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A quantitative investigation on the association between listening to music during exercise and hedonic well-being. By Janice Fung 2011.
 

A quantitative investigation on the association between listening to music during exercise and hedonic well-being. By Janice Fung 2011.

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    A quantitative investigation on the association between listening to music during exercise and hedonic well-being. By Janice Fung 2011. A quantitative investigation on the association between listening to music during exercise and hedonic well-being. By Janice Fung 2011. Document Transcript

    • 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
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