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The relationship between music-related mood regulation and psychopathology in young people Cassandra Jane Thomson, John E. Reece and Mirella Di Benedetto Musicae Scientiae published online 6 February 2014 DOI: 10.1177/1029864914521422 The online version of this article can be found at: http://msx.sagepub.com/content/early/2014/02/06/1029864914521422

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MSX0010.1177/1029864914521422Musicae ScientiaeThomson et al.

Article

The relationship between musicrelated mood regulation and psychopathology in young people

Musicae Scientiae 1–16 © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/1029864914521422 msx.sagepub.com

Cassandra Jane Thomson RMIT University, Australia

John E. Reece

RMIT University, Australia

Mirella Di Benedetto RMIT University, Australia

Abstract The aim of the present study was to investigate how music-related mood regulation relates to psychopathology – specifically depression, anxiety, and stress – in young people, through examining the nature of the relationships between individual music-related mood regulation strategies and psychopathology. The sample consisted of 146 (53 male and 93 female) university students aged between 17 and 24 years. Participants completed an online questionnaire addressing levels of psychopathology, music-related mood regulation behaviours, and personal music-related information. Results indicated that, as a whole, music-related mood regulation predicted levels of psychopathology. High use of the mood regulation strategy Discharge (venting of negative emotion through music) predicted high levels of depression, anxiety, and stress; Diversion (distraction from worries and stress) predicted high levels of anxiety and stress; and Entertainment (happy mood maintenance and enhancement) predicted low levels of depression. The results suggest that music-related mood regulation may perform a maladaptive function in certain individuals that promotes psychopathology; however, it is equally plausible that young people experiencing psychopathology are more likely to employ music in an attempt to reduce their symptoms. These are avenues for consideration in future research. The present study has practical implications for the use of music as a self-therapeutic resource and in the treatment of young people with psychopathology in music therapy settings.

Keywords adolescents, anxiety, depression, mood regulation, music therapy, psychopathology, use of music, stress, young people

Corresponding author: Cassandra Jane Thomson, RMIT University, PO Box 71, Bundoora, VIC 3083, Australia. Email: [email protected]


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Music has an undeniably powerful influence in the lives of young people. The World Health Organization (WHO, 2008)classifies “young people” as individuals aged between 10 and 24 – a critical transitional period characterised by continual brain maturation (Casey, Jones, & Hare, 2008). Both “adolescents” (10–19) and “youth” (15–24) are contained within this age range (WHO, 2008), and given that the sample of the current study included individuals who could be classified as either, the comprehensive term young people was adopted to describe the present sample. Through an investigation of the sample’s music listening habits, the current study aimed to demonstrate how music-related mood regulation relates to psychopathology, through examining the nature of the relationships between individual music-related mood regulation strategies and psychopathology. The attraction young people have towards music is evidenced in their average listening time, which increases to approximately two to four hours daily in the late teens, and in the fact they continually rate music as a high source of enjoyment and favoured leisure activity (Arnett, 1995; Miranda & Claes, 2009; North, Hargreaves, & O’Neill, 2000; Roberts, Henriksen, & Foehr, 2009; Zillman & Gan, 1997). The attraction this group has towards music is understandable when the functions it performs and developmental tasks it facilitates are considered. For most individuals, music serves a number of practical purposes including: relieving boredom, alleviating tension, increasing energy, and providing a distraction from concerns (Hallam, 2010). Additionally for young people, it has been suggested that music facilitates developmental and psychosocial tasks through fulfilment of individual and social motivations (Miranda & Claes, 2009; Schwartz & Fouts, 2003). For example, music aids self-actualisation, identity formation, and coping strategies, while also promoting a sense of belonging and social integration with peers (Arnett, 1995; Bakagiannis & Tarrant, 2006; Larson, 1995; North & Hargreaves, 1999). Saarikallio and Erkkilä (2007) identified two, perhaps more direct, aims motivating young people’s musical engagement: feeling good and controlling mood. Mood regulation is considered the process of modifying or maintaining the occurrence, duration, and intensity of moods, both positive and negative (Gross, 1998; Saarikallio & Erkkilä, 2007). Mood regulation is a critical function performed by music, especially for “moody adolescents”. Despite this being an excessively used phrase in popular discourse, there are empirical findings that endorse this label. For example, in comparison with other family members, young people experience more time at the extreme ends of the affective spectrum (Larson & Richards, 1994), and report an increased amount of time feeling unhappy, mildly negative, dysphoric, and often experience more stressful life events than their preadolescent selves (Larson, 1995). Given this, it is unsurprising that young people continually identify mood regulation as a primary motivation and integral aspect of their musical engagement (DeNora, 2000; Goethem & Sloboda, 2011; North et al., 2000; Saarikallio & Erkkilä, 2007; Sloboda & O’Neill, 2001; Thayer, Newman, & McClain, 1994). As young people make the transition from childhood to adulthood, their utilisation of music for mood regulation generally increases (Saarikallio, 2006), as does the implementation of other efficacious self-regulatory and coping strategies (Mullis & Chapman, 2000). However, this is also a critical period in which maladaptive regulatory strategies can develop and there is increased vulnerability for experiencing psychiatric disorders and their associated symptomology, otherwise known as psychopathology (Sims, 2002). The transitional years from childhood to adulthood see a marked increase in the prevalence of psychopathology, in particular mood disorders such as depression (Davey, Yücel, & Allen, 2008; Paus, Keshavan, & Giedd, 2008). The incidence of anxiety disorders and stress arousal also appears to increase during this period, which may be a possible response to the increased academic demands involved in completing secondary and tertiary level education (Larson, Downloaded from msx.sagepub.com at RMIT UNIVERSITY on February 18, 2014

