

Citation: Wu, J.; Zhang, L.; Yang, H.;
Lu, C.; Jiang, L.; Chen, Y. The Effect of
Music Tempo on Fatigue Perception
at Different Exercise Intensities. Int. J.
Environ. Res. Public Health 2022, 19,
3869. https://doi.org/10.3390/
ijerph19073869
Academic Editors: Mark Reybrouck,
Piotr Podlipniak and David Welch
Received: 15 February 2022
Accepted: 22 March 2022
Published: 24 March 2022
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International  Journal  of
Environmental Research
and Public Health
Article
The Effect of Music Tempo on Fatigue Perception at Different
Exercise Intensities
Jianfeng Wu 1
, Lingyan Zhang 2, Hongchun Yang 1, Chunfu Lu 1,*, Lu Jiang 2 and Yuyun Chen 2
1
Industrial Design and Research Institute, Zhejiang University of Technology, Hangzhou 310023, China;
jianfw@126.com (J.W.); yhc2016@zjut.edu.cn (H.Y.)
2
School of Design and Architecture, Zhejiang University of Technology, Hangzhou 310023, China;
lingyanzh@outlook.com (L.Z.); jl01103149@alibaba-inc.com (L.J.); 2111915006@zjut.edu.cn (Y.C.)
*
Correspondence: luchunfu@126.com
Abstract: Background: This study aimed to clarify the effect of music tempo on runners’ perception
of fatigue at different exercise intensities and while listening to music of different tempos through
running experiments. Methods: This study used a within-subject two-factor experimental design with
music tempo (fast music, slow music, no music) and exercise intensity (high intensity, low intensity)
as independent variables and the time to fatigue perception (TFP), the difference in heart rate (HR)
and the difference in the median frequency (MF) of surface electromyography (sEMG) signals as
observation indexes. Eighteen participants completed a total of 108 sets of running experiments.
Results: (1) The main effect of music tempo on the TFP was signiﬁcant (p < 0.001). (2) The main effect
of exercise intensity on the TFP was signiﬁcant (p < 0.001), and the main effect on the difference in HR
was signiﬁcant (p < 0.001). (3) The interaction effect of music tempo and exercise intensity on the TFP
was signiﬁcant (p < 0.05). Conclusions: Exercisers’ subjective perception of fatigue was affected by
music tempo and the interaction between music tempo and exercise intensity, and exercisers’ objective
fatigue perception was inﬂuenced mostly by exercise intensity. The ﬁndings of this study provide
guidance for runners’ choice of music at different intensities of exercise. Whether it is low-intensity
exercise or high-intensity exercise, listening to fast music while exercising can help runners perform
better mentally and physically during their runs.
Keywords: running; music tempo; exercise intensity; fatigue perception; heart rate; surface
electromyographic signals; median frequency
1. Introduction
Runners’ fatigue perception varies while running. Fatigue perception refers to the sub-
jective intensity of perception of tension, discomfort and fatigue during physical exercise [1].
Exercise fatigue usually manifests as soreness in muscles, an increased heart rate and de-
creased cognitive performance [2,3]. Although the continuous accumulation of fatigue will
affect people’s exercise intentions and performance [4] and excessive fatigue will lead to
physical injury [5,6], appropriate exercise fatigue can improve physical performance [7].
Therefore, to achieve a better ﬁtness effect, runners usually adjust their exercise intensity
continuously according to their subjectively perceived state after perceiving exercise fatigue
during exercise [8]. Listening to music to reduce the sense of boredom and fatigue during
running and to enhance motivation has become a common behavior during running [9].
In terms of whether music interferes with the perception of fatigue in exercisers, there
is a consensus among academics that the effects of music can be explained from three
perspectives: emotional regulation, attention diversion and fatigue recovery. Emotion
plays an important role in regulating motivation in sports. Music can cause exercisers
to experience positive emotions during exercise [10], alleviate their perception of fatigue
during running and enhance the pleasure and sense of participation in running [11]. Using
parallel information processing theory, Rejeski points out that the bandwidth of human
Int. J. Environ. Res. Public Health 2022, 19, 3869. https://doi.org/10.3390/ijerph19073869
https://www.mdpi.com/journal/ijerph
Int. J. Environ. Res. Public Health 2022, 19, 3869
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attention processing narrows during exercise. In the absence of the external stimuli of music,
runners may pay more attention to their running behaviors and feelings, thus increasing
their perception of fatigue, while in conditions with music, runners shift their attention
from unpleasant physical feelings to music, reducing their perception of fatigue and other
negative feelings [12]. It has also been pointed out that the tempo of music can inﬂuence
the internal movement tempo of the body [13], and music with a good tempo can induce
speciﬁc movement patterns [14] and enhance the arousal level of the exerciser, raising the
threshold of fatigue perception and speeding up recovery from exercise fatigue [15,16].
Further studies have shown that exercise fatigue perception is inﬂuenced by both
music tempo and exercise intensity. Different tempos stimulate different emotional states in
listeners, resulting in different effects on runners’ fatigue perception [17]. Exercise intensity
is another frequently discussed inﬂuencing factor. It has been argued that music can play
a role in reducing the perception of fatigue at different exercise intensities [18]. However,
some scholars believe that music only plays a regulatory role at speciﬁc intensities [19].
Subsequently, many scholars have conducted studies on music tempo and exercise in-
tensity, trying to ﬁnd the optimal combination of exercise intensity and music tempo for
modulating fatigue perception. For example, Maddigan et al. concluded that fast music
could improve the performance of exercisers during high-intensity exercise and reduce
fatigue perception [20], while Yamamoto et al. found that neither fast music nor slow music
changed the average power output of exercisers during high-intensity exercise [11], i.e.,
different music tempos did not disperse individuals’ perception of fatigue. Some scholars
have also discussed how movement perception is inﬂuenced by the interaction of music
tempo and movement-related fatigue, but the key lies in the music tempo, the consistency
of exercisers’ preference and in the music and movement tempos being synchronous or
asynchronous. No study compared music tempo to runner fatigue perception at different
intensities of exercise. In addition, some scholars have discussed whether exercisers’ fatigue
perception is affected by the interaction between music tempo and exercise intensity, but
the studies focused on the consistency of music tempo with exercisers’ preference [21] or
the inﬂuence of music tempo being synchronous or asynchronous with exercise tempo [22],
and there is no study comparing the effect of music tempo on runners’ fatigue perception
at different exercise intensities for the time being.
The aforementioned studies provide references for the positive effects of music on
exercise fatigue perception, but there is still some space for exploration on how to choose
the appropriate tempo of music for different intensities of exercise. For example, how
does musical tempo affect fatigue perception of exercisers during different intensities of
exercise? Is there an interaction effect between music tempo and exercise intensity on
runners’ fatigue perception? All these problems need our attention, and the effects of music
tempo on fatigue perception at different exercise intensities still need to be further explored.
To clarify the effect of music tempo on runners’ fatigue perception at different exercise
intensities, this study conducted experiments on running and analyzed and discussed the
changes in runners’ fatigue perception at different exercise intensities and with different
music tempo conditions by evaluating runners’ fatigue perception. The results provide
guidance for individual ﬁtness practitioners choosing music to listen to during exercise at
different intensities.
