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Emotional outcomes of regulation strategies used during personal music listening: A mobile experience sampling study

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Music is frequently used to support emotional health and well-being, with emotion regulation the most commonly reported mechanism. Music-based emotion regulation has not yet been extensively investigated within the broader emotion regulation framework. The effects of music-based emotion regulation on emotional state and well-being outcomes have also rarely been tested in real time. The current study aimed to determine the consequences of emotion regulation strategies used during music listening, in terms of hedonic outcomes, and associations with emotional health and well-being. A sample of 327 participants used the MuPsych application (app), a mobile experience sampling methodology designed for the real-time and ecologically-valid measurement of personal music listening. Results revealed that using music to regulate a recently experienced emotion (response-focused strategies) yielded the greatest hedonic success, but was associated with poorer emotional health and well-being. Music-based emotion regulation differed from non-music emotion regulation findings in several key ways, suggesting that music-based emotion regulation does not occur in accordance with the process model. This supported the notion that personal music listening is utilized as an independent regulatory resource, allowing listeners to reach specific emotional goals. Regulation strategies are selected to reach a desired hedonic outcome, based on initial mood, and influenced by emotional health and well-being.
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Musicae Scientiae
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DOI: 10.1177/1029864914536430
2014 18: 275Musicae Scientiae
William M. Randall, Nikki S. Rickard and Dianne A. Vella-Brodrick
A mobile experience sampling study
Emotional outcomes of regulation strategies used during personal music listening:
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DOI: 10.1177/1029864914536430
msx.sagepub.com
Emotional outcomes of regulation
strategies used during personal
music listening: A mobile
experience sampling study
William M. Randall and Nikki S. Rickard
Monash University, Australia
Dianne A. Vella-Brodrick
University of Melbourne and Monash University, Australia
Abstract
Music is frequently used to support emotional health and well-being, with emotion regulation the most
commonly reported mechanism. Music-based emotion regulation has not yet been extensively investigated
within the broader emotion regulation framework. The effects of music-based emotion regulation on
emotional state and well-being outcomes have also rarely been tested in real time. The current study
aimed to determine the consequences of emotion regulation strategies used during music listening, in
terms of hedonic outcomes, and associations with emotional health and well-being. A sample of 327
participants used the MuPsych application (app), a mobile experience sampling methodology designed
for the real-time and ecologically-valid measurement of personal music listening. Results revealed that
using music to regulate a recently experienced emotion (response-focused strategies) yielded the greatest
hedonic success, but was associated with poorer emotional health and well-being. Music-based emotion
regulation differed from non-music emotion regulation findings in several key ways, suggesting that
music-based emotion regulation does not occur in accordance with the process model. This supported the
notion that personal music listening is utilized as an independent regulatory resource, allowing listeners
to reach specific emotional goals. Regulation strategies are selected to reach a desired hedonic outcome,
based on initial mood, and influenced by emotional health and well-being.
Keywords
emotion regulation, experience sampling method, hedonic shift, mental health, mobile phone, music,
well-being
The development of adaptive emotion regulation strategies is essential for emotional health
and well-being. Emotion regulation encompasses a set of processes that are employed to
monitor, evaluate, or modify various aspects of an emotional experience (Thompson, 1994).
Corresponding author:
Nikki S. Rickard, School of Psychology & Psychiatry, Monash University, Wellington Rd, Clayton, Melbourne, Victoria,
3800, Australia.
Email: nikki.rickard@monash.edu
536430MSX0010.1177/1029864914536430Musicae ScientiaeRandall et al.
research-article2014
Article
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276 Musicae Scientiae 18(3)
These processes allow an individual to influence the subjective experience and expression of
emotion, in order to accomplish personal goals (Gross, 1998; Thompson, 1994). The impor-
tance of understanding emotion regulation processes is accentuated by the apparent links
between these processes and mental health. Deficits in emotion regulation are prominent in
many forms of psychopathology, and are implicated in the development, maintenance and
treatment of various mental disorders (Berking etal., 2012; Bradley, Cuthbert, & Lang, 1990;
Gross, 2002; Larsen, 2000; van Praag, 1990). Development of maladaptive regulation strate-
gies and the inability to cope with emotional stimuli are related to a wide range of disorders,
including mood, personality, eating and substance-abuse disorders (for a review, see Berking
et al., 2012). It is therefore crucial for adaptive psychological functioning that individuals
develop the ability to effectively self-regulate their emotional states (Larsen, 2000).
Music use has been frequently cited as an effective means of self-regulating emotions. For
instance, music assists the reduction of stress and improvement of negative moods (e.g. Larson,
1995; Ruud, 1997; Silk, Steinberg, & Morris, 2003; Sloboda, O’Neill, & Ivaldi, 2001). From a
wide range of regulation behaviors, Thayer, Newman and McClain (1994) found music listen-
ing to be the second most successful behavior for enhancing energy (after “control thoughts”),
reducing tension (after “religious/spiritual activity”), and changing a bad mood (after exer-
cise). It is equally effective as alternative regulation strategies such as talking to family or
friends, or engaging in a hobby (van Goethem & Sloboda, 2011). Furthermore, emotion regula-
tion is one of the primary motivations for engaging in music (Arnett, 1995; Denora, 1999;
Juslin & Laukka, 2004; Laiho, 2004; Laukka, 2007; Saarikallio & Erkkilä, 2007; Sloboda etal.,
2001; Tarrant, North, & Hargreaves, 2000). Questionnaire studies have indicated that 93% of
people use music to change their mood, with 49% doing so often (Juslin & Laukka, 2004).
Saarikallio and colleagues found that seven music-specific regulation strategies emerged
from their qualitative and quantitative analyses of how music is used for mood management:
entertainment, revival, strong sensation, diversion, discharge, mental work, and solace
(Saarikallio, 2008). These strategies are used to reach two main goals of mood control and
mood improvement, and have been observed in both adolescent and adult listeners (Saarikallio,
2011). Whether music is used to regulate emotions in the same way as non-music strategies has
not yet been demonstrated. An examination of music-based emotion regulation within the
general emotion regulation framework would be helpful in this regard.
The dominant framework for emotion regulation is the process model, developed by Gross
(1998). The central tenet of this model is that regulation strategies can occur at different times
in relation to an emotional response (Gross & John, 2003). Antecedent-focused strategies occur
before the activation of response tendencies, and include: situation selection (approaching or
avoiding stimuli based on their likely emotional impact); situation modification (modifying the
environment to alter emotional impact); attention deployment (distraction from or focus on
specific aspects of the situation); and cognitive change (evaluating the situation to alter its
emotional significance). Response-focused strategies occur following the activation of response
tendencies, and involve response modulation (diminishing or augmenting the response tenden-
cies: Gross, 1999).
The general hypothesis put forward in the process model is that antecedent-focused strate-
gies should be more effective in regulating emotion than response-focused strategies, as emo-
tional intensity will be lower during earlier stages of the emotion process. Testing of this
hypothesis has largely focused on the comparison of the antecedent strategy of cognitive reap-
praisal (interpreting an emotional situation to alter its emotional impact) with the response
strategy of expressive suppression (inhibiting emotion-expressive behaviour: Gross & John,
2003). Findings have consistently shown reappraisal to be a more successful strategy in the
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Randall et al. 277
short-term, producing decreases in the expression and experience of negative emotion (Gross,
2001; John & Gross, 2004). In contrast, suppression decreases expression and positive emo-
tional experience, but does not decrease negative emotional experience. Reappraisal also
appears to have long-term benefits, related to increased well-being and lower levels of depres-
sion, while suppression has displayed the opposite associations (Gross, 2002; Gross & John,
2003; John & Gross, 2004).
An extension to this process model hypothesis is the process-specific timing hypothesis,
which takes into account the information processing involved in regulation strategies (Sheppes
& Gross, 2011). This hypothesis states that strategies such as distraction, which prevent the
cognitive processing of emotional stimuli (‘early’ selection strategies), should be unaffected by
the emotional intensity of an experience. In contrast, ‘late’ selection strategies such as reap-
praisal, which provide semantic meaning to emotional information, should become less effec-
tive at higher emotional intensities (Sheppes & Gross, 2011). Sheppes and Gross (2011) further
postulated that while early selection strategies provide short-term relief from negative experi-
ences, they may be maladaptive in the long-term, as they prevent deep processing of emotional
information.
