The joy of heartfelt music: An examination of emotional and physiological responses

International Journal of Psychophysiology 120 (2017) 118–125

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The joy of heartfelt music: An examination of emotional and physiological responses

MARK

Emily Lynara, Erin Cvejica,b, Emery Schubertc, Ute Vollmer-Connaa,⁎ a b c

School of Psychiatry, University of New South Wales, Australia University of Sydney, School of Public Health, Australia School of the Arts and Media, University of New South Wales, Australia

A R T I C L E I N F O

A B S T R A C T

Keywords: Music-listening Mood Emotional state Autonomic responses Heart rate variability

Music-listening can be a powerful therapeutic tool for mood rehabilitation, yet quality evidence for its validity as a singular treatment is scarce. Specifically, the relationship between music-induced mood improvement and meaningful physiological change, as well as the influence of music- and person-related covariates on these outcomes are yet to be comprehensively explored. Ninety-four healthy participants completed questionnaires probing demographics, personal information, and musical background. Participants listened to two prescribed musical pieces (one classical, one jazz), an “uplifting” piece of their own choice, and an acoustic control stimulus (white noise) in randomised order. Physiological responses (heart rate, respiration, galvanic skin response) were recorded throughout. After each piece, participants rated their subjective responses on a series of Likert scales. Subjectively, the self-selected pieces induced the most joy, and the classical piece was perceived as most relaxing, consistent with the arousal ratings proposed by a music selection panel. These two stimuli led to the greatest overall improvement in composite emotional state from baseline. Psycho-physiologically, self-selected pieces often elicited a “eustress” response (“positive arousal”), whereas classical music was associated with the highest heart rate variability. Very few person-related covariates appeared to affect responses, and music-related covariates (besides self-selection) appeared arbitrary. These data provide strong evidence that optimal music for therapy varies between individuals. Our findings additionally suggest that the self-selected music was most effective for inducing a joyous state; while low arousal classical music was most likely to shift the participant into a state of relaxation. Therapy should attempt to find the most effective and “heartfelt” music for each listener, according to therapeutic goals.

1. Introduction

emotional states for those with low physical and psychosocial functioning, including older patients (Gotell et al., 2002; Lou, 2001), the medically ill (Li and Dong, 2012; Nilsson, 2009; Voss et al., 2004; Freeman et al., 2006), in the context of surgery (Bringman et al., 2009), and patients with depression (Riganello et al., 2010; Trappe, 2012b). The neural mechanisms underpinning the effects of music-based therapies across a range of clinic contexts are increasingly being explored (Särkämö et al., 2008; O'Kelly et al., 2016; Särkämö et al., 2016). The most salient goal of music-listening in therapy is a change in mood-state (North et al., 2000; Sloboda, 2011). As it is predominantly non-intrusive, non-harmful, inexpensive, and useful for both short- and long-term treatment, patients often prefer music-listening to pharmaceutical alternatives, which maximises compliance (Silverman, 2008). Moreover, its impressive adaptability across individuals and contexts can empower individuals to take control of their own therapy outside of the medical setting (Brandes et al., 2010; Chan et al., 2009).

Music forms an integral and powerful part of human experience (Trappe, 2012a). Not only can music invoke a large spectrum of emotions, but it can regulate arousal, enhance executive skills and concentration, improve sleep quality, and strengthen social connectedness (Harmat et al., 2008; Koelsch, 2010; Lesiuk, 2010). Indeed, neuroimaging and lesion studies have confirmed responses to music extending far beyond the auditory cortex, activating a complex neural network critical to the regulation of emotion and cognition (Ball et al., 2007; Brown et al., 2004; Gosselin et al., 2006; Koelsch et al., 2006). Music has a therapeutic tradition dating back to antiquity (Maratos et al., 2008). Simply listening to music has documented effects on mood and wellbeing in healthy adults (Sandstrom and Russo, 2010), and on mood regulation in adolescents (Saarikallio and Erkkilä, 2007). Musiclistening has also been found to be particularly helpful in improving

⁎

Corresponding author at: University of NSW (School of Psychiatry), Level 1, 30 Botany Street, UNSW Sydney, NSW, 2052, Australia. E-mail addresses: [email protected] (E. Lynar), [email protected] (E. Cvejic), [email protected] (E. Schubert), [email protected] (U. Vollmer-Conna).

http://dx.doi.org/10.1016/j.ijpsycho.2017.07.012 Received 12 October 2016; Received in revised form 24 July 2017; Accepted 25 July 2017 Available online 27 July 2017 0167-8760/ © 2017 Elsevier B.V. All rights reserved.

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including the effect of “self-selection” of music, on outcomes.

Despite reports that music-listening can be a powerful remedial tool (Silverman, 2008), quality evidence supporting its utility in the treatment of psychiatric conditions (including depression) is surprisingly scarce (Ellis et al., 2012). In this context, music has predominantly been evaluated as an adjunct to pharmaceutical and behavioural interventions, or to ‘care as usual’ (e.g. Erkkilä et al., 2011), and often as a last resort (Trappe, 2012a). This bias might be justified by the limited number of well-controlled studies confirming music-listening as a valid treatment in its own right, and those exploring the effects of music on the individual beyond self-report (Chanda and Levitin, 2013; Iwanaga and Moroki, 1999; Orini et al., 2010; Silverman, 2008). This point is highlighted by a Cochrane review (Maratos et al., 2008) that failed to find a sufficient number of suitable studies to enable a meta-analysis. Many studies have examined autonomic responses to music, including heart rate (HR), and galvanic skin response (GSR), as sensitive markers for emotional arousal (Bernardi et al., 2006; Hodges, 2009; Christensen et al., 2014; O'Kelly, 2016); however, these studies have yielded inconsistent findings (Ellis et al., 2012; Koelsch and Jancke, 2015). It has been suggested that simply employing average measures of autonomic activity (e.g., average HR) fails to capture the complexities of dynamic physiological responding (Ellis et al., 2012). More recently, studies utilizing parameters of beat-to-beat heart rate variability (HRV) have claimed to more adequately capture the dynamic responsivity and adaptability of the listener (Roque et al., 2013; Orini et al., 2010). HRV reflects the interaction of and balance between sympathetic and parasympathetic branches of the autonomic nervous system (ANS), which both innervate the heart. As conceptualised in a model of “neurovisceral integration” (Thayer and Lane, 2009) their interaction is part of a complex, dynamic network of afferent and efferent signals, which causes the inter-beat interval to be in constant flux. In healthy individuals, autonomic functions are flexible and adaptive to environmental change, reflected in relatively high HRV. Conversely, low HRV indicates a rigid system, and has been linked to a growing spectrum of pathologies, including psychiatric, inflammatory, and cardiovascular conditions (Beaumont et al., 2012; Kemp et al., 2010; Ellis et al., 2012). HRV is thus understood to provide a valid and sensitive measure of wellbeing. In the context of studying the beneficial effects of music on health, HRV measures may provide a useful means to reveal the dynamic autonomic forces underlying such benefits (Ellis et al., 2012). A number of studies have included HRV in their exploration of the effects of music-listening on cardiac parameters (Akar et al., 2015; Bernardi et al., 2006; Da Silva et al., 2014; Krabs et al., 2015; Olsson and Von Scheele, 2011; Orini et al., 2010; Pérez-Lloret et al., 2014; Roque et al., 2013; Wang et al., 2011; White, 1999; Zhou et al., 2010). Yet studies comparing the effects of music on both HRV and mood are scarce. Limited studies have suggested that increased HRV with musiclistening precedes, or even mediates, changes in mood-state (Ellis et al., 2012; Sokhadze, 2007). Others have reported correlations between the two and attempted to interpret findings that might reflect sympathetic versus parasympathetic activation (Chiu et al., 2003; Iwanaga et al., 2005; Iwanaga and Tsukamoto, 1997; Lee et al., 2012; Li and Dong, 2012). Of concern is the paucity of studies in this area that have employed standardised methodologies, adequate sample-sizes, consistent musical categories/descriptions, and generalizable findings. Furthermore, none has adequately addressed the influence of person-related (e.g., stress level, personality, musical preference and familiarity) or music-related (e.g., arousal, valence) variables (Chamorro-Premuzic and Furnham, 2007; Do Amaral et al., 2015; Patel et al., 2013; Rentfrow et al., 2011; Vuoskoski and Eerola, 2011). Thus, while music has intuitive therapeutic potential, a stronger and more comprehensive evidence-base is needed before it may be incorporated more widely into medical care. The aim of the current study was to investigate changes in emotional and physiological responses (including HRV) to different musical stimuli, and to examine the relationship between the two. Further, the study explored the influence of person- and music-related variables,