2. Methods
2.1. Subjects
We conducted an a priori analysis of the required sample size in the study using
G*power, with the presumption of the presence of a medium effect size of f = 0.25 [23],
a statistical test power = 0.8 and a signiﬁcance level α = 0.05. The results of the analysis
indicated that a sample size of 18 would be sufﬁcient to achieve a medium effect size
interaction effect. To obtain more generalizable research conclusions, the experimental
subjects were ordinary college students who did not have regular ﬁtness habits and were
not guided by scientiﬁc theories or methodological knowledge of running ﬁtness. They
Int. J. Environ. Res. Public Health 2022, 19, 3869
3 of 18
had no diseases of the muscular, skeletal, respiratory or cardiovascular systems. They were
not allergic to alcohol and had no cuts or scratches on the thigh muscles. All subjects had
no signiﬁcant differences in physical indicators, were between 20 and 30 years old, 170 and
180 cm in height and 55 and 75 kg.
A total of 18 healthy males were recruited as experimental subjects after the above
rigorous screening. The basic information of the experimental subjects is shown in Table 1.
Participants were required to conﬁrm compliance with the following requirements prior to
each experiment: (1) Rest at least 8 h the night before each experiment and refrain from
participating in any other physical activity other than the exercise tasks of this experiment
to prevent cardiopulmonary or muscle function injury or abnormalities. (2) Maintain a
previously habitual daily diet and ensure that the experiment is performed at least one hour
after the meal, and avoid eating, drinking alcohol or consuming excessive water within
one hour prior to the experiment.
Table 1. Basic information of the subjects (M ± SD).
Number of
Subjects
Age (Years)
Height (cm)
Weight (kg)
Resting Heart Rate
(Beats/min)
18
23.95 ± 1.49
173.78 ± 1.54
64.39 ± 4.55
78.97 ± 8.15
2.2. Experimental Design
The experiment was a two-factor within-subject experimental design with the inde-
pendent variables being music tempo (fast tempo, slow tempo, no music) and exercise
intensity (high intensity, low intensity). The two independent variables led to a total of six
experimental protocols. The dependent variables were the time to fatigue perception (TFP),
heart rate (HR) and the median frequency (MF) of surface EMG signals. For each of the
three music conditions, the experiment was completed for high- and low-intensity running.
To prevent interference between different groups of experiments, the interval between
each running experiment was 48 h for each subject, and the running experiments for each
subject were scheduled at the same time of day (from 9:00 a.m. to 11:00 a.m.) to control for
possible effects of diurnal patterns [24]. Fresh air circulation in the room was maintained
during the experiment, and the ambient temperature was kept at 23 ± 2 ◦C to reduce the
environmental load, promote normal body heat dissipation in the subjects during the exper-
iment and to avoid electrode shedding or short circuits caused by excessive sweating. Each
participant signed an informed consent form for the experiment before participating. The
study was conducted in accordance with the Declaration of Helsinki and approved by the
Ethics Committee of the Industrial Design Institute of Zhejiang University of Technology
(protocol code 0903/2021, date of approval 15 September 2021) (Supplementary Materials
Files S1 and S2).
2.3. Independent Variable
2.3.1. Music Tempo
Music can be divided into fast tempo music and slow tempo music according to the
speed of the tempo. The beat of fast tempo music is 150–160 bpm, and the beat is strong.
The beat of slow tempo music is 90–100 bpm, and such music is characterized by a narrow
range, soft sound and soothing changes [25]. In addition to the requirements of music
tempo, this study also followed the following principles of song selection: (1) The songs
chosen had a cheerful music style and obvious tempo and were able to induce positive
emotions in the subjects. (2) Music without any lyrics was selected to avoid the interference
of lyric content. (3) Songs with complex tunes were excluded. According to the principle
of music selection, 15 pieces of fast tempo music and 15 pieces of slow tempo music
were selected.
All subjects who participated in the experiment were invited to score the valence
and arousal effect of each piece of music, and the music with higher validity and arousal
Int. J. Environ. Res. Public Health 2022, 19, 3869
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effect among the fast music and slow music was selected for inclusion in the ﬁnal song list.
Finally, 15 pieces of music were obtained for the running experiment. Among them, 9 were
slow tempo pieces and 6 were fast tempo music pieces. Details of the selected music are
shown in Table 2.
Table 2. Details of music selection.
Music Tempo
Tracks
Duration/min
Beat/bpm
Slow music
Falcom Sound Team Jdk
4′23′′
90
Grass Harvest
3′14′′
96
The des Alizes
3′40′′
100
Harunouta
3′03′′
90
Sakurairo Contrail
2′28′′
90
Springtime Affair
2′49′′
95
Regrettably, You Know
2′04′′
96
A Tiny Sunshine
1′57′′
99
Sakura Residential Area
2′05′′
97
Fast music
Cigarette Daydreams
3′12′′
150
Hero
3′34′′
150
Shanghai Alice Magic Orchestra
3′36′′
152
Toy War
1′55′′
160
Where to Jun
3′57′′
160
Dream Land Days
3′17′′
155
2.3.2. Exercise Intensity
Exercise intensity refers to the amount of exercise an individual can perform per unit
of time and is a very important indicator in physical exercise and training. The American
College of Sports Medicine suggests that adults should exercise at an exercise intensity
of 50–85% of heart rate reserve (%HRR) to improve their cardiopulmonary function [26].
In view of the fact that this study included people who usually exercise less, the exercise
intensity during the experiment was within the recommended range of %HRR, with
50–60% HRR for low exercise intensity and 70–80% HRR for high exercise intensity.
To facilitate the experiment, the mean treadmill speed was adjusted according to
the %HRR interval to explore the relationship between %HRR and exercise intensity and
determine the treadmill speed to use for the running experiment [27]. Half of the subjects
(7 in total) were randomly selected to participate in the treadmill speed determination
experiment. Each subject was required to perform two separate running experiments
at 50–60% HRR and 70–80% HRR, respectively. To avoid the effect of fatigue, the two
running experiments were separated by more than 24 h. The experimental steps were
as follows: (1) the resting HR of the subjects was measured to determine the target HR
range; (2) the subjects wore an HR belt and then performed a 3 min warm-up exercise
on the treadmill; (3) an incremental running experiment was conducted, in which the
subjects started running at a speed of 5.5 km/h and the speed was increased by 0.5 km/h
every 2 min; (4) after each subject reached the upper limit of the target HR, the subject ran
continuously at that speed and stopped after 5 min. During the 5 min, the change in target
HR of the subjects was observed, and the HR of the subjects was kept at the upper limit of
the target HR range by increasing or decreasing the speed of the treadmill as appropriate.
The target HR was calculated by Equation (1):
Target HR = target intensity %HRR × (HRmax − resting HR) + resting HR
(1)
Based on the average speed of the treadmill while the subjects were in the target HR
range, the treadmill speed corresponding to low-intensity exercise (50–60% HRR) for this
experiment was 7 km/h, and the treadmill speed corresponding to high-intensity exercise
(70–80% HRR) was 9 km/h.
