INTRODUCTION
During most forms of sporting competition, it
is important for the athlete to make split-second decisions while also working
at high intensities, for example in a soccer match when deciding which teammate
to pass to or in a basketball game anticipating jumping into the passing lane
to steal the ball (Lemmink & Visscher, 2005). The Inverted-U relationship
between exercise intensity and cognitive function was initially proposed by
Gutin (1973). Researchers have since demonstrated that moderate intensity
exercise provides cognitive benefit (Browne, et al., 2017; Kashihara, Maruyama,
Murota & Nakahara, 2009; Piepmeier et al., 2014). However, when exercise at
high intensity is studied, the results are not as uniform (Brown, et al., 2017).
Smith et al. (2016) argued that high intensity exercise can slow reaction times
and significantly raise omission error and decision error rates when compared
to moderate intensity exercise. In contrast, high intensity exercise has been
shown to benefit cognitive performance in areas such as selective attention and
short-term memory tasks (Alves et al., 2014). Piepmeier et al. (2014) showed
little relationship between cognitive performance and exercise intensity. The
neuronal mechanisms behind these findings are still not fully understood
(Kashihara et al., 2009). In order to elucidate these mechanisms, it has been
suggested that future research be highly specific regarding inclusion criteria
such as fitness, exercise intensity, and exercise mode in order to create a
more consistent testing population between studies (Brown et al., 2017).
The impact of music on the human body is
complex and is still being studied in multiple areas such as psychology,
therapy, and performance (Hodges, 2016). Though great strides have been made
over the years, the effects of music on the human body and mind are not
completely understood. However, notable physiological responses (heart rate,
blood volume, skin conductance, muscular tension, etc.) have been reported in
the literature (Hodges, 2009) suggesting that music does have a significant
effect on human physiology.
“Sedative” music has been defined as music
having slower tempi, softer, more legato characteristics. “Stimulative” music
is the opposite; fast paced, louder, and wider pitch ranges (Hodges, 2016).
These different categories could be broadly applicable to calm classical music
and hard rock music. Adjusting these musical characteristics has demonstrated
capabilities of influencing individuals’ mood, energy level, or even emotional
states (Maranto, 1993). However, due to the complex nature and extreme
variation of music itself, some have criticized the “Sedative” and
“Stimulative” labels as too vague. This variation in musical characteristics is
reflected in the literature, where musical selections for exercise and
cognitive performance studies vary considerably in genre, as well as amount of
time subjects are exposed to music. Another issue with musical science is the
subjective nature of music. Cultural, developmental, biochemical, and musical
exposure levels all vary from person to person, so the same song may have
different effects not only between two subjects, but potentially even on the same
subject over time. These variables make it extremely difficult to tightly
control the effect of different music genres on physiological and cognitive
responses during high intensity exercise (Hodges, 2016). As a result of these variables,
it is nearly impossible to completely control music and its effects within a
study. An experimenter can either control the musical dynamics by playing the
same songs, which would in turn cause each participant to have a unique
reaction and opinion to the music. Or the experimenter could allow the
participant to select the music within set parameters of enjoyment or
motivation levels, which would result in different auditory stimuli between
each participant. One must look at each study to determine which method is best
for that situation, acknowledging the flaws each would exhibit.
There is a dearth of research on the effect of
music as a potential ergogenic aid that may improve cognitive performance in
individuals working at high intensities. There is no consensus on the effect of
music on the autonomic nervous system, however there is some evidence of
improved nervous system function (Ellis & Thayer, 2010). Szmedra and
Bacharach (1998) found music had significant benefits on heart rate, rate
pressure product, systolic blood pressure, relative perceived exertion, lactate
accumulation, and norepinephrine accumulation after trained athletes completed
a submaximal exercise test. While the Szmedra and Bacharach (1998) study
analyzed the physiological markers of exercise, the aim of the present study
was to determine if music improved or impaired cognitive performance after a
bout of high intensity exercise. It was hypothesized that different genres of
music would have different effects on cognitive performance. The Stroop Test
was used to measure selective attention of the subjects (Stroop, 1935).
Briefly, the Stroop test protocol involves a color word flashing randomly on
the screen. The color word may not match the color of the word. Subjects are
required to identify the color shown rather than read the color word. For
example, if the word “yellow” flashed on the screen and it was colored yellow,
the correct answer would be yellow. If the word “yellow” flashed again but was
colored green, the correct answer would be green. The “R”, “G”, “Y”, and “B”
keys on the subject’s computer were used to indicate the answer. The test was
completed when the subject answered 20 words correctly. This test has been
used previously to effectively evaluate selective attention after exercise
(Alves et al., 2014; Hogervorst, Riedel, Jeukendrup, & Jolles, 1996; Whyte,
Gibbons, Kerr & Moran, 2015). This test has been associated with frontal
lobe activation (Chayer & Morris, 2001). This area of the brain can be
linked to tasks such as problem solving, motor control, and executive function
(Chayer & Freedman, 2001) which are all important for athletic performance.