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1995; Paus et al., 2008; Pelletier, 2004). There is an almost linear increase in depressive symptoms from puberty until the mid-twenties, by which point around one in four young people will have experienced a depressive episode (Oakley Browne, Wells, Scott, & McGee, 2006). When young people experience psychopathology, such as depression, anxiety, and stress, it can have deleterious effects on psychosocial adjustment, academic development, global functioning, and it may increase the risk of early childbearing, subsequent relapses, and suicide (Davey et al., 2008; Paus et al., 2008). Even subclinical symptoms have the potential to significantly disrupt psychosocial functioning and can precipitate the onset of severe mood disorders (Lewinsohn & Essau, 2002). These negative outcomes highlight the need for further clarification of the ways in which music, a highly accessible resource, influences psychopathology in young people. Until recently, only a limited number of studies had considered the relationship, either protective or invoking, that music may have upon the experience of psychopathology during youth and adolescence. Miranda and Claes (2008) found this research caveat surprising given: the particularly high and problematic prevalence of depression amongst young people (Lewinsohn & Essau, 2002; Paus et al., 2008); music’s potent influence in the experience of emotions (Peretz & Zatorre, 2003); and the frequently identified potential for music to regulate mood (Goethem & Sloboda, 2011). Consequently, they conducted a series of studies (Miranda & Claes, 2007, 2008, 2009) that attempted to determine the possible role that music plays in the experience of adolescent depression. The researchers considered this relationship in light of certain factors, including: personality, musical preferences, depression in peers, and styles of coping by music. Miranda and Claes’ results indicated how such factors could play a substantial role in the relationship between music and psychopathology. For instance, with music coping styles, it was revealed that for girls the problem-oriented coping style was linked with lower depression levels, while the avoidance/disengagement coping style was linked with higher depression levels (2009), and for boys the emotion-oriented coping style was linked with higher depression levels. Depression has also been linked to certain music listening choices and motivations. It has been found that depressed people are more likely to use music to reflect mood and express emotion (Wilhelm, Gillis, Schubert, & Whittle, 2013). A number of studies have identified that choosing to listen to music that matches a negative mood, often with the intent of expressing or attending to negative emotion, can be a maladaptive mood regulation strategy that for certain individuals leads to rumination and a perpetuated negative mood (Garrido & Schubert, 2011b, 2013a, 2013b; Miranda, Gaudreau, Debrosse, Morizot, & Kirmayer, 2012; Västfjäll, Juslin, & Hartig, 2012). Music listening motivations and coping styles are concepts closely related to music-related mood regulation. It is of interest how the relationship between music-related mood regulation and psychopathology compares with that of similar concepts, such as coping styles and music listening motivations, especially with recent research providing theoretical and conceptual understanding of music as a unique method of mood regulation. Saarikallio investigated music-related mood regulation and the specific strategies involved in a series of studies (2006, 2008; Saarikallio & Erkkilä, 2007). Within these studies, a model of mood regulation through music consisting of seven specific strategies was established (the Music in Mood Regulation model; MMR) and subsequently converted into a quantifiable form – the Music in Mood Regulation scale (also abbreviated as MMR). Theoretical grounding for the model emerged from thorough qualitative research and the application of existing mood regulation models. The seven strategies included are: Revival – relaxation and new energy; Mental Work – mental contemplation and reappraisal of emotional preoccupations; Discharge – release and venting of negative emotions; Diversion – distraction from unwanted thoughts, worries, and stress; Solace – comfort, support, and emotional validation; Strong Sensation – intense emotion induction and strengthening; and Entertainment – happy mood enhancement and Downloaded from msx.sagepub.com at RMIT UNIVERSITY on February 18, 2014