Two meta-analyses have demonstrated reappraisal and distraction to be the most effective
strategies in the short-term, producing the greatest shift towards positive valence (Augustine &
Hemenover, 2009; Webb, Miles, & Sheeran, 2012). The outcomes of these strategies were
improved when distraction was active, rather than passive, and when reappraisal was directed
at the emotional stimulus, rather than the emotional response. In contrast, the strategy of con-
centration was found to be ineffective, leading to undesired hedonic outcomes (Webb etal.,
2012). Regulation was also found to be more likely when a negative state had been induced,
with greater induced negative affect leading to greater hedonic shifts (Augustine & Hemenover,
2009). Importantly, while no difference was found between avoidance and engagement strate-
gies across the entire set of included studies, avoidance was a more successful strategy in stud-
ies in which a negative state was induced in participants (Augustine & Hemenover, 2009).
The differential outcomes of antecedent and response-focused emotion regulation strategies
have not been directly compared with regard to music-based emotion regulation. The majority
of research on music and well-being has highlighted benefits of music listening (MacDonald,
Kreutz, & Mitchell, 2012; Rickard & McFerran, 2012), including protective effects against psy-
chopathology (Miranda & Claes, 2008). Recent research has also demonstrated that these asso-
ciations between music listening and mental health are at least partially mediated by emotion
regulation (Chin & Rickard, 2013; Thoma, Scholz, Ehlert, & Nater, 2012). However, certain
types of music listening can also be associated with poorer long-term well-being and mental
health outcomes in some contexts (Chin & Rickard, 2014). It is important then to investigate
the effects of music-based regulation strategies, on both short-term hedonic and longer-term
mental health outcomes.
A key limitation in previous research in this field has been the difficulty in obtaining valid
and sensitive data on everyday music use. While the majority of aforementioned emotion regu-
lation studies were performed under laboratory conditions (often involving emotion induction),
regulation through music listening involves a range of additional individual and situational
variables (Saarikallio, 2011). A more suitable approach is found in the experience sampling
method (ESM: Csikszentmihalyi & LeFevre, 1989), which offers an ecologically-valid means of
observing music use in real time (Randall & Rickard, 2013; Sloboda etal., 2001). A review of
ESM use in the measurement of emotion regulation has demonstrated that this methodology is
powerful in predicting and understanding the development of mental disorders (Bylsma &
Rottenberg, 2011). ESM has been successfully employed in a wide range of studies to observe
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278 Musicae Scientiae 18(3)
emotion dynamics and regulation in natural contexts (e.g. Carstensen etal., 2011; Shahar &
Herr, 2011; Yeung & Fung, 2012), with many of these utilizing smartphones and mobile elec-
tronic devices (Hill & Updegraff, 2012; Morris etal., 2010; Silk etal., 2011; Tan etal., 2012).
In terms of emotion regulation strategies, ESM results have shown that distraction is related to
higher positive affect (Stone, Kennedy-Moore, & Neale, 1995), and is effective when active, but
not passive (Fichman, Koestner, Zuroff, & Gordon, 1999). In contrast, avoidance and rumina-
tion fail to decrease negative affect, and lead to depression symptoms in adolescents (Silk etal.,
2003). The frequencies at which regulation strategies are utilized in daily life have been shown
to correlate with strategy effectiveness, as measured under laboratory conditions (Gross,
Richards, & John, 2006).
ESM provides the ideal means for observing how regulation strategies are used during music
listening, to assess both the hedonic outcomes, and associations with longer-term emotional
functioning. The current study utilized the MuPsych application (app), a mobile ESM design
developed to measure such emotion regulation variables (Randall & Rickard, 2013). MuPsych
is an ecologically-valid and real-time data collection method for observing personal music lis-
tening on mobile devices. Focusing on this personal and portable style of listening allows for
unprecedented insight into emotion regulation through music, as listeners have complete con-
trol in selecting music to fulfil their regulation needs, in any social context (Knobloch &
Zillmann, 2003). While music-based emotion regulation is believed to occur in accordance
with process model strategies, this has not yet been explicitly investigated within the process
model of emotion regulation developed by Gross (1998). The current study will utilize this eco-
logically-valid methodology to explore consistencies between music-based emotion regulation
and broader non-music emotion regulation. The effectiveness of music-based emotion regula-
tion will be assessed by measuring changes in emotional valence in real-time, and by surveying
associations between music regulation strategies and longer-term emotional health and
well-being.
The first aim was to determine the hedonic success of process model strategies used during
personal music listening. From the non-music literature, it was hypothesized that: (a) reap-
praisal would effectively decrease negative emotion experience, while suppression would not
(Gross, 2001, 2002; Gross & John, 2003; John & Gross, 2004). Conversely, suppression would
reduce positive emotion experience, but reappraisal would not, and that (b) distraction would
be effective regardless of emotional intensity, while reappraisal would become less effective at
higher intensities (Sheppes & Gross, 2011). The second aim was to assess the associations
between frequency of process model strategies used during personal music listening, and emo-
tional health and well-being. It was hypothesized that: (a) frequent use of reappraisal would be
positively correlated with measures of emotional health and well-being, while suppression
would be negatively correlated with these measures (Gross & John, 2003; John & Gross, 2004),
and that (b) frequent use of early selection strategies such as distraction would be associated
with poor emotional health and well-being, while use of later selection strategies would be
associated with higher levels of these measures (Sheppes & Gross, 2011).
Method
Participants
Data were collected initially from 594 participants via the MuPsych smartphone app.
Participants with less than five music episode reports were excluded from analysis, producing a
set of 327 participants (249 females, 78 males; age M = 21.02 years; SD = 6.18). Participants
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Randall et al. 279
were of diverse music background, with 9.79% of the sample reporting no musical training,
20.18% with primary school music education, 63.61% with secondary school music educa-
tion, and 6.42% with tertiary training. Approximately one third of the sample had played an
instrument for at least 6 years (32.36%), 14.07% for between 4 and 6 years, 30.28% for 1 to 3
years, and 22.94% reported never playing an instrument. Participants were recruited through
advertisement across a range of mediums, including magazine articles and social media, while
others were sourced from the Undergraduate Psychology Student Participant Pool at Monash
University. While these students received course credit for first-year psychology courses, partici-
pants did not receive any monetary incentive for participation.
Materials
The study employed the MuPsych mobile experience sampling methodology (m-ESM) to obtain
all data from participants. The complete design and testing of the m-ESM is detailed elsewhere
(Randall & Rickard, 2013). In brief, MuPsych is a mobile app that collects data through event-
based experience sampling reports (ESRs), which are administered when participants listen to
music on their own device. The app also administers non-music ESRs (at times with no music
present), and a series of psychometric questionnaires, which are completed over a two-week
collection period.
Music ESRs assess the emotional valence and arousal of the listener, on 7-point slider scales,
both at music onset, then again after 3 minutes of listening (automatically determined by the
app). Prior analyses have revealed that assessment of these two dimensions is an efficient and
reliable way of measuring music-induced emotion, explaining a high proportion of variance
(Thoma, Ryf, Mohiyeddini, Ehlert, & Nater, 2012; Vuoskoski & Eerola, 2011). For Aim 1,
hedonic success was defined as a significant shift towards the positive end of the valence scale
over this listening period (arousal data were analyzed, but are not the focus of the current
study).
Following the 3-minute assessment of valence and arousal, music ESRs immediately pre-
sented a series of additional screens, measuring a variety of listening context and music vari-
ables. Included within the series of ESR screens were items designed to assess whether listening
was occurring in relation to a process model strategy. The first screen determined whether the
participant was listening to music in preparation for (antecedent-focused), or in response to
(response-focused), an emotional event or situation. The answer to this question led partici-
pants to one of two alternate lists: one of antecedent-focused strategies, and one of response-
focused strategies, respectively. If it was reported on the first screen that listening was not
occurring in relation to an emotional event, listeners were presented with a screen that
allowed them to either confirm this, or change their answer to select from the antecedent or
response lists. This ensured that no shortcuts were possible for any particular answer, and that
non-serious responding could potentially result in an additional screen. As with all list-based
ESR questions, list options were randomized for each screen. Screens included a help icon,
which would provide users with explanatory text when required. The process model strategies
are shown in Table 1 (with the options presented to listeners included in the ‘Description’
column).