METHODS
Subjects
A convenience sample of 12 undergraduate
students completed this study. No power analysis was run, as this was a pilot
study that did not have the funding necessary to support a large number of
participants. Subject characteristics are presented in Table 1. Subjects
completed a pre-participation survey and provided voluntary informed consent.
The survey included questions about weekly exercise frequency in days per week
(3.166 ± 0.83) and exercise session length in ranges of minutes: 30-45 minutes
(n=4), 45-60 minutes (n=2), and >60 minutes (n=6). The total weekly training
volume in this study ranged from 30-60 minutes per week up to 360 minutes per
week. Additionally, most common type of exercise was surveyed; Resistance
training (n=7), Cardiovascular exercise (n=5), or team sports (n=0). Ethical
approval by the Longwood University Institutional Review Board was obtained
prior to the commencement of the study. Volunteers who indicated the presence
of cardiovascular disease risk factors/ symptoms or color blindness were
excluded. All subjects regularly performed either resistance training or
cardiorespiratory training for at least 30 minutes per week. All but one
subject listened to music during their regular exercise outside of this study.
Table 1
Subject Characteristics
|
Characteristics
|
M ± SD
|
|
Age (y)
|
20.3 ± 1.7
|
|
Weight (kg)
|
72.2 ± 14.9
|
|
Height (m)
|
1.70 ± 0.09
|
Note:
Data are presented as mean ± SD; n=7 males; n=5 females.
Protocol and Procedures
The subjects reported to the Exercise Physiology Lab on four
separate occasions with a minimum of 24-hours between visits. The initial visit
to the lab was a control session which all subjects attended at the same time.
During the control session subjects remained seated at tables in the lab while
listening to each musical selection over the lab speakers (no music, sedative,
stimulative) for approximately 12 minutes to simulate the amount of time of
subsequent workout sessions. After the appropriate length of musical exposure,
the subjects completed the Stroop Test online (http://ezyang.com/stroop/) with
the music still playing, as they would go on to do in an exercise session. The
subjects then completed the Brunel Music Rating Inventory (BMRI) (Karageorghis,
Bigliassi, Guérin & Delevoye-Turrell, 2018). The BMRI is a survey that
assesses music affinity for each participant in the form of a Likert scale.
Questions regarding the specific rhythm, tempo, genre of music, melody, sound
of the instruments, and the beat each musical condition were rated on a Likert
scale, with “1” being “not motivating at all for exercise” and “7” being
“strongly motivating for exercise”. The possible max score was 42. A higher
score equates to greater music affinity/preference. Though music preference
has still proved difficult to control for, this method has shown promising
results (Karageorghis et al., 2018). Subjects reported to the lab three more
times to complete each exercise condition. Exposure to music conditions was
randomized. Exercise sessions consisted of a 12-minute-high intensity interval
training workout (“No Equipment HIIT Cardio Home Workout-Quick and Intense
HIIT”, from the YouTube channel “Fitness Blender”) that contained body weight
exercises such as push-ups and jumping jacks. This workout was chosen
specifically because the video did not have a musical soundtrack; the only
sound came from the voice of the instructor and the movements of the instructor
on screen. This would make it possible to play the music that was specifically
selected for the study, which lasted through the entirety of both the workout
and the Stroop test (Except during a “no music” session). The exercises were
simple in nature, could be done with no equipment, and were demonstrated by the
instructor, making it easy for the subjects to follow along. Due to the
variability in training status, subjects were instructed to complete the
workout at their own self-selected interpretation of high intensity. Subjects
were encouraged to exert maximum energy with subsequent verbal encouragement.
Additionally, a subject was considered to have reached high-intensity exercise
if he or she reached at least 77% of age-predicted maximum heart rate. This
stipulation was met and surpassed in all trials.