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maintenance (Saarikallio & Erkkilä, 2007). Saarikallio (2008) suggests that these strategies represent the patterns through which music is commonly used to regulate mood. Interestingly, many of the pre-existing general models of mood regulation that facilitated the development of the MMR are predictors of depressive symptoms, affective wellbeing, and psychopathology (Aldao & Nolen-Hoeksema, 2012; Catanzaro, 1997; Gross & John, 2003; Lischetzke & Eid, 2003). Given that high scores on a range of mood regulation measures are generally associated with less depressive symptomatology and higher ratings of positive affect and wellbeing, presumably similar results would be found using the conceptually similar MMR scale. Despite these conceptual similarities, the MMR scale has been shown to measure a unique construct (Saarikallio, 2006), which highlights the need for further investigation of how this unique mood regulation resource is related to variables such as psychopathology. Through a diverse range of regulation strategies, music facilitates mood enhancement and maintenance, an especially salient function for young people who are commonly known, and have been empirically shown, to experience a fluctuating and pervasive affective range. Although a substantial body of research identifying and exploring music as a tool to influence mood exists, there is limited research exploring the dynamics of the relationship between mood regulation and psychopathology such as depression, anxiety, and stress. Saarikallio (2008, 2011) has proposed that these types of relationships should be investigated and now with a suitable model and measure of music-related mood regulation (the MMR) this is a tangible endeavour.

Hypotheses Accordingly, the present study aimed to determine how music-related mood regulation was related to psychopathology in young people and which specific strategies from the MMR model were most influential in this relationship. Therefore, the primary hypothesis was that musicrelated mood regulation scores would predict levels of psychopathology in young people. Establishing the specific nature of the relationships between individual music-related mood regulation strategies and psychopathology was included as an additional exploratory research question, as was clarification of whether or not musical training moderated the relationship between music-related mood regulation and psychopathology. A number of secondary hypotheses were also posed. Based on a previous study that used a brief measure of the MMR with a similarly aged sample (Saarikallio, 2012), it was hypothesised that there would be no association between age and MMR scores, or sex and MMR scores, but that the amount of time spent listening to music would be positively associated with MMR scores.

Method Participants The sample consisted of 146 (53 male and 93 female) RMIT University students aged between 17 and 24 years (M = 20.81, SD = 1.96). Participation was voluntary and respondents were recruited via RMIT University social media and on-campus advertisements.

Measures Demographic information. Participants were asked a range of basic demographic questions regarding age, sex, cultural background, educational level, university course, and socioeconomic Downloaded from msx.sagepub.com at RMIT UNIVERSITY on February 18, 2014