2. Material and methods 2.1. Participants Ninety-four healthy participants were recruited from staff and students at the University of New South Wales (UNSW), Sydney. Exclusion criteria included self-reported hearing impairment, significant medical illness (e.g., heart disease), and use of medications known to affect autonomic functioning (e.g., beta-blockers, benzodiazepines, corticosteroids). The study protocol was approved by the relevant institutional Human Research Ethics Committee (Ref#HC13063) and was performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki. Informed written consent was obtained from all participants prior to testing. 2.2. Study design All participants completed the study individually under controlled laboratory conditions in the early afternoon between 1 PM and 4 PM. On arrival, participants completed self-report questionnaires before being seated in a semi-reclined lounge chair and connected to non-invasive physiological sensors. After completing a short baseline questionnaire probing their current emotional state, participants were instructed to sit back and close their eyes. Four acoustic pieces, each five minutes in duration, were presented in randomised order over headphones at a comfortable volume. Participants were instructed to “listen and experience” each stimulus, and afterwards to rate their current state (on Likert scales) regarding how relaxed, engaged, joyful, sad, and anxious they felt (0 = “not at all”; 10 = “extremely”). 2.3. Self-report measures Questionnaires were used to obtain demographic and health data, and information relating to musical background (e.g., musical preference, training, experience); the Perceived Stress Questionnaire (PSQ; Levenstein et al., 1993) measured current life stress; and the Kessler 10 (K10; Kessler et al., 2002) assessed current psychological distress. The NEO Five-Factor Inventory (NEO-FFI-3; Costa and Mccrae, 2010) provided a measure of personality traits along five continua: neuroticism/ emotionality, extraversion, openness, agreeableness and conscientiousness. 2.4. Physiological measures HR was monitored by via three‑lead ECG with standard Ag/AgCl chest electrodes. Respiration was measured using a strain gauge transducer (Pneumotrace, California, USA) placed around the chest. Finger electrodes were placed around the index and ring fingers of the non-dominant hand to measure GSR (in microSiemens; μS). All sensors were connected to a computer-based data acquisition system (Power Lab 16/30SP, ADInstruments, Bella Vista, Australia) sampling at 1 kHz and analysed using LabChart Pro v7 and its HRV module. Raw data were used to calculate average responses to each stimulus. As recommended in the standard reference paper for the assessment of HRV for research and clinical use (Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, 1996), HRV was calculated from artefact-free ECG recordings of 5 min length. The high frequency spectral component (HF: 0.15–0.40 Hz; normalised units), a well-established marker of cardiac vagal activity used for analyses (Task Force, 1996). 2.5. Acoustic stimuli To select appropriate musical pieces, a panel of five academics from 119

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the Faculties of Medicine and Music at UNSW, each with substantial music playing experience offered subjective ratings on two key dimensions (as described in Russo et al., 2013, Schubert, 2004, Russell, 2003): arousal (the “mobilisation of energy”, i.e. from “calm” to “exciting”) and valence (the “hedonic dimension of emotion”, i.e. from “pleasant” to “unpleasant”) for more than fifty pieces of varying genres. The panel avoided suggesting pieces from the ‘pop music’ genre because of the assumption that these genres would be well represented by participant (self-) selected stimuli. Combined ratings were used to select two pieces of high positive valence: Flute and Harp Concerto in C, K. 299, 2nd Movement by W.A. Mozart (low arousal; classical genre); and Twelfth Street Rag by E.L. Bowman (high arousal; jazz genre). The selection of classical music for low arousal and jazz for high arousal was consistent with general associations attributed to these genres according to Zentner et al. (2008), and reflects long held beliefs about calming effects of classical music held by some music therapists (Gerdner, 2000; Trappe, 2012a; Trappe, 2012b). Participants themselves selected one piece that they found “uplifting”, and stated the reason/s for their choice. All pieces were either repeated or truncated to approach the desirable five-minute duration for HRV analysis (Task Force, 1996). Five minutes of gentle “white noise” served as an acoustically neutral control stimulus (Russo et al., 2013; Nyklíček et al., 1997).

school, and the majority (89.4%) was current tertiary students. Fourteen participants (14.9%) were on medication not considered exclusionary. Although this was not a clinical sample, K10 scores nevertheless varied widely. Specifically, 60 participants (63.8%) had K10 scores below 20, indicating no significant feelings of distress. The remaining 36% of the sample were likely to have at least mild mental disorder, of which 19 participants (20.4% of the total sample) achieved scores ≥ 25, indicating liklihood of moderate to severe disorder (Kessler et al., 2002; Andrews and Slade, 2001). Almost 80% of the sample had some experience playing a musical instrument or singing, of which 80.6% (64.5% of the total sample) had some formal training (Mean = 7.69 years, SD = 4.88). Of those with musical experience, 20.8% did not currently play or practice. The majority of participants (58.5%) listened to music “a lot”, while 39.4% listened to music “a small amount” and only 2.1% endorsed never listening to music for leisure. Most (78.7%) enjoyed popular music, while 26.6% enjoyed western classical music, 22.3% enjoyed non-western music, and 20.2% enjoyed jazz. The most common reason reported for music-listening was enjoyment (90.4%), followed by motivation (53.2%), distraction and procrastination (both 43.6%), and concentration (42.6%).