The questionnaires delivered by MuPsych in this study included measures of emotional
health and well-being (Aim 2). Emotional health was assessed by the Depression Anxiety and
Stress Scale (DASS), with three 14-item subscales of depression, anxiety, and stress (Lovibond
& Lovibond, 1995). For ethical reasons, this questionnaire was only presented to participants
over 18 years of age. Well-being was assessed using the 5-item Satisfaction With Life Scale
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280 Musicae Scientiae 18(3)
(SWLS: Diener, Emmons, Larsen, & Griffin, 1985), and the Positive and Negative Affect Schedule
(PANAS), with 14-item subscales of Positive and Negative affect (Watson, Clark, & Tellegen,
1988). An additional 8 items were added to the PANAS-20 in response to criticisms that low
arousal affect items are not duly represented in the original questionnaire. The Music Use ques-
tionnaire (MUSE: Chin & Rickard, 2012) was used to obtain information about participants’
music background. The Emotion Regulation Questionnaire (ERQ: Gross & John, 2003) was also
included in the study, to assess habitual use of the cognitive reappraisal and expressive suppres-
sion regulation strategies. This was used to determine whether the reported habitual use of
these strategies was related to how often each was utilized during personal music listening.
Procedure
Participants downloaded MuPsych on to their own mobile device (iPhone, or iPod touch1),
and used it as their personal music player for a 2-week period. All elements of the study were
presented electronically via the app, including consent and demographics forms, ESRs, and a
set of psychological surveys. Music ESRs were presented at times when participants chose to
listen to music, with a maximum of two completed per day. These ESRs followed the pro-
grammed timing rules of MuPsych, so that particular music episodes were not self-selected
by participants. Hedonic success of process model strategies was assessed through the valence
data collected by music ESRs. The change in valence ratings from commencement of music
to 3 minutes after commencement was utilized as a measure of affective change in response
to music. Affective change was also calculated in this way for non-music episodes for com-
parison. In testing the process-specific timing hypothesis, emotional intensity was defined as
the absolute value of valence reported at music onset. In addition to ESRs, psychological
surveys were made available across the data-collection period (four from the first day, and
four more after a week), which could be completed at any time of convenience. Data from
MuPsych was downloaded remotely and stored on a secure network site until ready for data
analysis.
Data analyses
Analyses for this study were performed at the participant level, by creating aggregate scores
from the ESRs of each individual listener. This approach is recommended for ESM (Hektner
Table 1. Process model strategies as assessed by MuPsych music ESRs.
Stage Process Strategy Description
Antecedent-focused Situation Selection Confront To confront the situation
Avoid To avoid the situation
Situation Modification Modify To modify the situation
Attention Deployment Distract To distract from the situation
Focus To help focus on the situation
Cognitive Change Reappraise To change thoughts about situation
Unspecified Unspecified None of these
Response-focused Response Modulation Suppress To reduce emotions from event
Enhance To enhance emotions from event
Unspecified Unspecified Neither of these
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Randall et al. 281
etal., 2007), and has been utilized in previous ESM studies of music use (Juslin etal., 2008), as
data points at the ESR level are not independent. The design for Aim 1 was a single factor (time)
repeated-measures design, comparing change in valence over the 3-minute listening period.
The design for Aim 2 was correlational.
For Aim 1, aggregate valence scores were created for each process model strategy used by
individual participants. In determining the hedonic success of each strategy, related-sample
t-tests (with Bonferroni adjusted alpha values of .005 to control the experiment-wise error
rate) were performed on these aggregated valence scores. Specifically, the valence of each strat-
egy at the time of music onset was compared to valence after 3 minutes of music listening, to
establish any significant change. This process was repeated for music episodes that began in
either a positive or negative emotional state, meaning that for each participant, aggregate
valence scores were produced from ESRs with either a positive or negative initial valence,
respectively. This was necessary to determine which strategies were successful in decreasing
negative emotional experience, and which influenced positive emotional experience. As a
majority of previous studies testing the process model have involved negative mood induction,
this allowed for a comparison of valence outcomes to previous results. All assumptions were
met for these analyses, except for the normality of difference scores assumption for the non-
music condition, due to the high frequency of zero valence change.
For Aim 2, average frequencies were created in a similar way to aggregate valence scores,
giving the relative frequency of each process model strategy used by individual participants.
These relative frequencies were correlated with DASS, SWLS and PANAS scores to establish any
associations between frequent use of a strategy by a participant and their emotional health and
well-being. Two-tailed Pearson correlations (with alpha value of p < .05) were used to deter-
mine these associations. Similarly, Pearson correlations were performed between the ERQ traits
of cognitive reappraisal and expressive suppression, and the frequencies of Reappraise and
Suppress strategies, respectively. Finally, Pearson correlations were used to explore the relation-
ship between initial valence of music episodes and levels of well-being (as measured by the
SWLS and PANAS).
Results
Aim 1: Hedonic success of process model strategies
Table 2 shows the relative frequencies of process model strategies for music episodes overall, as
well as for only those episodes that began in either a negative or positive emotional state. The
change in valence (across 3 minutes of music listening) is also presented for each of these three
conditions.
When considering all music episodes, music listening elicited a slight yet significant increase
in valence over a 3-minute listening period, t(326) = 3.94, p < .001, d = 0.22. This significant
positive shift in valence was amplified when the initial mood of the listener was negative, t(304)
= 13.39, p < .001, d = 0.65, while no change was observed for a positive mood. In contrast,
valence remained stable for all non-music control periods.
Approximately two-thirds of all music episodes involved no emotion regulation in relation to
a recent or upcoming emotional event, as subjectively reported by the listener. For the remain-
ing third, listeners were almost twice as likely to use music for self-regulation following an emo-
tional experience, rather than in preparation. For episodes with a negative initial mood, these
relative frequencies were substantially altered, with almost half of all music episodes involving
response-focused regulation. This difference was largely accounted for by more frequent use of
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282 Musicae Scientiae 18(3)
the Suppress strategy. In contrast, when listeners were in a positive mood, response-focused
strategies including Suppress were used less often.
Initial valence of the listener was noticeably more positive when no regulation strategy was
used, and more negative when response-focused strategies were used. Importantly, the strategy
of Suppress was associated with a negative mean initial valence, while all other strategies were
employed while the listener was in a positive emotional state. Intensity (as measured by abso-
lute initial valence) was negatively correlated with valence change for Reappraise, r(53) =
–.294, p = .032, while no relationship was found for Distract. Furthermore, intensity for ante-
cedent strategies (M = 1.34, SD = 0.82) was significantly higher than that for response strate-
gies (M = 1.11, SD = 0.87), t(141) = 2.81, p = .006, d = 0.24.
When considering all music episodes, the only significant change in valence was found for
those involving no regulation strategy, which elicited a positive outcome for listeners, t(315) =
4.90, p < .001, d = 0.28. Therefore, music listening was generally hedonically successful, but
this benefit was lost when music was used with any of the process model regulation strategies.
A very different set of valence change outcomes emerged when focusing only on music episodes
with negative initial valence. Under this condition, the positive valence change for episodes
with no regulation strategy was enhanced, t(234) = 11.12, p < .001, d = 0.73, while use of
either antecedent and response-focused strategies also yielded significant increases in valence
(Antecedent (overall): t(80) = 5.14, p < .001, d = 0.57; Response-focused (overall): t(265) =
8.10, p < .001, d = 0.50). Valence increased over time for the antecedent strategy of Distract,
t(34) = 3.23, p = .003, d = 0.55, and for the three response-focused options (Suppress (t(172)
Table 2. Relative frequencies, initial valence, and valence change for process model strategies.
