music psychology research paper

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• Published: 22 June 2021

Mental health and music engagement: review, framework, and guidelines for future studies

• Daniel E. Gustavson   ORCID: orcid.org/0000-0002-1470-4928 1 , 2 ,
• Peyton L. Coleman   ORCID: orcid.org/0000-0001-5388-6886 3 ,
• John R. Iversen 4 ,
• Hermine H. Maes 5 , 6 , 7 ,
• Reyna L. Gordon 2 , 3 , 8 , 9 &
• Miriam D. Lense 2 , 8 , 9  

Translational Psychiatry volume  11 , Article number:  370 ( 2021 ) Cite this article

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• Medical genetics
• Psychiatric disorders

Is engaging with music good for your mental health? This question has long been the topic of empirical clinical and nonclinical investigations, with studies indicating positive associations between music engagement and quality of life, reduced depression or anxiety symptoms, and less frequent substance use. However, many earlier investigations were limited by small populations and methodological limitations, and it has also been suggested that aspects of music engagement may even be associated with worse mental health outcomes. The purpose of this scoping review is first to summarize the existing state of music engagement and mental health studies, identifying their strengths and weaknesses. We focus on broad domains of mental health diagnoses including internalizing psychopathology (e.g., depression and anxiety symptoms and diagnoses), externalizing psychopathology (e.g., substance use), and thought disorders (e.g., schizophrenia). Second, we propose a theoretical model to inform future work that describes the importance of simultaneously considering music-mental health associations at the levels of (1) correlated genetic and/or environmental influences vs. (bi)directional associations, (2) interactions with genetic risk factors, (3) treatment efficacy, and (4) mediation through brain structure and function. Finally, we describe how recent advances in large-scale data collection, including genetic, neuroimaging, and electronic health record studies, allow for a more rigorous examination of these associations that can also elucidate their neurobiological substrates.

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Introduction

Music engagement, including passive listening and active music-making (singing, instrument playing), impacts socio-emotional development across the lifespan (e.g., socialization, personal/cultural identity, mood regulation, etc.), and is tightly linked with many cognitive and personality traits [ 1 , 2 , 3 ]. A growing literature also demonstrates beneficial associations between music engagement and quality of life, well-being, prosocial behavior, social connectedness, and emotional competence [ 4 , 5 , 6 , 7 , 8 ]. Despite these advances linking engagement with music to many wellness characteristics, we have a limited understanding of how music engagement directly and indirectly contributes to mental health, including at the trait-level (e.g., depression and anxiety symptoms, substance use behaviors), clinical diagnoses (e.g., associations with major depressive disorder (MDD) or substance use disorder (SUD) diagnoses), or as a treatment. Our goals in this scoping review are to (1) describe the state of music engagement research regarding its associations with mental health outcomes, (2) introduce a theoretical framework for future studies that highlight the contribution of genetic and environmental influences (and their interplay) that may give rise to these associations, and (3) illustrate some approaches that will help us more clearly elucidate the genetic/environmental and neural underpinnings of these associations.

Scope of the article

People interact with music in a wide variety of ways, with the concept of “musicality” broadly including music engagement, music perception and production abilities, and music training [ 9 ]. Table 1 illustrates the breadth of music phenotypes and example assessment measures. Research into music and mental health typically focuses on measures of music engagement, including passive (e.g., listening to music for pleasure or as a part of an intervention) and active music engagement (e.g., playing an instrument or singing; group music-making), both of which can be assessed using a variety of objective and subjective measures. We focus primarily on music engagement in the current paper but acknowledge it will also be important to examine how mental health traits relate to other aspects of musicality as well (e.g., perception and production abilities).

Our scoping review and theoretical framework incorporate existing theoretical and mechanistic explanations for how music engagement relates to mental health. From a psychological perspective, studies have proposed that music engagement can be used as a tool for encouraging self-expression, developing emotion regulation and coping skills, and building community [ 10 , 11 ]. From a physiological perspective, music engagement modulates arousal levels including impacts on heart rate, electrodermal activity, and cortisol [ 12 , 13 ]. These effects may be driven in part by physical aspects of music (e.g., tempo) or rhythmic movements involved in making or listening to music, which impact central nervous system functioning (e.g., leading to changes in autonomic activity) [ 14 ], as well as by personality and contextual factors (e.g., shared social experiences) [ 15 ]. Musical experiences also impact neurochemical processes involved in reward processing [ 10 , 13 , 14 , 16 , 17 , 18 ], which are also implicated in mental health disorders (e.g., substance use; depression). Thus, an overarching framework for studying music-mental health associations should integrate the psychological, physiological, and neurochemical aspects of these potential associations. We propose expanding this scope further through consideration of genetic and environmental risk factors, which may give rise to (and/or interact with) other factors to impact health and well-being.

Regarding mental health, it is important to recognize the hierarchical structure of psychopathology [ 19 , 20 ]. Common psychological disorders share many features and cluster into internalizing (e.g., MDD, generalized anxiety disorder (GAD), posttraumatic stress disorder (PTSD)), externalizing (e.g., SUDs, conduct disorder), and thought disorders (e.g., bipolar disorder, schizophrenia), with common variance shared even across these domains [ 20 ]. These higher-order constructs tend to explain much of the comorbidity among individual disorders, and have helped researchers characterize associations between psychopathology, cognition, and personality [ 21 , 22 , 23 ]. We use this hierarchical structure to organize our review. We first summarize the emerging literature on associations between music engagement and generalized well-being that provides promising evidence for associations between music engagement and mental health. Next, we summarize associations between music engagement and internalizing traits, externalizing traits/behaviors, and thought disorders, respectively. Within these sections, we critically consider the strengths and shortcomings of existing studies and how the latter may limit the conclusions drawn from this work.

Our review considers both correlational and experimental studies (typically, intervention studies; see Fig. 1 for examples of study designs). We include not only studies that examine symptoms or diagnoses based on diagnostic interviews, but also those that assess quantitative variation (e.g., trait anxiety) in clinical and nonclinical populations. This is partly because individuals with clinical diagnoses may represent the extreme end of a spectrum of similar, sub-clinical, problems in the population, a view supported by evidence that genetic influences on diagnosed psychiatric disorders or DSM symptom counts are similar to those for trait-level symptoms in the general population [ 24 , 25 ]. Music engagement may be related to this full continuum of mental health, including correlations with trait-level symptoms in nonclinical populations and alleviation of symptoms from clinical disorders. For example, work linking music engagement to subjective well-being speaks to potential avenues for mental health interventions in the population at large.

Within experimental studies, music interventions can include passive musical activities (e.g., song listening, music and meditation, lyric discussion, creating playlists) or active musical activities (e.g., creative methods, such as songwriting or improvisation and/or re-creative methods, such as song parody).

The goal of this scoping review was to integrate across related, but often disconnected, literatures in order to propose a comprehensive theoretical framework for advancing our understanding of music-mental health associations. For this reason, we did not conduct a fully systematic search or quality appraisal of documents. Rather, we first searched PubMed and Google Scholar for review articles and meta-analyses using broad search terms (e.g., “review” and “music” and [“anxiety” or “depression” or “substance use”]). Then, when drafting each section, we searched for additional papers that have been published more recently and/or were examples of higher-quality research in each domain. When giving examples, we emphasize the most recent and most well-powered empirical studies. We also conducted some targeted literature searches where reviews were not available (e.g., “music” and [“impulsivity” or “ADHD”]) using the same databases. Our subsequent framework is intended to contextualize diagnostic, symptom, and mechanistic findings more broadly within the scope of the genetic and environmental risk factors on psychopathology that give rise to these associations and (potentially) impact the efficacy of treatment efforts. As such, the framework incorporates evidence from review articles and meta-analyses from various literatures (e.g., music interventions for anxiety [ 26 ], depression [ 27 ]) in combination with experimental evidence of biological underpinnings of music engagement and the perspective provided by newly available methods for population-health approaches (i.e., complex trait genetics, gene–environment interactions).

Music engagement and well-being

A growing body of studies report associations between music engagement and general indices of mental health, including increased well-being or emotional competence, lending support for the possibility that music engagement may also be associated with better specific mental health outcomes. In over 8000 Swedish twins, hours of music practice and self-reported music achievement were associated with better emotional competence [ 5 ]. Similarly, a meta-ethnography of 46 qualitative studies revealed that participation in music activities supported well-being through management of emotions, facilitation of self-development, providing respite from problems, and facilitating social connections [ 28 ]. In a sample of 1000 Australian adults, individuals who engaged with music, such as singing or dancing with others or attending concerts reported greater well-being vs. those who engaged in these experiences alone or did not engage. Other types of music engagement, such as playing an instrument or composing music were not associated with well-being in this sample [ 4 ]. Earlier in life, social music experiences (including song familiarity and synchronous movement to music) are associated with a variety of prosocial behaviors in infants and children [ 6 ], as well as positive affect [ 7 ]. Thus, this work provides some initial evidence that music engagement is associated with better general mental health outcomes in children and adults with some heterogeneity in findings depending on the specific type of music engagement.

Music engagement and internalizing problems

MDD, GAD, and PTSD are the most frequently clustered aspects of internalizing psychopathology [ 19 , 24 , 29 , 30 ]. Experimental studies provide evidence for the feasibility of music intervention efforts and their therapeutic benefits but are not yet rigorous enough to draw strong conclusions. The most severe limitations are small samples, the lack of appropriate control groups, few interventions with multiple sessions, and publications omitting necessary information regarding the intervention (e.g., intervention fidelity, inclusion/exclusion criteria, education status of intervention leader) [ 31 , 32 , 33 ]. Correlational studies, by contrast, suggest musicians are at greater risk for internalizing problems, but that they use music engagement as a tool to help manage these problems [ 34 , 35 ].

Experimental studies

Randomized controlled trials have revealed that music interventions (including both music therapies administered by board-certified music therapists and other music interventions) are associated with reduced depression, anxiety, and PTSD symptoms [ 26 , 27 , 33 , 36 ]. A review of 28 studies reported that 26 revealed significantly reduced depression levels in music intervention groups compared to control groups, including the 9 studies which included active non-music intervention control groups (e.g., reading sessions, “conductive-behavior” psychotherapy, antidepressant drugs) [ 27 ]. A similar meta-analysis of 19 studies demonstrated that music listening is effective at decreasing self-reported anxiety in healthy individuals [ 26 ]. A review of music-based treatment studies related to PTSD revealed similar conclusions [ 36 ], though there were only four relevant studies. More recent studies confirm these findings [ 37 , 38 , 39 ], such as one randomized controlled trial that demonstrated reduced depression symptoms in older adults following musical improvisation exercises compared to an active control group (gentle gymnastic activities) [ 39 ].

This work is promising given that some studies have observed effects even when compared to traditional behavior therapies [ 40 , 41 ]. However, there are relatively few studies directly comparing music interventions to traditional therapies. Some music interventions incorporate components of other therapeutic methods in their programs including dialectic or cognitive behavior therapies [ 42 ], but few directly compare how the inclusion of music augments traditional behavioral therapy. Still other non-music therapies incorporate music into their practice (e.g., background music in mindfulness therapies) [ 43 , 44 ], but the specific contribution of music in these approaches is unclear. Thus, there is a great need for further systematic research relating music to traditional therapies to understand which components of music interventions act on the same mechanisms as traditional therapies (e.g., developing coping mechanisms and building community) and which bolster or synchronize with other approaches (e.g., by adding structure, reinforcement, predictability, and social context to traditional approaches).

