived from the L-V relationship ( L 0 , v 0 , and A line ). Statistical analyses Descriptive statistics are reported as means and standard deviations. The normality of the data distribution was verified through the Shapiro-Wilk test ( p > 0.05). Reliability evaluation for the 1RM and L-V relationship parameters (L 0 , v 0 , and A line ) involved a comparison of the pre-session incremental loading tests between the first two experimental sessions. The coefficient of variation (CV% = standard error of measurement / participants’ mean score × 100) and the intraclass correlation coefficient (ICC; model 3.1) were computed as indices of absolute and relative reliability, respectively. A two-way repeated measures analysis of variance (ANOVA; protocol [control, moderate-fatigue, and high-fatigue] × time [pre-session and post-session]) with Bonferroni post hoc corrections was applied to each dependent variable. Subsequently, the Pearson's correlation coefficient ( r ) was utilized to quantify the association between the percentage changes in 1RM and the corresponding percentage changes in L 0 , v 0 , and A line . The criteria employed to interpret the magnitude of the r coefficients were as follows: trivial (0.00–0.09), small (0.10–0.29), moderate (0.30–0.49), large (0.50–0.69), very large (0.70–0.89), nearly perfect (0.90–0.99), and perfect (1.00) (1). The CV assessments was conducted using Excel (Microsoft Corporation, Redmond, WA), while other statistical analyses were carried out with the statistical software (JASP version 0.18.3, Amsterdam, The Netherlands). RESULTS The reliability of all dependent variables was high: 1RM (CV = 1.16% and ICC = 0.99), L 0 (CV = 5.1% and ICC = 0.87), v 0 (CV = 2.10% and ICC = 0.86), and A line (CV = 3.23% and ICC = 0.96). Both fatigue protocols induced fatigue, as evidenced by the gradual decline in the fastest MV within each set with increasing number of sets (Table 1 ). As anticipated, the high-fatigue protocol exhibited a greater velocity loss and a more pronounced reduction in the fastest MV of the set compared to the moderate-fatigue protocol. [Table 1 ] Table 1 Description of the training variables for the moderate- and high-fatigue protocols. Protocol Set number Number of repetitions Fastest MV (m·s − 1 ) Final MV (m·s − 1 ) Velocity loss (%) Moderate-fatigue 1 6 ± 0.04 0.57 ± 0.03 0.46 ± 0.05 -20.1 ± 7.5 2 6 ± 0.04 0.54 ± 0.04 0.43 ± 0.04 -20.6 ± 6.3 3 6 ± 0.04 0.52 ± 0.04 0.41 ± 0.5 -21.8 ± 8.6 4 6 ± 0.04 0.50 ± 0.04 0.38 ± 0.06 -22.9 ± 10.3 5 6 ± 0.04 0.49 ± 0.04 0.35 ± 0.05 -27.4 ± 8.4 High-fatigue 1 13.7 ± 1.7 0.58 ± 0.02 0.25 ± 0.03 -56.6 ± 4.9 2 8.2 ± 1.5 0.51 ± 0.05 0.26 ± 0.03 -49.3 ± 6.5 3 6.3 ± 1.4 0.47 ± 0.05 0.26 ± 0.03 -44.2 ± 7.6 4 5.3 ± 1.2 0.43 ± 0.06 0.25 ± 0.04 -40.2 ± 10.4 5 4.2 ± 1.4 0.40 ± 0.07 0.26 ± 0.03 -34.2 ± 12.7 MV, mean velocity; Velocity loss (%) = (Final MV – Fastest MV) / Fastest MV × 1 The 1RM and the three L-V relationship variables ( L 0 , v 0 , and A line ) decreased at post-session compared to pre-session with the only exception of v 0 for the control protocol (Table 2 ). The interaction effect between protocol and time yielded significance for the 1RM, L 0 , and A line , as their decline at post-session was more pronounced for the high-fatigue protocol, followed by the moderate-fatigue protocol, and finally for the control protocol (Fig. 1 ). No significant interaction between protocol and time was observed for v 0 . [Table 2 ] Table 2 Comparison of the 1-repetition maximum (1RM) and load-velocity relationship parameters between the fatigue protocols. Variable Protocol Pre-session Post-session ANOVA 1RM (kg) Control 153.6 ± 23.4* 149.9 ± 23.6 a,b Protocol: F = 16.6; p = 0.001 Time: F = 749.9; p < 0.001 Protocol × Time: F = 143.9; p < 0.001 Moderate-fatigue 153.6 ± 22.9* 143.2 ± 22.6 b High-fatigue 153.3 ± 21.6* 134.0 ± 20.8 L 0 (kg) Control 205.2 ± 26.4 202.9 ± 25.8 a,b Protocol: F = 452.0; p = 0.001 Time: F = 32.1; p < 0.001 Protocol × Time: F = 112.4; p = 0.001 Moderate-fatigue 205.0 ± 26.2* 188.2 ± 28.1 b High-fatigue 203.8 ± 27.3* 171.2 ± 23.3 v 0 (m·s − 1 ) Control 1.18 ± 0.06 1.17 ± 0.06 Protocol: F = 0.4; p = 0.610 Time: F = 32.9; p < 0.001 Protocol × Time: F = 1.1; p = 0.325 Moderate-fatigue 1.18 ± 0.06* 1.16 ± 0.05 High-fatigue 1.19 ± 0.05* 1.16 ± 0.05 A line (kg·m·s − 1 ) Control 121.9 ± 17.9* 119.3 ± 17.7 a,b Protocol: F = 28.7; p = 0.001 Time: F = 719.5; p < 0.001 Protocol × Time: F = 184.3; p < 0.001 Moderate-fatigue 121.3 ± 18.2* 109.3 ± 17.9 b High-fatigue 121.3 ± 17.5* 99.8 ± 14.8 ANOVA, analysis of variance; L 0 , maximal theoretical load; v 0 , maximal theoretical velocity; A line , area under the load-velocity relationship line; *, significantly different than post-session; a , significantly different than moderate-fatigue; b , significantly different than high-fatigue [Figure 2 ] The percent change in the 1RM at post-session was significantly correlated with the percent change in A line ( r = 0.832) and L 0 ( r = 0.764), but not with the percent change in v 0 ( r = -0.012) (Fig. 2 .) [Figure 3 ] DISCUSSION This study was designed to examine the sensitivity and utility of the L-V parameters ( L 0 , v 0 , and A line ) in identifying instances of fatigue in response to diverse fatigue protocols (control, moderate-fatigue and high-fatigue). The primary hypothesis of this study was confirmed by the findings that the L-V variables demonstrated sensitivity in detecting fatigue at varying levels, leading to two key conclusions. Firstly, significant decrements were observed between the pre- and post-session for all variables and fatigue protocols, with the exception of v 0 and L 0 in the control protocol. Secondly, the L-V variables showed the most pronounced decrease in the high-fatigue, followed by the moderate-fatigue, and control protocols, with the exception of v 0 . Supporting our second hypothesis, very large correlations were observed between changes in 1RM and changes in L 0 and A line , but not with v 0 . These results collectively reveal that L-V relationship variables offer a highly sensitive and practical solution for fatigue monitoring. The present findings suggest that, in comparison with the moderate-fatigue protocol, the high-fatigue protocol resulted in a greater reduction in the fastest mean velocity (fastest MV) across sets (Δ: moderate-fatigue 14%, high-fatigue 31%) and a higher magnitude of velocity loss (Δ: moderate-fatigue 22%, high-fatigue 44%). These findings corroborate earlier findings in the literature, demonstrating that high-fatigue protocols involving greater velocity loss elicit greater neuromuscular fatigue and lead to a more progressive reduction in the fastest MV across sets [ 22 ]. Additionally, while 1RM, A line , L 0 , and v 0 showed high inter-session reliability, v 0 also exhibited strong test–retest reliability in our data (ICC = 0.86). This contrasts with previous research [ 20 ], which reported lower reliability for v 0 during the HBD exercise (ICC: 0.48). Previous studies have attributed the variability in v ₀ to the biomechanical characteristics of exercises performed with low loads, which cause the extrapolation point to be positioned farther from F ₀ [ 15 ]. However, the discrepancy between our findings and previous studies utilizing the HBD under identical protocols may be attributed to the following technical and mechanical differences between the two exercises. In the previous study, participants were instructed to avoid shoulder elevation during the HBD exercise while simultaneously performing all lifts at maximal velocity. Although all lifts were monitored throughout the study, this combination may have led participants, particularly under low-load, high-velocity conditions, to instinctively elevate their shoulders to maximize lifting velocity, thereby increasing variability across sessions [ 20 ]. In contrast, during the SMS exercise, participants can comfortably perform a standardized lift with low loads while maintaining their hips parallel to the ground, which likely reduces v ₀ test-retest variability across different days [ 23 ]. A significant decline in 1RM and all L-V variables ( L 0 , v 0 , and A line ) following the different fatigue protocols aligns with the findings of several studies with the similar design [ 24 – 26 ]. A key aspect of our findings is that, while the L ₀ and A line were sensitive enough to detect different levels of fatigue induced by the protocols (control > moderate-fatigue > high-fatigue), v ₀ failed to do so, implying that the sensitivity of L-V parameters is different when it comes to detecting fatigue levels during the SMS exercise. The present findings align with those of Şentürk et al. [ 29 ] indicating that L 0 and A line are highly sensitive markers capable of discriminating between different levels of neuromuscular fatigue, as they consistently decreased in a graded manner following high- and moderate-fatigue protocols, while v 0 failed to show such discriminatory capacity in both SMS and HBD exercises. However, two recent studies reported significant decreases in the A line and v ₀ variables after fatigue protocols were followed, but not in the L 0 variable [ 18 , 19 ]. This difference may be attributed to variations in the load ranges and methodological approaches used to determine the L–V relationship variables across studies. One study determined the L–V variables using a load corresponding to 20% of 1RM and a velocity of 0.55 m/s based on the individual LV profile [ 18 ]. The other study, however, calculated the L–V relationship variables using loads in the 20–80% 1RM range [ 19 ]. In the present study, the L–V variables were determined using