Strategy Frequency Initial
valence
Valence change
Initial valence Initial valence
All Negative Positive All Negative Positive
Music (Total) 0.91 0.08** 0.63** –0.04*
Non-Music
(Total)
0.93 0.02 0.22 –0.05
Antecedent-
focused
Total 11.57% 10.75% 11.85% 0.92 0.03 0.63** –0.03
Confront 1.00% 1.00% 0.70% 0.58 –0.07 –0.08 0.00
Avoid 0.33% 0.70% 0.46% 0.14 –0.07 0.67 –0.30
Modify 0.51% 0.31% 0.41% 1.42 0.07 0.00 –0.07
Distract 2.43% 3.76% 1.58% 0.28 0.05 0.69** –0.23
Focus 2.63% 1.01% 2.73% 1.30 0.18 0.94*0.10
Reappraise 1.94% 2.61% 2.12% 0.87 0.16 0.63*0.16
Unspecified (Ant) 2.73% 1.37% 3.85% 1.57 –0.03 0.50 –0.06
Response-
focused
Total 20.80% 48.66% 15.16% 0.24 0.01 0.44** –0.21**
Suppress 9.70% 27.11% 5.51% –0.03 0.01 0.42** –0.37**
Enhance 6.20% 11.37% 5.59% 0.75 0.04 0.41** –0.13
Unspecified (Res) 4.90% 10.17% 4.06% 0.30 –0.03 0.39** –0.21
No
Regulation
67.63% 40.59% 73.00% 1.14 0.12** 0.76** 0.01
Note. **Change is significant at the p < .005 level (2-tailed, adjusted); *change is significant at the p < .05 level (2-tailed,
unadjusted).
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Randall et al. 283
= 5.93, p < .001, d = 0.45), Enhance (t(75) = 4.07, p < .001, d = 0.47), and Unspecified (t(75)
= 4.30, p < .001, d = 0.49)). When initial valence was positive, valence was significantly
decreased by response-focused strategies overall, t(229) = –3.74, p < .001, d = 0.25, and by
Suppress, t(120)= –3.47, p = .001, d = 0.31, in particular.
Aim 2: Associations between process model strategies and emotional health and
well-being
Pearson’s correlations between the frequency of process model strategies and DASS, SWLS and
PANAS scores are shown in Table 3.
Inspection of this table shows music episodes involving no intentional regulation strategy to
be associated positively with well-being measures, and negatively with indicators of mental ill-
ness. In contrast, music listening in the presence of emotion regulation is associated negatively
with well-being, and positively with depression, anxiety and stress indicators, particularly for
response-focused strategies overall, and Suppress.
Frequent use of antecedent strategies Confront, Modify and Distract (along with overall
antecedent frequency) was associated positively with anxiety scores. This observation was
investigated further, revealing that high levels of anxiety were associated with decreases in
arousal levels for antecedent strategies, r(98) = –.244, p = .014. Modify and Distract frequen-
cies also displayed small positive correlations with depression scores. In a similar investigation
to that for anxiety, no associations were observed between depression and valence change for
any strategies.
Additional correlations were performed between Reappraise and Suppress frequencies, and
their corresponding trait measures of reappraisal and suppression, as measured by the ERQ.
Neither Reappraise (r(267) = .104, p = .088), nor Suppress (r(267) = .007, p = .910) signifi-
cantly correlated with their respective ERQ trait.
Finally, as initial valence was found to be a key variable in determining hedonic outcome,
associations between the initial valence participants reported when they sought music
Table 3. Correlations of regulation strategy frequencies with SWLS, PANAS, and DASS scores.
Strategy Life
satisfaction
Positive
affect
Negative
affect
Depression Anxiety Stress
Antecedent-
focused
Total –.006 .127*.033 .088 .223** .061
Confront –.026 .060 –.025 .089 .184*.058
Avoid –.064 .010 .052 .078 .052 –.009
Modify .063 .033 .072 .157*.241** .060
Distract –.036 –.076 .105 .162*.164*.063
Focus –.016 .158** –.011 –.045 .097 .033
Reappraise .059 .096 .010 .004 .062 .033
Unspecified (Ant) .001 .048 –.040 –.053 –.003 –.041
Response-
focused
Total –.167** –.089 .167** .197** .205** .152*
Suppress –.202** –.162** .187** .224** .134 .178*
Enhance –.026 .045 .027 .007 .116 .038
Unspecified (Res) –.063 –.044 .081 .154*.125 .058
No
regulation
.131*–.010 –.148** –.214** –.300** –.160**
Note. ** Correlation is significant at the p < .01 level (2-tailed); * Correlation is significant at the p < .05 level (2-tailed).
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284 Musicae Scientiae 18(3)
listening and emotional health and well-being measures were explored. This revealed that for
all music episodes, initial valence was positively correlated with life satisfaction (r(324) = .349,
p < .001) and positive affect (r(323) = .443, p < .001), and negatively correlated with negative
affect (r(323) = –.465, p < .001). Importantly, each of these correlations was markedly larger
than the non-music equivalent (r = .292, .298, and –.230, respectively, all p values < .001).
Discussion
The aims of the current study were to determine the hedonic success of emotion regulation
strategies used during personal music listening, and their associations with emotional health
and well-being. This was achieved by exploring music regulation in the context of general emo-
tion regulation strategies, using the process model of emotion regulation as a theoretical frame-
work (Gross, 1998). With regard to Aim 1, results revealed that for all music episodes, hedonic
success was only achieved when no intentional emotion regulation strategy was used, with
none of the process model strategies producing a significant change in valence. However, when
listeners were in a negative initial mood, the antecedent strategy Distract, along with all
response-focused strategies (Suppress, Enhance, and the Unspecified option), successfully
decreased this negative emotion experience. This is consistent with meta-analysis findings that
positive hedonic shifts are more likely when participants are in an induced negative state
(Augustine & Hemenover, 2009). When listeners were in a positive mood, the strategy of
Suppress significantly worsened this state, while other strategies had no effect.
It was hypothesized from an extensive body of non-music research (Gross, 2001, 2002;
Gross & John, 2003; John & Gross, 2004) that the strategy of Reappraise would decrease nega-
tive emotion experience, while Suppress would not. The current set of data suggests that the
opposite is true for music listening episodes, with an initial negative state significantly improved
by Suppress, but not by Reappraise. In contrast, results were as expected for listening episodes
with an initial positive state, with Suppress, but not Reappraise, reducing positive emotion
experience. The hedonic success of Distract was as expected from previous research (Augustine
& Hemenover, 2009; Stone etal., 1995; Webb etal., 2012).
In testing the process-specific timing hypothesis, it was expected that the early selection
strategy Distract would be effective at all emotional intensities, while Reappraise would become
less effective with increasing intensity (Sheppes & Gross, 2011). The current data supported
this expectation for the reappraisal strategy used with music, with a negative correlation
between intensity and valence change for Reappraise. No such correlation between intensity
and valence change was observed for the use of Distract with music. Thus, the data supported
the notion that strategies requiring heightened cognitive processing are not as beneficial in
highly emotional situations. However, as Reappraise was not found to significantly increase
valence, the implications of this result are limited.
With regard to Aim 2, correlational data revealed that frequent use of music without any
regulation strategy was associated with positive emotional health and well-being. Conversely,
Suppress was correlated negatively with these measures, and several antecedent strategies
were associated with anxiety and depression. This supports substantial previous research
which shows that passive listening to music can be beneficial for health and well-being (Västfjäll,
Juslin, & Hartig, 2012). It was unexpected, however, that passive music listening would be more
positively associated with well-being outcomes than any emotion-regulated use of music. This
diverges from previous survey research showing that using music intentionally for emotion
regulation is positively associated with psychological, social and emotional well-being (Chin &
Rickard, 2013), and highlights the importance of the experience sampling methodology.
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Randall et al. 285
The hypothesis that frequent use of Suppress would be negatively correlated with well-being,
and positively correlated with psychopathology indicators (Gross & John, 2003; John & Gross,
2004) was supported. In contrast, the hypothesis that frequent use of Reappraise would be
positively correlated with well-being was not supported in the current study, with no associa-
tion between music use for Reappraise and any of the emotional health and well-being out-
comes observed. Other antecedent findings are somewhat inconsistent with previous non-music
emotion research that has shown the strategies of Avoid and Focus to be related to higher levels
of depressive symptoms (Silk etal., 2003).
It was also hypothesized that the early selection strategies Distract and Avoid would be asso-
ciated with negative emotional health and well-being outcomes, as they do not allow for in-
depth processing of emotional situations. Conversely, the late selection strategies Reappraise
and Focus were expected to be associated with positive emotional health and well-being out-
comes (Sheppes & Gross, 2011). Evidence to support this was limited: while Distract was related
to depression and anxiety scores, and Focus to positive affect, no correlations with emotional
health and well-being outcomes were observed for either Reappraise or Avoid.