Aside from comparison with other therapeutic approaches, an earlier review of 98 papers from psychiatric in-patient studies concluded that promising effects of music therapy were limited by small sample sizes and methodological shortcomings including lack of reporting of adverse events, exclusion criteria, possible confounders, and characteristics of patients lost to follow-up [ 33 ]. Other problems included inadequate reporting of information on the source population (e.g., selection of patients and proportion agreeing to take part in the study), the lack of masking of interviewers during post-test, and concealment of randomization. Nevertheless, there was some evidence that therapies with active music participation, structured sessions, and multiple sessions (i.e., four or more) improved mood, with all studies incorporating these characteristics reporting significant positive effects. However, most studies have focused on passive interventions, such as music listening [ 26 , 27 ]. Active interventions (e.g., singing, improvising) have not been directly compared with passive interventions [ 27 ], so more work is needed to clarify whether therapeutic effects are indeed stronger with more engaging and active interventions.

Correlational studies

Correlational studies have focused on the use of music in emotional self-regulation. Specifically, individuals high in neuroticism appear to use music to help regulate their emotions [ 34 , 35 ], with beneficial effects of music engagement on emotion regulation and well-being driven by cognitive reappraisal [ 45 ]. Music listening may also moderate the association between neuroticism and depression in adolescents [ 46 ], consistent with a protective effect.

A series of recent studies have used validated self-reported instruments that directly assess how individuals use music activities as an emotion regulation strategy [ 47 , 48 , 49 , 50 ]. In adults, the use of music listening for anger regulation and anxiety regulation was positively associated with subjective well-being, psychological well-being, and social well-being [ 50 ]. In studies of adolescents and undergraduates, the use of music listening for entertainment was associated with fewer depression and anxiety symptoms [ 51 ]. “Healthy” music engagement in adolescents (i.e., using music for relaxation and connection with others) was also positively associated with happiness and school satisfaction [ 49 ]. However, the use of music listening for emotional discharge was also associated with greater depression, anxiety, and stress symptoms [ 51 ], and “unhealthy” music engagement (e.g., ‘hiding’ in music to block others out) was associated with lower well-being, happiness, school satisfaction, and greater depression and rumination [ 49 ]. Other work has highlighted the role of valence in these associations, with individuals who listen to happier music when they are in a bad mood reporting stronger ability for music to influence their mood than those who listen to sad music while in a negative mood [ 52 , 53 ].

This work highlights the importance of considering individuals’ motivations for engaging with music in examining associations with well-being and mental health, and are consistent with the idea that individuals already experiencing depression, anxiety, and stress use music as a therapeutic tool to manage their emotions, with some strategies being more effective than others. Of course, these correlational effects may not necessarily reflect causal associations, but could be due to bidirectional influences, as suggested by claims that musicians may be at higher risk for internalizing problems [ 54 , 55 , 56 ]. It is also necessary to consider demographic and socioeconomic factors in these associations [ 57 ], for example, because arts engagement may be more strongly associated with self-esteem in those with higher education [ 58 ].

It is also necessary to clarify if musicians (professional and/or nonprofessional) represent an already high-risk group for internalizing problems. In one large study conducted in Norway ( N  = 6372), professional musicians were higher in neuroticism than the general population [ 56 ]. Another study of musician cases ( N  = 9803) vs. controls ( N  = 49,015) identified in a US-based research database through text-mining of medical records found that musicians are at greater risk of MDD (Odds ratio [OR] = 1.21), anxiety disorders (OR = 1.25), and PTSD (OR = 1.13) [ 55 ]. However, other studies demonstrate null associations between musician status and depression symptoms [ 5 ] or mixed associations [ 59 ]. In N  = 10,776 Swedish twins, for example, professional and amateur musicians had more self-reported burnout symptoms [ 54 ]. However, neither playing music in the past, amateur musicianship, nor professional musicianship was significantly associated with depression or anxiety disorder diagnoses.

Even if musicians are at higher risk, such findings can still be consistent with music-making being beneficial and therapeutic (e.g., depression medication use is elevated in individuals with depressive symptoms because it is a treatment). Clinical samples may be useful in disentangling these associations (i.e., examining if those who engage with music more frequently have reduced symptoms), and wider deployment of measures that capture emotion regulation strategies and motivations for engaging with music will help shed light on whether high-risk individuals engage with music in qualitatively different ways than others [ 51 , 57 ]. Later, we describe how also considering the role of genetic and environmental risk factors in these associations (e.g., if individuals at high genetic and/or environmental risk self-select into music environments because they are therapeutic) can help to clarify these questions.

Music engagement and externalizing problems

The externalizing domain comprises SUDs, and also includes impulsivity, conduct disorder, and attention-deficit hyperactivity disorder (ADHD), especially in adolescents [ 20 , 24 , 60 , 61 ]. Similar to the conclusions for internalizing traits, experimental studies show promising evidence that music engagement interventions may reduce substance use, ADHD, and other externalizing symptoms, but conclusions are limited by methodological limitations. Correlational evidence is sparce, but there is less reason to suspect musicians are at higher risk for externalizing problems.

Intervention studies have demonstrated music engagement is helpful in patients with SUDs, including reducing withdrawal symptoms and stress, allowing individuals to experience emotions without craving substance use, and making substance abuse treatment sessions more enjoyable and motivating [ 62 , 63 , 64 ] (for a systematic review, see [ 65 ]). Similar to the experimental studies of internalizing traits, however, these studies would also benefit from larger samples, better controls, and higher-quality reporting standards.

Music intervention studies for ADHD are of similar quality. Such interventions have been shown to reduce inattention [ 66 ], decrease negative mood [ 67 ], and increase reading comprehension for those with ADHD [ 68 ]. However, there is a great amount of variability among children with ADHD, as some may find music distracting while others may focus better in the presence of music [ 69 ].

Little research has been conducted to evaluate music engagement interventions for impulsivity or conduct disorder problems, and findings are mixed. For example, a music therapy study of 251 children showed that beneficial effects on communication skills (after participating in a free improvisation intervention) was significant, though only for the subset of children above age 13 [ 70 ]. Another study suggested the promising effects of music therapy on social skills and problem behaviors in 89 students selected based on social/emotional problem behaviors, but did not have a control group [ 71 ]. Other smaller studies ( N  < 20 each) show inconsistent results on disruptive behaviors and aggression [ 72 , 73 ].

Correlational studies on externalizing traits are few and far between. A number of studies examined how listening habits for different genres of music relate to more or less substance use [ 74 , 75 , 76 , 77 ]. However, these studies do not strongly illuminate associations between music engagement and substance use because musical genres are driven by cultural and socioeconomic factors that vary over the lifespan. In the previously cited large study of American electronic medical records [ 55 ] where musicianship was associated with more internalizing diagnoses, associations were nonsignificant for “tobacco use disorder” (OR = 0.93), “alcoholism” (OR = 1.01), “alcohol-related disorders” (OR = 1.00), or “substance addiction and disorders” (OR = 1.00). In fact, in sex-stratified analyses, female musicians were at significantly decreased risk for tobacco use disorder (OR = 0.85) [ 55 ]. Thus, there is less evidence musicians are at greater risk for externalizing problems than in other areas.

Regarding other aspects of externalizing, some studies demonstrate children with ADHD have poor rhythm skills, opening a possibility that working on rhythm skills may impact ADHD [ 78 , 79 ]. For example, music might serve as a helpful scaffold (e.g., for attention) due to its regular, predictable rhythmic beat. It will be important to examine whether these associations with music rhythm are also observed for measures of music engagement, especially in larger population studies. Finally, musicians were reported to have lower impulsiveness than prior population samples, but were not compared directly to non-musicians [ 80 , 81 ].

Music engagement and thought disorders

Thought disorders typically encompass schizophrenia and bipolar disorder [ 20 ]. Trait-level measures include schizotypal symptoms and depression symptoms. Much like internalizing, music interventions appear to provide some benefits to individuals with clinical diagnoses, but musicians may be at higher risk for thought disorders. Limitations of both experimental and correlational studies are similar to those for internalizing and externalizing.

Music intervention studies have been conducted with individuals with schizophrenia and bipolar disorder. A recent meta-analysis of 18 music therapy studies for schizophrenia (and similar disorders) [ 82 ] demonstrated that music therapy plus standard care (compared to standard care alone) demonstrated improved general mental health, fewer negative symptoms of schizophrenia, and improved social functioning. No effects were observed for general functioning or positive symptoms of schizophrenia. Critiques echoed those described above. Most notably, although almost all studies had low risk of biases due to attrition, unclear risk of bias was evident in the vast majority of studies (>75%) for selection bias, performance bias, detection bias, and reporting bias. These concerns highlight the need for these studies to report more information about their study selection, blinding procedure, and outcomes.

More recent papers suggest similar benefits of music therapies in patients with psychosis [ 83 ] and thought disorders [ 84 ], with similar limitations (e.g., one study did not include a control group). Finally, although a 2021 review did not uncover more recent articles related to bipolar disorder, they argued that existing work suggests music therapy has the potential both to treat bipolar disorder symptoms and alleviate subthreshold symptoms in early stages of the disorder [ 85 ].

Much like internalizing, findings from the few existing studies suggest that musicians may be at higher risk for thought disorders. In the large sample of Swedish twins described earlier [ 54 ], playing an instrument was associated with more schizotypal symptoms across multiple comparisons (professional musicians vs. non-players; amateur musicians vs. non-players; still plays an instrument vs. never played). However, no associations were observed for schizophrenia or bipolar disorder diagnoses across any set of comparison groups. Another study demonstrated that individuals with higher genetic risk for schizophrenia or bipolar disorder were more likely to be a member of a creative society (i.e., actor or dancer, musician, visual artist, or writer) or work in a profession in these fields [ 86 ]. Furthermore, musician status was associated with “bipolar disorder” (OR = 1.18) and “schizophrenia and other psychotic disorders” (OR = 1.18) in US electronic health records (EHRs) [ 55 ].

Interim summary

There is promising evidence that music engagement is associated with better mental health outcomes. Music engagement is positively associated with quality of life, well-being, social connectedness, and emotional competence. However, some individuals who engage with music may be at higher risk for mental health problems, especially internalizing and thought disorders. More research is needed to disentangle these contrasting results, including clarifying how “healthy” music engagement (e.g., for relaxation or social connection) leads to greater well-being or successful emotion regulation, and testing whether some individuals are more likely to use music as a tool to regulate emotions (e.g., those with high neuroticism) [ 34 , 35 ]. Similarly, it will be important to clarify whether the fact that musicians may be an at-risk group is an extension of working in an artistic field in general (which may feature lower pay or lack of job security) and/or if similar associations are observed with continuous music engagement phenotypes (e.g., hours of practice). As we elaborate on later, genetically informative datasets can help clarify these complex associations, for example by tested whether musicians are at higher genetic risk for mental health problems but their music engagement mitigates these risks.

Music intervention studies are feasible and potentially effective at treating symptoms in individuals with clinical diagnoses, including depression, anxiety, and SUDs. However, it will be essential to expand these studies to include larger samples, random sampling, and active control groups that compare the benefits of music interventions to traditional therapies and address possible confounds. These limitations make it hard to quantify how specific factors influence the effectiveness of interventions, such as length/depth of music training, age of sample, confounding variables (e.g., socioeconomic status), and type of intervention (e.g., individual vs. group sessions, song playing vs. songwriting, receptive vs. active methods). Similarly, the tremendous breadth of music engagement activities and measures makes it difficult to identify the specific aspects of music engagement that convey the most benefits to health and well-being [ 87 ]. It is therefore necessary to improve reporting quality of studies so researchers can better identify these potential moderators or confounds using systematic approaches (e.g., meta-analyses).