Int. J. Environ. Res. Public Health 2022, 19, 3869
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2.3.3. Music Tempo with Exercise Intensity
The subjects performed constant-load running exercises on a home motorized tread-
mill in the general ﬁtness mode at different intensities under the conditions of music or
no music. A total of 6 exercise regimens were used for subjects within the 3 × 2 group:
no music × low intensity, no music × high intensity, slow tempo × low intensity, slow
tempo × high intensity, fast tempo × low intensity, fast tempo × high intensity. To prevent
the interference of order effects, the Latin square design method was used to arrange the
experimental order in this experiment.
2.4. Dependent Variable
2.4.1. Time to Fatigue Perception
In this study, Borg’s scale for rating of perceived exertion (RPE) was used to measure
the subjective exertion of exercisers during running. Scores on the RPE scale ranged from
6 (no exertion at all) to 20 (exercise limit), as shown in Table 3. The RPE scale is a subjective
assessment of an individual’s perception of muscular exertion, physical tension, discomfort
or fatigue during exercise and reﬂects the individual’s perception of his or her fatigue
state [28]. When the RPE reaches 15, the exerciser shows shortness of breath and signiﬁcant
muscle fatigue. Therefore, to ensure the safety of the experiment, RPE = 15 was chosen
as the index for the experiment. Running was stopped when the RPE value reported by
the subject reached 15, and the time from the start of running to the time when the RPE
reached 15, i.e., the time to fatigue perception, was recorded.
Table 3. Borg’s scale for rating of perceived exertion.
Score
Subjective
Exercise Intensity
Subjective
Exercise Fatigue
Score
Subjective
Exercise Intensity
Subjective
Exercise Fatigue
6
No exertion at all
Not hard at all
14
-
7
Extremely light
Extremely relaxed
15
Hard (heavy)
Tired
8
- 1
16
-
9
Very light
Very relaxed
17
Very hard
Very tired
10
-
18
-
11
Light
Relaxed
19
Extremely hard
Extremely tired
12
-
20
Maximal exertion
Trying one’s best
13
Somewhat hard
A little tired
1-represents the fatigue status between the two levels.
2.4.2. Instantaneous HR
HR can be used to objectively evaluate exercise fatigue and is the easiest indicator to
use to assess the intensity of current exercise and exercise fatigue [29]. Exercise fatigue
leads to a decrease in the HR regulation function, HR increases with fatigue and HR is
dynamic during exercise. In this study, the difference in HR before and after running was
used to characterize the degree of fatigue of exercisers. Two heart rate measurements were
taken in each set of experiments; the ﬁrst time the subject’s resting HR was measured before
the run. The second time was measured during the running process, where the subject was
wearing a heart rate belt, and we collected the HR signal of the subject during the whole
running process. However, in the data processing, only the heart rate signal of the subject
5 s before the end of the run was selected and averaged as an indicator of the immediate
post-run HR.
2.4.3. Surface Electromyography Signal
Electromyography (EMG) signals are indicative of biological electrical signals gener-
ated by the contraction of human muscles. Surface EMG (sEMG) measures the compre-
hensive electrical effect of the conduction of human muscle electrical signals that can be
sensed on the skin surface [30]. The sEMG signal is commonly used for the evaluation of
neuromuscular function because of its real-time, sensitive and ﬂexible characteristics. It
Int. J. Environ. Res. Public Health 2022, 19, 3869
6 of 18
has important practical and research value in the ﬁelds of sports science, clinical medicine,
ergonomics, etc. There have been many studies linking sEMG signals to fatigue [31]. The
sEMG uses the surface electrode bipolar conduction method, and the derived EMG signal
is increased by a signal ampliﬁer and then enters a converter for signal conversion and
storage in a computer. It has been shown that the frequency domain index of the sEMG
signal is more sensitive to the muscle fatigue state of runners [32]. Frequency domain anal-
ysis mainly relies on fast Fourier transformation (FFT) to obtain the frequency spectrum
or power spectrum of the EMG signal to reﬂect the variation of the EMG signal in the
frequency dimension [33]. The median frequency (MF) is one of the most commonly used
indicators in the frequency domain analysis of surface EMG signals, and the MF value is
more stable for assessing the muscle fatigue state [34,35]. MF refers to the middle value of
muscle ﬁber discharge frequency during skeletal muscle contraction and high frequency
discharge is the main expression of excitation of fast muscle ﬁbers, while slow muscle ﬁbers
are dominated by low-frequency potential activity [36]. Therefore, the difference in static
MF before and after running was used as the index of local muscle fatigue in this study.
The formula for MF is shown in Equation (2):
MF = 1
2
Z ∞
0
PSD( f )d f
(2)
where PSD(f) is the myoelectric power spectral density.
Running mainly mobilizes the leg muscle groups; therefore, in this experiment, the
sEMG signals of the local muscles in the runners’ legs were collected, and the rectus
femoris (RF) and vastus medialis (VM), which display obvious changes in sEMG signals
and are very stable during movement changes, were examined [37]. The electrode positions
for the RF and VM are shown in Figure 1. Figure 1a shows the electrode positions for
the RF, Figure 1b shows the electrode positions for the VM. The black electrode is the
reference electrode.
2.4.3. Surface Electromyography Signal 
Electromyography (EMG) signals are indicative of biological electrical signals gener-
ated by the contraction of human muscles. Surface EMG (sEMG) measures the compre-
hensive electrical effect of the conduction of human muscle electrical signals that can be 
sensed on the skin surface [30]. The sEMG signal is commonly used for the evaluation of 
neuromuscular function because of its real-time, sensitive and flexible characteristics. It 
has important practical and research value in the fields of sports science, clinical medicine, 
ergonomics, etc. There have been many studies linking sEMG signals to fatigue [31]. The 
sEMG uses the surface electrode bipolar conduction method, and the derived EMG signal 
is increased by a signal amplifier and then enters a converter for signal conversion and 
storage in a computer. It has been shown that the frequency domain index of the sEMG 
signal is more sensitive to the muscle fatigue state of runners [32]. Frequency domain 
analysis mainly relies on fast Fourier transformation (FFT) to obtain the frequency spec-
trum or power spectrum of the EMG signal to reflect the variation of the EMG signal in 
the frequency dimension [33]. The median frequency (MF) is one of the most commonly 
used indicators in the frequency domain analysis of surface EMG signals, and the MF 
value is more stable for assessing the muscle fatigue state [34,35]. MF refers to the middle 
value of muscle fiber discharge frequency during skeletal muscle contraction and high 
frequency discharge is the main expression of excitation of fast muscle fibers, while slow 
muscle fibers are dominated by low-frequency potential activity [36]. Therefore, the dif-
ference in static MF before and after running was used as the index of local muscle fatigue 
in this study. The formula for MF is shown in Equation (2): 
PSD f df


=
0
( )
2
1
MF
 
(2) 
where PSD(f) is the myoelectric power spectral density. 