loads in the 30–90% 1RM range. Furthermore, the decrease in 1RM values following the fatigue protocol in the present study resulted in modelling of the L–V relationship within the 30–100% 1RM range after moderate and high-fatigue protocol. This suggests that the decrease in L ₀ observed after fatigue may have occurred in the 90–100% 1RM range, where higher force is required, rather than at loads below 80% 1RM. Taken together, the results of the present study align with the findings of previous studies that indicated that the changes in the A line are influenced to varying degrees by the L 0 and v 0 variables depending on the source of fatigue (e.g., high load-low velocity v s. low load-high velocity). This finding underscores the practical utility of A line as a sensitive method for detecting different levels of fatigue. Confirming our second hypothesis, a significant correlation was observed between changes in 1RM and the A line and L ₀ variables following fatigue protocols, while no significant relationship was found with v ₀. Once again, our results align with previous studies that have reported a strong relationship between 1RM and L ₀ in both fatigued [ 27 ] non-fatigued [ 17 ] conditions, as well as with research examining the relationship between changes in these variables [ 20 ]. A key contribution of our study is its demonstration of the interaction between changes in 1RM and L-V variables during the SMS exercise, a frequently utilized movement pattern for monitoring lower-limb fatigue [ 28 ]. The findings presented above offer the following key conclusions regarding methods of fatigue monitoring. Traditionally, the 1RM test, which is commonly used to assess dynamic maximal strength [ 29 , 30 ] is impractical for daily fatigue monitoring due to technical challenges, the fatigue-inducing effects of the test [ 31 ] and the risk of injury [ 32 ]. Consequently, the L 0 and A line parameters have emerged as prominent indicators, as they not only detect statistically significant changes following different fatigue protocols but also exhibit a high correlation with 1RM values. It is notable that the test-retest reliability of the A line parameter is higher than that of L 0 , and its sensitivity varies depending on the source of fatigue. This highlights the importance of the A line parameter in comprehensively addressing a broader range of mechanical factors contributing to fatigue, underscoring the necessity for a more holistic approach in its evaluation. This study provides a novel and valuable contribution to both sports scientists and practitioners involved in RT programs by highlighting the importance of detailed analyses of mechanical performance decline (i.e., fatigue assessment) following training sessions. Nevertheless, several limitations should be acknowledged. First, the L–V parameters were assessed only 15 minutes after the fatigue protocols, limiting the understanding of longer-term recovery dynamics. Given that neuromuscular fatigue can persist beyond this acute phase [ 33 ]. future studies should examine changes in 1RM and L–V parameters over extended recovery periods (e.g., 24, 48, and 72 hours). Second, fatigue was assessed exclusively in the lower-body musculature, limiting the generalizability of the findings to other muscle groups, such as those in the upper body. Third, the study focused solely on mechanical aspects of fatigue (declines in 1RM and L–V parameters), without evaluating physiological markers that could provide additional insight into fatigue-related mechanisms particularly in high-fatigue conditions [ 7 , 10 , 20 ]. Fourth, this study did not compare the sensitivity of L–V parameters with other commonly used fatigue assessment tools such as the CMJ and other ballistics tests [ 5 ]. Considering that ballistic test parameters (e.g., peak velocity during CMJ) are more strongly associated with v ₀ [ 28 ], while maximal isometric strength test parameters are more closely related to L ₀ [ 34 ], investigating the interaction between changes in L-V variables and these test parameters following different fatigue protocols represents an important area for future research. Lastly, as lifting maximal loads and performing multiple-point tests with 5–6 loads may not always be feasible during training or routine testing, future studies should aim to identify the optimal combination of load and repetitions that enables accurate L-V assessment with minimal effort. This would improve the practicality and applicability of fatigue-monitoring protocols in athletic settings [ 15 ]. CONCLUSIONS The results of this study demonstrate that among the L–V relationship variables, L ₀ and A line possess the sensitivity to detect and discriminate between different levels of neuromuscular fatigue, showing a progressive decrease in the order of control > moderate-fatigue > high-fatigue and exhibiting strong correlations with changes in 1RM. In contrast, v ₀ was unable to distinguish between different fatigue levels and showed no significant relationship with changes in 1RM, indicating its limited sensitivity during submaximal and slow-velocity exercises. These findings collectively support L–V profile variables as a non-fatiguing, reliable, and informative practical alternative to traditional 1RM testing for integration into routine athlete monitoring protocols. In particular, A line provides a more holistic representation of fatigue-induced mechanical performance decline by integrating both force- and velocity-related components, making it especially valuable for practical applications. Abbreviations 1RM, one repetition maximum; A line, area under the load-velocity relationship line; ANOVA, analysis of variance CMJ, countermovement jump CV, coefficient of variation. SMS, smith-machine squat HBD, hexagonal barbell deadlift; HHT, heavy-load traditional; LLB, light-load ballistic; F 0, maximal theoretical force; L 0, maximal theoretical load; L-V, load-velocity; P max , maximal theoretical power; v 0, maximal theoretical velocity; MV, mean velocity; RT, resistance training; Declarations Ethics approval and consent to participate The study protocol was approved by the Ethics Committee of Istanbul Gelisim University (Approval no: IGU2024/04/67). All participants provided written informed consent prior to participation. Trial registration: ClinicalTrials.gov, NCT07307963 (First posted: 19 December 2025; retrospectively registered). Consent for publication Not applicable. Availability of data and materials The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This research received no external funding. Authors’ contributions Deniz Senturk (DS): Conceptualization, methodology, project administration, supervision, and writing original draft preparation. Aliasker Kumak (AK): Investigation, data curation, formal analysis, writing, review and editing. Danica Janicijevic (DJ): Statistical analysis, visualization, writing, review and editing. All authors read and approved the final manuscript. Acknowledgements The authors would like to thank Istanbul Gelişim University Sports Sciences Application and Research Center for providing access to laboratory facilities and equipment, and all participants for their contribution to this study. References Taylor JL, Todd G, Gandevia SC: Evidence for a supraspinal contribution to human muscle fatigue. Clin Exp Pharmacol Physiol2006, 33(4):400-405. Boyas S, Guével A: Neuromuscular fatigue in healthy muscle: Underlying factors and adaptation mechanisms. Ann Phys Rehabil Med 2011, 54(2):88-108. 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Cite Share Download PDF Status: Published Journal Publication published 27 Feb, 2026 Read the published version in BMC Sports Science, Medicine and Rehabilitation → Version 1 posted Editorial decision: Revision requested 27 Jan, 2026 Reviews received at journal 26 Jan, 2026 Reviews received at journal 25 Jan, 2026 Reviews received at journal 16 Jan, 2026 Reviewers agreed at journal 13 Jan, 2026 Reviewers agreed at journal 13 Jan, 2026 Reviewers agreed at journal 13 Jan, 2026 Reviewers invited by journal 13 Jan, 2026 Editor invited by journal 01 Jan, 2026 Editor assigned by journal 01 Jan, 2026 Submission checks completed at journal 31 Dec, 2025 First submitted to journal 31 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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17:33:59","extension":"html","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":148607,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8079121/v1/fe44df85c955feda0ff30290.html"},{"id":100444017,"identity":"b6776d3b-1b28-4e24-bc40-3d7c7f135e1e","added_by":"auto","created_at":"2026-01-16 17:33:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":519111,"visible":true,"origin":"","legend":"\u003cp\u003eGeneral overview of the study protocol. L-V, load-velocity; \u003cem\u003eL\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, theoretical maximal load; \u003cem\u003ev\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, theoretical maximal velocity, A\u003csub\u003eline\u003c/sub\u003e, area under the L-V relationship line; SMS, smith-machine squat; 1RM, 1-repetition maximum.