Various mechanisms have been proposed to explain the therapeutic effects of music on mental health, including psychological (e.g., building communities, developing coping strategies) [ 10 , 11 ] and specific neurobiological drivers (e.g., oxytocin, cortisol, autonomic nervous system activity) [ 12 , 13 , 14 ]. However, it will be vital to conduct more systematic research comparing the effects of music interventions to existing therapeutic methods and other types of creative activities (e.g., art [ 88 ]) to quantify which effects and mechanisms are specific to music engagement. Music interventions also do not have to be an alternative to other treatments, but may instead support key elements of traditional interventions, such as being engaging, enjoyable, providing social context, and increasing structure and predictability [ 89 ]. Indeed, some music therapists incorporate principals from existing psychotherapeutic models [ 42 , 90 ] and, conversely, newer therapeutic models (e.g., mindfulness) incorporate music into their practice [ 43 , 44 ]. It is not yet possible to disentangle which aspects of music interventions best synergize with or strengthen standard psychotherapeutic practices (which are also heterogeneous), but this will be possible with better reporting standards and quality experimental design.

To encapsulate and extend these ideas, we next propose a theoretical framework that delineates key aspects of how music engagement may relate to mental health, which is intended to be useful for guiding future investigations in a more systematic way.

Theoretical framework for future studies

Associations between music engagement and mental health may take multiple forms, driven by several different types of genetic predispositions and environmental effects that give rise to, and interact with, proposed psychological and neurobiological mechanisms described earlier. Figure 2 displays our theoretical model in which potential beneficial associations with music are delineated into testable hypotheses. Four key paths characterize specific ways in which music engagement may relate to (and influence) mental health traits, and thus represent key research questions to be addressed in future studies.

Progression of mental health problems is based on a diathesis-stress model, where genetic predispositions and environmental exposures result in later problems (which can be remedied through treatment). Potential associations with music engagement include (Path 1; blue arrows) correlated genetic/environmental influences and/or causal associations between music engagement and trait-level mental health outcomes; (Path 2; red arrows) interactions between music engagement and risk factors to predict later trait-level or clinical level symptoms; and (Path 3; gold arrow) direct effects of music engagement on reducing symptoms or improving treatment efficacy. Path 4 (orange arrows) illustrates the importance of understanding how these potential protective associations are driven by neuroanatomy and function. MDD major depressive disorder, GAD generalized anxiety disorder, PTSD posttraumatic stress disorder, SUD substance use disorder(s).

Path 1: Music engagement relates to mental health through correlated genetic and environmental risk factors and/or causation

The diathesis-stress model of psychiatric disease posits that individuals carry different genetic liabilities for any given disorder [ 91 , 92 , 93 ], with disorder onset depending on the amount of negative vs. protective environmental life events and exposures the individual experiences. Although at first glance music engagement appears to be an environmental exposure, it is actually far from it. Twin studies have demonstrated that both music experiences and music ability measures are moderately heritable and genetically correlated with cognitive abilities like non-verbal intelligence [ 94 , 95 , 96 , 97 ]. Music engagement may be influenced by its own set of environmental influences, potentially including socioeconomic factors and availability of instruments. Thus, music engagement can be viewed as a combination of genetic and environmental predispositions and availability of opportunities for engagement [ 98 ] that are necessary to consider when evaluating associations with mental health [ 54 ].

When examining music-mental health associations, it is thus important to evaluate if associations are in part explained by correlated genetic or environmental influences (see Fig. 3 for schematic and explanation for interpreting genetic/environmental correlations). On one hand, individuals genetically predisposed to engage with music may be at lower risk of experiencing internalizing or externalizing problems. Indeed, music engagement and ability appear associated with cognitive abilities through genetic correlations [ 3 , 99 ], which may apply to music-mental health associations as well. On the other, individuals at high genetic risk for neuroticism or psychopathology may be more likely to engage with music because it is therapeutic, suggesting a genetic correlation in the opposite direction (i.e., increased genetic risk for musicians). To understand and better contextualize the potential therapeutic effects of music engagement, it is necessary to quantify these potential genetic associations, while simultaneously evaluating whether these associations are explained by correlated environmental influences.

Variance in any given trait is explained by a combination of genetic influences (i.e., heritability) and environmental influences. For complex traits (e.g., MDD or depression symptoms), cognitive abilities (e.g., intelligence), and personality traits (e.g., impulsivity), many hundreds or thousands of independent genetic effects are combined together in the total heritability estimate. Similarly, environmental influences typically represent a multitude of factors, from individual life events to specific exposures (e.g., chemicals, etc.). The presence of a genetic or environmental correlation between traits indicates that some set of these influences have an impact on multiple traits. A Displayed using a Venn diagram. Identifying the strength of genetic vs. environmental correlations can be useful in testing theoretical models and pave the way for more complex genetic investigations. Beyond this, gene identification efforts (e.g., genome-wide association studies) and additional analyses of the resulting data can be used to classify whether these associations represent specific genetic influences that affect both traits equally (i.e., genetic pleiotropy ( B )) or whether a genetic influence impacts only one trait which in turn causes changes in the other (i.e., mediated genetic pleiotropy ( C )). Environmental influences can also act pleiotropically or in a mediated-pleiotropy manner, but only genetic influences are displayed for simplicity.

Beyond correlated genetic and environmental influences, music engagement and mental health problems may be associated with one another through direct influences (including causal impacts). This is in line with earlier suggestions that music activities (e.g., after-school programs, music practice) engage adolescents, removing opportunities for drug-seeking behaviors [ 100 ], increasing their social connections to peers [ 101 ], and decreasing loneliness [ 41 ]. Reverse causation is also possible, for example, if experiencing mental health problems causes some individuals to seek out music engagement as a treatment. Longitudinal and genetically informative studies can help differentiate correlated risk factors (i.e., genetic/environmental correlations) from causal effects of music engagement (Fig. 2 , blue arrows) [ 102 ].

Path 2: Engagement with music reduces the impact of genetic risk

Second, genetic and environmental influences may interact with each other to influence a phenotype. For example, individual differences in music achievement are more pronounced in those who engage in practice or had musically enriched childhood environments [ 97 , 98 ]. Thus, music exposures may not influence mental health traits directly but could impact the strength of the association between genetic risk factors and the emergence of trait-level symptoms and/or clinical diagnoses. Such associations might manifest as decreased heritability of trait-level symptoms in musicians vs. non-musicians (upper red arrow in Fig. 2 ). Alternatively, if individuals high in neuroticism use music to help regulate their emotions [ 34 , 35 ], those who are not exposed to music environments might show stronger associations between neuroticism and later depressive symptoms or diagnoses than those engaged with music (lower red arrow in Fig. 2 ). Elucidating these possibilities will help disentangle the complex associations between music and mental health and could be used to identify which individuals would benefit most from a music intervention (especially preventative interventions). Later, we describe some specific study designs that can test hypotheses regarding this gene-environment interplay.

Path 3: Music engagement improves the efficacy of treatment (or acts as a treatment)

For individuals who experience severe problems (e.g., MDD, SUDs), engaging with music may reduce symptoms or improve treatment outcomes. This is the primary goal of most music intervention studies [ 27 , 33 ] (Fig. 2 , gold arrow). However, and this is one of the central messages of this model, it is important to consider interventions in the context of the paths discussed above. For example, if music engagement is genetically correlated with increased risk for internalizing or externalizing problems (Path 1) and/or if individuals at high genetic risk for mental health problems have already been using music engagement to develop strategies to deal with subthreshold symptoms (Path 2), then may be more likely to choose music interventions over other alternatives and find them more successful. Indeed, the beneficial aspects of music training on cognitive abilities appear to be drastically reduced in samples that were randomly sampled [ 103 ]. Therefore, along with other necessary reporting standards discussed above [ 32 , 33 ], it will be useful for studies to report participants’ prior music experience and consider these exposures in evaluating the efficacy of interventions.

Path 4: Music engagement influences brain structure and function

Exploring associations between music engagement and brain structure and function will be necessary to elucidate the mechanisms driving the three paths outlined above. Indeed, there are strong links between music listening and reward centers of the brain [ 104 , 105 ] including the nucleus accumbens [ 106 , 107 ] and ventral tegmental areas [ 108 ] that are implicated in the reward system for all drugs of abuse [ 109 , 110 , 111 , 112 ] and may relate to internalizing problems [ 113 , 114 , 115 ]. Moreover, activity in the caudate may simultaneously influence rhythmic sensorimotor synchronization, monetary reward processing, and prosocial behavior [ 116 ]. Furthermore, music listening may help individuals control the effect of emotional stimuli on autonomic and physiological responses (e.g., in the hypothalamus) and has been shown to induce the endorphinergic response blocked by naloxone, an opioid antagonist [ 18 , 117 ].

This work focusing on music listening and reward processing has not been extended to music making (i.e., active music engagement), though some differences in brain structure and plasticity between musicians and non-musicians have been observed for white matter (e.g., greater fractional anisotropy in corpus callosum and superior longitudinal fasciculus) [ 118 , 119 , 120 , 121 ]. In addition, longitudinal studies have revealed that instrument players show more rapid cortical thickness maturation in prefrontal and parietal areas implicated in emotion and impulse control compared to non-musician children/adolescents [ 122 ]. Importantly, because the existing evidence is primarily correlational, these cross-sectional and longitudinal structural differences between musicians and non-musicians could be explained by genetic correlations, effects of music training, or both, making them potentially relevant to multiple paths in our model (Fig. 2 ). Examining neural correlates of music engagement in more detail will shed light on these possibilities and advance our understanding of the correlates and consequences of music engagement, and the mechanisms that drive the associations discussed above.

New approaches to studying music and mental health

Using our theoretical model as a guide, we next highlight key avenues of research that will help disentangle these music-mental health associations using state-of-the-art approaches. They include the use of (1) genetic designs, (2) neuroimaging methods, and (3) large biobanks of EHRs.

Genetic designs

Genetic designs provide a window into the biological underpinnings of music engagement [ 123 ]. Understanding the contribution of genetic risk factors is crucial to test causal or mechanistic models regarding potential associations with mental health. At the most basic level, twin and family studies can estimate genetic correlations among music ability or engagement measures and mental health traits or diagnoses. Genetic associations can be examined while simultaneously quantifying environmental correlations, as well as evaluating (bidirectional) causal associations, by testing competing models or averaging across different candidate models [ 102 , 124 ], informing Path 1.

By leveraging samples with genomic, music engagement, and mental health data, investigators can also examine whether individuals at higher genetic risk for psychopathology (e.g., for MDD) show stronger associations between music engagement measures and their mental health outcomes (Path 2). As a theoretical example, individuals with low genetic risk for MDD are unlikely to have many depressive symptoms regardless of their music engagement, so the association between depressive symptoms and music engagement may be weak if focusing on these individuals. However, individuals at high genetic risk for MDD who engage with music may have fewer symptoms than their non-musician peers (i.e., a stronger negative correlation). This is in line with recent work revealing the heritability of depression is doubled in trauma exposed compared to non-trauma exposed individuals [ 125 ].

Gene–environment interaction studies using polygenic scores (i.e., summed indices of genetic risk based on genome-wide association studies; GWAS) are becoming more common [ 126 , 127 ]. There are already multiple large GWAS of internalizing and externalizing traits [ 128 , 129 , 130 ], and the first large-scale GWAS of a music measure indicates that music rhythm is also highly polygenic [ 131 ]. Importantly, is not necessary to have all traits measured in the same sample to examine cross-trait relationships. Studies with only music engagement and genetic data, for example, can still examine how polygenic scores for depression predict music engagement, or interact with music engagement measures to predict other study outcomes. Figure 4 displays an example of a GWAS and how it can be used to compute and apply a polygenic score to test cross-trait predictions.