Running mainly mobilizes the leg muscle groups; therefore, in this experiment, the 
sEMG signals of the local muscles in the runners’ legs were collected, and the rectus fem-
oris (RF) and vastus medialis (VM), which display obvious changes in sEMG signals and 
are very stable during movement changes, were examined [37]. The electrode positions 
for the RF and VM are shown in Figure 1. Figure 1a shows the electrode positions for the 
RF, Figure 1b shows the electrode positions for the VM. The black electrode is the refer-
ence electrode. 
 
 
(a) 
(b) 
Figure 1. Muscle and electrode location. (a) Shows the electrode positions for the RF, (b) shows the 
electrode positions for the VM. 
The RF and VM sEMG signals of the subject’s dominant leg were collected. The sub-
ject was first asked to perform thigh flexion and extension movements to find the target 
muscle location. Once the target muscle was found, the location of the target muscle was 
marked [38]. Subsequently, cotton dipped in alcohol was used to clean the skin of surface 
dirt and remove sweat, sebum and other impurities on the skin surface. The electrodes 
were pasted after the skin was dry, and surface hairs were removed if necessary [39]. The 
purpose of taking the above measures was to reduce the impedance effect of the skin, 
Figure 1. Muscle and electrode location. (a) Shows the electrode positions for the RF, (b) shows the
electrode positions for the VM.
The RF and VM sEMG signals of the subject’s dominant leg were collected. The
subject was ﬁrst asked to perform thigh ﬂexion and extension movements to ﬁnd the target
muscle location. Once the target muscle was found, the location of the target muscle was
marked [38]. Subsequently, cotton dipped in alcohol was used to clean the skin of surface
dirt and remove sweat, sebum and other impurities on the skin surface. The electrodes
were pasted after the skin was dry, and surface hairs were removed if necessary [39]. The
purpose of taking the above measures was to reduce the impedance effect of the skin,
enhance the adhesion of the electrode patch to the skin and obtain the best recording effect.
Following the surface EMG for non-invasive assessment of muscles (SENIAM) guidelines,
bipolar sEMG electrodes were placed along the longitudinal midline of the muscle (in the
direction of the muscle ﬁbers) on the muscle abdomen at the muscle–tendon junction, and
the point spacing of the electrode patch was 2–3 cm [40,41]. After placement of the sensor
and the reference electrode, a test was performed to determine whether the electrodes
were placed properly on the muscle and connected to the equipment so that a reliable
Int. J. Environ. Res. Public Health 2022, 19, 3869
7 of 18
sEMG signal could be recorded [42]. EMG signal acquisition software (Acknowledge4.2,
Biopac) was connected to the sensor, and then the subject was asked to perform ﬂexion
and extension movements. It was then observed whether the corresponding EMG signal
in the interface of the acquisition software produced obvious changes. If the signal did
not show obvious changes, or the signal showed abnormal values, the muscle selection
position was further calibrated, and the electrode patch was checked to determine whether
the connection with skin was ﬁrm [38]. Testing continued until the signal was stable. The
above steps were followed before each acquisition of sEMG signals. The electrodes used
for testing were Ag/AgCl electrocardiographic electrodes. The EMG100c signal ampliﬁer
was then secured to the subject’s lower leg with a strap.
2.5. Experimental Apparatus
The experimental apparatus included a treadmill, a pair of Bluetooth headphones, a set
of barbell pieces with different weights, an HR belt, an RPE scale and an MP150 telemetry
physical recorder and its accessories. The treadmill had a rated power of 1100 W and a belt
area of 1350 × 480 mm, which was used to provide running conditions with a constant load.
Airpod Bluetooth headphones were used to play music at a constant volume of 50% of
the maximum volume, or approximately 65 dB. Barbells were hung on the legs before and
after running to collect sEMG signals during static muscle exertion. A Polar HR monitor
(Polar Electro Oy, Kempele, Finland) with a sampling rate of 125 Hz was used to acquire
the real-time HR of the subjects during running. Participants wore the Polar heart rate
band on their chest, which synced with their iPhone via Bluetooth. The MP150 telemetry
physiological recorder (Biopac Inc., Goleta, CA, USA) was used to acquire sEMG signals
at a sampling rate of 2048 Hz, equipped with two EMG100c signal ampliﬁers and several
disposable ECG electrode patches (Shanghai Huyou Medical Electrode Co., Ltd., Shangai,
China). The electrodes were used to connect the target muscle to the EMG signal ampliﬁer.
The bipolar electrode (Ag/AgCl) was attached with a 10 mm diameter gel medium for
reducing the impedance between the electrode and the skin. The size of the electrode
patches was trimmed to 3 cm before use so that they could meet the electrode patch 2–3 cm
spacing distance.
2.6. Experimental Procedure
The experimental procedure for this study is shown in Figure 2. Each subject was
required to follow the experimental procedure and complete a total of six sets of the
running experiment.
2.6.1. Acquisition of Resting HR
The HR of the subject was captured and recorded in real-time with an HR band. To
exclude the inﬂuence of the initial state among different subjects, the resting HR of the
subjects was collected before the experiment began. After the subjects sat in a comfortable
position for 5 min, their HR was collected for 1 min, and the average value was calculated
as the resting HR of the subjects [28].
2.6.2. Pre-Run Safety Instructions and Warmup
Before the formal experiment started, the subjects were introduced to the experiment
contents and procedures in detail and received instructions on the use of the treadmill,
correct running posture, a breathing adjustment method that could be used during running
and the use of the RPE scale to ensure the safety of the subjects and the smooth operation
of the experiment. All subjects underwent a 5-min warmup exercise before the experiment
to avoid the occurrence of muscle damage during the experiment.
Int. J. Environ. Res. Public Health 2022, 19, 3869
8 of 18
Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 
8
 
Figure 2. Experiment flow chart. 
2.6.1. Acquisition of Resting HR 
The HR of the subject was captured and recorded in real-time with an HR ban
exclude the influence of the initial state among different subjects, the resting HR 
subjects was collected before the experiment began. After the subjects sat in a comfo
position for 5 min, their HR was collected for 1 min, and the average value was calcu
as the resting HR of the subjects [28]. 
2.6.2. Pre-run Safety Instructions and Warmup 
Before the formal experiment started, the subjects were introduced to the exper
contents and procedures in detail and received instructions on the use of the trea
correct running posture, a breathing adjustment method that could be used during
ning and the use of the RPE scale to ensure the safety of the subjects and the smoot
eration of the experiment. All subjects underwent a 5-min warmup exercise befor
experiment to avoid the occurrence of muscle damage during the experiment. 
2.6.3. Acquisition of sEMG before Running 
Static sEMG signals of the abovementioned muscles were collected before and
running to complement the instability of the sEMG signals during running [28]. A 
barbell pieces of different weights was prepared for hanging on the dominant leg b
and after running to collect sEMG signals during static muscle exertion. Static sEM
nals were acquired as follows: the subject’s leg was subjected to a certain percenta
weight-bearing and knee extension so that the leg muscles were in a state of exertion
then data were recorded for the RF and VM with an MP150 telemetry physical rec
for 30 s [43]. In this case, the weight-bearing level was determined based on the 
weight of each subject: weight-bearing level = body weight (kg) × 10% [44]. The EMG
Figure 2. Experiment ﬂow chart.