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8079121/v1/ae009a901278124bdf82799c.png"},{"id":100444020,"identity":"4fbc1dc5-20ce-4d49-b261-8158a1791437","added_by":"auto","created_at":"2026-01-16 17:33:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":66385,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in the 1-repetition maximum (1RM; upper-left panel), theoretical maximal load (\u003cem\u003eL\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e; upper-right panel), theoretical maximal velocity (\u003cem\u003ev\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e; lower-left panel) and area under the load-velocity relationship line (A\u003csub\u003eline\u003c/sub\u003e; lower-right panel) before (Pre-session) and after (Post-session) implementing the control, moderate-fatigue, and high-fatigue protocols. Standard deviations for the different conditions are presented in Table 2. \u003csup\u003ea\u003c/sup\u003e, significantly different than moderate-fatigue; \u003csup\u003eb\u003c/sup\u003e, significantly different than high-fatigue.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8079121/v1/10f9fa8ab43f92f4fa81fcce.png"},{"id":100547546,"identity":"b1326101-5fab-42c7-bb03-e048fc5f0f86","added_by":"auto","created_at":"2026-01-19 08:15:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":89357,"visible":true,"origin":"","legend":"\u003cp\u003eThe association between the percent changes in 1-repetition maximum (1RM) and the percent changes in theoretical maximal load (\u003cem\u003eL\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e; upper panel), the area under the load-velocity relationship line (A\u003csub\u003eline\u003c/sub\u003e; lower panel), and theoretical maximal velocity (\u003cem\u003ev\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e; middle panel). r, Pearson correlation coefficient.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8079121/v1/89ab16c44d1e3127399bf82a.png"},{"id":103765442,"identity":"9f99f7de-d801-48e8-873a-9a6851473384","added_by":"auto","created_at":"2026-03-02 16:01:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1641260,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8079121/v1/3ef073a6-684d-420e-8e05-48e47596d1b9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sensitivity of the load-velocity relationship variables to discriminate the level of fatigue induced by multiple sets of the smith-machine squat exercise","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eMuscle fatigue can be defined as the loss of the ability of a specific muscle or muscle group to produce strength and power due to exercise-induced stress [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It is acknowledged that muscle fatigue can result from multiple mechanisms with the key contributing factors including (a) disruptions in ionic balance and neural stimulation, (b) accumulation of metabolic byproducts, and (c) reduced motor unit activation [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The level of fatigue induced during resistance training (RT) significantly influences how the neuromuscular system adapts in response to RT stimuli [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Therefore, the periodic assessment of RT-induced fatigue is paramount in optimizing strength adaptations and enhancing training efficacy [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Countermovement jump (CMJ) height [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], mean velocity against a fixed absolute load [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], and isometric maximal strength [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] are among the most commonly used metrics for monitoring fatigue. However, assessing fatigue based on a single mechanical variable is problematic, as it does not indicate whether the reduction in maximal power (P\u003csub\u003emax\u003c/sub\u003e) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] is due to impaired force (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e) or velocity (\u003cem\u003ev\u003c/em\u003e₀) capabilities [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This situation underscores the necessity of a more comprehensive evaluation of the fatigue process and its impact on performance variables.\u003c/p\u003e \u003cp\u003eA potential solution to this problem is the modeling of the force-velocity (F-V) relationship and the computation of the F-V parameters (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, \u003cem\u003ev\u003c/em\u003e₀ and P\u003csub\u003emax\u003c/sub\u003e) under different fatigue conditions. For example, Garcia-Ramos et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] found that a reduction in \u003cem\u003ev\u003c/em\u003e₀ was the primary cause of the decrease in P\u003csub\u003emax\u003c/sub\u003e following a light-load fatigue protocol involving bench press performed to failure at 60% of the one-repetition maximum (1RM). Conversely, the decline in P\u003csub\u003emax\u003c/sub\u003e during the non-failure high-load traditional (HLT) protocol involving bench press sets at 80% 1RM was mainly due to a decrease in \u003cem\u003eF\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, with no significant change in \u003cem\u003ev\u003c/em\u003e₀. Another study by Li et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] examined the sensitivity of the F-v parameters in detecting fatigue under a HLT protocol consisting of five squat repetitions at 80% 1RM and a light-load ballistic (LLB) protocol comprising five squat jump repetitions at 30% 1RM. Even though both RT protocols resulted in a decline in P\u003csub\u003emax\u003c/sub\u003e (10.1% and 12.2% for HLT and LLB, respectively), \u003cem\u003eF\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e and \u003cem\u003ev\u003c/em\u003e₀ contributed to this reduction to varying extents. In the LLB squat protocol, the decline in \u003cem\u003ev\u003c/em\u003e₀ (9.7%) was more pronounced than the minimal decrease in \u003cem\u003eF\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e (0.4%), whereas during the HLT squat protocol, \u003cem\u003eF\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e showed a greater reduction (8.4%) compared to \u003cem\u003ev\u003c/em\u003e₀ (4.1%). The findings of both studies consistently indicate that, in low-load, high-velocity exercises, the decline in P\u003csub\u003emax\u003c/sub\u003e is predominantly attributed to a reduction in \u003cem\u003ev\u003c/em\u003e₀. Conversely, in high-load, low-velocity exercises, the decrease in P\u003csub\u003emax\u003c/sub\u003e is attributed to a decrease in \u003cem\u003eF\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e. These observations substantiate the hypothesis that monitoring fatigue through a sole mechanical parameter may lack adequate sensitivity.\u003c/p\u003e \u003cp\u003eAlthough F\u0026ndash;V modeling provides a comprehensive framework to characterize neuromuscular mechanical capacities [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] some applications show substantial variability in \u003cem\u003ev\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, which may limit its usefulness for certain exercises [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. For example, Gonz\u0026aacute;lez-Hern\u0026aacute;ndez et al. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] explored the sensitivity of F-V parameters to selectively detect fatigue before and after three RT involving full squats and bench presses. Each session consisted of five repetitions at 10 RM, with varying inter-set rest durations in each session (1, 3, or 5 minutes). Contrary to predictions, the \u003cem\u003ev\u003c/em\u003e₀ significantly increased after RT sessions. For example, in the CMJ test conducted following a one-minute recovery period, \u003cem\u003ev\u003c/em\u003e₀ rose from a pre-training baseline of 3.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93 m\u0026middot;s⁻\u0026sup1; to 3.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77 m\u0026middot;s⁻\u0026sup1; post-training. Similar increase was observed in the bench press throw test, where \u003cem\u003ev\u003c/em\u003e₀ rose from 2.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34 m\u0026middot;s⁻\u0026sup1; to 2.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27 m\u0026middot;s⁻\u0026sup1; after a 3-minute rest period. The high variability in \u003cem\u003ev\u003c/em\u003e₀ observed in this and similar studies when performing exercises against gravity is likely explained by the large extrapolation needed from the experimental point representing the lowest load (e.g., unloaded jump) to the velocity-axis [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo address the issue of high \u003cem\u003ev\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e variability, load-velocity (L-V) modelling may offer a more suitable alternative to traditional F-V modelling, when it comes fatigue assessment as previous studies have established validity and reliability of the L-V modelling in exercises such as bench press throw [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and squat [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The parameters related to the L-V modelling include theoretical maximal load (\u003cem\u003eL\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e: load at 0 m\u0026middot;s⁻\u0026sup1;), theoretical maximal velocity (\u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e: velocity at 0 kg), and the area under the L-V relationship line (A\u003csub\u003eline\u003c/sub\u003e = \u003cem\u003eL\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e \u0026times; \u003cem\u003ev\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e/2). Recent studies have reported significant reductions in \u003cem\u003ev\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e and A\u003csub\u003eline\u003c/sub\u003e following the fatigue protocol, whereas \u003cem\u003eL\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e remained unchanged [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, a different study observed decreases in \u003cem\u003eL\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e and A\u003csub\u003eline\u003c/sub\u003e, but not \u003cem\u003ev\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Overall, these results suggest that there is currently no clear consensus in the literature on how L-V relationship variables respond to fatigue.