Keywords
borg scale - neuromuscular fatigue - resistance training - surface electromyography
Introduction
Fatigue is a common physiological phenomenon that occurs during physical activities
such as labor and sports. In resistance exercises, fatigue is inevitable [1] and leads to a reduced power output,
including decreases in peak velocity and power output, due to impaired force
production [2]
[3]. Furthermore, fatigue has been
associated with an increased risk of acute injuries caused by deteriorations in
exercise techniques and [4], in some
cases, a higher likelihood of developing chronic pain [5]
[6]. Thus, assessing fatigue is essential for optimizing athletic
performance, ensuring safety, and reducing injury risks during resistance
training.
Fatigue involves a range of physiological responses throughout the neuromuscular
system, from central to peripheral regions. It can be assessed using various
measures, such as blood lactate levels and maximal force output [2]
[7]
[8]. Surface electromyography
(sEMG) is a widely used non-invasive tool for evaluating neuromuscular functions in
research, sports science, and clinical applications [9]
[10]. sEMG signals can detect and quantify fatigue during isometric
contractions [10]
[11]. Extensive research over the past
century has established sEMG as a reliable method for identifying fatigue [10]. Fatigue-related changes in muscle
fiber conduction velocity cause shifts in the power spectral density toward lower
frequencies, which can be observed in sEMG signals [12]. As a result, the mean and median
frequencies of the power spectrum are widely used as key indicators of fatigue
during isometric contractions [10]
[11]
[13].
Accurately assessing fatigue is essential for athletes, coaches, and physical
therapists. While the use of sEMG for fatigue assessments is growing, particularly
in resistance exercises [11]
[14]
[15], its high cost and the expertise required for the proper use limit
its accessibility. Even if sEMG devices were more affordable, their application
requires advanced skills, particularly in processing unstable signals during
resistance exercises, which makes them less practical for the widespread or
individual use outside clinical and research settings [10]
[13].
Timely feedback on fatigue is critical during power-aimed resistance exercises; yet,
power spectrum changes occur over a relatively longer timescale. In power-aimed
training scenarios (e.g., peak power and velocity), athletes must lift weights
quickly while minimizing velocity decreases from fatigue [2]
[16]. sEMG-based fatigue assessments are impractical in these settings as
they cannot provide the immediate feedback required during resistance exercises. To
make fatigue assessments more practical, several indirect indicators have been
developed [2]
[17]
[18]. For example, a blood lactate concentration is considered a
significant indirect marker of fatigue [19]. However, its measurement typically requires invasive methods like
skin puncture, causing discomfort and additional burden to participants. Moreover,
blood lactate levels are easily affected by factors such as relative intensity and
rest intervals during resistance exercises [20]
[21], making it difficult to
control for these variables. Consequently, assessing fatigue using blood lactate
levels is only suitable in specific scenarios.
In the past decade, velocity-based training has gained popularity in resistance
exercises, with studies showing its positive impact on athletic performance [22]
[23]. Affordable devices using rotary encoders or laser speedometers to
measure lifting velocity have become widely available [24]
[25]. Velocity loss is recently recognized as a reliable fatigue
indicator, correlating significantly with metabolic parameters like blood lactate
and ammonia concentrations during power-aimed resistance exercises [2]
[26]
[27]. However,
high-precision velocity monitoring devices remain expensive for most users.
Additionally, explosive lifting may not be suitable for inexperienced individuals
due to the limited muscle strength, joint stability, and proper technique [28]. It is also unsuitable for resistance
training programs focused on rehabilitation or hypertrophy [29]. Consequently, the velocity-based
fatigue assessment is primarily applicable to experienced lifters in power-aimed
training, limiting its practicality for general practitioners.