2.6. Statistical analysis

Fig. 1 shows mean ratings of subjective experience in response to each stimulus. Ratings of engagement, F(3,279) = 46.8, p < 0.001, ηp2 = 0.34, and joyfulness, F(3,279) = 69.7, p < 0.001, ηp2 = 0.43, differed significantly across stimuli. Compared to each of the other stimuli, participants reported feeling most engaged and joyful during their self-selected piece, and least engaged and joyful during the white noise control condition (all p < 0.001). Engagement and joy during jazz and classical pieces were intermediate, and did not significantly differ to each other. While sadness ratings were low overall significant differences still emerged, F(3,279) = 6.8, p < 0.001, ηp2 = 0.07. Participants were significantly less sad following each of the musical pieces than after listening to white noise (all p < 0.02). Of the musical pieces, the jazz piece induced the least sadness, significantly less than the classical piece (p = 0.02). Average sadness ratings provided after the self-selected pieces were not significantly different from those after either prescribed piece. Anxiety ratings were low and did not differ significantly across stimuli. Ratings of relaxation differed significantly across stimuli, F(3,279) = 18.3, p ≤ 0.001, ηp2 = 0.16. Compared to other stimulus, participants reported feeling significantly more relaxed after listening to the classical piece (all p ≤ 0.001), and less relaxed following the jazz piece (all p ≤ 0.001). Relaxation ratings for self-selected pieces and white noise were intermediate, and not significantly different to each other.

3.2. Subjective experience

Statistical analysis was performed using IBM SPSS v22. Repeatedmeasures analysis of variance (ANOVA) was used to examine differences in subjective and physiological measures across stimuli. Principal components analysis was used to derive a composite emotional state index (ESI) which was compared across stimuli using repeated measures ANOVA. Chi-square analyses were used to determine if the frequency of best emotional outcome for each stimulus differed to that expected by chance. One-way ANOVAs and chi-square analyses were used to explore differences in emotional state change in response to the self-selected piece on the basis of several classification variables. Significance was set at p < 0.05 for all analyses. 3. Results 3.1. Participant characteristics Demographic, health, behavioural, and psychological variables are presented in Table 1. All participants had completed year 12 of high Table 1 Participant demographic, health, and behavioural characteristics (N = 94). Mean

SD

Range

3.3. Physiological responses Sex (female: male) Age (years) Body mass index (kg/m2) Physical health rating (1 −10) Moderate exercise (hrs/week) Vigorous exercise (hrs/week) Caffeine consumption (cups/day) Alcohol consumption (drinks/week) Psychological distress (K10) Perceived stress (PSQ)

67: 27 22.77 22.17 7.53 3.02 1.40 1.43 1.19 19.19 65.10

4.67 2.99 1.76 2.23 1.61 1.36 3.81 6.89 16.31

18–46 16.7–30.3 3–10 0–10 0–6 0–6 0–30 10–45 38–113

Personality (NEO-FFI-3) Neuroticism Extroversion Openness Agreeableness Conscientiousness

24.00 29.38 32.60 31.00 31.34

7.84 6.55 5.73 5.21 7.31

6–42 13–44 20–44 18–42 13–46

While the variation in average HR across the four stimuli was small (3 beats per minute overall), the difference was statistically significant, F(3,279) = 23.04, p < 0.001, ηp2 = 0.2. This effect was due to a significant increase in mean HR in response to listening to the self-selected pieces compared to each other stimulus (all p < 0.001), while there was no substantive difference between the mean HR induced by white noise, jazz or classical stimuli (Fig. 2A). GSR showed a similar pattern, F (3,279) = 5.69, p = 0.001, ηp2 = 0.06. Average GSR was significantly higher in response to the self-selected pieces when compared to each other stimulus (all p ≤ 0.005), but did not vary significantly across the other three stimuli (Fig. 2B). HRV (as HFnu) also varied significantly across the stimuli, F(3,279) = 3.05, p = 0.029, ηp2 = 0.03 (Fig. 2C). The classical piece elicited the highest HRV overall; and significantly greater than that associated with the self-selected pieces (p = 0.002), which was the lowest. HRV in

K10 = Kessler 10; PSQ = Perceived Stress Questionnaire; NEO-FFI-3 = Five-Factor Personality Inventory.

120

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Fig. 1. Average ratings of five subjective feeling states elicited by each stimulus. Error bars reflect the standard error of the mean. * indicates p < 0.05; ** indicates p < 0.01.

best mood enhancement in seven (8.2%). Chi-square analyses (χ2 = 24.17, p < 0.001) confirmed that this distribution was significantly different from that expected by chance. The possible impact of personal attributes or demographic factors on mood enhancement achieved by the different musical stimuli is shown in Table 2. While scores reflecting current psychological distress and personality attributes did not differentially influence which type of music was most effective as a mood-enhancing stimulus, participants whose K10 scores reflected moderate to severe psychological distress reported significantly greater mood enhancement than non-distressed individuals in response to all music pieces (all p ≤ 0.001). This association was supported by correlational analyses, which revealed highly significant associations between higher levels of perceived stress (r = 0.35, p = 0.001), higher psychological distress (r = 0.34, p = 0.001), and higher neuroticism/emotionality scores (r = 0.31, p = 0.004) and greater emotional benefit from listening to music. There was, however, no evidence of a consistent relationship between physiological responses (including HR, GSR and HRV) to the musical pieces and mood enhancement.

response to white noise or the jazz piece did not differ from each other or from the other musical pieces. Mean respiratory rate was comparable across stimuli, ranging from 14.26 to 16.62 breaths per minute. 3.4. Emotional state index Principal component analysis (PCA) was used to empirically derive a single index of emotional state (emotional state index; ESI) from baseline mood-state responses. The initial PCA was refined by removal of non-salient items (loading scores < 0.4). The optimal solution accounted for 55.35% of the variance, and included the following items (and loading scores): relaxed (0.81), sad (− 0.74), anxious (− 0.74), and joyful (0.68). Thus, a greater ESI reflects a more joyful and relaxed state, with lower levels of sadness and anxiety. To ensure that the derived index was a valid measure of emotional state at baseline, a series of bivariate correlations was conducted between baseline ESI and selfreported measures of distress and wellbeing. Indeed, baseline ESI was significantly associated with perceived stress (r = −0.56, p < 0.001), psychological distress (r = − 0.50, p < 0.001), weekly alcohol consumption (r = −0.28, p = 0.007), and self-rated physical health (r = 0.26, p = 0.01). The item loadings were subsequently applied to the subjective responses to the musical stimuli, generating an individual ESI for each piece. Notably, no participants' responses on the items comprising the ESI at baseline were the most extreme possible values across all items, thus allowing for the potential of positive change and avoiding a ceiling effect in response to musical stimuli. Commensurate with the differences observed on individual items of the emotional state profile, the ESI in response to the different stimuli (as a change from baseline) significantly differed across pieces (F (3,279) = 18.2, p < 0.001, ηp2 = 0.16), as presented in Fig. 3. Pairwise comparisons showed no differences between classical and self-selected pieces, however both elicited a significantly greater positive change in ESI (all p ≤ 0.001) than the jazz piece, which nevertheless induced significantly greater ESI enhancement than white noise (p = 0.01).