\u003c/p\u003e \u003cp\u003eTherefore, to address the shortcomings of the previous studies, the objective of this study was (i) to compare how L-V relationship parameters (L\u003csub\u003e0\u003c/sub\u003e, v\u003csub\u003e0\u003c/sub\u003e, and A\u003csub\u003eline\u003c/sub\u003e) change after two fatigue protocols involving several sets of smith-machine squat (SMS) exercises. One protocol aimed to induce moderate fatigue (performing half the maximum number of repetitions), and the other aimed to induce high fatigue (performing sets to exhaustion), and (ii) to determine whether changes in \u003cem\u003eL\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, \u003cem\u003ev\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, and A\u003csub\u003eline\u003c/sub\u003e following the fatigue protocols significantly correlate with the SMS 1RM test, a traditional measure of maximal strength. We hypothesized that (i) all L\u0026ndash;V parameters would decrease after the fatigue protocols, with larger decreases in \u003cem\u003eL\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e and A\u003csub\u003eline\u003c/sub\u003e than in \u003cem\u003ev\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, particularly after the high-fatigue protocol; and (ii) percentage changes in 1RM would correlate strongly with changes in \u003cem\u003eL\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e and A\u003csub\u003eline\u003c/sub\u003e, but not with \u003cem\u003ev\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSubjects\u003c/h2\u003e \u003cp\u003eTwenty-eight physically trained males, with an average age of 23.3 years (standard deviation [SD]: 3.0 years; range: 20\u0026ndash;36 years), willingly enrolled in this research endeavor. The participants exhibited a mean body mass of 78.1 kg (SD: 9.3 kg), a body height of 177.2 cm (SD: 3.3 cm), and a one-repetition maximum (1RM) for smith-machine squat (SMS) exercise of 150.9 kg (SD: 12.5 kg). All subjects possessed prior RT experience, averaging 5.0 years (SD: 2.6 years), and demonstrated proficiency in executing the SMS exercise during the familiarization session. Note that all athletes were using the SMS exercise in their RT programs. None of the participants exhibited any physical limitations or neuromuscular injuries that could impede their safe participation in the study. Participants were explicitly instructed to refrain from engaging in strenuous lower-body exercises throughout the study and were required to arrive at each testing session in a rested state. Prior to commencement, all subjects received a comprehensive verbal explanation of the testing procedures and subsequently provided informed consent by signing a consent form, acknowledging their willingness to participate in the study. The research protocol strictly adhered to the principles outlined in the Declaration of Helsinki and was approved by the Ethics Committee of Istanbul Gelisim University (Approval no: IGU2024/04/67). The study was retrospectively registered at ClinicalTrials.gov (Identifier: NCT07307963; First posted: 19 December 2025).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy design\u003c/h3\u003e\n\u003cp\u003eA crossover study design was applied to investigate the sensitivity of L-V relationship variables in distinguishing the extent of fatigue caused by multiple sets of SMS exercise. Subjects were required to visit the laboratory on five separate occasions. There was 72 hours of rest after the familiarization session and 96 hours of rest after each testing sessions. The initial session aimed to familiarize subjects with the SMS exercise, during which they performed the exercise at their maximal intended velocity while dealing with varying external loads. In the second session, participants underwent an incremental loading test to establish their SMS 1RM, after which participants rested 5 min and then performed one set to failure with 70% of their 1RM. The subsequent three experimental sessions were conducted in a randomized sequence. A consistent element across these experimental sessions was that, at the beginning and end of each experimental testing session, subjects underwent a complete incremental loading test using the SMS exercise, with the loads ranging from 30% of the 1RM determined in the second session to the actual 1RM. This test aimed to determine both the 1RM and L-V relationship parameters (\u003cem\u003eL\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, \u003cem\u003ev\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, and A\u003csub\u003eline\u003c/sub\u003e). The difference among the experimental sessions lay in the nature of the activity undertaken by subjects during the 30-minute interval that separated the two incremental loading tests. These activities included control, moderate-fatigue, and high-fatigue protocols. All sessions were meticulously conducted at the university research laboratory, supervised by the same researcher (DS), and maintained at approximately 22\u0026ndash;24\u0026deg;C with humidity of approximately 60%.\u003c/p\u003e\n\u003ch3\u003eFamiliarization session (session 1)\u003c/h3\u003e\n\u003cp\u003e Following a general warm-up that included jogging and joint mobilization exercises, participants performed a series of the SMS exercise against four different loads (30%, 50%, 70%, and 80% of their self-estimated 1RM) for three repetitions and one repetition against a load corresponding to 90% and 100% of their self-estimated 1RM. The participants initiated the exercise from a fully extended posture, maintaining a shoulder-width stance with the barbell positioned across the back at the acromion level, commonly referred to as the \"high-bar position.\" Instructed to execute a continuous descent, they were guided to lower themselves until their thighs were parallel to the ground. Upon reaching this bottom position, participants were required to swiftly return to the initial position as fast as possible [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. To ensure the adherence to prescribed technique, an iPad 10th generation (Apple Inc., Cupertino, CA, USA) was thoughtfully positioned on a tripod with a height of 0.5 meters, located 1.5 meters away from the subjects, diagonally behind them. This configuration facilitated the continuous observation of hip movements during all repetitions, which were diligently captured in slow-motion recordings. Any repetitions deviating from the desired technique were promptly identified and repeated for accuracy. The standardized technique requirements and instructions were consistently applied across all sessions throughout the course of this study. Emphasis was placed on maintaining constant downward pressure on the barbell throughout the entirety of the movement, and subjects were explicitly prohibited from utilizing any jumping motions. After a 5-minute rest period, subjects performed six consecutive repetitions against the 70% of their self-estimated 1RM using eccentric-concentric technique as described above.\u003c/p\u003e\n\u003ch3\u003ePreliminary testing session (session 2)\u003c/h3\u003e\n\u003cp\u003e After the completion of the identical general warm-up routine implemented in the familiarization session, which encompassed jogging and joint mobilization exercises, participants underwent a specific warm-up protocol. This entailed one set each of 8, 5, and 2 repetitions against loads corresponding to 40%, 60%, and 80% of their self-estimated 1RM, respectively. Subsequently, the load was incrementally augmented until reaching the 1RM for eccentric-concentric SMS. Load increments were determined by assessing the mean velocity (MV) of the repetitions, with increases of 20\u0026ndash;40 kg for MV above 0.80 m\u0026middot;s⁻\u0026sup1;, 10\u0026ndash;20 kg for MV ranging from 0.80 m\u0026middot;s⁻\u0026sup1; to 0.40 m\u0026middot;s⁻\u0026sup1;, and 1\u0026ndash;10 kg for MV below 0.40 m\u0026middot;s⁻\u0026sup1;. Performance involved three repetitions with light loads (MV\u0026thinsp;\u0026gt;\u0026thinsp;0.80 m\u0026middot;s⁻\u0026sup1;), two repetitions with medium loads (MV\u0026thinsp;=\u0026thinsp;0.50\u0026ndash;0.80 m\u0026middot;s⁻\u0026sup1;), and one repetition with heavy loads (MV\u0026thinsp;\u0026lt;\u0026thinsp;0.50 m\u0026middot;s⁻\u0026sup1;). Participants adhered to rest intervals of 2, 3, and 5 minutes when lifting light, medium, and heavy loads, respectively.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eExperimental testing sessions (sessions 3\u0026ndash;5)\u003c/h2\u003e \u003cp\u003eThe three experimental sessions commenced with the implementation of the identical general and specific warm-up routines delineated in the preliminary testing session. Subsequently, participants undertook the initial incremental loading test of the experimental session (pre-session L-V assessment): 3 repetitions at 30%1RM, 2 repetitions at 50%1RM and 70%1RM, and 1 repetition at 80%1RM and 90%1RM. Following the 90%1RM trial, participants executed single 1RM attempts until they were unable to complete a repetition. The maximal load lifted with proper technique was considered the 1RM. Recovery time was set at 3 minutes for submaximal lifts and 5 minutes for 1RM attempts. An identical incremental loading test was conducted at the conclusion of the experimental session (post-session L-V assessment).