These findings reveal several points of divergence between emotion regulation through
personal music listening and general emotion regulation findings. First, when listening to
music in a negative mood, several strategies successfully decreased negative affect, while no
regulation strategy exacerbated this negative affect. Most prominently, using music to sup-
press emotions was found to significantly improve a negative emotional state, despite a series
of studies consistently demonstrating that this is not the case for non-music regulation (e.g.,
Gross, 1998). Furthermore, while a meta-analysis has revealed strategies of concentration to
worsen affective state (Webb etal., 2012), no such effect was evident during music use for the
strategy of Focus. These findings suggest that music might not only be an effective means of
achieving hedonic improvement, but that it might be a means of reducing the risk associated
with strategies often perceived as maladaptive. A second major point of divergence was that
many of the associations with longer-term emotional functioning were not as predicted by the
non-music literature. Notably, antecedent-focused strategies did not produce the expected
positive correlations with well-being and emotional health, and were more consistently asso-
ciated with anxiety.
The general implication of these findings is that emotion regulation benefits of personal
music listening may not occur in accordance with the broader process model of emotion regu-
lation. The finding that music-based antecedent-focused strategies were associated with higher
emotional intensity than response-focused strategies further corroborates this suggestion.
Given that response-focused strategies occur following the activation of emotion response ten-
dencies, it would be expected that these strategies would be used at times of elevated intensity.
Another point that supports this is the finding that the Unspecified option was the most
frequently selected antecedent option. This suggests that the process model strategies pre-
sented may not be sufficient in explaining emotion regulation through music listening, and
there may be other strategies that are more appropriate (such as those of Saarikallio & Erkkilä,
2007). This is consistent with proposals that emotions experienced in response to music are
sufficiently different from non-musical emotions to warrant a domain-specific emotion clas-
sification (Zentner, Grandjean, & Scherer, 2008). Emotion regulation models for music use
may therefore also require distinct models from those described for more general emotion reg-
ulation (Gross, 1998).
Further support for this proposal was evident in the absence of correlation between general
suppression and reappraisal traits, as defined and measured by the ERQ, and the frequency of
these strategies used with music. Therefore, the regulation strategies people employ while
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286 Musicae Scientiae 18(3)
listening to music are not in accordance with the emotion regulation strategies they report as
generally using. Music may therefore serve as an independent regulatory resource, which is
utilized in a distinct manner from other forms of regulation. This could explain the various
predictions made from the non-music literature that were not supported by the current set of
empirical data. These discrepancies may be a consequence of the flexible nature of personal
music listening, and the ability of listeners to immediately select music of any emotional con-
tent to meet their specific regulatory needs (Knobloch & Zillmann, 2003).
When interpreting the current set of results, it is clear that response-focused strategies, and
the strategy of Suppress in particular, play an important role during personal music listening.
These strategies were utilized much more frequently when the listener was in a negative mood,
and were found to be hedonically successful when participants were in this emotional state.
Conversely, Suppress is used much less frequently when the listener is in a positive mood, and it
is during these music episodes that the strategy leads to a decrease in valence. Furthermore, the
frequent use of Suppress and response-focused strategies overall was found to be associated
with lower levels of emotional health and well-being. This is particularly relevant, as individu-
als with low well-being chose to listen to music at times of intensified negative affect, suggesting
a direction of causality. Taken together, these results seem to suggest that individuals in a nega-
tive mood are more likely to use music in response to an emotional event, and it is during such
times that these strategies produce the greatest hedonic benefits. A similar interpretation can
be made for antecedent-focused strategies, several of which were used more frequently by anx-
ious individuals, and led to a reduction in arousal for these listeners. This suggests that indi-
viduals experiencing anxiety about an upcoming event are using music to alleviate this negative
affect.
In addition, when individuals were in a positive or neutral mood, they were more likely to
listen to music without the use of any regulation strategy. Those with higher levels of emo-
tional health and well-being were also less likely to use emotion regulation strategies, and
selected to listen to music at times of heightened positive affect. These findings indicate that
individuals may simply be selecting regulation strategies to reach a desired hedonic outcome.
This is a notion that finds abundant support in the music emotion regulation literature (e.g.,
Juslin & Laukka, 2004; Knobloch & Zillmann, 2003). Furthermore, this goal-directed selection
of strategies is consistent with the observed correlation between the frequencies at which regu-
lation strategies are used, and their effectiveness (Gross etal., 2006). As there were no strate-
gies that exacerbated a negative state, it is highly possible that individuals susceptible to these
states chose to use music to regulate their negative emotions. Therefore, the associations
between strategy frequencies and emotional functioning may be due to the different emotional
goals associated with well-being and mental health conditions.
Another possible interpretation of the current data is that continued use of certain strate-
gies (such as Suppress) contributes to a decline in well-being and mental health in listeners.
This viewpoint holds that while these strategies are hedonically successful, providing short-
term emotional relief, repeated use may lead to long-term emotional detriment. This interpre-
tation is compatible with the assertion that mental disorder is closely tied with deficits in
emotion regulation, and the use of maladaptive strategies (Bradley etal., 1990; Larsen, 2000).
If the strategy of Suppress is in fact maladaptive—as has been established for non-music
regulation—then its frequent use while listening to music could lead to the development of
emotional disorders. This is especially pertinent for adolescent listeners, as this stage involves
the development of healthy regulation strategies into adulthood (Laiho, 2004). This interpre-
tation is consistent with the theory that regulatory strategies that prevent the processing of
emotional information can be maladaptive, as they do not allow for sufficient understanding
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Randall et al. 287
of an emotional event (Sheppes & Gross, 2011). In this way, while strategies may be effective
in the short-term, they may lead to emotional dysregulation, laying the foundations for future
psychopathologies (Kross & Ayduk, 2008; Sheppes & Gross, 2011). These two interpretations
are not mutually exclusive, and the current findings are likely due to a combination of these
effects. Thus, further longitudinal investigation is required to elaborate and clarify the current
findings.
While the current study was the first to directly link personal music listening to the process
model, the results may not purely reflect the differences between music and non-music emotion
regulation. The divergence in results from non-music research findings may be due to differ-
ences in measurement, as most previous studies were performed under laboratory conditions,
and often with induced emotions. Discrepancies in findings may also be partly explained by the
wording of strategy options, which resulted from space limitations in displaying options effec-
tively on a mobile screen. The mismatch observed between the emotion regulation traits (as
measured by the ERQ) and music-based emotion regulation strategies suggest there may have
been some differences in operational definitions. For example, the Suppress strategy was worded
as “To reduce emotions from event.” This does not include any specific reference to behavioral
suppression, or the inhibition of expressed emotion, as suppression is defined in many previous
studies (e.g., Gross, 2001). As this strategy was a major focus of analysis, the possibility of lis-
teners using non-behavioral variations of Suppress should be explored further. Another exam-
ple is the strategy Focus (“To help focus on the situation”), which does not distinguish between
rumination and other means of focusing on an emotional stimulus. While a meta-analysis has
found that concentration is a hedonically detrimental strategy (Webb etal., 2012), the current
study observed no such effect. In this context, it is of interest that while ruminating on sad
music is typically maladaptive (e.g., Garrido & Schubert, 2013), sad music can also be used
adaptively (e.g., by releasing negative emotions; Van den Tol & Edwards, 2011), so greater clari-
fication of the term “focus” may be required. Another design feature that may have influenced
the current results is the forced selection of either antecedent or response strategies. While this
increases answering efficiency, it is a simplification of emotion regulation processes, and
assumes that response tendencies are only elicited at a single point in time. This method is
therefore limited in capturing complex emotional scenarios, and results must be interpreted as
reflecting subjective and simple regulation processes. Finally, it should be noted that while the
current study design included non-music ESR controls, the random timing of these meant they
were not appropriate for capturing any general (non-music) emotion regulation events. Future
ESM studies would benefit from incorporating event-based sampling of both music and non-
music emotion regulation processes, to directly assess the similarities and differences between
these strategies.