3.6. Attributes of self-selected pieces Self-selected pieces were broadly categorised and described in Table 3. Tempi ranged from 44 to 192 bpm across the 94 pieces, averaging approximately 100 bpm. Participant-rated arousal was significantly correlated with tempo (r = 0.34, p = 0.001). Additionally, participants were asked to identify which aspect(s) of their chosen piece contributed to its “uplifting” potential. Fifty-four (76.1%) of those who selected pieces with vocals (57.4% of all participants) identified them as an “uplifting” factor. Fifty-nine (62.8%) participants identified the instrumentation, 70 (74.5%) the melody, 66 (70.2%) the rhythm or tempo, 50 (53.2%) the harmonies or key, 38 (40.4%) the dynamics, and 24 (25.5%) identified the loudness or intensity. Mean ΔESI and standard deviation were determined for each subcategory (Table 3). No significant difference was found between subcategory means for any of the analysed categories. However, while not significant, there appeared to be a trend for both tempo and participantrated arousal, with higher average ΔESI found in response to pieces that were slower (vs faster) and of low arousal (vs high arousal).

3.5. Correlates of music-induced mood enhancement - what works best for whom and why? For each participant, the “optimal” musical piece was determined on the basis of the greatest mood improvement from baseline (ΔESI). A small number of participants (n = 9) who had an equivalent ESI change across multiple stimuli were excluded from this analysis, resulting in a sample size of 85 participants. The self-selected musical piece produced the greatest enhancement in mood in the greatest number of participants (n = 40; 47.1%); the classical piece elicited the greatest positive mood change in 38 of the participants (44.7%); and jazz produced the

4. Discussion The findings of this study clearly demonstrate that listening to music can induce a powerful enhancement of mood state (reflected in greater joy and relaxation and lower anxiety and sadness). Moreover, the results provide strong support for self-selection of musical choice as a highly effective method to enhance mood. Participants with high levels 121

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Fig. 3. Change in emotional state index (ΔESI) from baseline in response to each stimulus. Error bars reflect the standard error of the mean. * indicates p < 0.05; ** indicates p < 0.01.

Table 2 Participant variables grouped by musical pieces that elicited the greatest change (Δ) in ESI. Demographics

Difference⁎

Greatest ΔESI Jazz n=7

Classical n = 38

Self-selected n = 40

Sex (F/M) Age

6/1 29.29

28/10 22.47

25/15 21.43

Musical experience (% yes) Formal music training (% yes) Musical enjoyment (%yes): Classical Jazz Popular Non-Western

57.14

76.32

80.00

ns J > C, p < 0.001 J > S, p < 0.001 ns

28.57

60.63

65.00

ns

14.29 28.57 71.43 14.29

36.84 18.42 73.68 39.47

20.00 15.00 90.00 5.00

Psychological distress Neuroticism/ emotionality Extraversion Openness Agreeableness Conscientiousness

21.71 25.57

17.97 23.82

20.05 24.05

ns ns ns χ2 = 14.09, p = 0.001 ns ns

31 33.71 30.14 31.29

28.68 32.18 30.42 30.68

29.65 32.80 31.75 31.35

ns ns ns ns

⁎ Only significant differences have been described further. J = jazz piece; C = classical piece; S = self-selected pieces; ns = no significant difference.

piece they found “uplifting”, resulting in unparalleled heterogeneity in the self-selected category. This differs from previous research asking participants to choose their preferred piece from a small prescribed list (Krumhansl, 1997). Despite the diversity of self-selected music in the current study, these pieces did induce the greatest joy and were the most engaging overall. Notably, differences in genre, tempo, arousal, lyrics, or memories did not engender differential emotional states. Keeping in mind that participants were asked to select a piece they themselves experienced as uplifting, this suggests that a key factor in inducing joy through music is a pre-existing positive emotional association. These findings strongly support personalised music therapy through self-selected music (Trappe, 2012a), especially where enjoyment rather than “calming the mind” or relaxation are the primary therapeutic goal, or where classical music is contraindicated (e.g., greatly disliked). The low arousal classical piece was found to be associated with the

Fig. 2. Mean physiological response to each stimulus: (A) HR = heart rate; (B) GSR = galvanic skin response; (C) HFnu = high frequency normalised units. Error bars reflect the standard error of the mean. * indicates p < 0.05; ** indicates p < 0.01.

of current psychological distress and trait emotionality benefitted most in terms of improved emotional state from listening to music. While the musical pieces used in this study elicited differential autonomic response profiles overall, closer analysis of individual responses failed to reveal consistent associations between changes in autonomic parameters and mood enhancement to the musical pieces. 4.1. Mood-enhancing effects of music Unique to this study, participants were able to select any musical 122

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wellbeing and health, rather than small and short-lived variations in emotional state. It should also be kept in mind that the musical pieces used in this study were all selected to be of positive emotional valance and thus differed mostly along the dimensions of relaxation, engagement, and enjoyment. Unexpectedly, the gentle white noise was associated with high HRV values and moderate relaxation ratings, despite the lowest composite mood scores. Despite its common use as a control auditory stimulus in psychophysiological research (Russo et al., 2013), white noise was clearly not an effective “neutral” stimulus, as it appeared to induce a state of relaxation in its own right. Rather, the present findings confirm reports of a therapeutic potential (Hiscock, 2006).

Table 3 Musical categorization of self-selected pieces. Category

Sub-category

N (%)

ΔESI [M (SD)]

Genre

Popular⁎ Classical/jazz Other Yes No Slow (< 70) Moderate (70–99) Fast (100–139) Very fast (140+) Low (− 5 to 0) High (1 to 5) Yes No

71 13 10 71 23 19 37 27 11 17 77 50 44

0.40 0.76 0.53 0.47 0.46 0.65 0.42 0.47 0.31 0.77 0.40 0.44 0.50

Vocals/Lyrics Tempo

Rated arousal Association/memory

(75.53) (13.83) (10.64) (75.53) (24.47) (20.21) (39.36) (28.72) (11.70) (18.09) (81.91) (53.19) (46.81)

(0.82) (1.00) (1.00) (0.80) (1.06) (0.99) (0.80) (0.71) (1.21) (0.89) (0.85) (0.91) (0.81)