\u003c/p\u003e \u003cp\u003eThe three experimental sessions differed in the activity participants engaged in during the 30-minute interval that separated pre-session and post-session incremental loading tests: (i) control protocol: passive rest for 30 min; (ii) After completing the pre-session 1RM test, 5 min of rest was provided. Participants then performed 5 sets of the SMS exercise at 70% of 1RM, each set consisting of half of the maximum number of repetitions determined during Session 2, with 2-min inter-set rest. After the final set, 15 min of rest was provided before the post-session incremental loading 1RM test; and (iii) high-fatigue protocol: identical to the moderate-fatigue protocol, except that all sets of the SMS exercise at 70% of 1RM were performed to failure. It is worth noting that in the medium fatigue protocol participants executed only half of the maximum number of repetitions performed in the set up to 70% failure determined during preliminary testing session (session 2), while in the high fatigue protocol all sets were executed up to failure.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData acquisition and analysis\u003c/h3\u003e\n\u003cp\u003eThe SMS exercise was executed utilizing a 10 kg smith-machine bar along with calibrated weight discs (Technogym; Italy, Europe) ranging from 0.5 to 25 kg. To capture the MV of all repetitions, a validated linear position transducer (GymAware RS PowerTool, Kinetic Performance Technologies, Canberra, Australia) was affixed to the right side of the barbell using a velcro strap, as detailed by [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The data acquired from the device were wirelessly transmitted through BluetoothTM to a tablet (iPad, Apple Inc., Cupertino, CA) via the GymAware v4.1.6 app, and subsequently, to an online cloud platform. Following this, the data were exported to Microsoft Excel (Microsoft Corporation, Redmond, WA) and prepared for subsequent analysis.\u003c/p\u003e \u003cp\u003eThe L-V relationship was established based on individual values of MV and the external load lifted (kg) across five loading conditions (30%, 50%, 70%, 80%, and 90% of 1RM). For each load, only the trial with the highest MV was considered in determining the individual L-V relationship. Utilizing a least-square linear regression model (L[v]\u0026thinsp;=\u0026thinsp;\u003cem\u003eL\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e \u0026ndash; sv), where \u003cem\u003eL\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e represents the load at zero velocity and s is the slope of the L-V relationship. Subsequently, the theoretical maximal velocity (\u003cem\u003ev\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e) and the area under the L-V relationship line (A\u003csub\u003eline\u003c/sub\u003e) were calculated as \u003cem\u003ev\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003eL\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e/s and A\u003csub\u003eline\u003c/sub\u003e = \u003cem\u003eL\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e\u0026middot;\u003cem\u003ev\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e/2. Hence, four dependent variables were taken into account in this study including the actual 1RM, and three variables derived from the L-V relationship (\u003cem\u003eL\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, \u003cem\u003ev\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, and A\u003csub\u003eline\u003c/sub\u003e).\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eDescriptive statistics are reported as means and standard deviations. The normality of the data distribution was verified through the Shapiro-Wilk test (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Reliability evaluation for the 1RM and L-V relationship parameters (L\u003csub\u003e0\u003c/sub\u003e, \u003cem\u003ev\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, and A\u003csub\u003eline\u003c/sub\u003e) involved a comparison of the pre-session incremental loading tests between the first two experimental sessions. The coefficient of variation (CV% = standard error of measurement / participants\u0026rsquo; mean score \u0026times; 100) and the intraclass correlation coefficient (ICC; model 3.1) were computed as indices of absolute and relative reliability, respectively. A two-way repeated measures analysis of variance (ANOVA; protocol [control, moderate-fatigue, and high-fatigue] \u0026times; time [pre-session and post-session]) with Bonferroni post hoc corrections was applied to each dependent variable. Subsequently, the Pearson's correlation coefficient (\u003cem\u003er\u003c/em\u003e) was utilized to quantify the association between the percentage changes in 1RM and the corresponding percentage changes in \u003cem\u003eL\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, \u003cem\u003ev\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, and A\u003csub\u003eline\u003c/sub\u003e. The criteria employed to interpret the magnitude of the \u003cem\u003er\u003c/em\u003e coefficients were as follows: trivial (0.00\u0026ndash;0.09), small (0.10\u0026ndash;0.29), moderate (0.30\u0026ndash;0.49), large (0.50\u0026ndash;0.69), very large (0.70\u0026ndash;0.89), nearly perfect (0.90\u0026ndash;0.99), and perfect (1.00) (1). The CV assessments was conducted using Excel (Microsoft Corporation, Redmond, WA), while other statistical analyses were carried out with the statistical software (JASP version 0.18.3, Amsterdam, The Netherlands).\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThe reliability of all dependent variables was high: 1RM (CV\u0026thinsp;=\u0026thinsp;1.16% and ICC\u0026thinsp;=\u0026thinsp;0.99), \u003cem\u003eL\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e (CV\u0026thinsp;=\u0026thinsp;5.1% and ICC\u0026thinsp;=\u0026thinsp;0.87), \u003cem\u003ev\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e (CV\u0026thinsp;=\u0026thinsp;2.10% and ICC\u0026thinsp;=\u0026thinsp;0.86), and A\u003csub\u003eline\u003c/sub\u003e (CV\u0026thinsp;=\u0026thinsp;3.23% and ICC\u0026thinsp;=\u0026thinsp;0.96). Both fatigue protocols induced fatigue, as evidenced by the gradual decline in the fastest MV within each set with increasing number of sets (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). As anticipated, the high-fatigue protocol exhibited a greater velocity loss and a more pronounced reduction in the fastest MV of the set compared to the moderate-fatigue protocol.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e]\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of the training variables for the moderate- and high-fatigue protocols.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtocol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSet\u003c/p\u003e \u003cp\u003enumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of repetitions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFastest MV (m\u0026middot;s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFinal MV\u003c/p\u003e \u003cp\u003e(m\u0026middot;s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVelocity loss (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eModerate-fatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e-20.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e-20.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e-21.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e-22.9\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e-27.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eHigh-fatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e13.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e-56.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e8.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e-49.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e-44.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e-40.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e-34.2\u0026thinsp;\u0026plusmn;\u0026thinsp;12.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eMV, mean velocity; Velocity loss (%) = (Final MV \u0026ndash; Fastest MV) / Fastest MV \u0026times; 1\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe 1RM and the three L-V relationship variables (\u003cem\u003eL\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, \u003cem\u003ev\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, and A\u003csub\u003eline\u003c/sub\u003e) decreased at post-session compared to pre-session with the only exception of \u003cem\u003ev\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e for the control protocol (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The interaction effect between protocol and time yielded significance for the 1RM, \u003cem\u003eL\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, and A\u003csub\u003eline\u003c/sub\u003e, as their decline at post-session was more pronounced for the high-fatigue protocol, followed by the moderate-fatigue protocol, and finally for the control protocol (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). No significant interaction between protocol and time was observed for \u003cem\u003ev\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e]\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of the 1-repetition maximum (1RM) and load-velocity relationship parameters between the fatigue protocols.