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

7

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.

12

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

13

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

14

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

References

Atkinson, G., Wilson, D., & Eubank, M. (2004). Effect of music on workrate

distribution during a cycle time trial. International Journal of Sports Medicine,

62, 413-419.

Bishop, D. T., Karageorghis, C. I., & Loizou, G. (2007). A grounded theory of young

tennis players' use of music to manipulate emotional state. Journal of Sport &

Exercise Psychology, 29, 584-607.

Carlson, K. J., Eisenstat, S. A., & Ziporyn, T. D. (2004). The new Harvard guide to

women's health. Massachusetts: Harvard University Press.

Chen, P. (1985). Music as a stimulus in teaching motor skills. New Zealand Journal of

Health, Physical Eudcation and Recreation, 18, 19-20.

Chin, T., & Rickard, N. (2010). The Music Use (MUSE) Questionnaire: An instrument

to measure engagement in music. Proceedings of the 11th International

Conference on Music Perception and Cognition, (pp. 1-4). Washington, USA.

Copeland, B. L., & Franks, B. D. (1991). Effects of types and intensities of background

music on treadmill endurance. Journal of Sports Medicine and Physical Fitness,

31, 100-103.

Diener, E. (1994). Assessing subjective well-being: Progress and opportunities. Social

Indicators Research, 31(2), 103-157.

Hayakawa, Y., Miki, H., Takada, K., & Tanaka, K. (2000). Effects of music on mood

during bench stepping exercise. Pereptual and Motor Skills, 90, 307-314.

18

Karageorghis, C. I., & Priest, D. L. (2008). Music in sport and exercise: An update on

research and application. The Sport Journal, 11(3).

Karageorghis, C. I., & Terry, P. C. (1997). The psychophysical effects of music in sport

and exercise: A review. Journal of Sport Behavior, 20, 54-68.

Mroczek, D. K., Kolarz, C. M. (1998). The effect of age on positive and negative affect:

A developmental perspective on happiness. Journal of Personality and Social

Psychology, 75(5), 1333-1349.

North, A. C., Hargreaves, D. J., & O'Neill, S. A. (2000). The importance of music to

adolescents. British Journal of Educational Psychology, 70, 255-272.

Pates, J., Karageorghis, C. I., Fryer, R., & Maynard, I. (2003). Effects of asynchronous

music on flow states and shooting performance among netball players.

Psychology of Sport and Exercise, 4, 413-427.

Priest, D. L., & Karageorghis, C. I. (2008). A qualitative investigation into the

characteristics and effects of music accompanying exercise. European Physical

Education Review, 14(3), 347-366.

Sanchez, X., Grundy, V. J., & Jones, M. A. (2005). Annual Convference of the British

Association of Sport and Exercise Sciences. Journal of Sports Sciences, 23(2),

158-159.

Szabo, A., Small, A., & Leigh, M. (1999). The effects of slow- and fast-rhythem

classical music on progressive cycling to physical exhaustion. Journal of Sports

Medicine and Physical Fitness, 39, 220-225.

19

Taylor, A. (2000). Physical activity, anxiety and stress. In S. Biddle, K. Fox, & S.

Boutcher, Physical activity and psychological well-being (pp. 10-45). London:

Routledge.

Terry, P. C., & Karageorghis, C. I. (2006). Psychophysical effects of music in sport and

exercise: An update on theory, research and application. In M. Katsikitis (Ed.),

Psychology bridging the Tasman: Science, culture, and practice - Proceedings of

the 2006 Joint Conference of the Australian Psychological Society and the New

Zealand Psychological Society (pp. 415-419). Melbourne, VIC: Australian

Psychological Society.

Thompson, E. R. (2007). Development and validation of an internationally reliable

short-form of the positive and negative affect schedule (PANAS). Journal of

Cross-Cultural Psychology, 38, 227.

Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief

measures of positive and negative affect: The PANAS Scales. Journal of

Personality and Social Psychology, 47, 1063-1070.

Werner, P. D., Swope, A. J., & Heide, F. J. (2006). The music experience questionnaire:

Development and correlates. The Journal of Psychology, 140, 329-345.

20

Appendix

21

22

23

24

25

26

27

28

29

30

31

32

33

34