4.3. Individual variation in response to music Individuals varied considerably not only in their subjective and physiological responses to each piece, but also in regard to which was their “best fit”. When categorised according to the piece that elicited the highest ESI, the majority of participants responded best to either the classical or self-selected pieces. Nevertheless, for some individuals, jazz provided the best fit for therapeutic use. Evidently, the piece inducing a positive therapeutic response for the majority is not guaranteed to be the best for all listeners. Like pharmaceutical medication, it is not “one size fits all”, although some therapeutic media have a higher likelihood of success than others (Giacomini et al., 2012; Garrido et al., 2016; Cvejic et al., 2016). Although this was a healthy sample, varying levels of psychological distress were reported by participants, with 20.4% scoring in the moderate to severe range on the K10. This proportion is consistent with results reported in other student populations (Stallman, 2010), but somewhat higher than that in the general population (Andrews and Slade, 2001). Not surprisingly, baseline emotional state was lower in this group, but they demonstrated a significantly greater enhancement in emotional state than their less-distressed counterparts in response to all music. Moreover, psychological distress scores consistently correlated significantly with music-induced mood enhancement. This is an encouraging finding, supporting the efficacy of music therapy with clinical populations, where mood improvement is the salient goal (Maratos et al., 2008). Interestingly, HRV responses to each musical piece were no different to those obtained from individuals with lower psychological distress, suggesting that in the immediate term emotional responses occurred independently of a corresponding change in HRV (Ellis et al., 2012). Although autonomic imbalance (reflected by low HRV) is common in clinical depression (Kemp et al., 2010; Kemp et al., 2012), it cannot be assumed that situational improvement in mood will lead to an increase in HRV (Ellis et al., 2012). It is possible that longerterm therapeutic interventions will lead to a more permanent remediation of psychological wellbeing, which could be expected to be accompanied by an increase in autonomic balance with concomitant higher HRV (Thayer and Lane, 2009). Preliminary analysis of the influence of music- and person-related variables on subjective and physiological responses revealed very little. The findings suggest that the jazz piece was more likely to be optimal for mood improvement with increased age. Those who identified nonWestern music as enjoyable were more likely to show improved mood to classical music than any other stimulus. The results indicated that at the level of the individual there is poor correspondence between the mood-enhancing effects of music and momentary changes in autonomic responses. In view of the substantive variations in emotional state change and autonomic responses across individuals, and the absence of a one-to-one relationship between these responses, it would be wise to consider each individual as a unique entity when using music for mood remediation. A variety of musical pieces could be trialled while monitoring physiological and emotional responses to determine the “best fit” in accordance with the intended therapeutic goal. Over time, boosting mood through “heartfelt” music

⁎

Music considered “popular” within any given culture. Tempo = beats per minute (bpm); Arousal was rated by participants on a Likert scale (−5 to 5).

highest relaxation and least anxiety ratings overall, regardless of whether participants reported enjoying classical music usually. The high arousal jazz piece induced a much smaller mood improvement overall despite its selection as a high valence piece for this study. This does not necessarily preclude the use of jazz music in therapy, as the data clearly indicate that some individuals will have the greatest mood change in response to jazz. 4.2. Physiological response to music The two prescribed pieces (jazz and classical) were comparable on measures of sympathetic arousal (HR and GSR). As the key difference between these pieces was their “arousal” quality (judged by a selection committee), this finding suggests that physiological activity may not directly reflect musical features thought to determine arousal (such as tempo, articulation, and dynamic intensity; Ellis and Thayer, 2010, Hodges, 2010, Lee et al., 2012). It might be hypothesised that emotional engagement is the main determinant of physiological arousal to positively-valenced music (Iwanaga and Tsukamoto, 1997; Orman, 2011). Indeed, self-selected pieces aroused the highest sympathetic activity (HR and GSR) and lowest parasympathetic activity (HFnu) overall. While this combination of physiological outcomes could be interpreted to be a “stress” response, the corresponding high engagement and maximal joy ratings suggest that participants were experiencing a “good stress” or “eustress” response to their chosen music (Hodges, 2010). Hegde et al. (2012) described this physiological response as reflecting “perceived emotional meaning”, linking increased GSR in particular to arousing emotions such as happiness. Chan et al. (2009) also wrote of the potential for familiar music to “resonate with the listener's feelings” in a psycho-physiological interaction. The utility of HRV as a sensitive and valid marker of health and wellbeing is well-established (Patel et al., 2013; Thayer and Lane, 2009; Kemp et al., 2010; Kemp et al., 2012; Ellis et al., 2012). Distinct profiles of autonomic responses were evident in group data. In particular, the average HRV response was highest during low arousal, classical music, which also rated as the most relaxing; and when listening to the selfselected pieces the average HR and GSR responses were highest, as were the ratings of joy and engagement. While it is important not to overstate the findings of the present study, we are able to suggest that a possible cause of inconsistent results for HR and GSR identified in the introduction could be due to the neglect of the significance of self-selected music. Self-selection may have an overpowering response that does not depend on specific musical features to the extent thought by some researchers (for more details, see Rickard, 2004). Yet we, as have others previously (Etzel et al., 2006), failed to uncover clear relationships between positive emotional states and autonomic responses, including HRV. It is likely that HRV measures better reflect enduring states of 123