A GWAS are conducted by examining whether individual genetic loci (i.e., single-nucleotide polymorphisms, or SNPs, depicted with G, A, C, and T labels within a sample (or meta-analysis) differentiate cases from controls. The example is based on a dichotomous mental health trait (e.g., major depressive disorder diagnosis), but GWAS can be applied to other dichotomous and continuous phenotypes, such as trait anxiety, musician status, or hours of music practice. Importantly, rather than examining associations on a gene-by-gene basis, GWAS identify relevant genetic loci using SNPs from across the entire genome (typically depicted using a Manhattan plot, such as that displayed at the bottom of A ). B After a GWAS has been conducted on a given trait, researchers can use the output to generate a polygenic score (sometimes called a polygenic risk score) in any new sample with genetic data by summing the GWAS effect sizes for each SNP allele present in a participant’s genome. An individual with a z  = 2.0 would have many risk SNPs for that trait, whereas an individual with z = −2 would have much fewer risk SNPs. C Once a polygenic score is generated for all participants, it can be applied like any other variable in the new sample. In this example, researchers could examine whether musicians are at higher (or lower) genetic risk for a specific disorder. Other more complex analyses are also possible, such as examining how polygenic scores interact with existing predictors (e.g., trauma exposure) or polygenic scores for other traits to influence a phenotype or predict an intervention outcome. Created with BioRender.com.

Finally, longitudinal twin and family studies continue to be a promising resource for understanding the etiology and developmental time-course of the correlates of mental health problems. Such designs can be used to examine whether associations between music and mental health are magnified based on other exposures or psychological constructs (gene-by-environment interactions) [ 132 ], and whether parents engaged with music are more likely to pass down environments that are protective or hazardous for later mental health (gene-environment correlations) in addition to passing on their genes. These studies also provide opportunities to examine whether these associations change across key developmental periods. The publicly available Adolescent Brain Cognitive Development study, for example, is tracking over 10,000 participants (including twin and sibling pairs) throughout adolescence, with measures of music engagement and exhaustive measures of mental health, cognition, and personality, as well as neuroimaging and genotyping [ 133 , 134 ]. Although most large samples with genomic data still lack measures of music engagement, key musical phenotypes could be added to existing study protocols (or to similar studies under development) with relatively low participant burden [ 135 ]. Musical questionnaires and/or tasks may be much more engaging and enjoyable than other tasks, improving volunteers’ research participation experience.

Neuroimaging

Another way to orient the design of experiments is through the exploration of neural mechanisms by which music might have an impact on mental health. This is an enormous, growing, and sometimes fraught literature, but there is naturally a great potential to link our understanding of neural underpinnings of music listening and engagement with the literature on neural bases of mental health. These advances can inform the mechanisms driving successful interventions and inform who may benefit the most from such interventions. We focus on two areas among many: (1) the activation of reward circuitry by music and (2) the impact music has on dynamic patterns of neural activity, both of which are likely vectors for the interaction of music and mental health and provide examples of potential interactions.

Music and reward

The strong effect of music on our emotions has been clearly grounded in its robust activation of reward circuitry in the brain, and motivational and hedonic effects of music listening have been shown to be specifically modulated by dopamine [ 16 , 105 , 136 ]. The prevalence of reward and dopaminergic dysfunction in mental illness makes this a rich area for future studies. For example, emotional responses to music might be used as a substitute for reward circuit deficiencies in depression, and it is intriguing to consider if music listening or music engagement could potentiate such function [ 137 , 138 ].

Music and brain network dynamics

The search for neuronally based biomarkers of aspects of mental illness has been a central thrust within the field [ 139 ], holding promise for the understanding of heterogeneity within disorders and identification of common mechanistic pathways [ 140 ]. A thorough review is beyond the scope of this paper, but several points of contact can be highlighted that might suggest neuro-mechanistic mediators of musical effects on mental health. For example, neurofeedback-directed upregulation of activity in emotion circuitry has been proposed as a therapy for MDD [ 141 ]. Given the emotional effects of music, there is potential for using musical stimuli as an adjuvant, or as a more actively patient-controlled output target for neurofeedback. Growing interest in measures of the dynamic complexity of brain activity in health and disease as measured by magnetic resonance imaging or magneto/electroencephalography (M/EEG) [ 142 ] provides a second point of contact, with abnormalities in dynamic complexity suggested as indicative of mental illness [ 143 ], while music engagement has been suggested to reflect and perhaps affect dynamic complexity [ 144 , 145 ].

The caveats identified in this review apply equally to such neuro-mechanistic studies [ 146 ]. High-quality experimental design (involving appropriate controls and randomized design) has been repeatedly shown to be critical to providing reliable evidence for non-music outcomes of music engagement [ 103 ]. For such studies to have maximal impact, analysis of M/EEG activity not at the scalp level, but at the source level, has been shown to improve the power of biomarkers, and their mechanistic interpretability [ 147 , 148 ]. Moreover, as with genetic influences that typically influence a trait through a multitude of small individual effects [ 149 ], the neural underpinnings of music-mental health associations may be highly multivariate. In the longer term, leveraging large-scale studies and large-scale data standardization and aggregation hold the promise of gleaning deeper cross-domain insights, for which current experimentalists can prepare by adopting standards for the documentation, annotation, and storage of data [ 150 ].

Biobanks and electronic health records

Finally, the use of EHR databases can be useful in quantifying associations between music engagement and mental health in large samples. EHR databases can include hundreds of thousands of records and allow for examination with International Statistical Classification of Diseases and Related Health Problems codes, including MDD, SUD, and schizophrenia diagnoses. This would allow for powerful estimates of music-mental health associations, and exploration of music engagement with other health outcomes.

The principal roadblock to this type of research is that extensive music phenotypes are not readily available in EHRs. However, there are multiple ways to bypass this limitation. First, medical records can be scraped using text-mining tools to identify cases of musician-related terms (e.g., “musician”, “guitarist”, “violinist”). For example, the phenome-wide association study described earlier [ 55 ] compared musician cases and controls identified in a large EHR database through text-mining of medical records and validated with extensive manual review charts. This study was highly powered to detect associations with internalizing and thought disorders (but showed null or protective effects for musicians for SUDs). Many EHR databases also include genomic data, allowing for integration with genetic models even in the absence of music data (e.g., exploring whether individuals with strong genetic predispositions for musical ability are at elevated or reduced risk for specific health diagnosis).

EHRs could also be used as recruitment tools, allowing researchers to collect additional data for relevant music engagement variables and compare with existing mental health diagnoses without having to conduct their own diagnostic interviews. These systems are not only relevant to individual differences research but could also be used to identify patients for possible enrollment in intervention studies. Furthermore, if recruitment for individual differences or intervention studies is done in patient waiting rooms of specific clinics, researchers can target specific populations of interest, have participants complete some relevant questionnaires while they wait, and be granted access to medical record data without having to conduct medical interviews themselves.

Concluding remarks

Music engagement, a uniquely human trait which has a powerful impact on our everyday experience, is deeply tied with our social and cultural identities as well as our personality and cognition. The relevance of music engagement to mental health, and its potential use as a therapeutic tool, has been studied for decades, but this research had not yet cohered into a clear picture. Our scoping review and framework integrated across a breadth of smaller literatures (including extant reviews and meta-analyses) relating music engagement to mental health traits and treatment effects, though it was potentially limited due to the lack of systematic literature search or formal quality appraisal of individual studies. Taken together, the current body of literature suggests that music engagement may provide an outlet for individuals who are experiencing internalizing, externalizing, or thought disorder problems, potentially supporting emotion regulation through multiple neurobiological pathways (e.g., reward center activity). Conducting more rigorous experimental intervention studies, improving reporting standards, and harnessing large-scale population-wide data in combination with new genetic analytic methods will help us achieve a better understanding of how music engagement relates to these mental health traits. We have presented a framework that illustrates why it will be vital to consider genetic and environmental risk factors when examining these associations, leading to new avenues for understanding the mechanisms by which music engagement and existing risk factors interact to support mental health and well-being.

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Acknowledgements

This work was supported by NIH grants DP2HD098859, R01AA028411, R61MH123029, R21DC016710, U01DA04112, and R03AG065643, National Endowment for the Arts (NEA) research lab grants 1863278-38 and 1855526-38, and National Science Foundation grant 1926794. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or National Endowment for the Arts. The authors would like to thank Navya Thakkar and Gabija Zilinskaite for their assistance.

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Gustavson, D.E., Coleman, P.L., Iversen, J.R. et al. Mental health and music engagement: review, framework, and guidelines for future studies. Transl Psychiatry 11 , 370 (2021). https://doi.org/10.1038/s41398-021-01483-8

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Psychology of Music publishes peer-reviewed papers directed at increasing the scientific understanding of any psychological aspect of music. These include studies on listening, performing, creating, memorising, analysing, describing, learning, and teaching, as well as applied social, developmental, attitudinal and therapeutic studies. Special emphasis is placed on studies, which address the interface between music psychology and music education.

"Without doubt, Psychology of Music is the pre-eminent journal in the field. Its reputation as the source of some of the most sophisticated and elegant research in music psychology has long been unparalleled." Professor Robert Walker

"... absorbing, well-researched and tidily presented, frequently thought-provoking and stimulating. The range of topics and educational levels covered is wide and varied. Journals like this serve an admirable purpose to make readily available reliable up-to-date research to support and encourage all musicians and teachers in their joint pursuits." Colin Touchin

"It is my considered opinion that Psychology of Music is now the premier journal of its kind in the world." Edward P. Asmus

Psychology of Music and SEMPRE provide an international forum for researchers working in the fields of psychology of music and music education, to encourage the exchange of ideas and to disseminate research findings.

View the institutional subscription rates : An institutional subscription to Psychology of Music includes a subscription to Research Studies in Music Education (two issues a year, also published by SAGE on behalf of SEMPRE). Subscriptions are available in the usual three ways: combined print and online, print only and e-access only. Please contact the customer services department to subscribe. Individual subscribers can purchase the journals separately in print only. If you are interested in becoming a member of SEMPRE and receiving a subscription to Psychology of Music as part of your membership dues please contact the SEMPRE membership secretary at: [email protected] This journal accepts supplementary materials, e.g. audio/video files, datasets, additional images etc. For more information please see our guidelines

All issues of Psychology of Music are available to browse online . Psychology of Music provides collections of free to access articles from the archive, centred around key topics and themes. The collections are collated by individuals across the field, and include an introduction to the topic or theme. Read them here .

This journal is a member of the Committee on Publication Ethics (COPE)

Published by the Society for Education, Music and Psychology Research (SEMPRE), the journal aims to increase the scientific understanding of all psychological aspects of music and music education. This includes studies on listening, performing, creating, memorizing, analyzing, describing, learning and teaching as well as applied social, developmental, attitudinal and therapeutic studies.

Submissions may be: theoretical critical papers or original empirical investigations containing systematic qualitative or quantitative analyses of relevant data; short research reports and notes which substantailly confirm or extend existing knowledge but which do not justify a full-length paper; or reviews of books, DVDs, CD Roms or online materials. Special emphasis is placed on studies carried out in naturalistic settings, especially those which address the interface between music psychology and music education.

Psychology of Music provides collections of free to access articles from the archive, centred around key topics and themes. The collections are collated by individuals across the field, and include an introduction to the topic or theme. Read them here .

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This Journal is a member of the  Committee on Publication Ethics .

Please read the guidelines below then visit the Journal’s submission site http://mc.manuscriptcentral.com/pom to upload your manuscript. Please note that manuscripts not conforming to these guidelines may be returned.

Only manuscripts of sufficient quality that meet the aims and scope of Psychology of Music will be reviewed.

There are no fees payable to submit or publish in this Journal. Open Access options are available - see section 3.3 below.