The rating of perceived exertion (RPE) scale is a subjective, perception-based tool
for quantifying exercise intensity [30]
[31]. Initially developed
for clinical settings, it has been widely adopted in sports science due to its
simplicity and reliability in reflecting physiological responses, such as heart
rate, muscle activation, and power output during endurance exercises [32]
[33]. Over the past two decades, the RPE has gained popularity in
resistance training, integrating key factors like %1RM, training volume, and rest
intervals [21]
[31]
[34]. Consequently, it is increasingly used as a physiological marker in
resistance exercises. Despite variations between scales (e.g., OMNI and Borg’s
scales), most RPE scales are validated and reliable for quantifying physiological
responses in resistance exercises. The RPE has also been found to correlate with
fatigue during non-explosive resistance exercises [14]
[35]
[36], suggesting its
potential as a marker for fatigue in power-aimed exercises. However, the
relationship between the RPE and fatigue in power-aimed resistance exercises remains
unclear as sEMG-based fatigue assessments in these contexts are inconsistent [10]
[13]. While the RPE-based fatigue assessment has been validated in
isometric and non-explosive exercises [14]
[15]
[37], further research is needed to confirm
its utility as a versatile tool for assessing fatigue in various settings.
Recent advancements in mathematical-based power spectrum analysis techniques have
enabled the potential for quantifying fatigue during dynamic contractions [11]
[13]. These methods may facilitate sEMG-based fatigue assessments in
explosive movements, paving the way to establish a relationship between the RPE and
fatigue. Such validation could position the RPE as an accessible and effective tool
for indirect fatigue assessments in power-aimed resistance exercises, benefiting
practitioners in sports, physiology, and clinical fields. This study aims to (1)
verify the relationship between the RPE and a new sEMG-based algorithm during power
bench press (BP) exercises and (2) compare the RPE with velocity loss to determine
a
more practical fatigue indicator for use in power-aimed resistance training
programs.
Materials and Methods
Experimental designs
This study investigated the validity of the RPE as a simplified fatigue indicator
and compared it with velocity loss in power BP exercises. A counterbalanced
crossover design was used, with BP selected due to its popularity in resistance
training. The protocol included two sessions, separated by at least 48 hours. In
the initial session, participants’ descriptive data were collected, and they
were instructed on using the RPE scale. An anchoring trial was conducted, where
participants performed a single set of power BP to physical failure,
establishing the upper and lower RPE limits and determining the required
repetitions for the experimental session [14]
[21]
[35]. In the second session,
participants completed three conditions corresponding to 30% (L), 60% (M), and
90% (H) of their repetitions from the anchoring trial. These conditions were
performed in a counterbalanced order. sEMG signals, RPE scores, and velocity
data were recorded across all conditions for analysis.
Participants
The sample size was determined using statistical power analysis software (G*Power
3.1, Bonn University, Germany). An analysis of variance (ANOVA) model with fixed
effects, main effects, and interaction analyses was used. The input parameters
were set at an effect size of 0.4, an alpha level of 0.05, and a power of 0.95
[14]
[15]. The minimum required number of
participants was calculated to be 14. Fifteen sub-elite collegiate athletes were
recruited for the study; however, one participant failed to complete the
experimental protocol, resulting in a final sample of 14 participants. The
participants were active collegiate athletes who regularly engaged in daily
resistance training and frequently competed at national and regional levels. All
participants were ranked between the Division II league (approximately
equivalent to National Collegiate Athletic Association Division II) and the
regional finals in Japanese collegiate athletics. Their competition experience
averaged 10.5±3.3 years. None of the participants had any neuromuscular or
skeletal injuries, nor were they undergoing medical treatment. All participants
were well-experienced in resistance training, with an average of 5.0±2.3 years
of experience. The descriptive characteristics of the participants were as
follows (mean±standard deviation): age, 20.5±1.1 years; body mass, 73.3±12.9 kg;
height, 172.3±5.7 cm; body fat percentage, 15.7±4.1%; and 1RM for BP, 92.3±17.5
kg. Before the experiment, all participants were informed about the experimental
schedule, measurement items, potential risks, discomforts, and benefits. Written
informed consent was obtained from all participants. The study was conducted in
accordance with the ethical guidelines outlined in the Declaration of Helsinki
and was approved by the Human Ethics Committee of an institution affiliated with
one of the authors.
