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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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Musicae Scientiae
2014, Vol. 18(3) 275 –291
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DOI: 10.1177/1029864914536430
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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 etal., 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 etal.,
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 etal.,
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 etal., 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 etal., 2011; Shahar &
Herr, 2011; Yeung & Fung, 2012), with many of these utilizing smartphones and mobile elec-
tronic devices (Hill & Updegraff, 2012; Morris etal., 2010; Silk etal., 2011; Tan etal., 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 etal.,
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
etal., 2007), and has been utilized in previous ESM studies of music use (Juslin etal., 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 etal., 1995; Webb etal., 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 etal., 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 etal., 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 etal., 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 etal., 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 etal., 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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