As part of the submission process you will be required to warrant that you are submitting your original work, that you have the rights in the work, and that you have obtained and can supply all necessary permissions for the reproduction of any copyright works not owned by you, that you are submitting the work for first publication in the Journal and that it is not being considered for publication elsewhere and has not already been published elsewhere. Please see our guidelines on prior publication and note that  Psychology of Music  may accept submissions of papers that have been posted on pre-print servers; please alert the Editorial Office when submitting (contact details are at the end of these guidelines) and include the DOI for the preprint in the designated field in the manuscript submission system. Authors should not post an updated version of their paper on the preprint server while it is being peer reviewed for possible publication in the journal. If the article is accepted for publication, the author may re-use their work according to the journal's author archiving policy. If your paper is accepted, you must include a link on your preprint to the final version of your paper.

• What do we publish? 1.1 Aims & Scope 1.2 Article types 1.3 Writing your paper
• Editorial policies 2.1 Peer review policy 2.2 Authorship 2.3 Acknowledgements 2.4 Funding 2.5 Declaration of conflicting interests 2.6 Research ethics and participant consent 2.7 Redundant publication 2.8 Editor/Assistant Editor Submissions 2.9 Research Data
• Publishing policies 3.1 Publication ethics 3.2 Contributor's publishing agreement 3.3 Open access and author archiving
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1. What do we publish?

1.1 Aims & Scope

Before submitting your manuscript to Psychology of Music , please ensure you have read the  Aims & Scope .

1.2 Article Types

Psychology of Music  publishes research articles of typically 4,000-6,000 words and shorter research notes.  Other types of format (e.g. theoretical critical papers, position papers, discussions, and reviews) are also welcomed providing they make a novel contribution to the field. The journal also publishes book reviews. Concise contributions are particularly welcome to facilitate timely publication.  Space is reserved for short and timely research articles (max. 3,000 words) that are identified as meriting more rapid publication which will be fast-tracked through the review process; the editorial board will identify such articles at submission, or authors may wish to flag them in their cover letter.

1.3 Writing your paper

The Sage Author Gateway has some general advice and on  how to get published , plus links to further resources. Sage Author Services also offers authors a variety of ways to improve and enhance their article including English language editing, plagiarism detection, and video abstract and infographic preparation.

1.3.1 Make your article discoverable

When writing up your paper, think about how you can make it discoverable. The title, keywords and abstract are key to ensuring readers find your article through search engines such as Google. For information and guidance on how best to title your article, write your abstract and select your keywords, have a look at this page on the Gateway:  How to Help Readers Find Your Article Online .

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2.1 Peer review policy

Sage does not permit the use of author-suggested (recommended) reviewers at any stage of the submission process, be that through the web-based submission system or other communication. Reviewers should be experts in their fields and should be able to provide an objective assessment of the manuscript. Our policy is that reviewers should not be assigned to a paper if:

•  The reviewer is based at the same institution as any of the co-authors

•  The reviewer is based at the funding body of the paper

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2.2 Authorship

All parties who have made a substantive contribution to the article should be listed as authors. Principal authorship, authorship order, and other publication credits should be based on the relative scientific or professional contributions of the individuals involved, regardless of their status. A student is usually listed as principal author on any multiple-authored publication that substantially derives from the student’s dissertation or thesis.

Please note that AI chatbots, for example ChatGPT, should not be listed as authors. For more information see the policy on Use of ChatGPT and generative AI tools .

2.3 Acknowledgements

All contributors who do not meet the criteria for authorship should be listed in an Acknowledgements section. Examples of those who might be acknowledged include a person who provided purely technical help, or a department chair who provided only general support.

Please supply any personal acknowledgements separately to the main text to facilitate anonymous peer review.

2.3.1 Third party submissions

Where an individual who is not listed as an author submits a manuscript on behalf of the author(s), a statement must be included in the Acknowledgements section of the manuscript and in the accompanying cover letter. The statements must:

• Disclose this type of editorial assistance – including the individual’s name, company and level of input
• Identify any entities that paid for this assistance
• Confirm that the listed authors have authorized the submission of their manuscript via third party and approved any statements or declarations, e.g. conflicting interests, funding, etc.

Where appropriate, Sage reserves the right to deny consideration to manuscripts submitted by a third party rather than by the authors themselves .

2.4 Funding

Psychology of Music requires all authors to acknowledge their funding in a consistent fashion under a separate heading. Please visit the Funding Acknowledgements  page on the Sage Journal Author Gateway to confirm the format of the acknowledgment text in the event of funding, or state that: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. 

2.5 Declaration of conflicting interests

Psychology of Music encourages authors to include a declaration of any conflicting interests and recommends you review the good practice guidelines on the  Sage Journal Author Gateway .

2.6 Research ethics and participant consent

Psychology of Music requires that any manuscripts involving human subjects or participants must include the following statements:

Ethical approval statement

Upon submission, authors will be asked to state the relevant ethics committee or institutional review board provided (or waived) approval. Please ensure that you have provided the full name and institution of the review committee, in addition to the approval number. Where exemption from ethics approval has been granted by an appropriate body, this should be specified and the reason for exemption should be provided. Manuscripts should include statements that provide a clear explanation as to why ethics approval and/or informed consent was not sought for a given study in a specific country or region.

Informed consent

Authors are required to state in the methods section whether participants provided informed consent (for inclusion, collection/use of data or samples, and/or publication, as applicable) and whether the consent was written or verbal.

2.7 Redundant publication

The Editors of Psychology of Music ask authors to declare if any data reported in their submission have been published previously wholly or in part. For example: the reanalysis of a previously published dataset by a different set of authors would need to be declared. The publication of multiple articles using the same dataset with somewhat related outcomes could be considered inappropriate. Within the cover letter and methods section, authors should declare if datasets or participants reported in their submission overlap with any prior published work to help a thorough Editorial assessment of the study.

2.8 Editor/Assistant Editor Submissions

In the interest of transparency, the journal’s policy on submissions by journal board members or Editors or Assistant Editors with a role in administering the peer review process or their families or associates, is as follows:

• Board members and Editors take no part in the review process of their own submissions, those of their family member or their associates and have no access to Editorial information concerning such submissions.
• No preference or favour is given in the decision or peer review process dependent on the author’s relationship to the journal.
• Papers are processed consecutively irrespective of the author’s relationship to the journal. That is, the journal does not ‘expedite’ papers at the expense of others as journal resources are distributed evenly.

2.9 Research Data

The journal is committed to facilitating openness, transparency and reproducibility of research, and has the following research data sharing policy. For more information, including FAQs please visit the Sage Research Data policy pages .

Subject to appropriate ethical and legal considerations, authors are encouraged to:

• share your research data in a relevant public data repository
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3.1 Publication ethics

Sage is committed to upholding the integrity of the academic record. We encourage authors to refer to the Committee on Publication Ethics’ International Standards for Authors  and view the Publication Ethics page on the  Sage Author Gateway .

3.1.1 Plagiarism

Psychology of Music and Sage take issues of copyright infringement, plagiarism or other breaches of best practice in publication very seriously. We seek to protect the rights of our authors and we always investigate claims of plagiarism or misuse of published articles. Equally, we seek to protect the reputation of the journal against malpractice. Submitted articles may be checked with duplication-checking software. Where an article, for example, is found to have plagiarised other work or included third-party copyright material without permission or with insufficient acknowledgement, or where the authorship of the article is contested, we reserve the right to take action including, but not limited to: publishing an erratum or corrigendum (correction); retracting the article; taking up the matter with the head of department or dean of the author's institution and/or relevant academic bodies or societies; or taking appropriate legal action.

3.1.2 Prior publication

If material has been previously published it is not generally acceptable for publication in a Sage journal. However, there are certain circumstances where previously published material can be considered for publication. Please refer to the guidance on the Sage Author Gateway  or if in doubt, contact the Editor at the address given below.

3.2 Contributor's publishing agreement

Before publication, Sage requires the author as the rights holder to sign a Journal Contributor’s Publishing Agreement. Sage’s Journal Contributor’s Publishing Agreement is an exclusive licence agreement which means that the author retains copyright in the work but grants Sage the sole and exclusive right and licence to publish for the full legal term of copyright. Exceptions may exist where an assignment of copyright is required or preferred by a proprietor other than Sage. In this case copyright in the work will be assigned from the author to the society. For more information please visit the  Sage Author Gateway .

3.3 Open access and author archiving

Psychology of Music offers optional open access publishing via the Sage Choice programme and Open Access agreements, where authors can publish open access either discounted or free of charge depending on the agreement with Sage. Find out if your institution is participating by visiting Open Access Agreements at Sage . For more information on Open Access publishing options at Sage please visit Sage Open Access . For information on funding body compliance, and depositing your article in repositories, please visit Sage’s Author Archiving and Re-Use Guidelines and Publishing Policies .

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The preferred format for your manuscript is Word. LaTeX files are also accepted. Word and (La)Tex templates are available on the Manuscript Submission Guidelines  page of our Author Gateway.

4.2 Artwork, figures and other graphics

For guidance on the preparation of illustrations, pictures and graphs in electronic format, please visit Sage’s Manuscript Submission Guidelines .   

Figures supplied in colour will appear in colour online regardless of whether or not these illustrations are reproduced in colour in the printed version. For specifically requested colour reproduction in print, you will receive information regarding the costs from Sage after receipt of your accepted article.

4.3 Supplemental material

This journal is able to host additional materials online (e.g. datasets, podcasts, videos, images etc) alongside the full-text of the article. For more information please refer to our  guidelines on submitting supplemental files .

4.4 Reference style

Psychology of Music adheres to the APA reference style. View the APA guidelines to ensure your manuscript conforms to this reference style.

4.5 English language editing services

Authors seeking assistance with English language editing, translation, or figure and manuscript formatting to fit the journal’s specifications should consider using Sage Language Services. Visit Sage Language Services  on our Journal Author Gateway for further information.

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• Open access
• Published: 21 March 2023

Changing positive and negative affects through music experiences: a study with university students

• José Salvador Blasco-Magraner 1 ,
• Gloria Bernabé-Valero 2 ,
• Pablo Marín-Liébana 1 &
• Ana María Botella-Nicolás 1  

BMC Psychology volume  11 , Article number:  76 ( 2023 ) Cite this article

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Currently, there are few empirical studies that demonstrate the effects of music on specific emotions, especially in the educational context. For this reason, this study was carried out to examine the impact of music to identify affective changes after exposure to three musical stimuli.

The participants were 71 university students engaged in a music education course and none of them were musicians. Changes in the affective state of non-musical student teachers were studied after listening to three pieces of music. An inter-subject repeated measures ANOVA test was carried out using the Positive and Negative Affect Schedule (PANAS) to measure their affective state.

The results revealed that: (i) the three musical experiences were beneficial in increasing positive affects and reducing negative affects, with significant differences between the interaction of Music Experiences × Moment (pre-post); (ii) listening to Mahler’s sad fifth symphony reduced more negative affects than the other experimental conditions; (iii) performing the blues had the highest positive effects.

Conclusions

These findings provide applied keys aspects for music education and research, as they show empirical evidence on how music can modify specific affects of personal experience.

Peer Review reports

Introduction

The studies published on the benefits of music have been on the increase in the last two decades [ 1 , 2 , 3 ] and have branched out into different areas of research such as psychology [ 4 , 5 , 6 , 7 , 8 ], education [ 1 , 9 , 10 ] and health [ 11 , 12 ] providing ways of using music as a resource for people’s improvement.