Initial session
Before the session, participants received detailed instructions on the study’s
purpose, measurements, and potential risks and benefits. Written consent was
then obtained. Descriptive characteristics, including body fat percentage
(measured using a bioelectrical impedance machine, InBody 970, InBody Co., Ltd,
South Korea), were recorded. Participants completed a warm-up consisting of 5
minutes of jogging, static and dynamic stretching, and two sets of power BP with
20 and 30 kg for eight and six repetitions, respectively. The rest interval was
determined by the participants, and the next set was performed when they
indicated that they were ready. This protocol has been shown to enhance power
[2]
[38]. The participants’ 1RM for BP was
measured using National Strength and Conditioning Association guidelines with a
standard Olympic barbell [28]. The 1RM
test involved progressively increasing the load until the participant achieved
their maximum weight for one repetition.
After a 5-minute rest, participants were instructed on Borg’s CR-10 scale (0–10),
which measures perceived exertion in terms of effort, discomfort, and fatigue in
the upper body [21]
[30]. The lifting cadence was
standardized with a 2-second lowering phase (eccentric) and an explosive pushing
phase (concentric), controlled using a smartphone metronome (one beep/s) [14]. The safety bar was set just above
the chest height to prevent barbell rebound and any potential impact on the sEMG
data. Participants then practiced lifting with only the barbell to familiarize
themselves with the cadence and technique. An anchoring trial followed, where
participants performed a single set of power BP at 65% 1RM until failure,
establishing RPE anchors. A score of 0 (“nothing at all”) represented a relaxed
state, while 10 (“extremely strong”) corresponded to failure [26]
[39]. Successful repetitions required lowering the bar to the safety
bar and pushing it back to the starting position.
Experimental session
Before the experimental session, participants completed the same warm-up routine
as in the initial session. Afterward, they practiced RPE reporting using Borg’s
CR-10 scale, placed vertically above their heads for easy visibility. The
lifting cadence was set to a 2-second lowering phase (eccentric), an explosive
raising phase (concentric), and a 2-second pause between repetitions, controlled
using a smartphone metronome (one beep/s). During the pause, participants
reported their subjective exertion for the most recent repetition based on the
RPE range established during the anchoring trial. If exertion exceeded the high
anchor (10, “extremely strong”), participants could report scores above 10 [30]
[40].
Following the warm-up and RPE reporting practice, the three experimental
conditions (L, M, and H) were performed in a counterbalanced order. The required
repetitions for each condition were calculated from the anchoring trial. To
reduce anticipatory effects (e.g., central governor influence) [41]
[42], participants were informed of the required repetitions only
during the second-to-last repetition and instructed to stop after completing the
final one. The RPE was recorded after each repetition during the 2-second pause.
If participants struggled to maintain the cadence, they were encouraged to
follow the metronome as closely as possible. After each condition, participants
reported an overall RPE for the trial. A 5-minute rest was provided between
conditions.
Surface electromyography
sEMG signals were recorded from the
pectoralis major, lateral head of the triceps brachii, and anterior deltoid
muscles on the dominant side. Bipolar surface electrodes (ADMEDEC Co., Ltd,
Japan) were placed on each muscle following established anatomical guidelines
[43]
[44]. Before the electrode placement,
the skin was shaved, abraded, and cleaned with alcohol to reduce impedance. The
signals were amplified using an active differential preamplifier and transmitted
wirelessly to a device (MARQ MQ-8, Kissei-Com Tech, Nagano, Japan). Sampling
occurred at 1,000 Hz, and a synchronized high-speed camera (Grasshopper
GRAS-03K2C, FLIR Systems Inc., Canada) recorded a motion video. The raw sEMG
signals were captured using Vital Recorder 2 (Kissei-Com Tech, Nagano, Japan),
segmented into individual repetitions, and exported for analysis, focusing only
on the concentric phases.