2.6.3. Acquisition of sEMG before Running
Static sEMG signals of the abovementioned muscles were collected before and after
running to complement the instability of the sEMG signals during running [28]. A set of
barbell pieces of different weights was prepared for hanging on the dominant leg before
and after running to collect sEMG signals during static muscle exertion. Static sEMG
signals were acquired as follows: the subject’s leg was subjected to a certain percentage of
weight-bearing and knee extension so that the leg muscles were in a state of exertion, and
then data were recorded for the RF and VM with an MP150 telemetry physical recorder for
30 s [43]. In this case, the weight-bearing level was determined based on the body weight
of each subject: weight-bearing level = body weight (kg) × 10% [44]. The EMG amplitudes
of the RF and VM on the dominant side of the leg were recorded. To ensure signal stability
and data reliability, the middle 20 s of the 30-s static EMG signal were extracted, and the
20-s MF values were calculated with a 2048-point window.
The experimental procedure is shown in Figure 3. Figure 3a shows the electrode posi-
tioning for the target muscle and the actual situation of wearing and using the instrument;
Figure 3b shows the setup for measuring sEMG signals when loading and stretching the
knee; and Figure 3c shows the running experimental procedure.
2.6.4. Running Experiment and Data Collection
Each runner ran under low-intensity and high-intensity conditions, with no music,
fast music and slow music. The subjects started exercising at 7 km/h for the low-intensity
exercise condition and at 9 km/h for the high-intensity exercise condition. The subjects
were observed and asked about their current fatigue at 1-min intervals, and the RPE values
were recorded. The subjects stopped running when their subjective RPE reached 15. The
time taken for the RPE value to reach 15 was recorded, and the HR signal was collected for
5 s before the end of the run. Immediately after the subjects stopped running, the weighted
knee extension experiment was performed, and the sEMG signals of the RF and VM were
Int. J. Environ. Res. Public Health 2022, 19, 3869
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collected from the subjects. Throughout the experiment, the experimenter observed the
subjects’ movement status and ensured their safety.
Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 
9 of 18 
 
 
The experimental procedure is shown in Figure 3. Figure 3a shows the electrode po-
sitioning for the target muscle and the actual situation of wearing and using the instru-
ment; Figure 3b shows the setup for measuring sEMG signals when loading and stretch-
ing the knee; and Figure 3c shows the running experimental procedure. 
 
 
 
(a) 
(b) 
(c) 
Figure 3. (a) The electrode position for the target muscles and the actual situation of wearing and 
using the instrument; (b) the setup for measuring sEMG signals when loading and stretching the 
knee; and (c) the running experimental procedure. 
2.6.4. Running Experiment and Data Collection 
Each runner ran under low-intensity and high-intensity conditions, with no music, 
fast music and slow music. The subjects started exercising at 7 km/h for the low-intensity 
exercise condition and at 9 km/h for the high-intensity exercise condition. The subjects 
were observed and asked about their current fatigue at 1-min intervals, and the RPE val-
ues were recorded. The subjects stopped running when their subjective RPE reached 15. 
The time taken for the RPE value to reach 15 was recorded, and the HR signal was col-
lected for 5 s before the end of the run. Immediately after the subjects stopped running, 
the weighted knee extension experiment was performed, and the sEMG signals of the RF 
and VM were collected from the subjects. Throughout the experiment, the experimenter 
observed the subjects’ movement status and ensured their safety. 
After the experiment, the HR band, electrode pads and other experimental equip-
ment were removed, and the subjects were asked if there was any discomfort and were 
instructed to perform appropriate stretching and recovery exercises to regulate HR recov-
ery. Then, the next experiment was scheduled. 
2.7. Data Processing and Analysis 
All results are presented as group means and standard deviations. The normality of 
the data distribution was confirmed using the Shapiro–Wilk test. To determine the effect 
of the intervention on the dependent variable, a two-way analysis of variance (ANOVA) 
for repeated measures was used to calculate between-group differences. If the interaction 
between music tempo and exercise intensity was significant, Bonferroni post hoc tests 
were calculated. The effect size was calculated as partial eta squared (η2). The criteria for 
classifying Cohen d were as follows: small (0 < d < 0.5), medium (0.5 ≤ d < 0.8) and large 
(d ≥ 0.8) [23]. The significance level was 0.05. All statistical analyses were performed using 
SPSS 26.0 (SPSS Inc., Chicago, IL, USA). 
3. Results 
3.1. Examination of the Effects of Music Tempo and Exercise Intensity 
A two-way repeated-measures ANOVA was conducted for TFP and the differences 
in HR and MF to determine whether there were main and interaction effects for music 
Figure 3. (a) The electrode position for the target muscles and the actual situation of wearing and
using the instrument; (b) the setup for measuring sEMG signals when loading and stretching the
knee; and (c) the running experimental procedure.
After the experiment, the HR band, electrode pads and other experimental equipment
were removed, and the subjects were asked if there was any discomfort and were instructed
to perform appropriate stretching and recovery exercises to regulate HR recovery. Then,
the next experiment was scheduled.
2.7. Data Processing and Analysis
All results are presented as group means and standard deviations. The normality of
the data distribution was conﬁrmed using the Shapiro–Wilk test. To determine the effect
of the intervention on the dependent variable, a two-way analysis of variance (ANOVA)
for repeated measures was used to calculate between-group differences. If the interaction
between music tempo and exercise intensity was signiﬁcant, Bonferroni post hoc tests
were calculated. The effect size was calculated as partial eta squared (η2). The criteria for
classifying Cohen d were as follows: small (0 < d < 0.5), medium (0.5 ≤ d < 0.8) and large
(d ≥ 0.8) [23]. The signiﬁcance level was 0.05. All statistical analyses were performed using
SPSS 26.0 (SPSS Inc., Chicago, IL, USA).
3. Results
3.1. Examination of the Effects of Music Tempo and Exercise Intensity
A two-way repeated-measures ANOVA was conducted for TFP and the differences
in HR and MF to determine whether there were main and interaction effects for music
tempo and exercise intensity, and the results are shown in Table 4. The main effect of music
tempo on TFP was signiﬁcant (p < 0.001), but the main effects of the differences in HR and
MF were not signiﬁcant (p > 0.05). The main effect of exercise intensity on TFP and the
difference in HR was signiﬁcant (p < 0.001), but the main effect for the difference in MF was
not signiﬁcant (p > 0.05). The interaction effect of music tempo and exercise intensity was
signiﬁcant for TFP (p < 0.05) but not for the difference in HR or MF (p > 0.05).
Int. J. Environ. Res. Public Health 2022, 19, 3869
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Table 4. Examination of the effects of music tempo and exercise intensity.