The publication in 1996 of the famous report “Education Hides a Treasure” submitted to the UNESCO by the International Commission was an important landmark in the educational field. This report pointed out the four basic pillars of twenty-first century education: learning to know, learning to do, learning to live together, and learning to be [ 13 ]. The two last ones clearly refer to emotional education. This document posed a challenge to Education in terms of both academically and emotionally development at all levels from kindergarten to university. In this regard, there has been a notable increase in the number of studies that have shown the strong impact of music on the emotions in the different stages of education and our lives. For example, from childhood to adolescence, involving primary, secondary and university education, music is especially relevant for its beneficial effects on developing students’ emotional intelligence and prosocial skills [ 1 , 14 ]. In adults, music benefits emotional self-regulation [ 15 ], while in old age it helps to maintain emotional welfare and to experience and express spirituality [ 16 ]. This underlines the importance of providing empirical evidence on the emotional influence of music.

Influence of music on positive affects

Numerous studies have used the Positive and Negative Affect Schedule (PANAS) to evaluate the emotional impact of music [ 17 ]. This scale is valid and effective for measuring the influence of positive and negative effects of music on listeners and performers [ 10 , 18 , 19 ]. Thus, for example, empirical evidence shows that exposure to a musical stimulus favours the increase of positive affects [ 20 , 21 ] found a significant increase in three positive affects in secondary school students after listening to music, and the same results has been found after listening to diverse musical styles. These results are consistent with Schubert [ 22 ], who demonstrated that music seems to improve or maintain well-being by means of positive valence emotions (e. g. happiness, joy and calm). Other research studied extreme metal fans aged between 18 and 34 years old and found statements of physiological excitement together with increased positive affects [ 21 ]. Positive outcomes after listening to sad music have also been found [ 23 ], who played Samuel Barbers’ Adagio for Strings , described by the BBC as the world’s saddest piece of classical music, to 20 advanced music students and 20 advanced psychology students with no musical background and subsequently found that the music only had positive affects on both groups.

Several experimental designs that used sad music on university students noticed that they experienced both sadness and positive affects [ 24 , 25 ] and also found that music labeled as “happy” increased positive affects while the one labeled as “sad” reduced both positive and negative affects [ 26 ]. For other authors the strongest and most pleasant responses to sad music are associated with empathy [ 27 ]. Moreover, listening to sad music had benefits since attributes of empathy were intensified [ 27 , 28 ]. In relation to musical performances, empirical evidence found a significant increase in positive affects [ 29 ]. Thus, music induces listeners to experience positive affects, which could turn music into an instrument for personal development.

Following on from Fredrickson’s ‘broaden‐and‐build’ framework of positive emotions [ 30 ], positive affects cause changes in cognitive activities which, in turn, can cause behaviour changes. They can also expand the possibilities for action and improve physical resources. According to Fredrickson [ 30 ], positive affects trigger three sequential effects: (1) amplification of the scope for thought and action; (2) construction of personal resources to deal with difficult simplifications; (3) personal transformation by making one more creative, with a better understanding of situations, better able to face up to difficulties and better socially integrated. This leads to an “upward spiral” in which even more positive affects are experienced. A resource such as music that can increase positive affects, can therefore be considered as a step forward in personal transformation. Thus, music teachers could have a powerful tool to help students enhance their personal development.

Influence of music on negative affects

There is a great deal of controversy as regards the influence of music on negative affects. Blasco and Calatrava [ 20 ] found a significant reduction of five negative affects in secondary school students after listening to Arturo Marquez’s typically happy Danzón N O 2. Different results were found in an experiment in which the change in participants ‘affects was assessed after listening the happy "Eye of the Tiger" by Survivor and the sad "Everybody Hurts" by REM [ 26 ]. They found that the happy piece only increased the positive affects but did not reduce the negative ones, while the sad piece reduced both positive and negative affects. However, neither of these findings agree with Miller and Au [ 31 ], who carried out an experiment to compare the influence of sad and happy music on undergraduates ‘mood arousal and found that listening to both types had no significant changes on negative affects. Shulte [ 32 ] conducted a study with 30 university students to examine the impact that nostalgic music has on affects, and found that after listening to different songs, negative affects decreased. Matsumoto [ 33 ] found that sad music reduced sad feelings in deeply sad university students, while Vuoskoski and Eerola [ 34 ] showed that sad music could produce changes in memory and emotional judgements related to emotions and that experiencing music-induced sadness is intrinsically more pleasant than sad memories. It therefore seems that reducing negative affects has mostly been studied with sad and nostalgic musical stimuli. In this way, if music can reduce negative affects, it can also be involved in educational and psychological interventions focused on improving the emotional-affective sphere. Thus, for example, one study examined the effects of a wide range of music activities and found that it would be necessary to specify exactly what types of music activity lead to what types of outcomes [ 2 ]. Moore [ 3 ] also found that certain music experiences and characteristics had both desirable and undesirable effects on the neural activation patterns involved in emotion regulation. Furthermore, recent research on university students shows that music could be used to assess mood congruence effects, since these effects are reactions to the emotions evoked by music [ 35 ].

These studies demonstrate that emotional experience can be actively driven by music. Moreover, they synthesize the efforts to find ways in which music can enhance affective emotional experience by increasing positive affects and reducing the negative ones (e. g. hostility, nervousness and irritability). Although negative emotions have a great value for personal development and are necessary for psychological adjustment, coping with them and self-regulation capacities are issues that have concerned psychology. For example, Emotional Intelligence [ 36 ], which has currently been established in the educational field, constitutes a fundamental conceptual framework to increase well-being when facing negative emotions, providing keys for greater control and management of emotional reactions. It also establishes how to decrease the intensity and frequency of negative emotional states [ 37 ], providing techniques such as mindfulness meditation that have proven their effectiveness in reducing negative emotional experiences and increasing the positive ones [ 38 ]. The purpose of this research is to find whether music can be part of the varied set of resources that can be used by a teacher to modify students’ emotional experience.

Thus, although empirical evidence of the effects of music on the emotional sphere is still incipient. It seems that they can increase positive effects, but it is not clear their impact on the negative ones, since diverse and contradictory results (no change and reduction of negative affects after listening to music) were found. In addition, the effects of the type of musical piece (e.g. happy or sad music) need further investigation as different effects were found. Moreover, previous studies do not compare between the effects of listening to versus performing music. Such an approach could provide keys to highlight the importance of performing within music education. Therefore, this study aims to contribute to this scientific field, providing experimental evidence on the effects of listening to music as compared to performing music, as well as determining the effects of different types of music on positive and negative affects.

To this end, the effects of three different types of music experiences were compared: (1) listening to a sad piece, (2) listening to an epic and solemn piece, and (3) performing of a rhythm and a blues piece, to determine whether positive and negative affects were modified after exposure to these experimental situations. In particular, two hypotheses guided this study: (1) After exposure to each musical experience (listening to a sad piece; listening to a solemn piece and playing a blues), all participants will improve their emotional experience, increasing their positive affects and reducing their negative ones; and (2) the music performance will induce a greater change as compared to the listening conditions.

Participants

A total of 71 students were involved in this study, 6 men and 65 women between the ages of 20 and 40, who were studying a Teaching Grade. These students were enrolled in the "Music Education" program as part of their university degree’s syllabus. None of them had special music studies from conservatories, academies or were self-taught; thus, all had similar musical knowledge. None of them had previously listened to music in an instructional context nor had performed music with their fellow students. In addition, none of them had listening before to the musical pieces selected for this experiment.

All signed an informed consent form before participating and no payment was given for taking part in the study. As the experiment was carried out in the context of a university course, they were assured that their participation and responses would be anonymous and would have no impact on their qualifications. The research was approved by the ethical committee at the Universidad Católica de Valencia San Vicente Mártir: UCV2017- 18-28 code.

Questionnaire

To assess emotional states, the Positive and Negative Affective States scales (PANAS), was administered [ 39 ]. In particular, the Spanish version of the scale [ 17 ], whose study shows a high degree of internal consistency; in males 0.89 in positive affects and 0.91 in negative affects; in women 0.87 in positive affects and 0.89 in negative affects. In this study, good reliability level in each experimental condition was obtained (0.836–0.913 for positive affects and 0.805–0.917 for negative affects (see Table 1 for more information on Cronbach’s α for each experimental condition).

The PANAS consists of 20 items which describe different dimensions of emotional experience. Participants must answer them regarding to their current affective state. The scale is composed of 20 items; 10 positive affects (PA) and 10 negative affects (NA). Answers are graded in a 5-options (Likert scale), with reversed items, ranging from extremely (1) to very slightly or not at all (5).

Musical pieces

The musical pieces choice stemmed from the analysis of some of the music elements that most influence the perception of emotions: mode, melody and intervals. Within the melody, range and melodic direction were distinguished. The range or amplitude of the melodic line is commonly divided into wide or narrow, while the melodic direction is often classified as ascending or descending. Chang and Hoffman [ 10 ] associated narrow amplitude melodies with sadness, while Schimmark and Grob [ 40 ] related melodic amplitude with highly activated emotions. Regarding the melodic direction, Gerardi and Gerken [ 41 ] found a relationship between ascending direction and happiness and heroism, and between descending direction and sadness.

In relation to the mode, Tizón [ 42 ] stated that the major one is completely happy, while the minor one represents sadness. Thompson and Robitaille [ 43 ] considered that, in order to cause emotions such as happiness, solemnity or joy, composers use tonal melodies, while to obtain negative emotions, they use atonality and chromaticism.

In this research, the selected pieces (“Adagietto” from Gustav Mahler's Fifth Symphony, MML; and “Titans” from Alexander The Great from Vangelis, VML) are representative examples of the melodic, intervallic and modal characteristics previously exposed. Mahler's and Vangelis's pieces completely differ in modes and melodic amplitude (sad vs. heroism). Likewise, Mahler's piece is much more chromatic than Vangelis' one, which has a broader melody made up of third, fourth and fifth intervals, often representative of heroism. Those features justify the fact that they have been used as soundtracks in two films belonging to the epic genre (Alexander The Great, 2004) and drama (Death in Venice, 1971).

The musical piece that was performed by the students was chosen in order to be easy to learn in a few sessions, since they were not musicians. So, three musical pieces were used for the experimental conditions, the first two musical pieces were recordings in a CD, while the third one was performed by the subjects.

The three chosen pieces are described below:

Condition 1 (MML): “Adagietto” from Gustav Mahler’s Fifth Symphony (9:01 min), performed by the Berlin Philharmonic conducted by Claudio Abbado [ 44 ]. This is a sad, melancholic and dramatic piece that Luchino Visconti used in the film Death in Venice, made in 1971 and based on the book by Thomas Mann.

Condition 2 (VML): “Titans Theme” from Alexander the Great (3:59 min), directed by Oliver Stone and premiered in 2004, whose music was composed, produced and performed by Vangelis [ 45 ]. It has a markedly epic character with large doses of heroism and solemnity.

Condition 3 (BP): “Rhythm’s Blues” composed and played by Ana Bort (4:00 min). This is a popular African-American piece of music with an insistent rhythm and harmonically sustained by tonal degrees. This piece was performed by the participants using percussion instruments (carillons and a range of xylophones and metallophones).

The sample was divided into two groups (N 1  = 36 and N 2  = 35) that participated separately in all the phases of the study. The first two conditions (MML and VML) were carried out in each group's classroom, while the performance (BP) was developed in the musical instruments room. This room had 52 percussion instruments, including different types of chimes, xylophones and metallophones (soprano, alto and bass). It is a large space where there are only chairs and musical instruments and stands. The first group was distributed as follows: 6 chimes (3 soprano and 3 alto), 5 soprano xylophones, 5 alto xylophones, 5 bass xylophones, 5 soprano metallophones, 5 alto metallophones and 5 bass metallophones. The distribution of the second group was similar, but with one less alto metallophone.

Prior to the experiment, participants received two practical lessons in order to learn how to collectively perform the music score (third experimental condition). After the two practical lessons, during the next three sessions (leaving two weeks between each session), the experiment was carried out. In each session, an experimental condition was applied and PANAS was on-line administered online beforehand and afterwards (Pre-Post design). All participants were exposed to the three experimental conditions and completed the scale before and after listening to music.