A fourth-order Butterworth bandpass filter
(20–450 Hz) was applied to remove noise. Fatigue was quantified using the
spectral fatigue index (SFI), a validated parameter shown to be sensitive during
dynamic contractions [11]
[13]. A fast Fourier transformation was
applied to compute the power spectrum, and spectral moments were calculated
using the following [equation
(1)]:
The SFI was determined as the ratio of spectral moments of
orders −1 and 5, as shown in [equation
(2)]:
SFI values were calculated for each repetition, normalized to
the first repetition, and averaged across the three muscles to create a single
parameter for statistical analysis. All calculations were performed using Matlab
2024a (Mathworks, Natick, MA, USA).
Velocity loss calculations
Barbell velocity during the BP was measured using a high-speed video recorded at
120 frames/s from the sagittal plane. The camera was positioned 5 m from the
bench, facing the participant’s left side. A marker on the barbell’s end was
used to track its trajectory. Pixel-to-distance calibration was conducted with a
1-m stick placed near the barbell edge to convert pixel measurements into
real-world distances [45]. The
barbell’s trajectory coordinates were tracked, and the concentric phase velocity
was calculated using a KineAnalyzer (Kissei-Com Tech, Nagano, Japan). The
coordinates and velocities were segmented into individual repetitions and
exported for analysis. The peak concentric phase velocity for each repetition
was extracted with a custom Matlab code (Matlab 2024a, Mathworks, Natick, MA,
USA). Intra-set velocity loss was determined by comparing each repetition’s
velocity to the fastest repetition within the same conditions, calculated on a
repetition-by-repetition basis [2].
Statistical analysis
The Shapiro–Wilk test assessed the normality of the overall RPE, average velocity
loss, and average SFI for each condition. The average SFI and velocity loss were
normally distributed, so one-way ANOVA with the Bonferroni correction was
applied. However, the overall RPE under the M and H conditions did not meet the
normality assumption, prompting the use of Friedman’s test with the Bonferroni
correction for multiple comparisons.
To examine changes in the RPE, SFI, and velocity loss during the lifting process,
data from specific repetitions (first, median, and last) were analyzed for each
condition. Given the varying repetition numbers (L: 3.8±0.7; M: 7.2±1.3; H:
11.0±1.0), a two-way ANOVA (three conditions×three repetitions) was used to test
for main and interaction effects, followed by Bonferroni-corrected post hoc
tests if significant effects were detected. The sphericity assumption was
verified for the ANOVA tests. If sphericity could not be assumed, the
Greenhouse–Geisser correction was applied. If ε>0.75, a Huynh-Feldt
correction was applied. The effect size for the ANOVA was indicated with partial
η
2 and 95% confidence intervals (CIs). Partial
η
2 and 95% CIs were calculated using the customized R code
(R Studio 2024.12.0, Posit Software, PBC, MA, USA).
For correlation analysis, intra-set RPE, velocity loss, and SFI data were
included. To avoid overlapping data, specific volume ranges were used: 0–30% for
the L condition, 30–60% for the M condition, and 60–90% for the H condition
[35]
[46]. Correlations between SFI and RPE,
as well as SFI and velocity loss, were compared using Fisher’s
r-to-z transformation and paired two-tailed tests. Statistical
analysis was performed using SPSS 29.0 (SPSS Inc., USA), with significance set
at p<0.05.
Results
Significant differences in the overall RPE were observed across the experimental
conditions (χ
2=27.527, p<0.001, and Kendall’s
W=0.983), with specific differences noted between L vs. M (p=0.042), M
vs. H (p=0.018), and L vs. H (p<0.001) comparisons ([Fig. 1A]). For the average SFI,
significant effects for conditions were observed (p<0.001, partial
η
2=0.365, and 95% CI [0.115, 0.528]). Bonferroni post hoc
tests indicated significant differences in L vs. H (p<0.001), and M vs. H
(p=0.015) comparisons ([Fig.