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changes induced by different types of music in musicians and non-musicians: the importance of silence. Heart 92, 445–452. http://dx.doi.org/10.1136/hrt.2005. 064600. Brandes, V., Terris, D.D., Fischer, C., Loerbroks, A., Jarczok, M.N., Ottowitz, G., Titscher, G., Fischer, J.E., Thayer, J.F., 2010. Receptive music therapy for the treatment of depression: a proof-of-concept study and prospective controlled clinical trial of efficacy. Psychother. Psychosom. 79, 321–322. http://dx.doi.org/10.1159/000319529. Bringman, H., Giesecke, K., Thorne, A., Bringman, S., 2009. Relaxing music as premedication before surgery: a randomised controlled trial. Acta Anaesthesiol. Scand. 53, 759–764. http://dx.doi.org/10.1111/j.1399-6576.2009.01969.x. Brown, S., Martinez, M.J., Parsons, L.M., 2004. Passive music listening spontaneously engages limbic and paralimbic systems. Neuroreport 15, 2033–2037. Chamorro-Premuzic, T., Furnham, A., 2007. Personality and music: can traits explain how people use music in everyday life? Br. J. Psychol. 98, 175–185. http://dx.doi.org/10. 1348/000712606X111177. Chan, M.F., Chan, E.A., Mok, E., Kwan Tse, F.Y., 2009. Effect of music on depression levels and physiological responses in community-based older adults. Int. J. Ment. Health Nurs. 18, 285–294. http://dx.doi.org/10.1111/j.1447-0349.2009.00614.x. Chanda, M.L., Levitin, D.J., 2013. The neurochemistry of music. Trends Cogn. Sci. 17, 179–193. http://dx.doi.org/10.1016/j.tics.2013.02.007. Chiu, H.-W., Lin, L., Kuo, M., Chiang, H., Hsu, C., 2003. Using heart rate variability analysis to assess the effect of music therapy on anxiety reduction of patients. In: Proceedings of Computers in Cardiology Conference, pp. 469–472. Christensen, J.F., Gaigg, S.B., Gomila, A., Oke, P., Calvo-Merino, B., 2014. Enhancing emotional experiences to dance through music: the role of valence and arousal in the cross-modal bias. Front. Hum. Neurosci. 8, 757. http://dx.doi.org/10.3389/fnhum. 2014.00757. Costa, P., Mccrae, R.R., 2010. The NEO Inventories: NEO Five Factor Inventory-3 (NEOFFI-3). Lutz, FL, USA, PAR. Cvejic, E., Lynar, E.C., Chung, Y.M., Vollmer-Conna, U., 2016. One size does not fit all: individual differences in cardiac autonomic and subjective responses to brief relaxation activities. Int. J. Cardiol. 223, 265–267. http://dx.doi.org/10.1016/j.ijcard. 2016.08.252. Da Silva, S.A., Guida, H.L., Dos Santosantonio, A.M., Vanderlei, L.C., Ferreira, L.L., De Abreu, L.C., Sousa, F.H., Valenti, V.E., 2014. Auditory stimulation with music influences the geometric indices of heart rate variability in men. Int. Arch. Med. 7, 27. http://dx.doi.org/10.1186/1755-7682-7-27. Do Amaral, J.A., Guida, H.L., Vanderlei, F.M., Garner, D.M., De Abreu, L.C., Valenti, V.E., 2015. The effects of musical auditory stimulation of different intensities on geometric indices of heart rate variability. Altern. Ther. Health Med. 21, 16–23. Ellis, R.J., Thayer, J.F., 2010. Music and autonomic nervous system (Dys)function. Music. Percept. 27, 317–326. http://dx.doi.org/10.1525/mp.2010.27.4.317. Ellis, R.J., Koenig, J., Thayer, J.F., 2012. Getting to the heart: autonomic nervous system function in the context of evidence-based music therapy. Music Med. 4, 90–99. http://dx.doi.org/10.1177/1943862112437766. Erkkilä, J., Punkanen, M., Fachner, J., Ala-Ruona, E., Pöntiö, I., Tervaniemi, M., Vanhala, M., Gold, C., 2011. Individual music therapy for depression: randomised controlled trial. Br. J. Psychiatry 199, 132–139. http://dx.doi.org/10.1192/bjp.bp.110.085431. Etzel, J.A., Johnsen, E.L., Dickerson, J., Tranel, D., Adolphs, R., 2006. Cardiovascular and respiratory responses during musical mood induction. Int. J. Psychophysiol. 61, 57–69. http://dx.doi.org/10.1016/j.ijpsycho.2005.10.025. Freeman, L., Caserta, M., Lund, D., Rossa, S., Dowdy, A., Partenheimer, A., 2006. Music thanatology: prescriptive harp music as palliative care for the dying patient. Am. J. Hosp. Palliat. Med. 23, 100–104. Garrido, S., Schubert, E., Bangert, D., 2016. Musical prescriptions for mood improvement: an experimental study. In: The Arts in Psychotherapy. 51. pp. 46–53. Gerdner, L.A., 2000. Effects of individualized versus classical “relaxation” music on the frequency of agitation in elderly persons with Alzheimer's disease and related disorders. Int. Psychogeriatr. 12, 49–65. Giacomini, K.M., Yee, S.W., Ratain, M.J., Weinshilboum, R.M., Kamatani, N., Nakamura, Y., 2012. Pharmacogenomics and patient care: one size does not fit all. Sci. Transl. Med. 4, 153ps118. http://dx.doi.org/10.1126/scitranslmed.3003471. Gosselin, N., Samson, S., Adolphs, R., Noulhiane, M., Roy, M., Hasboun, D., Baulac, M., Peretz, I., 2006. Emotional responses to unpleasant music correlates with damage to the parahippocampal cortex. Brain 129, 2585–2592. http://dx.doi.org/10.1093/ brain/awl240. Gotell, E., Brown, S., Ekman, S.L., 2002. Caregiver singing and background music in dementia care. West. J. Nurs. Res. 24, 195–216. Harmat, L., Takacs, J., Bodizs, R., 2008. Music improves sleep quality in students. J. Adv. Nurs. 62, 327–335. http://dx.doi.org/10.1111/j.1365-2648.2008.04602.x. Hegde, S., Kumar, P.S., Rai, P., Mathur, G.N., Varadan, V.K., 2012. Music close to one's heart: heart rate variability with music, diagnostic with e-bra and smartphone. In: Proceedings of SPIE 8344, Nanosensors, Biosensors, and Info-Tech Sensors and Systems, pp. 8344. http://dx.doi.org/10.1117/12.916793. Hiscock, H., 2006. The crying baby. Aust. Fam. Physician 35, 680–684. Hodges, D.A., 2009. Bodily responses to music. In: HALLAM, S., CROSS, I., THAUT, M. (Eds.), The Oxford Handbook of Music Psychology. Oxford University Press, Oxford. Hodges, D., 2010. Psychophysiological measures. In: JUSLIN, P., SLOBODA, J. (Eds.), Handbook of Music and Emotion: Theory, Research, Applications. Oxford University Press, Oxford. Iwanaga, M., Moroki, Y., 1999. Subjective and physiological responses to music stimuli controlled over activity and preference. J. Music. Ther. 36, 26–38. Iwanaga, M., Tsukamoto, M., 1997. Effects of excitative and sedative music on subjective and physiological relaxation. Percept. Mot. Skills 85, 287–296. http://dx.doi.org/10. 2466/pms.1997.85.1.287. Iwanaga, M., Kobayashi, A., Kawasaki, C., 2005. Heart rate variability with repetitive