In each of these three sessions, a different music condition was applied: MML in the first one, VML in the second one and BP in the third one.

As conditions VML and MML were listening to pieces of music, the instructions received by the subjects were: “You are going to listen to a musical piece, you ought to listen actively, avoiding distractions. You can close your eyes if you feel like to”. For the BP condition, they were said to play the musical sheet all together.

The aim of the study was to examine the effect of the music experience variable (with three levels: MML, VML and BP) in the Positive and Negative Affects subscales from the PANAS scale. The variable Moment was also studied to control biases and to analyze differences between the Pre and Post conditions.

The experiment was designed as a two-way repeated measure (RM) ANOVA with two dependent variables: Positive Affects and Negative Affects, one for each PANAS’ subscales.

The two repeated measures used in the experiment were the variables Musical Experience (ME), with three levels (MML, VML and BP) and the variable Moment, with two levels (PRE and POST). All participants were exposed to the three experimental conditions.

The design did not include a control group, similar to many other studies in the field of music psychology [ 27 , 30 ]. The control was carried out from the intra-subject pre-post measurement of all the participants. The rationale for this design lies in the complexity of the control condition (or placebo) design in psychology [ 46 ]. While placebos in pharmacological trials are sugar pills, in psychology it is difficult to establish an equivalent period of time similar to the musical pieces (e. g. 9 min) without activity, so that cognitive activity occurred during this period of time (e. g. daydreaming, reading a story, etc.) could bias and limit the generalization of results.

Additionally, one of the goals of this study was to compare the effects of listening to music compared to performance on affects. For this reason, two music listening experiences (MML and VML) and a musical performance experience (BP) were designed. In order to control potential biases, participants did not know the musical pieces in the experimental conditions and they had a low level of musical performance competence (musicians were excluded).

It was used SPSS statistics v.26 for the statistical analyzes.

Two ANOVA were performed. The first one, analyzed two dependent variables at the same time: Positive Affects (PA) and Negative Affects (NA).

In the second ANOVA, the 20 items of the PANAS scale were taken as dependent variables. The rest of the experimental design was similar to the first one, a two-way RM ANOVA with variables Musical Experience (ME) and Moment as repeated measures.

Examination of frequency distributions, histograms, and tests of homogeneity of variance and normality for the criterion measures indicated that the assumptions for the use of parametric statistics were met. Normality was met in all tests except for one, but the ANOVA is robust against this assumption violation. All the analyses presented were performed with the significance level (alpha) set at 0.05, two-tailed tests. Means and standard deviations for the 6 experimental conditions for both subscales, Positive Affects and Negative Affects, are presented in Table 1 .

Mauchly’s test of sphericity was statistically significant for Musical Experience and Musical Experience*Moment focusing on NA as the dependent variable ( p  < 0.05). The test only was significant for Musical Experience for PA as dependent variable ( p  < 0.05). The rest of the W’s Mauchly were not significant ( p  > 0.05), so we assumed sphericity for the non-mentioned variables and worked with the assumed sphericity univariate solution. For the variables which the W’s Mauchly was significant, the univariate solution was also taken, but choosing the corrected Greenhouse–Geisser epsilon approximation due to its conservativeness.

A significant principal effect of the Musical Experience variable F(1.710,119.691) = 22.505, p  < 0.05, η 2  = 0.243; the Moment variable F(1,70) = 45.291, p  < 0.05, η 2  = 0.393; and the Musical Experience*Moment interaction F(2,140) = 32.502, p  < 0.05, η 2  = 0.317 were found for PA.

Statistically significance was found for Moment F(1, 70) = 70.729, p  < 0.05, η 2  = 0.503 and Musical Experience*Moment interaction F(1.822, 127.555) = 8.594, p  < 0.05, η 2  = 0.109, but not for Musical Experience F(1.593, 111.540) = 2.713, p  < 0.05, η 2  = 0.037, for the other dependent variable, NA.

Table 2 shows pairwise comparisons between Musical Experience levels. Bonferroni’s correction was applied in order to control type I error. We only interpret the results for the Positive Affects because the Musical Experience effect was not statistically significant for Negative Affects. Results show that condition VML presents a significant higher punctuation in Positive Affects than the other two conditions ( p  < 0.05). It also shows that the musical condition MML is significantly above BP in Positive Affects ( p  < 0.05).

As regards Moment variable (Table 3 ), all but one Pre-Post differences were statistically significant ( p  < 0.05) for all the three conditions for both Positive and Negative Affects dependent variables. The Pre-Post difference found in Positive Affects for the VML Musical Experience did not reach the statistical level ( p  = 0.319).

Focusing on these statistically significant differences, we observe that conditions MML and BP, for PA, decreased from Pre to Post condition, indicating that positive emotions increased significantly between pre and post measures. On the other hand, for NA, all conditions increased from Pre to Post conditions, indicating that negative affects were decreased between pre and post conditions. Once again, one should bear in mind that items were reversed, thus, a higher scores in NA means a decrease in affects.

In order to measure the interaction effect, significant differences between simple effects were analysed.

The simple effect of Moment (level2-level1) in the first Music Experience condition (MML) in PA was compared with the simple effect of Moment (level2-level1) in the second Musical Experience condition (VML). Music Experience conditions 2–3 (VML-BP) and 1–3 (MML-BP) were compared in the same way. Thus, taking into account PA and NA variables, a total of 6 comparisons, 3 per dependent variable, were made.

The results of these comparisons are shown in Table 4 . Comparisons for PA range from T1 to T3 and comparisons for NA range from T4 to T6. All of them are significant ( p  < 0.05) which means that there are statistically significant differences between all the Musical Experience conditions when comparing the Moment (pre/post) simple effects.

In Table 5 , we can look at the differences’ values. As we said before the differences between Pre and Post conditions are significant when comparing the three musical conditions. The biggest difference for positive affects is between MML and BP (T3 = 8.443), and between VML and MML (T4 = − 6.887) for negative affects.

In this second part, the results obtained from the second two-way RM ANOVA with the 20 items as dependent variables are considered. Results of the descriptive analysis of each item: Interested, Excited, Strong, Enthusiastic, Proud, Alert, Inspired, Determined, Attentive, Active, Distressed, Upset, Guilty, Afraid, Hostile, Irritable, Ashamed, Nervous, Jittery, Scared ; in each musical condition: MML, VML and BP; and for the PRE and POST measurements, can be found in the Additional file 1 (Appendix A).

As regards the ANOVA test that compares the three experimental conditions in each mood, Mauchly’s Sphericity Test indicates that sphericity cannot be assumed for the musical experience in most of the variables of the items of effects, except for Interested, Alert, Inspired, Active and Irritable . For these items, the highest observed power index among Greenhouse–Geisser, Huynh–Feldt and Lower-bound epsilon corrections was taken for each variable. For the interaction Musical Experience*Moment, sphericity was not assumed for Distressed, Guilty, Hostile and Scared . For these items, the same above-cited criterion was followed.

Musical experience has a principal effect on all the positive affects, but only has it for 5 negative affects ( Nervous, Jittery, Scared, Hostile and Upset ) ( p  < 0.05). For more detail see Table S1 from Additional file 1 : Appendix B.

The principal effect of Moment is also statistically significant ( p  < 0.05) for all (positive and negative), but two items: Guilty ( p  = 0.073) and Hostile ( p  = 0.123). All the differences between Pre and Post for positive affects are positive, which means that scores in conditions Pre were significantly higher than in condition Post. The other way around occurs for negative affects, all the differences Pre-Post are negative, meaning that the Post condition is significantly higher than the Pre condition. For more detail, see Table S2 from Additional file 1 : Appendix B. In this way, Pre-post changes (Moment) improve affective states; the positive affects increase while the negative are reduced, except for Guilty ( p  = 0.073) and Hostile ( p  = 0.123).

Comparing the proportion of variance explained by the musical experienced and Moment (Tables s1 and s2 from the Additional file 1 : Appendix B), it is observed that most of the η 2 scores in musical experience are below 0.170, except Active and Alert , which are higher. On the other hand, the η 2 scores for Moment are close to 0.300. From these results we can state that, taking only one of the variables at a time, the proportion of the dependent variable’s variance explained by Moment is higher than the proportion of the dependent variable’s variance explained by Musical Experience.

The effect of interaction, shown in Table S3 from the Additional file 1 : Appendix B is significant in 7 positive moods ( Interested, Excited, Enthusiastic, Alert, Determined, Active and Proud ) and 4 negative moods ( Hostile , Irritable, Nervous , and Jittery ).

The pairwise comparisons of Musical Experience’s levels show a wide variety of patterns. Looking at Positive Affects, there is only one item ( Active ) which present significant differences between the three musical conditions. Items Concentrated and Decided do not present any significant difference between any musical conditions. The rest of the Positive items show at least one significant difference between conditions VML and BP. All differences are positive when comparing VML-MML, VML-BP MML-BP, except for Alert and Proud. So, in general, scores are higher for the first two conditions in relation to the third one, meaning that third musical condition presents the biggest increase for Positive Affects (remember items where reversed). For more detail see Additional file 1 : Appendix C.

As regard pairwise comparisons of Musical Experience’s for negative affects, only the items which had a significant principal effect of the variable Musical Experience are shown here. There is a significant difference between conditions VML and MML in item Nervous ; between VML and BP for Scared ( p  < 0.05). For Jittery ; all three conditions differed significantly from each other ( p  < 0.05). Conditions MML and BP differed significantly for Hostile ( p  < 0.05) and conditions VML and BP almost differed significantly for Upset item, but null hypothesis cannot be rejected as p  = 0.056. For more detail see Additional file 1 : Appendix C. All differences were negative when comparing VML-MML, VML-BP MML-BP, except for Nervous and Jittery . So, in general, scores are lower for the first and second condition in relation to the third one.

Positive effects increased significantly during the post phase of all the music experiences, showing that exposure to any of the three music stimuli improved positive affectivity. There were also significant differences between the three experiences in this phase, according to the following order of improvements in positive affectivity: (1) the rhythm and blues performance (BP), (2) listening to Mahler (MML) and (3) listening to Vangelis (VML). As regards the effects of the musical experience x Moment interaction , all the comparisons were significant, with bigger differences in the interpretation of the blues (BP) than in listening to Mahler (MML) and Vangelis (VML). However, the comparison between both experiences, although significant, was smaller. These results indicate that performing music is significantly effective in increasing positive effects. We will explain these results in greater detail below as regards the specific affective states.

As regards Negative Affects, the comparison of the simple effects showed that these decreased after the musical experiences, although in this first analysis the VML musical experience did not differ from the other two. However, the results of the effects of the interaction between musical experiencie x Moment showed that all the comparisons were significant, with a larger difference between MML and VML than the one between BP and each of the other experiences. Listening to Mahler (MML) was more effective in reducing negative affects, compared to both listening to Vangelis and interpreting the blues (BP). These results agree with previous studies [ 26 , 32 ], in which listening to sad music helped to reduce negative affectivity. In this study, it was the most effective condition, although exposure to all three musical experiences reduced negative affects.

The analysis of the specific affective states shows that most items that belong to Positive Affect scale are the most sensitive ones to the PRE-POST change, the different musical conditions and the interpretation of both effects. However, some items of the Negative Affect scale did not differ in the different music conditions or in the music experience × Moment interaction . For example, there were two items (Guilty and Hostile) that did not obtain significance. These results are consistent with the fact that music has certain limits as regards its impact on people’s affects and does not influence all equally. For example, Guilty has profound psychological implications that cannot be affected by simple exposure to certain musical experiences. This means we should be cautious in inferring that music alone can have therapeutical effects on complex emotional states whose treatment should include empirically validated methods. Also, emotional experiences are widely diverse so that any instrument used to measure them is limited as regards the affective/emotional state under study. These results suggest the importance of reviewing the items that compose the PANAS scale in musical studies to adapt it in order to include affective states more sensitive to musical experiences and eliminate the least relevant items.