1B]). Finally, significant effects for conditions were observed in the
average velocity loss (p<0.001, partial η
2=0.496, and
95% CI [0.243, 0.632]). Bonferroni post hoc tests indicated that significant
differences in the average velocity loss were observed between L vs. M
(p=0.002) and L vs. H (p<0.001) comparisons ([Fig. 1C]).
Fig. 1 Overall RPE (A), the average SFI (B), and the
average velocity loss (C) of bench press tasks. The p values
represent the differences and significant levels between experimental
conditions. RPE, ratings of perceived exertion; SFI, spectral fatigue
index.
Intra-set data were analyzed using a two-way ANOVA, with the first, mid-point, and
last repetitions extracted for this analysis. Significant interaction effects
between conditions and repetitions were observed for the RPE (p<0.001,
F=39.159, partial η
2=0.751, and 95% CI [0.600, 0.808]),
SFI (p<0.001, F=20.744, partial η
2=0.615, and 95%
CI [0.407, 0.702]), and velocity loss (p<0.001, F=12.029, partial
η
2=0.481, and 95% CI [0.241, 0.592]). There was also a
significant overall main effect of the number of repetitions on the RPE
(p<0.001, F=77.572, partial η
2=0.856, and 95% CI
[0.712, 0.901]), velocity loss (p<0.001, F=79.732, partial
η
2=0.860, and 95% CI [0.719, 0.904]), and SFI
(p<0.001, F=44.499, partial η
2=0.774, and 95% CI
[0.563, 0.845]) throughout the BP trials. In pairwise comparisons, significant
changes over repetitions were observed under the L, M, and H conditions for both the
RPE and the SFI (all p<0.001). However, significant velocity loss over
repetitions was not observed under the L conditions (p=0.238, F=1.517,
partial η
2=0.104; [Fig
2C], circle with solid lines). Significant differences in RPE were noted
between experimental conditions at the mid-point (p<0.001,
F=35.911, partial η
2=0.734, and 95% CI [0.496, 0.818]) and
last repetition (p<0.001, F=63.952, partial
η
2=0.831, and 95% CI [0.665, 0.884]). The SFI also showed
significant differences at the mid-point (p=0.005, F=6.526, partial
η
2=0.334, and 95%CI [0.042, 0.526]), and the last repetition
(p<0.001, F=20.909, partial η
2=0.617, and 95%
CI [0.320, 0.736]). Velocity loss significantly differed at the mid-point
(p=0.002, F=11.181, partial η
2=0.462, and 95% CI
[0.141, 0.625]) and last repetition (p<0.001, F=30.489, partial
η
2=0.701, and 95% CI [0.443, 0.795]).
Fig. 2 The RPE (A), spectral fatigue index (SFI) (B),
and velocity loss (C) during low (L, circle with solid lines), medium
(M, square with dashed lines), and high (H, triangle with dotted lines)
volume conditions of exercises. *A significant difference as L compared with
M conditions, **p<0.01, ***p<0.001; #a
significant difference as M compared with H conditions, #p<0.05,
###p<0.001; †a significant difference as L compared
with H conditions, †p<0.05, †††p<0.001. The p
values indicate the differences and significant levels between selected
repetitions. RPE, ratings of perceived exertion; SFI, spectral fatigue
index.
The results of the Spearman correlation analysis, which included a total of 154 BP
repetitions, are shown in [Fig. 3].
Significant relationships were found between the SFI and RPE (r=0.547,
R
2=0.299, p<0.001; [Fig. 3A]) and those between the SFI and
the velocity loss (r=0.603, R
2=0.363, and
p<0.001) were found to be significant ([Fig. 3B]). No significant difference was
observed between the SFI–RPE and SFI–velocity loss correlations (z=−0.723 and
p=0.467). Additionally, a significant relationship was found between RPE
and velocity loss (r=0.667, R
2=0.444, and
p<0.001; [Fig. 3C]).
