1
Abstract
The aim was to examine whether music engagement for the purpose of exercise is a
significant predictor of hedonic well-being. A sample of 518 participants (315 females
and 205 males, mean age= 26.75 years) was randomly recruited by convenient sampling
through Monash University students. Music engagement was measured using the Music
Use questionnaire (Chin & Rickard, 2010); physical activity was measured with an
Exercise Overall Index subscale; and hedonic well-being was measured using the
international Positive and Negative Affective Schedule Short Form (Thompson, 2007).
Questionnaires were administered online. The hypothesis was supported as music
engagement for the purpose of exercise was a better predictor of hedonic well-being,
compared to exercising without music, or demographic variables alone. It was concluded
that habitual music engagement for the purpose of exercise improves our general
hedonic well-being with enhanced positive affect. These findings provide understanding
on how music engagement could be beneficial in physical education, psychological
therapy, and entrepreneurial contexts.
2
A quantitative investigation on the association between listening to music during
exercise and hedonic well-being.
Janice Fung
The beneficial effects of music usage in sports contexts have long been an
intuitive assumption throughout history. Music has demonstrated the power to induce
motivation; increase endurance; elicit, change, or regulate emotions; evoke memories;
reduce inhibitions; and encourage rhythmic movement (Terry & Karageorghis, 2006). It
is due to these observations that researchers have speculated that music would exert a
measurable motivational influence on performance during exercise (Priest &
Karageorghis, 2008).
Music engagement is the level of active participation that an individual
undertakes in music activities, measured by the frequency and regularity of participation,
and the personally evaluated importance of the music activity (Chin & Rickard, 2010).
As music engagement is commonly used as a powerful means enhancing positive
affective states (North, Hargreaves, & O'Neill, 2000), it is often used as a pre-
performance motivator (Bishop, Karageorghis, & Loizou, 2007), or as a background
accompaniment to tasks such as exercising (Karageorghis & Priest, 2008). Moreover,
the motivational characteristics of music are considered to be subjective, as their
influences vary according to musical taste, age, gender, and cultural-upbringing (Pates,
Karageorghis, Fryer, & Maynard, 2003; Bishop et al., 2007; Karageorghis & Priest,
2008). Previous studies suggest a distinction between music engagement with exercise
and dancing. During exercise, the use of music is for the benefit of physical and
psychological health. In comparison; dancing is the treatment, rather than usage, of
3
music, as it is a form of self-expression involving the integration of music and
movement (Werner, Swope, & Heide, 2006; Chin & Rickard, 2010).
A review of various studies by Karageorghis and Terry (1997) reported that
athletes benefit from music engagement during exercise, as music can create a more
pleasant learning state (Chen, 1985) with increased positive moods, such as optimism,
motivation, and confidence; reduced negative moods, such as tension and depression
(Karageorghis & Terry, 1997); and increased intrinsic motivation to endure (Chen,
1985). Evidence suggests that music can act as a distractor, drawing attention away from
internal sensations of pain and fatigue, and directing it towards external cues (music)
(Szabo, Small, & Leigh, 1999). This effect is known as „dissociation‟ (Copeland &
Franks, 1991). A study supported this notion with the analyses of interview and diary
data from fourteen young tennis players. Results reported that music has the power to
increase positive affective states with the effect of „dissociation‟ (Bishop et al., 2007).
Music has been found to more effectively enhance affective states at the medium
and high levels of work intensity (Karageorghis & Terry, 1997). In support of this
finding, other research data have shown that the synchronization of music tempi with
repetitive exercise greatly enhances the regulation of movement (Karageorghis & Priest,
2008), and promotes positive moods (Hayakawa, Miki, Takada, & Tanaka, 2000). With
the effect of „dissociation‟ (Copeland & Franks, 1991), athletes‟ can benefit from
improved endurance (Bishop et al., 2007), increased work output efficiency (Priest &
Karageorghis, 2008) and a reduction in perceived exertion (Karageorghis & Terry, 1997;
Atkinson, Wilson, & Eubank, 2004). Subsequently, it can be proposed that increased
4
work output is associated with improved subjective well-being, as studies have indicated
that exercise can decrease levels of negative affect (Taylor, 2000).
Karageorghis and Priest (2008) conducted a study on thirteen musically
experienced participants. The interview data indicated that improved endurance due to
music engagement is only apparent in exercise of low to moderate intensities, as
perceptions of fatigue would overpower the influence of music in high intensity
exercises. Therefore it can be questionable whether reported increases of positive affect
is directly explained by music engagement; or whether it is largely caused by exercise
itself, with music as a predictor for exercise intensity. However, studies have indicated
the possibility of music to be the dominant influence for emotional states across all
exercise intensities. Bishop et al. (2007) proposed that although music does not decrease
the perceived effort during high intensity exercise; it may still enhance the experience,
making hard work seem more enjoyable by altering the way in which the mind interprets
symptoms of fatigue. Moreover, a study by Sanchez, Grundy, and Jones (2005)
examined the effect of music on emotions during exercise. The participants reported
reduced perceived effort and improved emotional states, in contrast to the findings from
the „no music‟ conditions.