Parameter
High
Low
Music Tempo
Exercise
Intensity
Music Tempo
× Exercise
Intensity
Fast
Slow
None
Fast
Slow
None
p-Value
η2
p-Value
η2
p-Value
η2
TFP (min)
7.18 ± 2.36
6.23 ± 2.44
5.40 ± 1.94
12.68 ± 6.46
10.79 ± 4.86
9.06 ± 4.36
0.000
0.632
0.000
0.540
0.031
0.207
HR
difference 1
69.56 ± 10.66
67.39 ± 11.14
71.11 ± 10.18
57.89 ± 10.30
61.83 ± 10.81
62.06 ± 8.95
0.077
0.140
0.000
0.796
0.075
0.141
MFRF
difference 2
−0.64 ± 4.98
−2.02 ± 2.95
0.53 ± 4.45
−0.86 ± 6.34
−0.42 ± 6.81
−0.96 ± 5.11
0.672
0.023
0.967
0.000
0.210
0.088
MFVM
difference 2
−0.75 ± 5.91
−0.89 ± 3.81
1.01 ± 4.2
0.38 ± 3.81
−1.08 ± 5.6
−0.79 ± 4.41
0.609
0.029
0.670
0.011
0.323
0.064
1 HR difference value is post-run HR−resting HR. 2 MF difference is MF before running−MF after running. RF: rectus femoris. VM: vastus medialis.
Int. J. Environ. Res. Public Health 2022, 19, 3869
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3.2. Time to Fatigue Perception
The effects of music tempo on TFP at different exercise intensities are shown in Figure 4.
The combined results show that the TFP of the group with music was signiﬁcantly greater
than that of the group without music. Speciﬁcally, in the low-intensity exercise experiment,
the TFP in the no music condition (9.06 min) was shorter than the TFP in the slow music
condition (10.79 min) and the fast music condition (12.68 min). During high-intensity
exercise, the TFP in the no music condition (5.40 min) was shorter than the TFP in the slow
tempo music condition (6.23 min) and the TFP in the fast tempo music condition (7.18 min).
 
difference 2 
0.64 ± 4.98 
2.02 ± 2.95 0.53 ± 4.45 
0.86 ± 6.34 0.42 ± 6.81 0.96 ± 5.11 
0.672 0.023 0.967 0.000 0.210 
0.088 
MFVM  
difference 2 −0.75 ± 5.91 −0.89 ± 3.81 1.01 ± 4.2 
0.38 ± 3.81 −1.08 ± 5.6 −0.79 ± 4.41 
0.609 0.029 0.670 0.011 0.323 
0.064 
1 HR difference value is post-run HR−resting HR. 2 MF difference is MF before running−MF after 
running. RF: rectus femoris. VM: vastus medialis. 
3.2. Time to Fatigue Perception 
The effects of music tempo on TFP at different exercise intensities are shown in Figure 
4. The combined results show that the TFP of the group with music was significantly 
greater than that of the group without music. Specifically, in the low-intensity exercise 
experiment, the TFP in the no music condition (9.06 min) was shorter than the TFP in the 
slow music condition (10.79 min) and the fast music condition (12.68 min). During high-
intensity exercise, the TFP in the no music condition (5.40 min) was shorter than the TFP 
in the slow tempo music condition (6.23 min) and the TFP in the fast tempo music condi-
tion (7.18 min). 
 
Figure 4. Time to fatigue perception for different experimental conditions. 
Since the interaction effect of music tempo and exercise intensity on the TFP was sig-
nificant, a further Bonferroni post hoc test was performed to show the results (Table 5). 
The results of the Bonferroni post hoc test showed that the difference in the effect on TFP 
between no music and slow music at different exercise intensities was statistically signif-
icant (p < 0.01), the difference in the effect on TFP between no music and fast music was 
Figure 4. Time to fatigue perception for different experimental conditions.
Since the interaction effect of music tempo and exercise intensity on the TFP was
signiﬁcant, a further Bonferroni post hoc test was performed to show the results (Table 5).
The results of the Bonferroni post hoc test showed that the difference in the effect on
TFP between no music and slow music at different exercise intensities was statistically
signiﬁcant (p < 0.01), the difference in the effect on TFP between no music and fast music
was statistically signiﬁcant (p < 0.001) and the difference in the effect on TFP between slow
music and fast music was statistically signiﬁcant (p < 0.001).
Table 5. Post hoc test of TFP under different experimental conditions.
Exercise Intensity
Pairwise Comparisons
No Music vs.
Slow Music vs.
Fast Music vs.
Low intensity
Slow music: p = 0.004
No music: p = 0.004
No music: p = 0.000
Fast music: p = 0.000
Fast music: p = 0.000
Slow music: p = 0.000
High intensity
Slow music: p = 0.000
No music: p = 0.000
No music: p = 0.0000
Fast music: p = 0.000
Fast music: p = 0.000
Slow music: p = 0.000
3.3. HR Changes
The effect of music tempo on the differences in HR at different exercise intensities
is shown in Figure 5. As seen from the difference in HR between the before and after
measurements, the difference in HR in the high-intensity exercise group was higher than
that in the low-intensity group overall. This is evident in the following: during low-
intensity exercise, the no music group had the highest HR variation (62.06 beats/min),
followed by the slow music group (61.83 beats/min) and ﬁnally the fast music group
(57.89 beats/min); during high-intensity exercise, the no music group had the highest HR
variation (71.11 beats/min), followed by the fast music group (69.56 beats/min) and ﬁnally
the slow music group (67.39 beats/min).
Int. J. Environ. Res. Public Health 2022, 19, 3869
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,
g
y
g
p
g
in the low-intensity group overall. This is evident in the following: during low-intensity 
exercise, the no music group had the highest HR variation (62.06 beats/min), followed by 
the slow music group (61.83 beats/min) and finally the fast music group (57.89 beats/min); 
during high-intensity exercise, the no music group had the highest HR variation (71.11 
beats/min), followed by the fast music group (69.56 beats/min) and finally the slow music 
group (67.39 beats/min). 
 
Figure 5. Differences in HR before and after running under different experimental conditions. 
3.4. sEMG Changes 
The results of the MF difference of EMG signals of the RF and VM of the subjects 
under the six exercise protocols are shown in Figure 6a,b. From the figure, it can be seen 
that the MF difference values of both the RF and VM have two different directions of pos-
itive and negative results, failing to show a regular variation in surface EMG signal 
changes of the RF and VM. 
Figure 5. Differences in HR before and after running under different experimental conditions.
3.4. sEMG Changes
The results of the MF difference of EMG signals of the RF and VM of the subjects under
the six exercise protocols are shown in Figure 6a,b. From the ﬁgure, it can be seen that the
MF difference values of both the RF and VM have two different directions of positive and
negative results, failing to show a regular variation in surface EMG signal changes of the
RF and VM.
Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 
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(a) 
(b) 
Figure 6. (a) MF differences of rectus femoris before and after running under different experi-
mental conditions; (b) MF differences of vastus medialis before and after running under different 
experimental conditions. 