Fig. 3 Spearman’s Rho of SFI–RPE scores (A), SFI–velocity loss
(B), and RPE–velocity loss (C) during bench press tasks.
*The significant level of correlation coefficients, p<0.001. RPE,
ratings of perceived exertion; SFI, spectral fatigue index.
Discussion
The purpose of this study was two-fold: (1) to examine the validity of using RPE as
a
fatigue marker by analyzing the relationship between RPE and fatigue, quantified
using a new sEMG-based algorithm during power BP exercises and (2) to compare RPE
with velocity loss to determine a more practical and versatile fatigue indicator for
use in power-aimed resistance exercise programs. The most important findings of this
study are as follows: (1) RPE and velocity loss significantly increased with
fatigue, indicating that fatigue results in an increase in RPE and a decrease in
velocity during power BP exercises, (2) significant correlations were observed
between the RPE and the SFI, as well as between the velocity loss and the SFI,
suggesting that both RPE and velocity loss can serve as fatigue markers during power
BP exercises. (3) A significant correlation between RPE and velocity loss was found,
indicating that RPE could also be used as a reliable indicator for velocity loss
when a velocity assessment device is unavailable during power BP exercises. However,
(4) significant velocity loss was not observed under the L condition, suggesting
that velocity loss may not effectively reflect fatigue when performing power
training with low prescribed volume.
Subjective exertion is associated with various central and peripheral physiological
responses, such as firing frequency and motor unit recruitment during resistance
exercises [21]
[47]. Lagally et al. found that RPE, blood
lactate, and muscle activation levels increased correspondingly with the relative
intensity of biceps curl exercises, suggesting that RPE is linked to both central
and peripheral responses [21]. To control
for the influence of relative intensity, a constant relative intensity was
maintained across the three experimental conditions. The significant differences in
overall RPE observed in this study may be related to the differences in the number
of repetitions prescribed under each condition. For instance, Hiscock et al.
indicated that resistance exercise programs with higher volumes (more repetitions)
elicit more severe metabolic, endocrine, and perceptual responses, even when the
relative intensity is lower [18]
[48]. Consistent with these findings, the H
condition, which had the highest volume, likely accelerated intramuscular
perturbation and disruption of homeostasis (e.g., accumulation of hydrogen ions)
[49]. These physiological responses
increase afferent feedback from group III/IV muscle fibers, resulting in a
significant rise in subjective exertion and discomfort [50]
[51]. Moreover, disruptions in homeostasis can affect muscle fiber
conductivity, which can be quantified by analyzing the power spectral density
characteristics of the sEMG signal [13]
[49]. Significant changes in
the SFI reflect a shift in the power spectrum from high to low frequencies, a shift
induced by fatigue-related mechanisms [52]. Consequently, the corresponding increases in RPE and SFI suggest that
fatigue leads to similar changes in perceived exertion, indicating that RPE can
effectively reflect fatigue responses during power BP exercises.
During resistance exercises, particularly in power-aimed lifting programs, it is
crucial for practitioners, such as athletes and coaches, to manage fatigue levels
to
prevent significant velocity decreases, as highlighted in the Introduction section.
With the growing trend of velocity-based training, practitioners have increasingly
turned to velocity-measuring devices to monitor fatigue in power-aimed exercise
programs. While these devices have become more affordable in recent years, they
remain unrealistic for individuals to purchase and may not be suitable for the
large-scale use. To address this challenge, we selected RPE, a widely used and
nearly zero-cost alternative, as a more practical fatigue indicator for power-aimed
resistance exercises. Previous studies have validated the relationship between
fatigue and RPE in isometric core exercises (e.g., prone plank) [15]
[37]. In the present study, significant differences in the average SFI
were observed between conditions (L vs. H: p<0.001; M vs. H:
p=0.015). Similar significant differences were found in overall RPE (L vs. M:
p=0.014; L vs. H: p<0.001; M vs. H: p=0.006) and average
velocity loss (L vs. M: p=0.002; L vs. H: p<0.001). These results
indicate that the average fatigue level, overall RPE, and average velocity loss
increased correspondingly during power BP exercises. Thus, both the overall RPE and
average velocity loss could serve as indicators of total fatigue of past sets or
sessions. For example, practitioners could assess the overall RPE after a single set
of power BP or an entire power-aimed lifting program to gauge the overall fatigue
level of the lifting contents. Similarly, they could average the velocity loss of
the latest set to achieve the same purpose. However, given the ease of the use of
RPE, it can be concluded that RPE is a more practical and global indirect fatigue
marker during power BP.