may lead to increased HRV, while elevated HRV through music may engender further mood improvement (Kemp et al., 2010; Kemp et al., 2012). Thus, chosen with discretion, either path has the potential to successfully rehabilitate mood, and thereby enhance wellbeing and quality of life. The pathway to be given clinical priority remains to be determined. 4.4. Limitations and future directions Although intended as a control piece due to its affective neutrality whilst still engaging auditory processing neural pathways, gentle white noise was found to induce a state of relaxation. Further research might experiment with alternate control stimuli, including variations in loudness of presentation, and in isolating the influence of genre and arousal, such as employing high arousal classical music and low arousal jazz music. One might predict arousal over genre to be the dominating variable, and such an approach would allow researchers to question the long held ‘low arousal’ belief about classical music (Pérez-Lloret et al., 2014; White, 1999). The number of prescribed pieces could also be expanded in future research to more accurately determine whether responses are genre- or piece-specific. Longitudinal research would uncover the cumulative and plausibly interactive effects of music-listening on mood enhancement and autonomic functioning. Furthermore, generalisation to clinical populations can only be speculative until validated in specific patient groups. Nevertheless, the present study makes important new contributions to the relationship between physiological states and corresponding self-reported emotions felt when listening to music. 5. Conclusions There is no doubt that music-listening is a powerful tool for improving mood, and has great potential for use in therapy. Our findings strongly support the beneficial short-term effects of music chosen by the listener as ‘uplifting’. This study provides unique evidence for individual differences in response to music, and highlights a complex relationship between subjective and physiological responses. These insights may open new avenues for research contributing to evidencebased therapeutic frameworks and an increased recognition of the potential for music in medical settings. Acknowledgements The authors would like to thank all participants that volunteered their time to take part in this study, and Dr. Yuen Ming Chung for her assistance with participant recruitment and assessment. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Conflicts of interest None to declare. References Akar, S.A., Kara, S., Latifoglu, F., Bilgic, V., 2015. Analysis of heart rate variability during auditory stimulation periods in patients with schizophrenia. J. Clin. Monit. Comput. 29, 153–162. http://dx.doi.org/10.1007/s10877-014-9580-8. Andrews, G., Slade, T., 2001. Interpreting scores on the Kessler psychological distress scale (K10). Aust. N. Z. J. Public Health 25, 494–497. Ball, T., Rahm, B., Eickhoff, S.B., Schulze-Bonhage, A., Speck, O., Mutschler, I., 2007. Response properties of human amygdala subregions: evidence based on functional MRI combined with probabilistic anatomical maps. PLoS One 2, e307. http://dx.doi. org/10.1371/journal.pone.0000307. Beaumont, A., Burton, A.R., Lemon, J., Bennett, B.K., Lloyd, A., Vollmer-Conna, U., 2012. Reduced cardiac vagal modulation impacts on cognitive performance in chronic fatigue syndrome. PLoS One 7, e49518. http://dx.doi.org/10.1371/journal.pone. 0049518. Bernardi, L., Porta, C., Sleight, P., 2006. Cardiovascular, cerebrovascular, and respiratory

124

International Journal of Psychophysiology 120 (2017) 118–125

E. Lynar et al.

Rentfrow, P.J., Goldberg, L.R., Levitin, D.J., 2011. The structure of musical preferences: a five-factor model. J. Pers. Soc. Psychol. 100, 1139–1157. http://dx.doi.org/10.1037/ a0022406. Rickard, N.S., 2004. Intense emotional responses to music: a test of the physiological arousal hypothesis. Psychol. Music 32, 371–388. Riganello, F., Candelieri, A., Quintieri, M., Conforti, D., Dolce, G., 2010. Heart rate variability: an index of brain processing in vegetative state? An artificial intelligence, data mining study. Clin. Neurophysiol. 121, 2024–2034. http://dx.doi.org/10.1016/ j.clinph.2010.05.010. Roque, A.L., Valenti, V.E., Guida, H.L., Campos, M.F., Knap, A., Vanderlei, L.C., Ferreira, C., De Abreu, L.C., 2013. The effects of different styles of musical auditory stimulation on cardiac autonomic regulation in healthy women. Noise Health 15, 281–287. http://dx.doi.org/10.4103/1463-1741.113527. Russell, J.A., 2003. Core affect and the psychological construction of emotion. Psychol. Rev. 110, 145–172. Russo, F.A., Vempala, N.N., Sandstrom, G.M., 2013. Predicting musically induced emotions from physiological inputs: linear and neural network models. Front. Psychol. 4, 468. http://dx.doi.org/10.3389/fpsyg.2013.00468. Saarikallio, S., Erkkilä, J., 2007. The role of music in adolescents' mood regulation. Psychol. Music 35, 88–109. Sandstrom, G.M., Russo, F.A., 2010. Music hath charms: the effects of valence and arousal on recovery following an acute stressor. Music Med. 2, 137–143. http://dx.doi.org/ 10.1177/1943862110371486. Särkämö, T., Tervaniemi, M., Laitinen, S., Forsblom, A., Soinila, S., Mikkonen, M., Autti, T., Silvennoinen, H.M., Erkkilä, J., Laine, M., Peretz, I., Hietanen, M., 2008. Music listening enhances cognitive recovery and mood after middle cerebral artery stroke. Brain 131, 866–876. http://dx.doi.org/10.1093/brain/awn013. Särkämö, T., Alternmüller, E., Rodriguez-Fornells, A., Pertz, I., 2016. Editorial: music, brain, and rehabilitation: emerging therapeutic applications and potential neural mechanisms. Front. Hum. Neurosci. http://dx.doi.org/10.3389/fnhum.2016.00103. Schubert, E., 2004. Modeling perceived emotion with continuous musical features. Music. Percept. 21, 561–585. http://dx.doi.org/10.1525/mp.2004.21.4.561. Silverman, M.J., 2008. Quantitative comparison of cognitive behavioral therapy and music therapy research: a methodological best-practices analysis to guide future investigation for adult psychiatric patients. J. Music. Ther. 45, 457–506. Sloboda, J.A., 2011. Music in everyday life: the role of emotions. In: Juslin, P.N., Sloboda, J.A. (Eds.), Handbook of Music and Emotion: Theory, Research, Applications. Oxford University Press, Oxford. Sokhadze, E.M., 2007. Effects of music on the recovery of autonomic and electrocortical activity after stress induced by aversive visual stimuli. Appl. Psychophysiol. Biofeedback 32, 31–50. http://dx.doi.org/10.1007/s10484-007-9033-y. Stallman, H.M., 2010. Psychological distress in university students: a comparison with general population data. Aust. Psychol. 45, 249–257. http://dx.doi.org/10.1080/ 00050067.2010.482109. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, 1996. Heart rate variability: standards of measurement, physiological interpretation and clinical use. Circulation 93, 1043–1065. http://dx.doi.org/10.1161/01.CIR.93.5.1043. Thayer, J.F., Lane, R.D., 2009. Claude Bernard and the heart-brain connection: further elaboration of a model of neurovisceral integration. Neurosci. Biobehav. Rev. 33, 81–88. http://dx.doi.org/10.1016/j.neubiorev.2008.08.004. Trappe, H.-J., 2012a. The effect of music on human physiology and pathophysiology. Music Med. 4, 100–105. http://dx.doi.org/10.1177/1943862112438106. Trappe, H.-J., 2012b. Music and medicine: the effects of music on the human being. Appl. Cardiopulm. Pathophysiol. 16, 133–142. Voss, J.A., Good, M., Yates, B., Baun, M.M., Thompson, A., Hertzog, M., 2004. Sedative music reduces anxiety and pain during chair rest after open-heart surgery. Pain 112, 197–203. http://dx.doi.org/10.1016/j.pain.2004.08.020. Vuoskoski, J.K., Eerola, T., 2011. The role of mood and personality in the perception of emotions represented by music. Cortex 47, 1099–1106. http://dx.doi.org/10.1016/j. cortex.2011.04.011. Wang, H.-M., Lee, Y.-C., Yen, B.S., Wang, C.-Y., Huang, S.-C., Tang, K.-T., 2011. A physiological valence/arousal model from musical rhythm to heart rhythm. In: Proceedings of the 2011 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1013–1016. White, J.M., 1999. Effects of relaxing music on cardiac autonomic balance and anxiety after acute myocardial infarction. Am. J. Crit. Care 8, 220–230. Zentner, M.R., Grandjean, D., Scherer, K.R., 2008. Emotions evoked by the sound of music: characterization, classification, and measurement. Emotion 8, 494–521. Zhou, P., Sui, F., Zhang, A., Wang, F., Li, G., 2010. Music Therapy on Heart Rate Variability. In: Proceedings of the 3rd IEEE International Conference on Biomedical Engineering and Informatics (BMEI). 3. pp. 965–968.