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status. Participants were also required to indicate whether they had ever been diagnosed with depression or anxiety in the past, and if so, whether they had sought treatment. Personal music-related information. Questions covering participants’ musical history and behaviours were also included. Participants answered questions about their musical history, including whether or not they had played a musical instrument, and if so, for how many years and how frequently they practised. An estimated indication of daily music listening (less than one hour; between one and three hours; or more than three hours) was also required. Music in Mood Regulation scale (MMR). The MMR is a 40-item scale developed by Saarikallio (2008) measuring seven subscale strategies of mood regulation through music. The seven subscales from Saarikallio and Erkkilä’s (2007) model mentioned earlier include: Revival (7 items; e.g., “I listen to music to perk up after a rough day”), Mental Work (5 items; e.g., “Music inspires me to think about important issues”), Discharge (6 items; e.g., “When I’m really angry, I feel like listening to some angry music”), Diversion (5 items; e.g., “For me, music is a way to forget about my worries”), Solace (6 items; e.g., “Music is like a friend who understands my worries”), Strong Sensation (7 items; e.g., “Music offers me unforgettable moments”), and Entertainment (4 items; e.g., “I listen to music to make cleaning the house more pleasant”). Responses are made on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). To reduce the development of response patterns, the scale includes five reverse-scored items. The MMR scale was modelled upon and compared with psychometrically sound measures of general mood regulation, including: the scale for Negative Mood Regulation expectancies (Catanzaro & Mearns, 1990), the Emotion Regulation Questionnaire (Gross & John, 2003), the Trait Meta Mood Scale (Salovey, Mayer, Goldman, Turvey, & Palfai, 1995) and the mood regulation scale by Lischetzke and Eid (2003). The MMR demonstrated high internal reliability when used with an adolescent population, with the overall scale possessing a Cronbach’s alpha of .96 and individual subscales ranging between .76 and .92. Although a brief 21-item revision of the original MMR exists (Saarikallio, 2012), the comprehensive 40-item measure was selected based on its superior internal reliability. Depression Anxiety Stress Scales (DASS). To assess levels of psychopathological symptomology the DASS 42-item inventory of negative emotional states was used (Lovibond & Lovibond, 1993). The specific emotional states measured are depression (14 items; e.g., “I couldn’t seem to experience any positive feeling at all”), anxiety (14 items; e.g., “I was aware of the dryness of mouth”) and stress (14 items; e.g., “I found it hard to wind down”). The DASS rates the extent to which an individual has experienced symptoms of each state during the past week on a 4-point scale ranging from 0 (did not apply to me at all) to 3 (applied to me very much, or most of the time). Each scale demonstrates satisfactory psychometric properties with Cronbach’s alpha scores of .91 for depression, .84 for anxiety and .90 for stress. Unlike the standard Beck Depression Inventory, the DASS only contains items that discriminate between depression and other affective states; for instance, there are no somatic items regarding weight loss or insomnia. Despite the scale’s ability to discriminate between affective states, the high level of inter-correlation is recognised by the scale developers (Lovibond & Lovibond, 1995). The test was normed on a sample aged 17–69 years.

Procedure Following ethical approval from the RMIT University Human Research Ethics Committee, advertisements containing a link to the online survey were posted around the university’s Downloaded from msx.sagepub.com at RMIT UNIVERSITY on February 18, 2014

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campuses and on social media pages (Facebook, Twitter). The survey was administered using Qualtrics Labs, Inc. (2012) software. A Plain Language Statement preceded the survey, which outlined the purpose of the study, the nature of the questions, and participants’ rights. Being an RMIT University student aged 17–24 years was the only inclusion criteria, and consent was implied by submission of the completed survey (students aged under 18 were considered sufficiently independent by the RMIT University Human Research Ethics Committee to not require the conventional parental consent). A correlation design based on canonical correlation and multivariate multiple regression analyses was used.

Results Participants came from a variety of disciplines and educational levels with undergraduate students forming the majority (78%), and the remaining being Victorian Certificate of Education, Technical and Further Education, postgraduate, and Doctor of Philosophy students. Participants came from a range of cultural backgrounds, but were predominantly Australian-born English speakers (80%). The majority of the participants play or have played an instrument at some point in their life (76.7%). Of the sample, 10.6% reported having been previously diagnosed with depression, 6.6% with anxiety, and 14.4% with both, which is consistent with statistics for this population (Oakley Browne et al., 2006).

Data preparation and screening Data were entered into a single SPSS 19 spreadsheet for analysis, with variables coded as required. The MMR subscales were entered both individually and as a total score, while DASS subscales were entered individually only. A musical training variable was created by combining years played with level of practice scores. Continuous variables were assessed for normality through direct hypothesis tests of the assumption and visual inspection of histograms and boxplots. Although some variables violated the assumption of normality, this commonly occurs with large samples (Norman, 2010); therefore, further use of parametric tests was considered appropriate. DASS variables were positively skewed, a common outcome for non-clinical samples. The MMR subscales Entertainment and Strong Sensation demonstrated some negative skew; however, this was considered unlikely to have any substantive influence upon subsequent analyses given the adequate sample size (Tabachnick & Fidell, 2007). No outliers required deletion. In instances where it appeared participants had unknowingly left items unanswered, person mean substitution was used (i.e., means based on non-missing items from the relevant scales replaced missing items; Downey & King, 1998).