To compare the validity of RPE and velocity loss as fatigue indicators not only after
a specific set but also during the lifting process, the SFI, RPE, and velocity loss
were examined throughout the exercise. Given that participants performed different
numbers of repetitions across experimental conditions, the first, mid-point, and
last repetitions were extracted for two-way ANOVA. Significant differences in the
SFI were observed at the mid-point (L vs. H: p=0.014) and the last repetition
(L vs. M: p=0.007; M vs. H: p=0.013; L vs. H: p<0.001).
Similarly, significant differences in intra-set velocity loss were noted at the
mid-point (L vs. M: p=0.005; L vs. H: p<0.001) and the last
repetition (L vs. M: p<0.001; L vs. H: p<0.001). A significant
correlation was also observed between the SFI and velocity loss, providing further
evidence of the relationship between fatigue and velocity loss, consistent with
previous studies that examined this relationship through blood lactate and ammonia
concentrations [2]. In this study, using a
highly sensitive sEMG-based algorithm, similar results were observed, confirming
that the velocity loss is a reliable indicator of fatigue in upper-body power-aimed
resistance exercises such as power BP. For RPE, significant differences were found
at the mid-point (L vs. M: p=0.004; M vs. H: p<0.001; L vs. H:
p<0.001) and the last repetition (all p<0.001). The
significant correlation between the SFI and RPE supports and extends previous
findings on the relationship between RPE and fatigue. Previous studies demonstrated
that the RPE is a valid marker of fatigue in non-explosive resistance exercises,
including non-explosive BP [14]
[35]
[36]. The present study shows that the RPE is also a reliable marker for
indirect fatigue assessments in both non-explosive and power-aimed lifting settings.
Based on the correlation comparison, no significant difference was found between the
SFI–RPE and SFI-velocity loss correlation coefficients. Therefore, it can be
concluded that both RPE and velocity loss effectively predict fatigue and can be
used interchangeably as indirect fatigue indicators. Strength coaches and personal
trainers can monitor RPE or velocity loss on a repetition-by-repetition basis or at
predetermined points (e.g., the first, mid-point, and last repetition) to track
fatigue during the lifting process. However, when using RPE-based fatigue
assessments, there is no need for special devices or equipment (e.g., linear
encoders), making the RPE a more practical option for assessing fatigue in
power-aimed resistance exercise settings.
An interesting finding in this study was the significant correlation observed between
velocity loss and RPE. Previous studies focusing on submaximal set configurations
found a significant correlation between the OMNI scale (a resistance
exercise-specific RPE scale) and mean concentric velocity during leg press exercises
[26]. While these studies demonstrated
that RPE could be used as a fatigue indicator, direct evidence linking RPE to
fatigue was lacking. In the present study, we chose Borg’s CR-10 scale due to its
broader applicability compared to the OMNI scale. We aimed to address the gap in
previous research by assessing both velocity loss and fatigue. Our results
successfully established a relationship between RPE, velocity loss, and fatigue.
Consequently, it can be concluded that the RPE not only serves as a fatigue
indicator but also reflects velocity decrease during power-aimed resistance
exercises. In situations where velocity-assessing devices are unavailable,
practitioners can use RPE as a simplified velocity indicator. By ceasing the set
when RPE increases significantly, they can effectively avoid substantial velocity
loss.