Heart rate was measured telemetrically (FirstBeat
Technologies OY, Jyväskylä, Finland) throughout the workout and Stroop Test
directly after the completion of the workout (within 30 seconds) the subjects
reported to an assigned laptop and completed the online version of the Stroop
test to assess selective attention performance. No talking was allowed during
the Stroop test; however, the musical condition of the session was maintained
until all subjects had completed the Stroop Test. All subjects completed the
Stroop test within 3 minutes after the completion of the workout. The Stroop
effect can be defined as the difference in response time for matched and
mismatched color words in milliseconds. For example, if the average response
time for matched color word pairs is 50 ms, while the average response time for
mismatched color word pairs is 75 ms, the “Stroop Effect” would be 25 ms. This
delay can be attributed to the neural interference upon receiving two different
color stimuli, and the ability to limit the effect of this interference is
called “Selective Attention” (Stroop, 1935). This was presented by the software
upon completion of the test. The order of exposure to control (no music),
sedative, and stimulative music was randomized.
Music Selection
The playlists for the sedative and stimulative conditions are
presented in Table 2. The order that the music was played was randomized each
session using the “Shuffle” command on the music streaming platform. The
sedative playlist consisted of all instrumental classical music pieces with low
tempos (BPM average = 69.85) and a general calming and smooth timbre. The
stimulative playlist (BPM average = 148) was fully composed of instrumental
hard rock pieces in order to sonically contrast the legato and consonant nature
of the classical music. As part of the control session subjects completed the
BMRI (Karageorghis et al., 2006) directly after each musical condition
concluded in order to determine the differences in musical enjoyment between
the sedative and stimulative playlists. Music of both categories was lyric-free
on the basis that lyrics have been shown to decrease productivity and
concentration (Shih, Huang & Chiang, 2012). Additionally, no “instrumental”
versions of popular songs with lyrics were used. This was to discourage
association with lyrics that may have been previously heard by the subjects,
and by extension, disallow the subconscious singing of songs while testing. All
music was played at a consistent volume of 70 decibels.
Statistical Analysis
Results were analyzed using SPSS 22.0 software (IBM, Armonk,
NY), and are presented as mean ± SD. A series of 2x3 (exercise x music)
repeated measures ANOVAs were conducted to test the significance of
within-subjects factors on dependent variables including reaction time on the
Stroop Task, as well as Heart Rate Response. Significance was set a priori at
alpha < 0.05. Where appropriate, post-hoc analyses were conducted and
Bonferroni adjustments were made to reduce the likelihood of Type II errors.
RESULTS
For all repeated measures ANOVAs conducted, Mauchly’s
assumption of sphericity was met at p > 0.05. Mean Stroop reaction
times during exercise with the various music conditions were: No Music 132.39 ±
88.93 ms; Classical 137.05 ± 61.74 ms; Rock 102.6 ± 83.1 ms). Without exercise,
reaction times were: No Music 166.6 ± 118.17 ms; Classical 138.42 ± 86 ms; Rock
139.67 ± 74.47 ms. There was no significant main effect of either exercise (F(1,2)
= 0.585, p = 0.524) or music (F(2,4) = 1.939, p = 0.258) on
Stroop reaction time, nor was there a significant interaction effect of
exercise and music on Stroop reaction time (F(2,4) = 0.045, p = .956;
Figure 1). There was a significant main effect of exercise on heart rate response
(F(1,2) = 564.005, p < 0.01). Mean heart rates were
consistently higher during exercise (HRex= 148.2 ± 14.7) than during
control sessions (HRcon= 76.1 ± 9.1D). There was no other
significant main effect for music (F(2,4) = 2.537, p = 0.194) or
interaction effect of music and exercise (F(2,4) = 2.980, p = 0.161)
on heart rate response (Figure 2). BMRI results showed that most subjects
preferred the stimulative (average score: 24.07 ± 13.52 AU) compared to the
sedative (13.35 ± 6.95AU), though many indicated that they preferred another
genre as their top choice. Despite this, there was no significant correlation
between BMRI scores and Stroop effect (Pearson r = 0.-117, p = 0.724)
or heart rate response (Pearson r = -0.215, p = 0.525).
DISCUSSION
The results of the present study suggest that selective
attention is resistant to the effects of a short high intensity interval
exercise bout and the distraction of either sedative or stimulative music. The
lack of response to the differing musical genre refutes some of the existing
literature, showing that selective attention has the potential to be more
resistant to musical distraction than the conductors of the present study had
hypothesized. It was hypothesized that performance on the Stroop task would
decrease in the presence of music (especially hectic, hard rock) due to the
addition of a distracting auditory stimulus alone. It appears that the music in
this study was not enough of an external stimulus to impair cognitive
performance, at least from the measure of selective attention. Future research
should consider this and experiment with new musical variables such as volume,
genre, and selection process. The results also suggest that music may lower
average heart rate during high intensity interval exercise. This supports the
findings of some previous literature (Szmedra & Bacharach, 1998), though
the existing body of work still appears to be divided on the effects of music
(Hodges, 2016; Maranto, 1993). If music could be used to lower the heart rate during
high intensity exercise, performance may be improved due to slower onset of
fatigue. However, these results are independent of the neural benefits or
detriments of music that could affect motivation or self-efficacy (Ballmann,
Maynard, Lafoon, Williams & Rogers, 2019). If the results of the present
study could be replicated, they could have practical uses in a variety of
intense training sessions, and may already be at play in settings such as spin
classes, HIIT classes, or independent music listeners in the gym. It may be
worthwhile for coaches to experiment with music in their team practices for the
possible physiological benefit displayed by these results. In order to make
future research more applicable to a sport competition setting, researchers
should experiment with cognitive performance while actively performing exercise
in order to more realistically simulate a live competition where decisions must
be made.