Correlations Pearson correlations were conducted to investigate the nature of the relationships among variables, some of which are presented in Table 1, along with means and standard deviations. The majority of the inter-correlations between MMR strategies were consistent with previous studies (Saarikallio, 2008, 2012), with the exception of the non-significant positive association between Discharge and Entertainment. DASS variables displayed typical large1 positive inter-correlations with each other (Lovibond & Lovibond, 1993). Notably, Discharge revealed significant positive correlations with each of the DASS variables, while Entertainment revealed small-moderate negative correlations with Depression and Anxiety. Downloaded from msx.sagepub.com at RMIT UNIVERSITY on February 18, 2014


– .83** .88** .62** .82** .89** .77** .60** –.03 .04 .04

22.69 (9.39) 20.60 (7.09) 24.77 (8.58)

1

143.34 (23.30) 26.63 (4.95) 18.47 (3.72) 19.26 (5.65) 17.04 (3.63) 21.18 (4.76) 26.76 (4.98) 17.21 (2.86)

M (SD)

–.13 –.08 –.08

– .64** .28** .73** .69** .63** .60**

2

–.05 .01 .01

– .52** .67** .80** .67** .47**

3

Note. MMR = Music in Mood Regulation Scale, DASS = Depression, Anxiety, Stress Scale. * p < .05; **p < .01.

MMR 1. Total 2. Revival 3. Mental Work 4. Discharge 5. Diversion 6. Solace 7. Strong Sensation 8. Entertainment DASS 9. Depression 10. Anxiety 11. Stress

Variable

.25** .33** .32**

– .45** .51** .26** .09

4

.07 .15 .12

– .74** .50** .39**

5

–.02 .04 .02

– .66** .42**

6

–.15 –.15 –.15

– .54**

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Table 1. Summary of inter-correlations, means, and standard deviations for scores on the MMR and DASS (N = 146).

–.28** –.18* –.08

–

8

–

9

.64** –

10

.71** .76** –

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The only DASS variable to demonstrate a statistically significant positive correlation with sex was stress, r = .16, p = .047, indicating that females reported higher levels of stress symptomology than males. Significant moderate correlations were found between previous diagnoses of depression and anxiety and all DASS subscales, r = .21–.36, p = .001, indicating a moderate positive association between previous psychiatric disorders and the presence of recent psychiatric symptomology. Additional correlations were conducted to address the secondary hypotheses, with no significant associations revealed between total MMR and sex, r = -.05, p = .56, or total MMR and age, r = -.01, p = .89. A large positive correlation was found between total MMR and daily listening, r = .47, p = .001.

Canonical correlation As a screening procedure prior to hypothesis testing, a canonical correlation was performed using SPSS to analyse the nature of the relationship between the sets of MMR and DASS variables. Canonical correlation is frequently used as an initial exploratory procedure when analysing multivariate combinations of independent and dependent variables, and can be used as a precursor to more focused regression modelling (Tabachnick & Fidell, 2007). All seven MMR subscales were included in the first set as independent variables and all three DASS subscales as dependent variables in the second set. Out of three canonical correlations produced, the first and only significant canonical correlation was .49 (24% overlapping variance). With the three canonical correlations included, χ2 (21) = 56.73, p < .001, indicating a significant multivariate relationship between the two sets of variables. Data for the first pair of canonical variates appear in Table 2. With a .3 correlation cut-off (Tabachnick & Fidell, 2007, p. 603), the variables in the MMR set that correlated with the single significant canonical variate were Discharge (.69), Strong Sensation (-.35), and Entertainment (-.46). All three variables in the DASS set – depression (.88), anxiety (.94), and stress (.86) – correlated with the canonical variate. This indicates that young people who identify with, and frequently use, the MMR strategy Discharge, but not Strong Sensation or Entertainment, are likely to experience more depression, anxiety, and stress symptomology. A multivariate multiple regression analysis provided a more detailed analysis of these results.

Multivariate multiple regression To establish how well the combined MMR variables predicted the combined DASS variables, a multivariate multiple regression analysis was conducted. For the multivariate combinations of independent and dependent variables, which were inter-correlated, multicollinearity was within acceptable limits as assessed using both VIF and tolerance measures (Field, 2013). A significant multivariate effect was found: Wilks’ Λ = .67, F(21, 391.07) = 2.83, p < .001, η2 = .13, 95% CI [.03, .23], and subsequent univariate analyses focusing on the prediction of the combined MMR subscales of each of the dependent variables found significant results for all three DASS subscales: depression - R2 = .14, F(7, 138) = 4.43, p < .001, 95% CI [.04, .24]; anxiety - R2 = .17, F(7, 138) = 5.22, p < .001, 95% CI [.07, .27]; and stress - R2 = .16, F(7, 138) = 4.82, p < .001, 95% CI [.06, .26]. Analysis of the relative contribution of each MMR sub-scale on each dependent variable separately for the three significant multiple regression models revealed that for depression, Discharge, t(138) = 2.98, p = .003, and Entertainment, t(138) = -2.36, p = .02, provided significant levels of unique predictive variance to depression scores. In the case of anxiety, Downloaded from msx.sagepub.com at RMIT UNIVERSITY on February 18, 2014