Thus far, considerable research has been ascertaining the use of music as an
emotional regulator prior to physical performance (Bishop et al., 2007), or as a
background accompaniment to exercising (Terry & Karageorghis, 2006; Karageorghis
and Priest, 2008). Much of the existing literature seems to be concerned with
operationalized studies examining the immediate psychophysical changes before or after
engagement with exercise involving music. However, little has been explored on the
5
effect of exercise music engagement on general well-being, where well-being is not
measured immediately before or after exercise. Much previous research utilised
interview data obtained from small sample groups, which may be disadvantageous if
participants have insufficient knowledge of musical structure to be able to articulate
musical properties (Bishop et al., 2007). In attempt to circumvent these limitations, the
current study evaluated a larger sample size; thus the findings are better suited to
generalise to the population. Surveys were used to collate qualitative data. This allowed
more meticulousness and accuracy in statistically analysing the predictor variables.
The aim of the current study was to examine whether music engagement for the
purpose of exercise is a better predictor of hedonic well-being than demographic
variables (age and gender) and exercise alone. Hedonic well-being was indicated by
increased Positive and Negative Affect Schedule (PANAS) scores of positive affect and
decreased scores of negative affect (Watson, Clark, & Tellegen, 1988; Diener, 1994).
Three hypotheses were devised. Firstly, it was hypothesized that demographic variables
will be significant predictors of improved hedonic well-being. This combination of
independent variables was denoted as „model 1‟. Secondly, it was hypothesized that
exercise, in addition to demographics (model 2), will be a significantly better predictor
of affectivity than „model 1‟. Lastly, it was hypothesized that music engagement for the
purpose of exercise, with demographics and exercise held constant (model 3), will be a
significantly better predictor of hedonic well-being than „model 2‟.
6
Method
Participants
The sample comprised 518 valid participants (mean age= 26.75 years, SD=
11.20), with 315 females (mean age= 26.83, SD= 11.28) and 205 males (mean age=
26.49, SD= 11.11), each recruited by convenient sampling via word-of-mouth through
students of Monash University. The inclusion criteria imposed that participants were to
be aged 18 years and above as of 1st January, 2011; and frequently engaged in music and
exercise.
Materials
Measure of Music Engagement
The measurement of music engagement was identified using the Music Use
(MUSE) questionnaire (Chin & Rickard, 2010) that provides a unique profile of each
participant‟s music engagement. Responses for the 24-item questionnaire were made on
a 6-point Likert-scale, where “0” indicated “not at all/not applicable to me” and “5”
indicated “strongly agree”. This study was only interested in the fourth Music
Engagement Style (MES-IV) (Cronbach‟s Alpha = .80) that consisted of 6 items
determining music engagement for the purpose of dance and physical exercise (Chin &
Rickard, 2010).
Measure of Physical Activity
Physical activity engagement was measured by the (Exercise Overall Index) EOI
subscale specially designed for this study, and included in the MUSE questionnaire. The
EOI was calculated using three items, by multiplying the values of (a) the usual
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frequency of engagement in purposeful exercise (instances per week) and (b) the typical
average hours of exercise per day, then dividing the product by (c) the regularity of
engagement in purposeful exercise. The overall score indicated the quantity of exercise
engagement within the recent timeframe of one week, with the consideration of general
regularity of exercise in the past.
Measure of Hedonic Well-being
Hedonic well-being was measured using the 10-item international Positive and
Negative Affective Schedule Short Form (I-PANAS-SF), which has been tested to have
adequate reliability and validity (Cronbach‟s alpha = .78) (Thompson, 2007).
Procedure
After reading the explanatory statement and agreeing to participate in this study,
participants were given the website link- www.surveymethods.com- to complete the
MUSE and I-PANAS-SF. There was no time limit assigned, and participants spent
approximately 45 minutes to complete the questionnaires. Participants were able to
withdraw at any time, and informed consent was confirmed upon submission of the
questionnaires. All procedures were approved by the ethics committee of Monash
University. Collated qualitative data was then computerised to be statistically analysed.