While the MuPsych methodology has been shown to have high ecological validity (Randall
& Rickard, 2013), experience sampling is still subject to personal biases and expectations
(Bylsma & Rottenberg, 2011). A limitation of the current study is that it relies on subjective
self-report of emotional experience, while emotion responses and regulation also involve behav-
ioral and physiological aspects (Gross, 2001). These three components are not always congru-
ent, so without convergence with additional measures, subjective reporting must be treated
with some caution. In addition, future longitudinal research is required to clarify the relation-
ship between individual variables and regulation strategy frequencies. This would help deter-
mine whether regulation strategy selection leads to declines in emotional functioning, or vice
versa. Further research using the MuPsych app will involve the development of a model of
emotion regulation through personal music listening. This model will integrate variables
related to the listener, music and social context of personal music listening, and identify the
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288 Musicae Scientiae 18(3)
salient variables in determining affective outcome (for more details see http://www.MuPsych.
com/research).
Conclusion
This study investigated the consequences of emotion regulation strategies employed during
personal music listening in terms of both hedonic outcomes, and longer-term emotional health
and well-being. This examination was performed in the context of non-music forms of regula-
tion, through the framework of the process model of emotion regulation (Gross, 1998). A series
of discrepancies emerged between the current set of empirical music listening data, and expec-
tations from the non-music literature. A major divergence was the success of music-based
response-focused strategies in decreasing negative emotion experience, and the failure of most
music-based antecedent-focused strategies to do so. Taken as a whole, the current study sug-
gests that emotion regulation through music listening does not occur in accordance with the
process model. It is proposed that personal music listening may be utilized as a flexible inde-
pendent regulatory resource, allowing individuals to meet their specific emotional goals sepa-
rately from non-music forms of regulation.
A major implication of the current results is that individuals in a negative affective state are
able to select from a wide range of suitable music regulation strategies in order to alleviate their
negative experience. This supports the notion that listeners select regulation strategies to reach
a desired hedonic outcome, based on their current mood, and influenced by their emotional
health and well-being. This study has provided valuable insight into emotion regulation
through personal music listening, and explored emotional outcomes of strategies in relation to
the process model of emotion regulation. The understanding of how music is used as a regula-
tory resource, and how it differs from non-music forms of regulation, will help in promoting
music listening behaviors to achieve desirable emotional outcomes.
Acknowledgements
MuPsych was programmed by Pinion Systems, in association with Monash University.
Funding
This research received no specific grant from any funding agency in the public, commercial, or not-for-
profit sectors.
Note
1. iPhone and iPod are registered trademarks of Apple Inc.
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... Music Music's influence on wellbeing is realised through emotion regulation (McFerran et al., 2015;Randall et al., 2014;Saarikallio & Erkkilä, 2007), coping styles (Miranda & Claes, 2009;Semenza, 2018), and personality and motivation factors (Miranda, 2013;Thompson et al., 2019). Broadly, 'music-in-mood regulation strategies' involve the use of music to distract, reinforce, express or simply explore and work through emotions or mood states (Saarikallio, 2008(Saarikallio, , 2011(Saarikallio, , 2012. ...
... More research in the domain of music in mood regulation is needed to understand the emotional and psychological outcomes for heavy metal fans who use particular emotion regulation strategies, and with systematic consideration given to other individual factors (e.g., gender, personality). This endeavour is critical for highlighting the implications of adopting emotion regulation strategies for longer-term mental health (Blais-Rochette & Miranda, 2016;Carlson et al., 2015;McFerran & Saarikallio, 2014;Randall et al., 2014;van den Tol, 2016) and for recommending specific strategies in therapeutic settings (e.g., Dingle et al., 2016). For instance, advising a heavy metal music fan to listen to calming music to reduce distress may be detrimental when their preference is to listen to their preferred genre to discharge negative emotions. ...
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Concerns have been raised that prolonged exposure to heavy metal music with aggressive themes can increase the risk of aggression, anger, antisocial behaviour, substance use, suicidal ideation, anxiety and depression in community and psychiatric populations. Although research often relies on correlational evidence for which causal inferences are not possible, it is often claimed that music with aggressive themes can cause psychological and behavioural problems. This narrative review of theory and evidence suggests the issues are more complicated, and that fans typically derive a range of emotional and social benefits from listening to heavy metal music, including improved mood, identity formation, and peer affiliation. In contrast, non-fans of heavy metal music-who are often used as participants in experimental research on this topic-invariably report negative psychological experiences. Our review considers a comprehensive set of empirical findings that inform clinical strategies designed to identify fans for whom heavy metal music may confer psychological and behavioural risks, and those for whom this music may confer psychosocial benefits.
... Music Music's influence on wellbeing is realised through emotion regulation (McFerran et al., 2015;Randall et al., 2014;Saarikallio & Erkkilä, 2007), coping styles (Miranda & Claes, 2009;Semenza, 2018), and personality and motivation factors (Miranda, 2013;Thompson et al., 2019). Broadly, 'music-in-mood regulation strategies' involve the use of music to distract, reinforce, express or simply explore and work through emotions or mood states (Saarikallio, 2008(Saarikallio, , 2011(Saarikallio, , 2012. ...
... More research in the domain of music in mood regulation is needed to understand the emotional and psychological outcomes for heavy metal fans who use particular emotion regulation strategies, and with systematic consideration given to other individual factors (e.g., gender, personality). This endeavour is critical for highlighting the implications of adopting emotion regulation strategies for longer-term mental health (Blais-Rochette & Miranda, 2016;Carlson et al., 2015;McFerran & Saarikallio, 2014;Randall et al., 2014;van den Tol, 2016) and for recommending specific strategies in therapeutic settings (e.g., Dingle et al., 2016). For instance, advising a heavy metal music fan to listen to calming music to reduce distress may be detrimental when their preference is to listen to their preferred genre to discharge negative emotions. ...
Article
Full-text available
Concerns have been raised that prolonged exposure to heavy metal music with aggressive themes can increase the risk of aggression, anger, antisocial behaviour, substance use, suicidal ideation, anxiety and depression in community and psychiatric populations. Although research often relies on correlational evidence for which causal inferences are not possible, it is often claimed that music with aggressive themes can cause psychological and behavioural problems. This narrative review of theory and evidence suggests the issues are more complicated, and that fans typically derive a range of emotional and social benefits from listening to heavy metal music, including improved mood, identity formation, and peer affiliation. In contrast, non-fans of heavy metal music — who are often used as participants in experimental research on this topic — invariably report negative psychological experiences. Our review considers a comprehensive set of empirical findings that inform clinical strategies designed to identify fans for whom heavy metal music may confer psychological and behavioural risks, and those for whom this music may confer psychosocial benefits.
... Music Music's influence on wellbeing is realised through emotion regulation (McFerran et al., 2015;Randall et al., 2014;Saarikallio & Erkkilä, 2007), coping styles (Miranda & Claes, 2009;Semenza, 2018), and personality and motivation factors (Miranda, 2013;Thompson et al., 2019). Broadly, 'music-in-mood regulation strategies' involve the use of music to distract, reinforce, express or simply explore and work through emotions or mood states (Saarikallio, 2008(Saarikallio, , 2011(Saarikallio, , 2012. ...
... More research in the domain of music in mood regulation is needed to understand the emotional and psychological outcomes for heavy metal fans who use particular emotion regulation strategies, and with systematic consideration given to other individual factors (e.g., gender, personality). This endeavour is critical for highlighting the implications of adopting emotion regulation strategies for longer-term mental health (Blais-Rochette & Miranda, 2016;Carlson et al., 2015;McFerran & Saarikallio, 2014;Randall et al., 2014;van den Tol, 2016) and for recommending specific strategies in therapeutic settings (e.g., Dingle et al., 2016). For instance, advising a heavy metal music fan to listen to calming music to reduce distress may be detrimental when their preference is to listen to their preferred genre to discharge negative emotions. ...
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Full-text available
Concerns have been raised that prolonged exposed to heavy metal music with aggressive themes can increase the risk of aggression, anger, antisocial behaviour, substance use, suicidal ideation, anxiety and depression in community and psychiatric populations. Although research often relies on correlational evidence for which causal inferences are not possible, it is often claimed that music with aggressive themes can cause psychological and behavioural problems. This narrative review of theory and evidence suggests the issues are more complicated, and that fans typically derive a range of emotional and social benefits from listening to heavy metal music, including improved mood, identity formation, and peer affiliation. In contrast, non-fans of heavy metal music — who are often used as participants in experimental research on this topic — invariably report negative psychological experiences. Our review considers a comprehensive set of empirical findings that inform clinical strategies designed to identify fans for whom heavy metal music may confer psychological and behavioural risks, and those for whom this music may confer psychosocial benefits.