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Thomson et al. Table 2. Canonical variate and corresponding correlations, standardized canonical coefficients, variance percentages, redundancies, and canonical correlation between MMR and DASS variables. Canonical variate MMR set Revival Mental Work Discharge Diversion Solace Strong Sensation Entertainment Percent of variance Redundancy DASS set Depression Anxiety Stress Percent of variance Redundancy Canonical correlation

Correlation

Coefficient

–.22 –.03 .69 .27 .04 –.35 –.46 .13 .03

–.34 –.28 –.77 –.66 –.08 –.28 –.26

.88 .94 .86 .78 .18 .49

.37 .60 .14

Note. MMR = Music in Mood Regulation scale, DASS = Depression, Anxiety, Stress Scale.

Discharge, t(138) = 3.74, p = .001, and Diversion, t(138) = 2.44, p = .016, provided significant levels of unique predictive variance, and similarly for stress: Discharge, t(138) = 4.01, p = .001, and Diversion, t(138) = 2.18, p = .03.

Musical training as a moderator To address the research question as to whether musical training moderated the relationship between music-related mood regulation and psychopathology, three regression models were analysed based on the Baron and Kenny (1986) approach to moderation testing. A DASS subscale served as the dependent variable in each, with total MMR score as the predictor and the calculated musical training score as the moderator. Moderation was assessed by considering the interaction between MMR score and musical training. No significant interaction was found between musical training and total MMR in any of the analyses: Depression, F(1, 108) = 0.38, p = .54, η2 < .01, Anxiety, F(1, 108) = 0.002, p = .96, η2 < .01, or Stress, F(1, 108) = 0.04, p = .84, η2 < .01, indicating that musical training was not a significant moderator in any of the relationships between total MMR and DASS variables.

Discussion The purpose of the current study was to investigate how music-related mood regulation relates to psychopathology in young people through examining the nature of the relationships between individual music-related mood regulation strategies and psychopathology. The primary hypothesis that MMR scores would predict levels of psychopathology was supported, with MMR scores Downloaded from msx.sagepub.com at RMIT UNIVERSITY on February 18, 2014

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accounting for a moderate amount of the DASS score variance. Secondary hypotheses were also supported, with no associations found between either total MMR and age, or total MMR and sex. In a previous study with a younger sample (M = 15.01 years; Saarikallio, 2006), it was found that use of music-related mood regulation increased with age and was more prevalent amongst girls; however, with an older sample (M = 23.97 years; Saarikallio, 2012), neither of these results were upheld. Therefore, it was suggested that music-related mood regulation behaviour increases throughout adolescence before stabilising in adulthood (Saarikallio, 2012), and our findings support this. Unsurprisingly, listening time was positively associated with MMR scores, which is also consistent with previous findings (Saarikallio, 2006, 2012). Musical training did not moderate the relationship between music-related mood regulation and psychopathology. This suggests that the experience of mood regulation through music and the way in which this relates to psychopathology is independent of one’s musical training. Given the lack of research addressing this topic, this question was exploratory in nature; therefore, these results are not viewed as expected or otherwise. The most critical findings, and those which hold the greatest practical implications, are those clarifying the nature of the relationships between individual MMR strategies and psychopathology.

Discharge Through a comprehensive range of analyses, the influence of individual MMR strategies was revealed. Discharge appeared to have the most substantial influence, demonstrating notable positive associations with each DASS variable in all conducted analyses. This suggests that high use of Discharge is both associated with, and predictive of, high levels of psychopathology. Discharge involves selecting music that is congruent with an already established negative mood, followed by venting and releasing of negative emotions. Given that individuals with psychopathology are likely to experience more negative emotions, it is perhaps unsurprising that they employ this strategy more readily. Discharge shares conceptual similarities with a coping strategy proposed by Miranda and Claes (2009) known as emotion-oriented coping by music listening. As mentioned earlier, this strategy was linked with high levels of depression in young males, with the authors proposing that maladaptive emotion-oriented coping, involving unregulated negative emotional release or venting, is a potential link to depressive symptoms. Discharge also draws parallels with a behaviour that is frequently identified as inconsistent with mood regulation theories. It has been recognised that some individuals will actively attempt to maintain or enhance an already established negative mood by selecting mood-congruent music (Feldman-Barrett & Gross, 2001; Laiho, 2004); however, this behaviour contradicts the propositions of most mood regulation theories, including Zillmann’s mood management theory (1988a, 1988b), which is “based on a hedonistic proposition, that individuals arrange stimulus conditions to minimize bad moods and maximize good moods” (Laiho, 2004, p. 56). A number of studies have attempted to rationalise the reasons why individuals choose to perform this counter-hedonistic behaviour, including its potential to be an enjoyable or even pleasurable experience (Garrido & Schubert, 2011a; Huron, 2011; Van den Tol & Edwards, 2013). Larsen (2000) describes this behaviour as delayed hedonic gratification, where after a cathartic release of negative emotion an individual can feel better. Zillmann and Gan (1997) suggest music provides both solace and a protected solitude in which a negative mood can be maintained to improve understanding and insight into one’s current situation. Provided a young person is resilient enough to move beyond their enhanced negative state, this behaviour is generally considered psychologically beneficial (McFerran, 2010; Zillmann & Downloaded from msx.sagepub.com at RMIT UNIVERSITY on February 18, 2014