4. Discussion 
4.1. The Effect of Music Tempo on Runners’ Subjective Perception of Fatigue at Different Exer-
cise Intensities 
The TFP results indicate that music can effectively influence runners’ perception of 
fatigue during running, a finding that is consistent with previous studies [45,46]. The sig-
nificant main effect of music tempo (p < 0.001) also confirms the positive effect of music 
on fatigue perception (0.50 ≤ effect size d < 0.8). The results show that music tempo had a 
moderate effect on the time of fatigue perception. This result is consistent with Nethery’s 
study, where listening to music reduced subjective fatigue induced by exercise at different 
exercise intensities [18]. The mechanism of the effect can be explained by attentional lim-
itation theory and selective sensory filtering theory [47,48]. That is, listening to music dur-
ing exercise can reduce the excitability of the sympathetic nervous system, thereby reduc-
ing subjective fatigue perception and enhancing exercise tolerance. This study further ex-
plored the influence of music tempo on the TFP at different exercise intensities. This also 
suggests that in the absence of musical stimuli, participants may be more focused on their 
efforts and feel fatigued more quickly. 
The significant main effect of exercise intensity (p < 0.001) indicates that there was a 
significant difference between the effects of high and low exercise intensity on runners’ 
subjective perception of fatigue, and there was a moderate effect of exercise intensity on 
subjective fatigue perception (0.50 ≤ effect size d < 0.8), which is consistent with common 
Figure 6. (a) MF differences of rectus femoris before and after running under different experimental
conditions; (b) MF differences of vastus medialis before and after running under different experimen-
tal conditions.
4. Discussion
4.1. The Effect of Music Tempo on Runners’ Subjective Perception of Fatigue at Different
Exercise Intensities
The TFP results indicate that music can effectively inﬂuence runners’ perception of
fatigue during running, a ﬁnding that is consistent with previous studies [45,46]. The
signiﬁcant main effect of music tempo (p < 0.001) also conﬁrms the positive effect of music
on fatigue perception (0.50 ≤ effect size d < 0.8). The results show that music tempo had a
moderate effect on the time of fatigue perception. This result is consistent with Nethery’s
study, where listening to music reduced subjective fatigue induced by exercise at different
exercise intensities [18]. The mechanism of the effect can be explained by attentional
limitation theory and selective sensory ﬁltering theory [47,48]. That is, listening to music
during exercise can reduce the excitability of the sympathetic nervous system, thereby
reducing subjective fatigue perception and enhancing exercise tolerance. This study further
explored the inﬂuence of music tempo on the TFP at different exercise intensities. This also
suggests that in the absence of musical stimuli, participants may be more focused on their
efforts and feel fatigued more quickly.
Int. J. Environ. Res. Public Health 2022, 19, 3869
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The signiﬁcant main effect of exercise intensity (p < 0.001) indicates that there was
a signiﬁcant difference between the effects of high and low exercise intensity on runners’
subjective perception of fatigue, and there was a moderate effect of exercise intensity on
subjective fatigue perception (0.50 ≤ effect size d < 0.8), which is consistent with common
knowledge about the positive correlation between exercise intensity and fatigue perception.
Meanwhile, the signiﬁcant interaction effect of music tempo and exercise intensity indicated
that the effects of music tempo and exercise intensity (p < 0.05) on runners’ subjective
perception of fatigue were inﬂuenced by each other, but the effect is small. However, the
effect size result (0 < effect size d < 0.5) shows that music tempo and exercise intensity
interacted to a lesser extent. The results of further post hoc analyses indicate that whether in
high intensity or low intensity exercise, listening to fast music can prolong the running time
and reduce the subjective fatigue of runners compared to listening to slow music. During
high-intensity exercise, the effect of fast music and slow music on the TFP is signiﬁcant
(p < 0.001), indicating that fast music was to some extent more effective than slow music in
reducing fatigue perception. This conclusion is also supported by previous studies, such
as Cental’s study, which mentioned that listening to fast music increased overall exercise
tolerance and the neuromuscular fatigue threshold [18]. Furthermore, post-experiment
interviews with all subjects revealed that listening to fast music resulted in a more uplifted
state of mind and a more positive mood, which effectively distracted them and reduced the
perception of fatigue. Some subjects also believed that fast music made them unconsciously
adjust their running pace, which helped to counteract the perception of fatigue. The
combined results of the experiments show that fast music effectively reduced the subjective
perception of fatigue at different exercise intensities in runners.
4.2. The Effect of Music Tempo on HR Differential at Different Exercise Intensities
The ANOVA results show that the difference in HR before and after running was
only affected by the main effect of exercise intensity (p < 0.001) and reached an effect
level close to the high intensity (0.50 ≤ effect size d < 0.8). It indicates that HR changes
were largely inﬂuenced by the exercise intensity. This ﬁnding indicates that there is a
signiﬁcant difference in the magnitude of HR variation in runners at different intensities of
exercise, and this ﬁnding is consistent with the common knowledge that there is a strong
correlation between the magnitude of HR variation in runners and the intensity of exercise.
In addition, a study by Szabo et al. provides evidence for the idea that music not only has
an effect on fatigue perception during progressive exercise, but can still provide effects for
a period of time at higher intensities, depending on the quality of intrinsic arousal [49]. The
main reason is that the greater intensity of exercise causes a greater cardiac load, and the
maintenance of cardiac output depends on an increase in HR; the fatigue of the body also
increases, resulting in a higher HR immediately after running [50].
However, the main effect of music tempo was not signiﬁcant (p > 0.05), and the
level of effect size was small (0 < effect size d < 0.5), indicating that the magnitude of HR
change during exercise was inﬂuenced by music tempo to a lesser extent. This ﬁnding is
inconsistent with some current studies which indicate that the magnitude of HR variation
during exercise is inﬂuenced by music tempo [51]. This ﬁnding is also inconsistent with
the signiﬁcant main effect of music tempo on the TFP, probably because the TFP is a
subjective index of fatigue that reﬂects runners’ self-evaluation of their physical state
and is signiﬁcantly inﬂuenced by mental aspects. In contrast, under control conditions,
the HR variation of the subjects mainly depends on subjects’ physical fatigue [52]. As
some studies pointed out, the effect of music on fatigue perception would be reﬂected in
the shift of runners’ attention to the fatigue state; that is, it would be shown ﬁrst in the
shift in perception of mental fatigue and then in the perception of physical fatigue [53,54].
However, the duration of this experiment was determined by the runners’ subjective
fatigue, and the runners ended the run when their subjective fatigue reached the level of
RPE = 15, which led to the intervention effect of music tempo on the runners’ physical
fatigue perception not being fully demonstrated in this experiment. This further led to
Int. J. Environ. Res. Public Health 2022, 19, 3869
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a nonsigniﬁcant interaction effect between music tempo and exercise intensity (p > 0.05,
0 < effect size d < 0.5). However, there are studies that have come to similar conclusions
as the present study, such as Edworthy and Waring and Dyck et al., who also did not ﬁnd
a link between music tempo changes and HR changes [17,55]. HR and music tempo can
be considered as interacting oscillatory systems that will start at the same period, but this
alignment strategy only works at the beginning of the experiment.