... Affect regulation involves the up or down regulation of emotion or mood and is frequently conveyed as a key function of music listening (Saarikallio, 2012;Schäfer et al., 2013). Music listening is a method of selfregulation that can be conscious or unconscious, as well as adaptive and maladaptive, allowing listeners to reach specific emotional goals (Randall et al., 2014). This is in line with the uses and gratifications theory (Katz et al., 1974;Lonsdale & North, 2011) which proposes that individuals use media, such as music, to fulfil a goal or need. ...
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... The differences in reading comprehension score results necessitate further study of how music training may play a key role in how individuals listen to music (Hallam & MacDonald, 2016), especially as more applications of background music are being used across media (Kuo et al., 2013;Randall et al., 2014) and in performance-based learning settings (Moreno & Woodruff, 2021). The scores in reading comprehension tests between both music and non-music major groups suggest that music majors are able to regulate themselves in the presence of background music of varying speeds, resulting in them not being as emotionally "distracted" or swayed by the music when it came time to complete their test. ...
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This study examined how music major and non-music major participants’ performances and emotional expressions differed when exposed to background music of varying tempi while completing a reading comprehension task. Two questions were addressed: (1) What effect does background music of varying tempi have on the performance of music and non-music majors while completing a reading comprehension task? and (2) What real-time expressed emotions and self-report measures may predict the reading comprehension scores between music and non-music majors? A total of 34 participants completed a reading comprehension task under each of three musical test conditions while facial-muscular emotion software analyzed their facial expressions, to understand the effect on performance. Results indicated that music majors displayed significantly different ( p < .05) emotional expressions of joy and contempt, which corresponded to no significant differences in performance scores, in comparison to non-majors, whose performance varied greatly across the background music test conditions that were given. These findings support the growing literature surrounding the possible effects of music training and how differences between trained and non-musicians may be understood through emotion detection software.
... The findings in this study illustrate that music intervention in some cases can improve sleep and relaxation and our ability to regulate our affective state using music. Regulation takes place when individuals select music to achieve a psychological goal and this use of music for change is a common everyday occurrence (Baltazar & Saarikallio, 2016MacDonald et al., 2012a;Randall et al., 2014;Saarikallio et al., 2017). Saarikallio et al. (2017) reported positive effects in adolescents who used music to lower arousal levels highlighting how music listening can be employed to actively enhance psychological and physiological wellbeing. ...
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Background: Insomnia in depression is common and difficult to resolve. Unresolved depression-related sleep disturbances increase risk of relapse at high costs for individuals and society. Trials have suggested music for insomnia in various populations, but there is little research on the effectiveness of music for depression-related insomnia. Methods: We examined the efficacy of a music intervention on insomnia, depression symptoms and quality of life in adults with depression-related insomnia. A two-armed randomized controlled trial was conducted, including depression outpatients with insomnia (n = 112) in a 1:1 ratio to music intervention and waitlist control group. The intervention group listened to music at bedtime for 4 weeks. Participants received treatment as usual during 8 weeks with assessments at baseline, at 4 and 8 weeks. The primary outcome measure was Pittsburgh Sleep Quality Index (PSQI), secondary outcomes comprised Actigraphy, the Hamilton Depression Rating Scale (HAMD-17) and World Health Organisation well-being questionnaires (WHO-5, WHOQOL-BREF). Results: The music intervention group experienced significant improvements in sleep quality and well-being at 4 weeks according to global PSQI scores (effect size = -2.1, 95%CI -3.3; -0.9) and WHO-5 scores (effect size 8.4, 95%CI 2.7;14.0). At 8 weeks, i.e. 4 weeks after termination of the music intervention, the improvement in global PSQI scores had decreased (effect size = -0.1, 95%CI -1.3; 1.1). Actigraphy sleep assessments showed no changes and there was no detection of change in depression symptoms. Conclusions: Music intervention is suggested as a safe and moderately effective sleep aid in depression-related insomnia. Trial registration: Clinicaltrials.gov. ID NCT03676491.
... On a more individual level, participants' scores indicate that the absence of their phones would make them miss the opportunity to "play games" and "listen to music" significantly more. As prior research has shown, video games (Sarsenbayeva, Tag, et al., 2020) and music (Randall, Rickard, & Vella-Brodrick, 2014) are popular and effective ER tools. Clearly, the breaking off of social interactions, for example, by denying access to digital devices, or caused by restrictions imposed on our everyday life, causes people distress (Yamada et al., 2021). ...
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Technology plays an increasingly prominent role in emotional lives. Researchers have begun to study how people use devices to cope with and shape emotions: a phenomenon that has been called Digital Emotion Regulation. We report a study of the impact of the COVID-19 pandemic upon young people's digital habits and emotion regulation behaviors. We conducted a two-wave longitudinal survey, collecting data from 154 university students both before and during the COVID-19 pandemic. During the pandemic, participants were subject to increased emotional distress as well as restrictions on movement and social interaction. We present evidence that participants' emotion regulation strategies changed and became more homogeneous during the pandemic, with participants resorting to digital tools when offline strategies were less available, while also becoming more emotionally dependent upon their devices. This study underscores the growing significance of the digital for contemporary emotional experience, and contributes to understanding the potential role for technology in supporting well-being during high-impact events.
... The regulation strategies are driven by ones goals and subsequently leading to different behavioural and mental health outcomes (Berking et al., 2019;Coulacoglou & Saklofske, 2017;Kobylińska & Kusev, 2019). This is consistent with prior evidence that people engage with music to reach specific goals that serve their needs (Randall et al., 2014;Schäfer, 2016;Sloboda et al., 2001;van Goethem & Sloboda, 2011). ...
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People across cultures often use music to evoke positive emotions and moods. Yet, some people tend to employ maladaptive strategies such as rumination, avoidant coping, or social isolation when they listen to music. The present research investigated how strongly maladaptive musical engagement is linked with depression and wellbeing in a sample of 1415 Indians (17–65 years) across four cities and two countries. Participants completed a battery of assessments on trait affect, depression, adaptive and maladaptive musical engagement strategies, music preferences, and music skills. 1329 participants were included for analysis. All nine symptoms ( r = .16, to .30) and the sum score of depression ( r = .39), as well as trait negative affect ( r = .36) were correlated with maladaptive music engagement. Six of the symptoms of depression were significant predictors of maladaptive music engagement. Among those, suicidal ideation, worthlessness, and fatigue were the most important. Maladaptive music engagement increased the odds of experiencing all the depression symptoms ( OR = 1.04 to 1.14). Trait positive affect ( r = .29) and having music as a hobby ( r = .22) correlated with adaptive music engagement. Musicians who had been playing an instrument for six years and above had lower levels of maladaptive music engagement ( d = .84). Furthermore, the results show that depression symptoms might have a bidirectional relationship with maladaptive music engagement, with suicidal thoughts being the most important symptom. The current study also validated the Healthy-Unhealthy Music Scale (HUMS; Saarikallio et al., 2015 ) in India and provides a cut-off score based on the sensitivity (.86) and specificity (.66) in identifying people at risk for depression. Overall, the results reveal that socio-demographic factors (age, gender, relationship status, occupation status, geographical location), psychological factors (trait affect, depressive symptoms), and music skills play an important role in engaging with music.
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Emotions play an important role in overeating, yet there is little research looking at practical strategies to reduce overeating in response to a negative mood. In three different experimental studies, we tested if exposure to music can reduce food consumption in a negative mood. Female undergraduates (N = 120–121 in each study) completed a measure of emotional eating and reported baseline hunger. Mood ratings were taken at baseline, post-mood induction and post-eating. All participants were given a mood induction (sadness for study 1, stress for studies 2 and 3) and allocated to one of three music conditions (self-chosen in study 3) or a silent (control) condition. Music was selected from three pieces reported by each participant as being listened to regularly when experiencing the negative mood being examined (sadness or stress) in order to provide solace (comforting music), diversion (distracting positive music), or discharge (angry and/or sad music). Participants were provided with several snack foods to consume whilst completing a mock taste test and intake (in grams) was compared between conditions. In study 1 participants in the music for discharge condition consumed less than those in the control condition. Moreover, participants with high levels of self-reported EE ate more crisps in the control than in the distraction condition. In study 2 participants in the solace condition consumed less than those in the control and discharge conditions. In study 3 most participants chose music for diversion; this did not, however, lead to lower consumption, despite a reduction in reported stress. Overall, the results of these studies indicate that listening to certain types of music might reduce emotion-related eating after controlling for hunger using a standardized pre-session snack.