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Gan, 1997). Accordingly, the results of the current study may indicate that some young people identify with the Discharge strategy, but do not experience a beneficial cathartic release of negative emotion and, therefore, cannot shift beyond their negative affective state. Rather than an opportunity for gaining insight and understanding into one’s state as Zillmann and Gan (1997) suggested, this immersion in negatively valenced music might simply promote rumination (Garrido & Schubert, 2011a, 2011b, 2013a; Miranda et al., 2012). It has been suggested that those prone to rumination are attracted to sad and negatively valenced music because they perceive it as an opportunity for catharsis and are able to relate to the emotional content (Garrido & Schubert, 2013a, 2013b). For such individuals, this perception of sad music listening as a psychologically beneficial process is often inaccurate, with dysphoria and rumination simply perpetuated (Garrido & Schubert, 2011b). Even when ruminators are aware of not enjoying listening to sad music and recognise the negative impact it has upon their mood, they can still remain attracted to it (Garrido & Schubert, 2013a, 2013b). Rumination is not only a behaviour commonly associated with depression (Nolen-Hoeksema, 1991), it is also considered a vulnerability factor for the onset of depression in young people, especially in those displaying high levels of neuroticism (Kuyken, Watkins, Holden, & Cook, 2006). This highlights the risk involved in the use of a mood regulation strategy that could potentially lead to rumination. The relationship between Discharge and depression is clearer to rationalise than that between Discharge and anxiety or stress. It is difficult to ascertain whether these relationships are a byproduct of comorbidity with depression or whether Discharge legitimately promotes anxiety, stress, and their associated symptomology (e.g., worry, nervous arousal, agitation). Although an adaptive and beneficial strategy for most, it is possible that for some Discharge is an ineffective or even maladaptive self-regulatory strategy.

Diversion Diversion, in addition to Discharge, contributed significant levels of unique predictive variance to Anxiety and Stress scores. Diversion, which involves using music as a distraction from unwanted thoughts, worries, and stress, has the potential to be conceptualised as an avoidance coping strategy. Music can provide a distraction from a range of stressors present in a young person’s life, including: parents, friends, relationships, school, or university (Miranda & Claes, 2009). However, recurrent denial or avoidance of stressors, paradoxically, can increase one’s susceptibility to anxiety and perpetuate its symptoms (Carver & Scheier, 1998; SaltersPedneault, Tull, & Roemer, 2004). Again, it appears that a strategy that generally performs an adaptive function has the potential to be detrimental. Conversely, the adaptive quality of Entertainment was confirmed, with evidence of it being a potential protective factor against psychopathology.

Entertainment On the basis of all analyses conducted, Entertainment demonstrated a notable negative association with Depression, suggesting young people who frequently use this particular strategy experience less depressive symptomology. Given Entertainment involves creating a pleasant atmosphere to maintain or enhance a current positive mood, this intuitively makes sense. The fact that this strategy lies on the premise of an already established positive mood indicates why young people experiencing psychopathology – and presumably more negative moods – would be less likely to use it. Conversely, for young people who experience little to no psychopathology, Downloaded from msx.sagepub.com at RMIT UNIVERSITY on February 18, 2014