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProtocol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePre-session\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePost-session\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eANOVA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1RM (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e153.6\u0026thinsp;\u0026plusmn;\u0026thinsp;23.4*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e149.9\u0026thinsp;\u0026plusmn;\u0026thinsp;23.6 \u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eProtocol: F\u0026thinsp;=\u0026thinsp;16.6; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003eTime: F\u0026thinsp;=\u0026thinsp;749.9; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003eProtocol \u0026times; Time: F\u0026thinsp;=\u0026thinsp;143.9; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate-fatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e153.6\u0026thinsp;\u0026plusmn;\u0026thinsp;22.9*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e143.2\u0026thinsp;\u0026plusmn;\u0026thinsp;22.6 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh-fatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e153.3\u0026thinsp;\u0026plusmn;\u0026thinsp;21.6*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e134.0\u0026thinsp;\u0026plusmn;\u0026thinsp;20.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eL\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e205.2\u0026thinsp;\u0026plusmn;\u0026thinsp;26.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e202.9\u0026thinsp;\u0026plusmn;\u0026thinsp;25.8 \u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eProtocol: F\u0026thinsp;=\u0026thinsp;452.0; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003eTime: F\u0026thinsp;=\u0026thinsp;32.1; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003eProtocol \u0026times; Time: F\u0026thinsp;=\u0026thinsp;112.4; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate-fatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e205.0\u0026thinsp;\u0026plusmn;\u0026thinsp;26.2*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e188.2\u0026thinsp;\u0026plusmn;\u0026thinsp;28.1 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh-fatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e203.8\u0026thinsp;\u0026plusmn;\u0026thinsp;27.3*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e171.2\u0026thinsp;\u0026plusmn;\u0026thinsp;23.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003ev\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e (m\u0026middot;s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eProtocol: F\u0026thinsp;=\u0026thinsp;0.4; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.610\u003c/p\u003e \u003cp\u003eTime: F\u0026thinsp;=\u0026thinsp;32.9; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003eProtocol \u0026times; Time: F\u0026thinsp;=\u0026thinsp;1.1; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.325\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate-fatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh-fatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eA\u003csub\u003eline\u003c/sub\u003e (kg\u0026middot;m\u0026middot;s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e121.9\u0026thinsp;\u0026plusmn;\u0026thinsp;17.9*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e119.3\u0026thinsp;\u0026plusmn;\u0026thinsp;17.7 \u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eProtocol: F\u0026thinsp;=\u0026thinsp;28.7; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003eTime: F\u0026thinsp;=\u0026thinsp;719.5; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003eProtocol \u0026times; Time: F\u0026thinsp;=\u0026thinsp;184.3; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate-fatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e121.3\u0026thinsp;\u0026plusmn;\u0026thinsp;18.2*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109.3\u0026thinsp;\u0026plusmn;\u0026thinsp;17.9 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh-fatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e121.3\u0026thinsp;\u0026plusmn;\u0026thinsp;17.5*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.8\u0026thinsp;\u0026plusmn;\u0026thinsp;14.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eANOVA, analysis of variance; \u003cem\u003eL\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, maximal theoretical load; \u003cem\u003ev\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, maximal theoretical velocity; A\u003csub\u003eline\u003c/sub\u003e, area under the load-velocity relationship line; *, significantly different than post-session; \u003csup\u003ea\u003c/sup\u003e, significantly different than moderate-fatigue; \u003csup\u003eb\u003c/sup\u003e, significantly different than high-fatigue\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e[Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e]\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe percent change in the 1RM at post-session was significantly correlated with the percent change in A\u003csub\u003eline\u003c/sub\u003e (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.832) and \u003cem\u003eL\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.764), but not with the percent change in \u003cem\u003ev\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e (\u003cem\u003er\u003c/em\u003e = -0.012) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e[Figure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e]\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study was designed to examine the sensitivity and utility of the L-V parameters (\u003cem\u003eL\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, \u003cem\u003ev\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, and A\u003csub\u003eline\u003c/sub\u003e) in identifying instances of fatigue in response to diverse fatigue protocols (control, moderate-fatigue and high-fatigue). The primary hypothesis of this study was confirmed by the findings that the L-V variables demonstrated sensitivity in detecting fatigue at varying levels, leading to two key conclusions. Firstly, significant decrements were observed between the pre- and post-session for all variables and fatigue protocols, with the exception of \u003cem\u003ev\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e and \u003cem\u003eL\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e in the control protocol. Secondly, the L-V variables showed the most pronounced decrease in the high-fatigue, followed by the moderate-fatigue, and control protocols, with the exception of \u003cem\u003ev\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e. Supporting our second hypothesis, very large correlations were observed between changes in 1RM and changes in \u003cem\u003eL\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e and A\u003csub\u003eline\u003c/sub\u003e, but not with \u003cem\u003ev\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e. These results collectively reveal that L-V relationship variables offer a highly sensitive and practical solution for fatigue monitoring.\u003c/p\u003e \u003cp\u003eThe present findings suggest that, in comparison with the moderate-fatigue protocol, the high-fatigue protocol resulted in a greater reduction in the fastest mean velocity (fastest MV) across sets (Δ: moderate-fatigue 14%, high-fatigue 31%) and a higher magnitude of velocity loss (Δ: moderate-fatigue 22%, high-fatigue 44%). These findings corroborate earlier findings in the literature, demonstrating that high-fatigue protocols involving greater velocity loss elicit greater neuromuscular fatigue and lead to a more progressive reduction in the fastest MV across sets [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Additionally, while 1RM, A\u003csub\u003eline\u003c/sub\u003e, \u003cem\u003eL\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, and \u003cem\u003ev\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e showed high inter-session reliability, \u003cem\u003ev\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e also exhibited strong test\u0026ndash;retest reliability in our data (ICC\u0026thinsp;=\u0026thinsp;0.86). This contrasts with previous research [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], which reported lower reliability for \u003cem\u003ev\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e during the HBD exercise (ICC: 0.48). Previous studies have attributed the variability in \u003cem\u003ev\u003c/em\u003e₀ to the biomechanical characteristics of exercises performed with low loads, which cause the extrapolation point to be positioned farther from \u003cem\u003eF\u003c/em\u003e₀ [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, the discrepancy between our findings and previous studies utilizing the HBD under identical protocols may be attributed to the following technical and mechanical differences between the two exercises. In the previous study, participants were instructed to avoid shoulder elevation during the HBD exercise while simultaneously performing all lifts at maximal velocity. Although all lifts were monitored throughout the study, this combination may have led participants, particularly under low-load, high-velocity conditions, to instinctively elevate their shoulders to maximize lifting velocity, thereby increasing variability across sessions [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In contrast, during the SMS exercise, participants can comfortably perform a standardized lift with low loads while maintaining their hips parallel to the ground, which likely reduces \u003cem\u003ev\u003c/em\u003e₀ test-retest variability across different days [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA significant decline in 1RM and all L-V variables (\u003cem\u003eL\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, \u003cem\u003ev\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, and A\u003csub\u003eline\u003c/sub\u003e) following the different fatigue protocols aligns with the findings of several studies with the similar design [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. A key aspect of our findings is that, while the \u003cem\u003eL\u003c/em\u003e₀ and A\u003csub\u003eline\u003c/sub\u003e were sensitive enough to detect different levels of fatigue induced by the protocols (control\u0026thinsp;\u0026gt;\u0026thinsp;moderate-fatigue\u0026thinsp;\u0026gt;\u0026thinsp;high-fatigue), \u003cem\u003ev\u003c/em\u003e₀ failed to do so, implying that the sensitivity of L-V parameters is different when it comes to detecting fatigue levels during the SMS exercise. The present findings align with those of Şent\u0026uuml;rk et al. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] indicating that \u003cem\u003eL\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e and A\u003csub\u003eline\u003c/sub\u003e are highly sensitive markers capable of discriminating between different levels of neuromuscular fatigue, as they consistently decreased in a graded manner following high- and moderate-fatigue protocols, while \u003cem\u003ev\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e failed to show such discriminatory capacity in both SMS and HBD exercises. However, two recent studies reported significant decreases in the A\u003csub\u003eline\u003c/sub\u003e and \u003cem\u003ev\u003c/em\u003e₀ variables after fatigue protocols were followed, but not in the \u003cem\u003eL\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e variable [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This difference may be attributed to variations in the load ranges and methodological approaches used to determine the L\u0026ndash;V relationship variables across studies. One study determined the L\u0026ndash;V variables using a load corresponding to 20% of 1RM and a velocity of 0.55 m/s based on the individual LV profile [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The other study, however, calculated the L\u0026ndash;V relationship variables using loads in the 20\u0026ndash;80% 1RM range [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In the present study, the L\u0026ndash;V variables were determined using loads in the 30\u0026ndash;90% 1RM range. Furthermore, the decrease in 1RM values following the fatigue protocol in the present study resulted in modelling of the L\u0026ndash;V relationship within the 30\u0026ndash;100% 1RM range after moderate and high-fatigue protocol. This suggests that the decrease in \u003cem\u003eL\u003c/em\u003e₀ observed after fatigue may have occurred in the 90\u0026ndash;100% 1RM range, where higher force is required, rather than at loads below 80% 1RM. Taken together, the results of the present study align with the findings of previous studies that indicated that the changes in the A\u003csub\u003eline\u003c/sub\u003e are influenced to varying degrees by the \u003cem\u003eL\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e and \u003cem\u003ev\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e variables depending on the source of fatigue (e.g., high load-low velocity \u003cem\u003ev\u003c/em\u003es. low load-high velocity). This finding underscores the practical utility of A\u003csub\u003eline\u003c/sub\u003e as a sensitive method for detecting different levels of fatigue.\u003c/p\u003e \u003cp\u003eConfirming our second hypothesis, a significant correlation was observed between changes in 1RM and the A\u003csub\u003eline\u003c/sub\u003e and \u003cem\u003eL\u003c/em\u003e₀ variables following fatigue protocols, while no significant relationship was found with \u003cem\u003ev\u003c/em\u003e₀. Once again, our results align with previous studies that have reported a strong relationship between 1RM and \u003cem\u003eL\u003c/em\u003e₀ in both fatigued [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] non-fatigued [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] conditions, as well as with research examining the relationship between changes in these variables [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. A key contribution of our study is its demonstration of the interaction between changes in 1RM and L-V variables during the SMS exercise, a frequently utilized movement pattern for monitoring lower-limb fatigue [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The findings presented above offer the following key conclusions regarding methods of fatigue monitoring. Traditionally, the 1RM test, which is commonly used to assess dynamic maximal strength [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] is impractical for daily fatigue monitoring due to technical challenges, the fatigue-inducing effects of the test [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and the risk of injury [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Consequently, the L\u003csub\u003e0\u003c/sub\u003e and A\u003csub\u003eline\u003c/sub\u003e parameters have emerged as prominent indicators, as they not only detect statistically significant changes following different fatigue protocols but also exhibit a high correlation with 1RM values. It is notable that the test-retest reliability of the A\u003csub\u003eline\u003c/sub\u003e parameter is higher than that of L\u003csub\u003e0\u003c/sub\u003e, and its sensitivity varies depending on the source of fatigue. This highlights the importance of the A\u003csub\u003eline\u003c/sub\u003e parameter in comprehensively addressing a broader range of mechanical factors contributing to fatigue, underscoring the necessity for a more holistic approach in its evaluation.\u003c/p\u003e \u003cp\u003eThis study provides a novel and valuable contribution to both sports scientists and practitioners involved in RT programs by highlighting the importance of detailed analyses of mechanical performance decline (i.e., fatigue assessment) following training sessions. Nevertheless, several limitations should be acknowledged. First, the L\u0026ndash;V parameters were assessed only 15 minutes after the fatigue protocols, limiting the understanding of longer-term recovery dynamics. Given that neuromuscular fatigue can persist beyond this acute phase [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. future studies should examine changes in 1RM and L\u0026ndash;V parameters over extended recovery periods (e.g., 24, 48, and 72 hours). Second, fatigue was assessed exclusively in the lower-body musculature, limiting the generalizability of the findings to other muscle groups, such as those in the upper body. Third, the study focused solely on mechanical aspects of fatigue (declines in 1RM and L\u0026ndash;V parameters), without evaluating physiological markers that could provide additional insight into fatigue-related mechanisms particularly in high-fatigue conditions [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Fourth, this study did not compare the sensitivity of L\u0026ndash;V parameters with other commonly used fatigue assessment tools such as the CMJ and other ballistics tests [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Considering that ballistic test parameters (e.g., peak velocity during CMJ) are more strongly associated with \u003cem\u003ev\u003c/em\u003e₀ [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], while maximal isometric strength test parameters are more closely related to \u003cem\u003eL\u003c/em\u003e₀ [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], investigating the interaction between changes in L-V variables and these test parameters following different fatigue protocols represents an important area for future research. Lastly, as lifting maximal loads and performing multiple-point tests with 5\u0026ndash;6 loads may not always be feasible during training or routine testing, future studies should aim to identify the optimal combination of load and repetitions that enables accurate L-V assessment with minimal effort. This would improve the practicality and applicability of fatigue-monitoring protocols in athletic settings [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThe results of this study demonstrate that among the L\u0026ndash;V relationship variables, \u003cem\u003eL\u003c/em\u003e₀ and A\u003csub\u003eline\u003c/sub\u003e possess the sensitivity to detect and discriminate between different levels of neuromuscular fatigue, showing a progressive decrease in the order of control\u0026thinsp;\u0026gt;\u0026thinsp;moderate-fatigue\u0026thinsp;\u0026gt;\u0026thinsp;high-fatigue and exhibiting strong correlations with changes in 1RM. In contrast, \u003cem\u003ev\u003c/em\u003e₀ was unable to distinguish between different fatigue levels and showed no significant relationship with changes in 1RM, indicating its limited sensitivity during submaximal and slow-velocity exercises. These findings collectively support L\u0026ndash;V profile variables as a non-fatiguing, reliable, and informative practical alternative to traditional 1RM testing for integration into routine athlete monitoring protocols. In particular, A\u003csub\u003eline\u003c/sub\u003e provides a more holistic representation of fatigue-induced mechanical performance decline by integrating both force- and velocity-related components, making it especially valuable for practical applications.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e1RM, one repetition maximum;\u003c/p\u003e\n\u003cp\u003eA\u003csub\u003eline,\u0026nbsp;\u003c/sub\u003earea under the load-velocity relationship line;\u003c/p\u003e\n\u003cp\u003eANOVA, analysis of variance\u003c/p\u003e\n\u003cp\u003eCMJ,\u0026nbsp;countermovement jump\u003c/p\u003e\n\u003cp\u003eCV, coefficient of variation.