exposure to music. Biol. Psychol. 70, 61–66. http://dx.doi.org/10.1016/j.biopsycho. 2004.11.015. Kemp, A.H., Quintana, D.S., Gray, M.A., Felmingham, K.L., Brown, K., Gatt, J.M., 2010. Impact of depression and antidepressant treatment on heart rate variability: a review and meta-analysis. Biol. Psychiatry 67, 1067–1074. http://dx.doi.org/10.1016/j. biopsych.2009.12.012. Kemp, A.H., Quintana, D.S., Felmingham, K.L., Matthews, S., Jelinek, H.F., 2012. Depression, comorbid anxiety disorders, and heart rate variability in physically healthy, unmedicated patients: implications for cardiovascular risk. PLoS One 7, e30777. http://dx.doi.org/10.1371/journal.pone.0030777. Kessler, R.C., Andrews, G., Colpe, L.J., Hiripi, E., Mroczek, D.K., Normand, S.L., Walters, E.E., Zaslavsky, A.M., 2002. Short screening scales to monitor population prevalences and trends in non-specific psychological distress. Psychol. Med. 32, 959–976. Koelsch, S., 2010. Towards a neural basis of music-evoked emotions. Trends Cogn. Sci. 14, 131–137. http://dx.doi.org/10.1016/j.tics.2010.01.002. Koelsch, S., Jancke, L., 2015. Music and the heart. Eur. Heart J. 36, 3043–3049. http:// dx.doi.org/10.1093/eurheartj/ehv430. Koelsch, S., Fritz, T., Dy, V.C., Muller, K., Friederici, A.D., 2006. Investigating emotion with music: an fMRI study. Hum. Brain Mapp. 27, 239–250. http://dx.doi.org/10. 1002/hbm.20180. Krabs, R.U., Enk, R., Teich, N., Koelsch, S., 2015. Autonomic effects of music in health and Crohn's disease: the impact of isochronicity, emotional valence, and tempo. PLoS One 10, e0126224. http://dx.doi.org/10.1371/journal.pone.0126224. Krumhansl, C.L., 1997. An exploratory study of musical emotions and psychophysiology. Can. J. Exp. Psychol. 51, 336–353. Lee, K.C., Chao, Y.H., Yiin, J.J., Hsieh, H.Y., Dai, W.J., Chao, Y.F., 2012. Evidence that music listening reduces preoperative Patients' anxiety. Biol. Res. Nurs. 14, 78–84. http://dx.doi.org/10.1177/1099800410396704. Lesiuk, T., 2010. The effect of preferred music on mood and performance in a highcognitive demand occupation. J. Music. Ther. 47, 137–154. Levenstein, S., Prantera, C., Varvo, V., Scribano, M.L., Berto, E., Luzi, C., Andreoli, A., 1993. Development of the perceived stress questionnaire: a new tool for psychosomatic research. J. Psychosom. Res. 37, 19–32. Li, Y., Dong, Y., 2012. Preoperative music intervention for patients undergoing cesarean delivery. Int. J. Gynaecol. Obstet. 119, 81–83. http://dx.doi.org/10.1016/j.ijgo. 2012.05.017. Lou, M.F., 2001. The use of music to decrease agitated behaviour of the demented elderly: the state of the science. Scand. J. Caring Sci. 15, 165–173. http://dx.doi.org/10. 1046/j.1471-6712.2001.00021.x. Maratos, A.S., Gold, C., Wang, X., Crawford, M.J., 2008. Music therapy for depression. Cochrane Database Syst. Rev. 1, CD004517. http://dx.doi.org/10.1002/14651858. CD004517.pub2. Nilsson, U., 2009. The effect of music intervention in stress response to cardiac surgery in a randomized clinical trial. Heart Lung 38, 201–207. http://dx.doi.org/10.1016/j. hrtlng.2008.07.008. North, A.C., Hargreaves, D.J., O'neill, S.A., 2000. The importance of music to adolescents. Br. J. Educ. Psychol. 70, 255–272. Nyklíček, I., Thayer, J.F., Van Doornen, L.J., 1997. Cardiorespiratory differentiation of musically-induced emotions. J. Psychophysiol. 11, 304–321. O'Kelly, J., 2016. Music Therapy and Neuroscience: Opportunities and Challenges. Voices: a World Forum for Music Therapypp. 16. http://dx.doi.org/10.15845/voices.v16i2. 872. O'Kelly, J., Fachnr, J.C., Tervaniemi, M., 2016. Editorial: dialogues in music therapy and music neuroscience: collaborative understanding driving clinical advances. Front. Hum. Neurosci. http://dx.doi.org/10.3389/fnhum.2016.00585. Olsson, E.M., Von Scheele, B., 2011. Relaxing on a bed of nails: an exploratory study of the effects on the autonomic, cardiovascular, and respiratory systems, and saliva cortisol. J. Altern. Complement. Med. 17, 5–12. http://dx.doi.org/10.1089/acm. 2010.0135. Orini, M., Bailon, R., Enk, R., Koelsch, S., Mainardi, L., Laguna, P., 2010. A method for continuously assessing the autonomic response to music-induced emotions through HRV analysis. Med. Biol. Eng. Comput. 48, 423–433. http://dx.doi.org/10.1007/ s11517-010-0592-3. Orman, E.K., 2011. The effect of listening to specific musical genre selections on measures of heart rate variability. In: Update: Applications of Research in Music Education. 30. pp. 64–69. http://dx.doi.org/10.1177/8755123311418479. Patel, V., Giesebrecht, S., Burton, A.R., Cvejic, E., Lemon, J., Hadzi-Pavlovic, D., Dain, S., Lloyd, A., Vollmer-Conna, U., 2013. Reliability revisited: autonomic responses in the context of everyday well-being. Int. J. Cardiol. 166, 743–745. http://dx.doi.org/10. 1016/j.ijcard.2012.09.177. Pérez-Lloret, S., Diez, J., Domé, M.N., Delvenne, A.A., Braidot, N., Cardinali, D.P., Vigo, D.E., 2014. Effects of different" relaxing" music styles on the autonomic nervous system. Noise Health 16, 279. http://dx.doi.org/10.4103/1463-1741.140507.

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