4.3. Effect of Music Tempo on the Difference in MF at Different Exercise Intensities
The differences in MF for the RF and VM for different exercise regimens varied widely
and did not show regular changes. The results show that the main effects of music tempo
and exercise intensity on both MFRF difference and MFVM difference were not signiﬁcant
(p > 0.05) and that both had small effect sizes (0 < effect size d < 0.5). The interaction
effects of music tempo and exercise intensity on MFRF and MFVM differences were also not
signiﬁcant (p > 0.05) and both effect sizes were small (0 < effect size d < 0.5). Meanwhile,
the experimental results for the differences in MF were negative, which indicates decreased
local muscle fatigue after running, which is inconsistent with the results of most previous
studies that used the difference in MF as an indicator [56,57]. The reason may be due to
the different distribution patterns of fast and slow muscle ﬁbers in different individuals
and muscles, which are genetically determined and almost impossible to change later by
exercise [58]. In the post-experiment communication with the subjects, we learned that
runners may use different force generation methods for different running conditions, which
also indirectly inﬂuenced the experimental results. Meanwhile, the ANOVA results also
show that the main effects of both exercise intensity and music tempo were not signiﬁcant,
indicating that the change in muscle fatigue before and after running was not signiﬁcant.
However, in the post-experiment questioning of the subjects, we found that the subjects all
had some soreness and swelling in their lower limb muscles after ﬁnishing the running
experiment. This phenomenon was not monitored during the experiment, and the sEMG
signal changes for the two muscles before and after running were not regular; there were
more negative differences in MF, which shows that the MF values in this experiment did
not show a decreasing trend but an increasing trend. The reasons for this result in this study
may be twofold. (1) The experimental test time was short, and the runners reached the
speciﬁed value of subjective fatigue in a short period of time, during which the local muscle
tissue continuously recruited fast muscle ﬁbers to participate in the exercise so that the
muscle ﬁring frequency increased, and the neural excitability continued during the running
process [59,60]. (2) The energy consumption during muscle contraction during running
did not exceed the energy recovery during relaxation, and the lactic acid accumulation was
not signiﬁcant, so the driving strategy of the central nervous system for the muscles and
the conduction speed of the muscle ﬁbers were not affected [61]. Other scholars have also
found different trends in MF frequency domain indicators during exercise [62]. Combining
the above views to see the limitations of using the decrease in the MF frequency domain
index pre- to post-exercise can determine the fatigue of local muscles during dynamic
exercise such as running.
4.4. Pratical Applications
This study aimed to determine the effect of music tempo on runners’ fatigue perception
at different exercise intensities and provided a physiological and psychological explanation
of the role of music tempo in resisting fatigue perception by observing time of fatigue
perception, heart rate changes and sEMG signal changes during the running experiment.
The combined results of the three indicators revealed that the use of music with different
tempos at different exercise intensities caused runners to exhibit different mental and
physical performances. Speciﬁcally, runners’ TFP was inﬂuenced by the music tempo, and
in addition, runners’ TFP was also inﬂuenced by the interaction between music tempo and
exercise intensity. The change in HR of runners during running was mainly inﬂuenced
by the exercise intensity. Whether it is low-intensity exercise or high-intensity exercise,
Int. J. Environ. Res. Public Health 2022, 19, 3869
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listening to fast music may mitigate individuals’ perception of fatigue to some extent. This
was also pointed out in the study by Centala et al. The effect of listening to fast music
during exercise would be better than slow music and could regulate mental emotions and
reduce the perception of fatigue during exercise [63].
In addition, this study also provides many practical implications. Running is an
endurance sport, and runners will feel fatigue to different degrees during running, es-
pecially for many non-professional runners, who often do not persist in running due to
insufﬁcient exercise, limited physical strength and lack of perseverance. The results of this
study can help runners to develop a more beneﬁcial exercise program. For example, both
low-intensity exercisers and high-intensity exercisers are advised to listen to fast music
while exercising, which would help them maintain a better mental state, make the exercise
process less tedious and better control the physiological state, including heart rate changes.
This research could lead to better effects of music on perceived fatigue, allowing runners to
have better mental and physical performance while running.
4.5. Limitations
There were some limitations to this study. First, the subject population was not
subdivided, and none of the runners had undergone professional training. Second, we only
examined male subjects. Future studies could use sex as an independent variable to discuss
the effects of music tempo on subjects of different sexes and could also include people
of all ages and from different occupational backgrounds to make the study results more
generalizable. Third, the observation indexes used in this study were relatively limited,
and more measurement techniques could be integrated in the future to more accurately
assess the fatigue perception status of runners. Fourth, based on this study, future studies
could reﬁne the characteristics of the music, such as the cultural background of the music,
or use music with lyrics to explore other methods of music intervention during exercise to
promote the popularity of the research results.
5. Conclusions
In this paper, the effect of music tempo on runners’ subjective and objective fatigue
perception at different exercise intensities was investigated through running experiments.
The inﬂuence of music on runners’ perception of fatigue was explored, and the interaction
between music tempo and exercise intensity was considered. The results of the study
showed that (1) there were signiﬁcant main effects and interaction effects of music tempo
and exercise intensity on the TFP; (2) in terms of the difference in HR, the main effect of
exercise intensity was signiﬁcant, the main effect of music tempo was not signiﬁcant and
the interaction effect of music tempo and exercise intensity was not signiﬁcant; and (3) in
terms of the difference in the MF of sEMG, the main effect and interaction effect of music
tempo and exercise intensity were not signiﬁcant, and the results did not show regular
changes. The combined results of the study indicate that fast music can effectively reduce
the perception of fatigue of runners during running. The results of this study provide
a scientiﬁc basis for ordinary runners to select evidence-based for running, help them
effectively overcome their perception of fatigue during exercise and optimally modify their
perception of fatigue with music.
Supplementary Materials: The following are available online at https://www.mdpi.com/article/10
.3390/ijerph19073869/s1, File S1: Ethic reviews; File S2: Informed Consent.
Author Contributions: Conceptualization, J.W.; methodology, L.J. and H.Y.; investigation, Y.C.;
resources, C.L.; formal analysis, L.J. and L.Z.; writing—original draft preparation, L.J.; writing—
review and editing, L.Z.; supervision, J.W. and H.Y. All authors have read and agreed to the published
version of the manuscript.
Funding: This research was funded by the Key Industrial Projects of Zhejiang Province Science and
Technology Department, China, grant number 2019C03124.
Int. J. Environ. Res. Public Health 2022, 19, 3869
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Institutional Review Board Statement: The study was conducted in accordance with the Declaration
of Helsinki and approved by the Ethics Committee of the Industrial Design Institute of Zhejiang
University of Technology (protocol code 0903/2021, date of approval 15 September 2021).
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: Not applicable.
Conﬂicts of Interest: The authors declare no conﬂict of interest.
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The Effect of Music Tempo on Fatigue Perception at Different Exercise Intensities.
Wu, Jianfeng,Zhang, Lingyan,Yang, Hongchun,Lu, Chunfu,Jiang, Lu,Chen, Yuyun