In previous research involving music and performance, there
have been a series of methods for determining the musical selections. The major
distinction lies in self-selected vs. non self-selected music. In self-selected
trials, subjects choose music that aligned or was opposite to individual
preferences. Anaerobic work with preferred vs. non-preferred music has been
shown to have motivational implications, but it remains to be seen if any
tangible performance gains can be achieved (Ballman et al., 2019). A drawback
of the self-selected design is that it potentially creates large variation in
the type of music each subject is exposed to. In non-self-selected models, the
musical dynamics (beats per minute (BPM), timbre, consonance/dissonance) can be
controlled. However, the individual preference will almost certainly shift
between subjects. Due to the complex and not yet fully understood relationships
between music, exercise performance, and cognitive performance, it is important
to consider all of these factors when designing an experiment. In the present
study, it was necessary to control each playlist in order to meet the
“sedative” or “stimulative” criteria. The sedative playlist consisted of all
instrumental classical music pieces with low tempos (BPM average = 69.85) and a
general calming and smooth timbre. The stimulative playlist (BPM average = 148)
was fully composed of instrumental hard rock pieces in order to sonically
contrast the legato and consonant nature of the classical music. The popularity
of these musical genres among college students can be called into question.
However, the present study was more concerned with presenting two contrasting
music genres in order to determine the effects on selective attention. In an
effort to determine the differences in musical enjoyment between the two
conditions, the subjects completed the Brunel Music Rating Inventory survey
(Karageorghis et al., 2018). While music preference had no influence on the
results of the present study this additional variable may be able to further
explain potential cognitive performance enhancements or detriments. Music of
both categories was lyric-free on the basis that lyrics have been shown to
decrease productivity and concentration (Shih et al., 2012).
It is worth noting that the Stroop test is by no means a
complete measure of cognitive functioning, but rather specifically assesses
selective attention. Future research should utilize other cognitive tests in
order to obtain a more holistic analysis of cognitive function. Future research
should also continue to explore a variety of auditory stimulation media and variables,
for example; self-selected vs non self-selected music, exercise with other
media such as podcasts or audiobooks, and different volumes of the auditory
stimulus. Additionally, studies should incorporate longer and more intense
exercise protocols and different exercise modalities in addition to tighter
control of subject training status. One major limitation of this study was the
inability to standardize the intensity of exercise between participants. While
they all performed the same workout, the only requirement was to reach 77% of
their max heart rate, which leaves a large window of intensities that meet the
requirement and thus create variability in each individual’s workout stimulus.
This could have significantly different implications for each subject’s
physiology based on training status. Future studies should consider this and
standardize exercise intensity. One way to do this may be to use maximal effort
testing similarly to Ballmann et al. (2019). Anecdotal data from two subjects
who completed the high intensity interval workout followed by the Stroop test
two times in a row showed a substantially larger Stroop effect after the second
exercise bout (i.e., identifying the correct color took longer in mismatched
color word pairs). This anecdote came after two subjects were interested in
doing a second workout after their data for the day had already been recorded.
This observation suggests that a longer duration of high intensity exercise may
have greater negative effects on selective attention. Mechanisms for this
effect remain to be elucidated.
CONCLUSION
Many people enjoy listening to music during exercise. The
results of this study suggest that heart rate response to exercise is reduced
when listening to music but that music does not affect selective attention.
Exercise participants should be able to continue to make rapid decisions in
response to changing stimuli while listening to either sedative or stimulative
music. The significant lowering of the heart rate in the presence of music
regardless of the type may have implications for future research regarding
stress, anxiety, cardiac rehabilitation, or human performance.
Acknowledgements
This original research paper was conducted by Derek Jones under
the Mentorship of Dr. Jo Morrison at Longwood University and has not been
published in any other journals.
Funding
N/A
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