An important finding in this study was the lack of significant velocity loss during
the L condition (the main effect for repetition: p=0.238). Previous studies
advocating for velocity loss as a marker of fatigue did not establish clear limits
for its application [2]
[26]. This finding introduces a new boundary
for using velocity-based fatigue assessments, suggesting that velocity loss may not
accurately reflect fatigue under certain power-aimed lifting conditions. For
instance, significant velocity loss may not be observed when the prescribed volume
is well below physical failure (e.g., ≤30% of the until-failure volume). In such
cases, relying solely on velocity assessments could lead to an oversight of fatigue.
This finding emphasizes the need for coaches and trainers to be cautious when using
velocity-based fatigue assessments as the significant velocity loss is more likely
to be absent when the volume settings are less than 30% of the until-failure volume.
Additionally, velocity loss may offer limited precision in detecting subtle fatigue
conditions. To enhance the accuracy of fatigue assessments in low-volume power-aimed
training programs, it is advisable not to rely solely on velocity loss. Coaches and
trainers should combine velocity loss with other fatigue-related markers, such as
RPE and maximal countermovement jump height, to improve the precision of fatigue
assessments, especially when fatigue may already be present despite an absence of
the significant velocity decrease. This unexpected finding could also be interpreted
in a reverse manner. As mentioned previously, practitioners should focus on avoiding
significant velocity loss when performing power-aimed training programs. In the
present study, significant velocity loss was not observed under the L condition.
Accordingly, it could be concluded that lifting performance remains stable or
decreases only slightly when the prescribed volume is low (e.g., ≤30% of the
until-failure volume) with moderate relative intensity (e.g., 65% 1RM). Coaches and
athletes could consider 30% of the until-failure volume as a threshold if their goal
is to minimize performance loss during power-aimed resistance training programs.
However, practitioners aiming to achieve both hypertrophy and power within a single
training session, or applying velocity loss as an acute fatigue indicator during
hypertrophy-aimed training programs, should exercise caution when using velocity
loss as the sole fatigue indicator [53].
In such cases, velocity loss may provide misleading results, potentially
underestimating the actual fatigue condition.
The present study has several limitations. First, to obtain intra-set RPE, a 2-second
pause was included between repetitions. It is challenging to completely avoid the
influence of this pause phase on fatigue, as it resembles isometric contraction,
which may affect the results. Second, this study only assessed power BP exercises
as
it is a key exercise for developing upper-body power. However, these findings may
not be directly applicable to other resistance exercises, particularly those
involving the lower body. Previous research, along with our own studies, suggests
that fatigue in the lower body is more complex due to physiological differences such
as muscle fiber composition and lactate kinetics [36]
[54]. In a recently published work, velocity loss failed to increase even
as the power back squat process approached physical failure [55]. Third, although the sample size was
predetermined using statistical power analysis, it remains relatively small, which
may have limited the statistical significance of some analyses. For instance,
significant differences were not observed in certain pairwise comparisons. Future
studies should aim to reduce the impact of the pause phase on intra-set data,
directly compare the differences between upper- and lower-body power-aimed
exercises, and involve a larger sample size to confirm the statistical power of the
current findings.
Conclusions
The present study demonstrated that the RPE, velocity loss, and SFI changed in a
similar manner, indicating a strong relationship between subjective exertion,
velocity decrease, and fatigue levels. Significant correlations were observed in
SFI–RPE and SFI–velocity loss, leading us to conclude that both the RPE and velocity
loss are valid indicators of fatigue during power BP exercises. Additionally, we
found that the RPE is significantly correlated with velocity loss during power BP,
suggesting that the RPE can serve as a simplified marker for velocity loss when
velocity-measuring devices are unavailable in power-aimed resistance exercises.
However, it is important to note that the velocity loss may not accurately reflect
fatigue when the prescribed volume is low. Overall, regarding the similar changes
and significant relationship between the RPE and the SFI, the RPE is a valid and
practical indicator of fatigue, especially in power-aimed upper-body exercises.