Results
Using the computer software IBM SPSS version 19, 169 participants were
excluded from the raw data of 687 respondents due to having missing data, outlier
values, or complying the exclusion criteria. The criteria imposed that participants who
8
do not purposefully engage in exercise (EOI= 0) were to be excluded, as the research
hypotheses was not concerned with the factor of no-exercise. Moreover, participants
who indicated „dance‟ as their only form of physical exercise were also excluded. This
was in concurrence with previous research suggesting that music engagement in dance is
distinguished from music engagement in exercise (Werner et al., 2006; Chin & Rickard,
2010). As a result, the cleaned data to be statistically analysed comprised 518
participants.
Primary analyses were performed to ensure no violation of assumptions.
Violation was found in the homoscedasticity for negative affect, thus data was
interpreted with caution. The relationships between positive affect (M=20.85, SD=4.72),
negative affect (M=13.39, SD=5.46), gender (M=1.61, SD=11.22), age (M=26.75,
SD=11.22), exercise (M=3.75, SD=3.14), and music engagement (M=3.32, SD=1.39),
were investigated using Pearson correlation coefficients. Alpha was set at .05 for all
statistical analyses. There were eight significant but weak relationships found, as shown
in Table 1.
9
Table 1
Intercorrelations Between Predictor and Outcome Variables
1 2 3 4 5 6
1. Positive Affect * * -.075† .067 .217† .071
2. Negative affect * .111† -.174† -.137† .048
3. Gender * .01 -.09† .126†
4. Age * -.029 -.225†
5. Exercise (EOI) * .037
6. Music Engagement *
N= 518, †= significant at p<.05
EOI= Exercise Overall Index
Afterwards, two separate multiple hierarchical regression analyses were
conducted to deterine whether age, gender, exercise, and music engagement could be
used to predict the two facets of hedonic well-being – Positive affect and negative affect.
Firstly, the regression anlyses revealed that age and gender were not significant
predictors of positive affect scores, adjusted R2=.006, F (2,515) = 2.65, p>.05. However,
these variables were found to significantly predict negative affect, F (2,515) =11.63,
p<.001, accounting for 3.9% (adjusted R2=.039) of its variability.
Secondly, it was found that, together, exercise engagement and demographic variables
significantly accounted for 5% (adjusted R2= .05) of the variance in positive affect
scores, F (3,514) = 10.11, p<.001; of which the exercise variable alone explained 4.6%
(R2
changed= 0.046) of the variance, Fchanged(1,514)=24.77, p<.001. Furthermore, the
combined variables accounted for 5.5% (adjusted R2= .055) of variance in negative
10
affect scores, F (3,514) = 11.06, p<.001, of which exercise alone explained 1.7%
(R2
changed= 0.017) of the variance, Fchanged(1,514)=9.55, p<.05.
Finally, results showed that music engagement, in addition to all other variables,
significantly accounted for 5.6% (adjusted R2=.056) of variance in positive affect scores,
F(4,513)=8.73, p<.001, of which music engagement alone accounted for 0.8%
(R2
changed=.008) of the variance, Fchanged(1,513)=4.39, p<.05. Furthermore, all the
variables combined significantly predicted 5.3% (adjusted R2=.053) of variance in
negative affect, F(4,513)=8.28, p<.001, of which 0% (adjusted R2=.00) was accounted
by music engagement alone.
Table 2 and 3 below present the standardized and unstandardized regression
coefficients for the regression analyses, together with the squared semi-partial
correlations.
11
Table 2
Regression analyses with dependent variable: PANAS-SF Positive Affect
Model Unstandardized (B) Standardized (Beta) sr2
1. (Constant)
Gender
Age
21.268
-.732
.028
-.076
.067
.006
.005
2. (Constant)
Gender
Age
EOI†
19.694
-.546
.031
.322
-.057
.073
.214
.003
.005
.045
3. (Constant)
Gender
Age†
EOI†
ME†
18.623
-.664
.040
.316
.316
-.069
.094
.210
.093
.005
.008
.044
.008
N= 518, †= significant at p<.05
EOI= Exercise Overall Index, ME= Music Engagement
It was found that when only demographic variables were included as predictors
(i.e. in model 1), neither gender nor age were significant predictors of positive affect.
When both exercise and demographic variables were included as predictors (i.e. in
model 2), increased positive affect was associated with significantly greater EOI. When
music engagement was included as a variable (i.e. in model 3); age, exercise, and music
engagement were the significant predictors of positive affect with positive association.