\u003c/p\u003e\n\u003cp\u003eSMS, smith-machine squat\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHBD, hexagonal barbell deadlift;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHHT, heavy-load traditional;\u003c/p\u003e\n\u003cp\u003eLLB, light-load ballistic;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003csub\u003e0,\u0026nbsp;\u003c/sub\u003emaximal theoretical force;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eL\u003c/em\u003e\u003csub\u003e0,\u0026nbsp;\u003c/sub\u003emaximal theoretical load;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eL-V, load-velocity;\u003c/p\u003e\n\u003cp\u003eP\u003csub\u003emax\u003c/sub\u003e, maximal theoretical power;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ev\u003c/em\u003e\u003csub\u003e0,\u0026nbsp;\u003c/sub\u003emaximal theoretical velocity;\u003c/p\u003e\n\u003cp\u003eMV, mean velocity;\u003c/p\u003e\n\u003cp\u003eRT, resistance training;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Ethics Committee of Istanbul Gelisim University (Approval no: IGU2024/04/67). All participants provided written informed consent prior to participation. Trial registration: ClinicalTrials.gov, NCT07307963 (First posted: 19 December 2025; retrospectively registered).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDeniz Senturk (DS): Conceptualization, methodology, project administration, supervision, and writing original draft preparation.\u003c/p\u003e\n\u003cp\u003eAliasker Kumak (AK): Investigation, data curation, formal analysis, writing, review and editing.\u003c/p\u003e\n\u003cp\u003eDanica Janicijevic (DJ): Statistical analysis, visualization, writing, review and editing.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank Istanbul Gelişim University Sports Sciences Application and Research Center for providing access to laboratory facilities and equipment, and all participants for their contribution to this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTaylor JL, Todd G, Gandevia SC: Evidence for a supraspinal contribution to human muscle fatigue. 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gender.\u003cstrong\u003e\u003cem\u003eJ Sports Sci Med\u003c/em\u003e\u003c/strong\u003e2012, 11(2):221-225.\u003c/li\u003e\n\u003cli\u003eSigvaldsen E, Loturco I, Larsen F, Bruusgaard J, Kalhovde JM, Haugen T: Validity and reliability of upper body push and pull tests to determine one-repetition maximum. \u003cem\u003ePloS One \u003c/em\u003e2023, 18(7):e0288649.\u003c/li\u003e\n\u003cli\u003eEston R, Evans HJ: The validity of submaximal ratings of perceived exertion to predict one repetition maximum. \u003cstrong\u003e\u003cem\u003eJ Sports Sci Med\u003c/em\u003e\u003c/strong\u003e2009, 8(4):567-573.\u003c/li\u003e\n\u003cli\u003eNiewiadomski W, Laskowska D, Gąsiorowska A, Cybulski G, Strasz A, Langfort J: Determination and prediction of one repetition maximum (1RM): safety considerations. \u003cstrong\u003e\u003cem\u003eJ Hum Kinet\u003c/em\u003e\u003c/strong\u003e2008, 19(2008):109-120.\u003c/li\u003e\n\u003cli\u003eThomas K, Brownstein CG, Dent J, Parker P, Goodall S, Howatson G: Neuromuscular Fatigue and Recovery after Heavy Resistance, Jump, and Sprint Training. \u003cem\u003eMed Sci Sports Exerc\u003c/em\u003e2018, 50(12):2526-2535.\u003c/li\u003e\n\u003cli\u003eMcGuigan MR, Winchester JB: The relationship between isometric and dynamic strength in college football players. \u003cem\u003eJ Sports Sci Med\u003c/em\u003e2008, 7(1):101-105.\u003c/li\u003e\n\u003cli\u003e\u003c/li\u003e\n \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-sports-science-medicine-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssmr","sideBox":"Learn more about [BMC Sports Science, Medicine and Rehabilitation](http://bmcsportsscimedrehabil.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ssmr/default.aspx","title":"BMC Sports Science, Medicine and Rehabilitation","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"fatigue, resistance training, strength, testing, velocity-based training","lastPublishedDoi":"10.21203/rs.3.rs-8079121/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8079121/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe aim of the research was to examine the sensitivity of load-velocity (L-V) relationship parameters (\u003cem\u003eL\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, \u003cem\u003ev\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, and A\u003csub\u003eline\u003c/sub\u003e (area under the L\u0026ndash;V line; Aline\u0026thinsp;=\u0026thinsp;\u003cem\u003eL\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e \u003cb\u003e\u0026times;\u003c/b\u003e \u003cem\u003ev\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e \u003cb\u003e/ 2)\u003c/b\u003e) in detecting fatigue after different fatigue protocols as well as their correlation with changes in 1-repetition maximum (1RM).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eAfter a familiarization and preliminary testing session which was used for 1RM smith-machine squat (SMS) determination and performing a set of repetition to failure with 70%1RM load, 28 resistance-trained men randomly performed three fatigue protocols. All fatigue protocols were carried out between two incremental loading tests, conducted at the beginning (pre-session) and end (post-session) of the training session. The characteristics of the fatigue protocols were as follows: (i) control protocol: no training, (ii) moderate-fatigue protocol: 5 sets of the SMS exercise at 70%1RM performing half the maximum possible number of repetitions, and (iii) high-fatigue protocol: 5 sets of the SMS exercise performed to failure against the 70%1RM.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePost-session declines in 1RM (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), \u003cem\u003eL\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) and A\u003csub\u003eline\u003c/sub\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were the greatest after the high fatigue protocol, followed by the moderate fatigue protocol and finally the control protocol. Changes in \u003cem\u003ev\u003c/em\u003e₀ did not differentiate between the fatigue protocols (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.325). The post-session percentage change in 1RM was significantly correlated with the percentage change in A\u003csub\u003eline\u003c/sub\u003e (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.832) and \u003cem\u003eL\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.764), but not with the percentage change in \u003cem\u003ev\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e (\u003cem\u003er\u003c/em\u003e= -0.012).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThese results suggest that L-V relationship variables offer a highly sensitive and practical solution for fatigue monitoring. Trial registration: Trial registration: ClinicalTrials.gov, NCT07307963 (First posted: 19 December 2025; retrospectively registered).\u003c/p\u003e","manuscriptTitle":"Sensitivity of the load-velocity relationship variables to discriminate the level of fatigue induced by multiple sets of the smith-machine squat exercise","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 17:33:54","doi":"10.21203/rs.3.rs-8079121/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-27T11:58:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-26T12:43:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-25T11:04:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-16T06:34:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"51805601230398736006505612011379783850","date":"2026-01-13T17:00:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"160143977283919312178506462479637619627","date":"2026-01-13T09:53:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"267341203715849436166648188497577371803","date":"2026-01-13T09:31:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-13T09:00:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-02T04:40:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-01T08:05:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-31T10:42:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Sports Science, Medicine and Rehabilitation","date":"2025-12-31T10:38:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-sports-science-medicine-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssmr","sideBox":"Learn more about [BMC Sports Science, Medicine and Rehabilitation](http://bmcsportsscimedrehabil.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ssmr/default.aspx","title":"BMC Sports Science, Medicine and Rehabilitation","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"52816739-deca-43e6-8e1c-5332a2cca14b","owner":[],"postedDate":"January 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-02T16:00:38+00:00","versionOfRecord":{"articleIdentity":"rs-8079121","link":"https://doi.org/10.1186/s13102-026-01615-x","journal":{"identity":"bmc-sports-science-medicine-and-rehabilitation","isVorOnly":false,"title":"BMC Sports Science, Medicine and Rehabilitation"},"publishedOn":"2026-02-27 15:57:32","publishedOnDateReadable":"February 27th, 2026"},"versionCreatedAt":"2026-01-16 17:33:54","video":"","vorDoi":"10.1186/s13102-026-01615-x","vorDoiUrl":"https://doi.org/10.1186/s13102-026-01615-x","workflowStages":[]},"version":"v1","identity":"rs-8079121","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8079121","identity":"rs-8079121","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}