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Article Info. Abstract What is the soundtrack to a global pandemic in the 21 st Century? Music has been used to improve individual wellbeing during periods of uncertainty, discomfort and illness throughout history. Personal music consumption, in particular, has been used as a distraction, emotion regulation technique and communication tool. From 16 th Century plagues to COVID-19, the method of consumption may have evolved but the instinct to turn to music has remained. This research brings together a novel dataset collected from anonymised Spotify users via the Spotify API comprising 25,800 playlists and 2,782,025 pieces of music. The playlists were all created by users between January 1 st (before any countries announced lockdown) and July 29 th 2020 (date of data collection). The playlists have been identified by their titles which all contain phrases relating directly to aspects of the pandemic (lockdown, coronavirus, COVID, social distancing). The tracks and the audio features, such as key, tempo, valence, danceability, energy etc. of each track contained within each playlist have been captured, as has any descriptive metadata users added to their playlists. The article presents a weighted analysis of the tracks that were included within COVID-19-associated playlists most frequently and the musical features that were included within playlists. Within the article, a series of typical COVID-19-associated tracks and playlists are suggested from the weighted analysis and Spotify's audio features. Further to this, a thematic analysis of the descriptive metadata entered by listeners into their playlists reveals a multifaceted dataset displaying stark divides between optimistic or socially minded playlists compiled for personal and collective benefit, and heavily pessimistic, negatively-valenced descriptions of plague and worldwide disaster.
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Most previous studies investigating music-induced emotions have applied emotion models developed in other fields to the domain of music. The aim of this study was to compare the applicability of music-specific and general emotion models – namely the Geneva Emotional Music Scale (GEMS), and the discrete and dimensional emotion models – in the assessment of music-induced emotions. A related aim was to explore the role of individual difference variables (such as personality and mood) in music-induced emotions, and to discover whether some emotion models reflect these individual differences more strongly than others. One hundred and forty-eight participants listened to 16 film music excerpts and rated the emotional responses evoked by the music excerpts. Intraclass correlations and Cronbach alphas revealed that the overall consistency of ratings was the highest in the case of the dimensional model. The dimensional model also outperformed the other two models in the discrimination of music excerpts, and principal component analysis revealed that 89.9% of the variance in the mean ratings of all the scales (in all three models) was accounted for by two principal components that could be labelled as valence and arousal. Personality-related differences were the most pronounced in the case of the discrete emotion model. Personality, mood, and the emotion model used were also associated with the intensity of experienced emotions. Implications for future music and emotion studies are raised concerning the selection of an appropriate emotion model when measuring music-induced emotions. © 2011, European Society for the Cognitive Sciences of Music. All rights reserved.
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This chapter presents a novel approach to music and health that focuses on exploring health benefits within everyday life contexts, with particular consideration of possible underlying mechanisms. The focus is on listening rather than performing, and on public health rather than clinical populations. A central role is attached to emotion in bringing about desired health outcomes, and it is argued that music may be uniquely suited to managing or regulating emotions and stress in everyday life. The rest of this chapter is organized into three major sections. First, it explains the background and theoretical basis of the current approach. Then, it illustrates this approach by summarizing a set of empirical studies. Finally, it discusses the implications of the results from these studies for future research on music and health.
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Choosing to listen to self-identified sad music after experiencing negative psychological circumstances seems paradoxical given the commonly-held view that people are motivated to seek a positive affective state when distressed. We examined the motivations people described to listen to music they identified as sad, particularly when experiencing negative circumstances, and the self-reported effects of this activity. We asked adults to respond to an online survey and analyzed their narrative reports using a modified grounded theory approach. Responses were received from 65 adults across five countries. The process that underlies choosing to listen to sad music as well as the self-regulatory strategies and functions of sad music were identified. The music-selection strategies included: connection; selecting music based on memory triggers; high aesthetic value; and message communicated. The functions of these strategies were in the domains of (re-)experiencing affect, cognitive, social, retrieving memories, friend, distraction, and mood enhancement. We additionally modelled the underlying psychological process that guides sad music listening behaviour and the effects of listening. These findings present core insights into the dynamics and value of choosing to listen to self-identified sad music when coping with negative psychological circumstances.
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The mediating effects of emotion regulation (reappraisal and suppression) were examined in the relationship between music engagement and well-being. Emotion regulation strategies (Emotion Regulation Questionnaire; Gross & John, 2003) and styles of music engagement (Music Use questionnaire; Chin & Rickard, 2012) were assessed in a large diverse sample of 637 participants. A battery of hedonic and eudaimonic well-being measures (International Positive and Negative Affect Schedule Short Form; Thompson, 2007; Satisfaction With Life Scale; Diener, Emmons, Larsen, & Griffin, 1985; Mental Health Continuum-Short Form; Keyes et al., 2008) was also administered. Results demonstrated that the path of mediation was dependent on the type of emotion regulation strategy utilized, as well as the way in which one engages with music. Findings provide initial evidence that engaging with music for the purposes of cognitive and emotion regulation may enhance wellbeing primarily through the habitual use of cognitive reappraisal. In contrast, various other aspects of music engagement (music listening, engaged production, and social connection) if coupled with a tendency to regulate emotions and thoughts by expressive suppression may yield undesirable wellbeing outcomes. This study highlights the important role emotion regulation plays in the complex relationship between music engagement and well-being.
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Emotional self-regulation is acknowledged as one of the most important reasons for musical engagement at all ages. Yet there is little knowledge on how this self-regulatory use of music develops across the life span. A qualitative study was conducted to initially explore central processes and strategies of the emotional self-regulation during adulthood. The data were collected through group interviews and analyzed through qualitative content analysis. Participants were 21 interviewees with an age range of 21-70 years. The results clarified conceptual features of music-related emotional self-regulation in adulthood and revealed two main trends. First, the basic nature of regulation, including various regulatory goals and strategies, remained highly similar throughout adulthood. Second, however, several changes were also evident, and they could be further categorized into three types: change by age, event-related fluctuations, and retirement transition. The study provided knowledge about the role of music-related emotional experiences as a functional and meaningful part of human behavior and psychosocial development during adulthood.
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Musical experiences are often reported to influence emotions (Juslin & Västfjäll, 2008; Sloboda, O'Neill, & Ivaldi, 2001): people consciously and unconsciously use music to change, create, maintain or enhance their emotions and moods (affect) on a daily basis for their personal benefit (DeNora, 1999; Schramm, 2005). This is known as affect regulation. However, existing research has not yet answered questions of how music regulates affect, especially beyond the expressive properties of music (Meyer, 1956). The aims of the studies presented here were to investigate (a) how music functions to regulate affect, (b) which affects it regulates, and (c) whether music listening can be considered a successful affect regulation device. A one-week diary study with interviews and a three-week diary study were conducted. The main findings were: (1) music helps through broader affect regulation strategies like distraction, introspection, and active coping; music can for example distract someone from the affect or situation, or help to think about the affect or situation in a rational way; (2) music plays a major role in creating happiness and relaxation; (3) music overall is a successful regulation device with a range of underlying mechanisms helping different strategies. The current paper is a valuable addition to the existing literature and provides several new insights into the function of music for affect regulation in everyday life. The insight gained into which strategies and underlying mechanisms are involved when music is used for affect regulation might be used for the benefit of people's emotional wellbeing.
Book
Music psychology is the study of how humans experience and perceive music, and the impact this has on individuals, groups and communities. Engaging with music - whether by performing, creating, learning or listening - can have significant benefits across the lifespan. This book explores how music can promote mental health and functioning in diverse settings, from supporting cognitive development in premature babies to establishing identity and emotional well-being in adolescents, to enhancing brain function in adults and challenging cognitive decline in dementia patients. A lifespan approach is used to illustrate that the benefits of musical engagement need not be reserved for the vulnerable, but can also serve people of all ages to enhance health and well-being.