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Table 3
Regression analyses with dependent variable: PANAS-SF Negative Affect
Model Unstandardized (B) Standardized (Beta) sr2
1. (Constant)
Gender†
Age†
13.651
1.261
-.085
.113
-.176
.013
.031
2. (Constant)
Gender†
Age†
EOI†
14.78
1.128
-.087
-.231
.101
-.179
-.133
.01
.032
.017
3. (Constant)
Gender†
Age†
EOI†
ME
14.782
1.129
-.087
-.231
-.001
.101
-.179
-.133
.000
.01
.031
.017
.000
N= 518, †= significant at p<.05
EOI= Exercise Overall Index, ME= Music Engagement
It was found that when only demographic variables were included as predictors
(i.e. in model 1), both age and gender were significant predictors of negative affect, with
increased negative affect associated with younger age. When both exercise and
demographic variables were included as predictors (i.e. in model 2); gender, age, and
exercise were significantly associated with negative affect, with increased negative
affect associated with decreased EOI. Exercise was a stronger predictor than age. When
music engagement is included as a variable (i.e. in model 3); gender, age, and exercise
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were the significant predictors of negative affect with negative association. Exercise was
the strongest predictor.
Discussion
The aim of the current study was to examine whether music engagement for the
purpose of physical exercise is a better predictor of hedonic well-being than
demographic variables (age and gender) and exercise alone. Three hypotheses were
tested. The first hypothesis stated that age and gender are significant predictors of
improved hedonic well-being. Results of the current study indicated a weak correlation
between gender and affect scales, exercise, and music engagement. Exercise has
previously been associated with enhanced well-being (Taylor, 2000); therefore the
gender differences in affective states may be explained by differences in exercise and
music engagement. In support of this notion, a previous American study suggested that
women exercise less often than do men (Carlson, Eisenstat, & Ziporyn, 2004).
Furthermore, past research has suggested that gender affects the perception of the
motivational characteristics of music (Karageorghis & Priest, 2008; Pates at al., 2003;
Bishop et al., 2007). The results of the current study showed weak association between
age and gender on affect scores, with significant influence on negative affect, but no
significant association with positive affect. Similarly, previous research by Mroczek and
Kolarz (1998) found negative affect to significantly decrease with age; however this
association was only found among men. Furthermore, positive affect was found to
generally increase with age; however the association was weak, as it was moderated by
combinations of personality and other sociodeographic factors. The results of the current
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study could not establish a notable improvement of well-being according to age and
gender; thus the first hypothesis was not supported.
Secondly, the findings of the current study were congruent with previous
research (Taylor, 2000), reporting that increased exercise was associated with increased
positive affect and decreased negative affect. Furthermore, engagement in exercise
explained more of the variance of positive and negative affect, than did the demographic
variables alone. Therefore the second hypothesis was supported as results showed
exercise, in addition to age and gender, to be a significantly better predictor of affectivity
than demographic profiles alone.
Lastly, results showed that the listening to music for the purpose of exercise
served as a significantly better predictor of positive affect, compared to exercising
without music. This is congruent with previous studies that reported music engagement
during exercise to increase positive moods (Chen, 1985; Hayakawa et al., 2000; Bishop
et al., 2007). Past research found negative moods to also be reduced (Karageorghis &
Terry, 1997; Bishop et al., 2007); however, the current study did not find music
engagement to be a significant predictor of negative affect. The dissimilarity with
previous studies may be for the reason that previous studies were operationalized, where
participants‟ affect scores were assessed immediately after exercise; thus the reduction
of negative affect induced from exercise (i.e. fatigue, stress, pain) would have been
reflected in the PANAS scores. Improved hedonic well-being is marked by high positive
affect and low negative affect (Diener, 1994); hence the current study reported improved
hedonic well-being, as positive affect increased, and negative affect remained low.
Therefore the third hypothesis was supported, that listening to music for the purpose of
15
exercise was a better predictor of hedonic well-being, compared to exercising without
music engagement.
Several limitations were identified for the current study. As previous research
have distinguished between the fundamentals of dance and exercise (Werner et al.,
2006), particpants who indicated dance as their only form of exercise were excluded
from the raw data. However, there was no means to acertain how much of the remaining
participants‟ score for „physical excercise‟ consisted of dance. This may have affected
the results. Moreover, the MUSE questionnaire used for this study included many test
items irrelevant to the study in focus, and required approximately 45 minutes to
complete; hence the responses were prone to lack in accuracy due to participant fatigue.
To overcome these limitations, future studies could utilize a shorter questionnaire
containing only the items relevant to the study. Moreover, questions may be included to
obtain more accurate information regarding the participation of dance and exercise. A
cut-off ratio could be proposed to control for participants who engage in dance more
than other forms of exercise.
In summary, the influence of age and gender on well-being is small, as exercise
is a stronger predictor of hedonic well-being. Moreover, music engagement with
exercise can further increase measures of positive affect. In conclusion, habitually
engaging in music for the purpose of exercise improves our general hedonic well-being,
well after the positive emotional effects of exercise have passed. This study provides
understanding on how music engagement could be beneficial in educational,
psychological, and business contexts. For example, to help professional athletes improve
16
performance during training, help motivate people trying to lose weight, or help to
improve general hedonic well-being.
17
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Appendix
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