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Entertainment is a far more accessible and efficacious strategy. Additionally, in one study that investigated both depressed and non-depressed peoples’ reasons for listening to music (Wilhelm et al., 2013), it was revealed that non-depressed individuals were more likely to use music for energy and motivation, and inspiration and stimulation – reasons that share similarities with the strategy of Entertainment. Entertainment may also provide added protection for individuals who are close to crossing a threshold into psychopathology. As mentioned previously, rumination is a common feature in the onset and later presentation of depression and dull and uneventful situations can promote its occurrence (Kuyken et al., 2006). Many of the MMR items addressing Entertainment focus on the use of music for making potentially boring situations more pleasant and eventful; therefore, use of Entertainment may discourage opportunities for rumination. Additionally, according to the canonical correlation analysis, Strong Sensation also demonstrated a negative association with DASS variables. Strong Sensation involves induction and strengthening of intense, positive emotions. Similar to Entertainment, Strong Sensation relies on an already established positive state. Those experiencing psychopathology, specifically depression, have a reduced capacity for experiencing joy and positive emotions (NolenHoeksema & Girgus, 1994); therefore, it is understandable these individuals would not readily employ a strategy that involves experiencing intense, positive musical moments.

Limitations and suggestions for future research The current study contains a number of limitations, which in combination with the current findings inform recommendations for future research. Although the results provide some indication that certain music-related mood regulation strategies may influence psychopathology in a particular direction, causality has by no means been established. It could legitimately be argued that either frequent use of certain strategies promotes the onset of psychopathology or that psychopathology encourages the adoption of these strategies. The sequence of the causal pathways could be addressed through longitudinal assessments of music-related mood regulation and psychopathology throughout adolescence, but rather than focusing on causality, it may be beneficial to investigate the ways in which certain MMR strategies either maintain or prevent psychopathology, and the impact adjustments to these strategies has upon subsequent symptomology. Although the DASS is a reliable measure, superior at discriminating between disorders and providing clear indications of symptom severity and frequency, it is based on an individual’s experiences within the past week. The limitation here is that events within the past week, such as a death, a relationship break-up, or an upcoming exam, could influence responses and inaccurately reflect an individual’s typical level of symptomology. However, the DASS assesses a number of symptom categories, so fluctuations due to the week’s events would unlikely appear as a pervasive problem. A clinical interview is the ideal way to diagnose a disorder, or the level of its symptoms present, but with a large sample this becomes highly impractical. Rather than focus on psychopathology and its presence or absence, it could be beneficial to conduct a similar study that instead focuses on wellbeing and positive features, such as self-esteem, satisfaction with life, and subjective wellbeing; although, some consideration has already been given to this approach (Miranda & Gaudreau, 2011). The final limitation of the study relates to the generalisability of the findings. Although a specific age group was the focus of the study, this limits the size of the population to which the results can be generalised. The strategies employed by adults appear virtually identical to those employed by young people (Saarikallio, 2011); however, conducting a study with an older Downloaded from msx.sagepub.com at RMIT UNIVERSITY on February 18, 2014

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sample would clarify whether the same relationships with psychopathology occur in adulthood. Additionally, the fact that all participants were students of some form renders the findings less applicable to individuals who are not studying.

Implications and conclusions The findings of the current study hold a number of implications, including the need to adapt the view of music-related mood regulation as a strictly psychologically beneficial process. Incorporated into this view should be the notion that music-related mood regulation does not necessarily guarantee mood improvement in all individuals and in some circumstances may even maintain the presence of psychopathology. Our findings also add to the growing literature that suggests music listening habits can have both an adaptive and maladaptive impact upon psychological wellbeing (Garrido & Schubert, 2011b, 2013a; Miranda et al., 2012). The study holds practical implications in terms of its potential application in a clinical setting. In music therapy, mood regulation is already used as part of the treatment of young people with depression (Hendricks, 2001); however, these findings could aid practitioners in identifying ineffective or maladaptive mood regulation strategies. Subsequently, practitioners could discourage and reduce the use of such strategies and in their place implement more efficacious strategies. Additionally, the findings hold implications for everyday listening habits. From a self-therapy perspective, music has many advantages for young people experiencing varying levels of psychopathology (Skånland, 2011). Its high accessibility (with the popularity of personal mobile listening devices) is a notable advantage (Heye & Lamont, 2010; Västfjäll et al., 2012); however, on the basis of these and other recent findings (Garrido & Schubert, 2011b, 2013a; Miranda et al., 2012), it appears a healthy listening regimen that performs an adaptive function is necessary in order to gain the full benefits of music as a self-therapeutic resource. Funding This research received no specific grant from any funding agency in the public, commercial, or not-forprofit sectors.

Note 1. Interpretations of effect sizes were based on Hopkins’s (2011) criteria.

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