The analysis of the results in the specific affective states, allows us to delve deeper into each experimental condition. Thus, regarding the results obtained in the complete scale of PANAS, listening to Mahler (MML), causes desirable changes by raising two positive affects ( Inspired and Attentive ) and reducing 10 negative affects ( Distressed, Upset, Afraid, Hostile, Irritable, Ashamed, Nervous, Jittery, and Scared ). This shows that this music condition had a greater effect on the negative affects than the other ones. These results agree with previous studies [ 26 , 32 ], which found that sad music could effectively reduce negative affects, although other studies came to the opposite conclusion. For instance, Miller and Au [ 31 ] found that sad music did not significantly change negative affects. Some authors [ 47 , 48 ] have argued that adults prefer to listen to sad music to regulate their feelings after a negative psychological experience in order to feel better. Taruffi and Koelsch [ 49 ] concluded that sad music could induce listeners to a wide range of positive effects, after a study with 772 participants. In order to contribute to this debate. It would be interesting to control personality variables that might explain these differences on the specific emotions evoked by sad music. In this study, it has been shown that a sad piece of music can be more effective in reducing negative affects than in increasing positive ones. Although the results come from undergraduate students, similar outcomes could be obtained from children and adolescents, although further research is required. In fact, Borella et al. [ 50 ] studied the influence of age on the effects of music and found that the emotional effects influenced cognitive performance (working memory) in such a way that the type of music (Mozart vs. Albinoni) had a stronger influence on young people than on adults. Kawakami and Hatahira [ 28 ], in a study on 84 primary schoolchildren, also found that exposure to sad music pleased them and their level of empathy correlated with their taste for sad music.

Listening to Vangelis (VML) increased 3 positive affects ( Excited, Inspired and Attentive ) and reduced 8 negative affects ( Distressed, Upset, Afraid, Irritable, Ashamed, Nervous, Jittery , and Scared ). Surprisingly, two positive affects were reduced in this experimental condition ( Alert and Attentive ). It could be explained due to the characteristic ostinato rhythm of this piece of music. It was found a similar effect in the study by Campbell et al., [ 26 ] in which sad music reduced both positive and negative affects. This musical condition also managed to modify negative affects more than positive ones.

Performing the blues (BP) increased all 10 positive affects, indicating that performing is more effective in increasing positive affects than listening. These results agree with the study by Dunbar et al. [ 29 ], who found that music performance significantly increased positive affects.

Performing the blues (BP) reduced 6 negative affects, although it was more effective in increasing positive affective states. Vigorous rhythmic music was also found to be positively associated with the use of all the forms of regulating emotions, which suggests that this type of music is especially useful for emotion modulation [ 51 ]. It was found an exception, since Jittery increased after the blues performance. It could be explained by the negative experience that is sometimes associated with music performance. Therefore, it should be taken into account that music performance could increase some negative effects. For example, Dimsdale et al. [ 52 ] found that a strong negative emotional response to a certain type of music in adolescents was related to risk behaviour, indicating that research into the repertory of music experiences needs to be broadened to diverse styles in different age groups to identify all the types of emotional response and their psychological consequences. However, this result should be taken with caution and further research should focus on whether the effect of increased agitation is usual after music performances.

To sum up, this study contributes to the scientific field on the following points: (1) all the musical experiences had significant effects on improving emotional states, increasing positive affects and decreasing the negative ones, which shows the importance of musical experiences on improving the affective sphere; (2) the specific affects that increased, decreased or did not change for each musical experience were identified, providing specific and useful keys for the design of future interventions; and (3) the differences between various types of musical experiences were analyzed, finding more improvements in the performing conditions than in the listening ones.

Limitations and future directions

Limitations.

The sample, made up of university students with a very homogeneous profile in terms of age and sociodemographic characteristics, could limit the generalization of the results. In addition, the low percentage of men in the sample could also affect the generalizability of the results, although no previous studies have reported gender-based differential effects on the positive and negative affects after musical experiences.

Besides, the choice of the pieces of music was based on theoretical criteria and students’ music preferences were not taken into account. This will be included in future research, since the specific choice of the pieces could affect the positive or negative valence of participants’ emotions. However, the goal of using pieces of music not chosen by participants was to elicit new musical experiences for them. Furthermore, no participant was a musician and none of them had previous knowledge of any of the pieces, which may lead to a bias in the results.

In relation to this, the huge amount of available pieces of music, all of them influenced by their cultural and historical context, make it difficult to generalize that certain music parameters correlate with specific emotions. It would be necessary a cross-cultural approach to reach that conclusion.

Future directions

It is recommended to introduce the variables of music preferences and music history to control their effect on the results and to be able to compare the different musical parameters of the pieces together with participants’ preferences.

Likewise, it would be interesting to identify the affects with a greater or lesser degree of influence by music, to adjust the psychological evaluation instrument to the characteristics of the experiment, including items of emotions that can be modified after exposure to a music experience.

The PANAS manual [ 39 ] indicates that a wide variety of affective states (60) and eight different temporal instructions were included in its construction, showing its great versatility. In further research, this instrument should be adapted to for a more specific application to music studies. For instance, by including other emotional states that could be related with the influence of music (e.g. Tranquility , Gratitude , Elevation ), in order to measure more exactly the effects of music on people’s affective experiences.

Accordingly, it would be interesting to evaluate participants' affective traits to establish a baseline and control personality variables, helping to delve into the different levels of the hierarchical structure of affectivity and its relationship with the various music parameters.

Finally, it is recommended that the psychology of music include objective psychophysiological measurements together with self-report evaluations, so that conclusions arising from the experiments have greater robustness and can increase the impact of the contribution to the scientific community.

This study have shown how different music experiences, such as listening and performing, influence the changes in positive and negative affects in student teachers. The results show that the three musical experiences studied are effective in improving the affects by comparing the emotional states before and after the music experiences. It was also showed that there are differences between the effects obtained in each of the music experiences. Besides, improving both types of affects will depend largely on the selected music for the purpose. Although further evidence is required, the results support the importance of music in education, since it provides tools to increase positive affects and to decrease the negative ones, which is important for emotional intelligence development [ 53 , 54 ].

The three music experiences studied are more effective in reducing negative emotional states than in increasing the positive ones. This finding provides useful clues for music teachers to provide strategies that favor emotional regulation. For instance, in order to reduce hostility, irritability and nervousness, students could be exposed to musical auditions of both sad and solemn pieces, choosing musical pieces with similar characteristics to those described in this study. These auditions will be a resource for stress management in the classroom, as well as a tool that students can adopt and generalize to other contexts. Moreover, it is highly likely that students have not heard this type of music before and this experience could increase their repertoire of musical preferences, enhancing their emotional regulation.

The blues performance had a greater impact on participants' positive affects than listening to the other two pieces so, if any teacher wants to increase them (e.g., enthusiasm, interest, etc.), students could be asked to perform simple pieces such as Rhythm's Blues. In this way, musical performance could increase students' resources, contributing to higher levels of motivation, concentration and interest, which promotes learning [ 55 , 56 , 57 , 58 ]. Likewise, it could be very useful for elementary and secondary music teachers, who will be able to contribute to socio-emotional improvement and personal development of their students. Particularly, musical experiences could be a valuable resource for secondary teachers, since music is important in adolescents' lives and can be an interesting tool for meeting their emotional needs [ 59 ]. This is supported by Kokotsaki and Hallam [ 60 ], who consider that performing music helps students feel like active agents of a group, develop a strong sense of belonging, gain popularity, make "like-minded" relationships, improve their social skills and foster a strong sense of self-esteem and satisfaction.

This study shows that experiencing with various unknown musical pieces can have positive effects on emotions. According to this finding, university professors of Teaching grade in music education should encourage future teachers to experience various musical styles, rhythms and tonalities, avoiding prejudices. Thereby, future music teachers will be able to use a diversity of musical experiences that broaden the emotional effects and fulfill the socio-emotional function of music education. In relation to Fredrickson's 'broaden‐and‐build' framework of positive emotions [ 30 ], music can become a mean of widening other positive emotional states, constructing personal resources and transforming people, and contribute to an upward spiral of positive emotions. Taking into account the underlying psychological mechanisms of the impact of music on the emotional states it will be possible to use it to improve emotional area and other aspects of the personal sphere, as Chang et al., [ 10 ] maintain. Therefore, music education is an important resource to improve the emotional development of students.

Availability of data and materials

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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We should like to express our gratitude to the Valencia University student teachers for their disinterested and valuable contribution to this study.

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José Salvador Blasco-Magraner, Pablo Marín-Liébana & Ana María Botella-Nicolás

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JSBM and GBV contributed to the study conception and design. Material preparation, data collection and analysis were performed by JSBM and GBV. The first draft of the manuscript was written by JSBM, GBV and PML. PML and ABN review, translate and editing the manuscript. All authors read and approved the final manuscript.

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Blasco-Magraner, J.S., Bernabé-Valero, G., Marín-Liébana, P. et al. Changing positive and negative affects through music experiences: a study with university students. BMC Psychol 11 , 76 (2023). https://doi.org/10.1186/s40359-023-01110-9

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Performing Music Research: Methods in Music Education, Psychology, and Performance Science

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Performing Music Research is a comprehensive guide to research in music performance. It reviews the knowledge and skills needed to critique existing studies in music education, psychology, and performance science, and to design and carry out new investigations. Methodological approaches are highlighted across the book in ways that help aspiring researchers bring precision to their research questions, select methods that are appropriate for addressing their questions, and apply those methods systematically and rigorously. Each chapter contains a study guide, comprising a chapter summary, a list of keywords, and suggestions for further discussion. The book concludes with a resources section, including a glossary and supplementary material to support advanced statistical analysis. The book’s companion website provides information designed to facilitate access to original research and to test knowledge and understanding.

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Reviewing the Effectiveness of Music Interventions in Treating Depression

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Depression is a very common mood disorder, resulting in a loss of social function, reduced quality of life and increased mortality. Music interventions have been shown to be a potential alternative for depression therapy but the number of up-to-date research literature is quite limited. We present a review of original research trials which utilize music or music therapy as intervention to treat participants with depressive symptoms. Our goal was to differentiate the impact of certain therapeutic uses of music used in the various experiments. Randomized controlled study designs were preferred but also longitudinal studies were chosen to be included. 28 studies with a total number of 1,810 participants met our inclusion criteria and were finally selected. We distinguished between passive listening to music (record from a CD or live music) (79%), and active singing, playing, or improvising with instruments (46%). Within certain boundaries of variance an analysis of similar studies was attempted. Critical parameters were for example length of trial, number of sessions, participants' age, kind of music, active or passive participation and single- or group setting. In 26 studies, a statistically significant reduction in depression levels was found over time in the experimental (music intervention) group compared to a control ( n = 25) or comparison group ( n = 2). In particular, elderly participants showed impressive improvements when they listened to music or participated in music therapy projects. Researchers used group settings more often than individual sessions and our results indicated a slightly better outcome for those cases. Additional questionnaires about participants confidence, self-esteem or motivation, confirmed further improvements after music treatment. Consequently, the present review offers an extensive set of comparable data, observations about the range of treatment options these papers addressed, and thus might represent a valuable aid for future projects for the use of music-based interventions to improve symptoms of depression.

Introduction

“If I were not a physicist, I would probably be a musician. I often think in music. I live my daydreams in music. I see my life in terms of music.” −Einstein, 1929 .

Depression is one of the most serious and frequent mental disorders worldwide. International studies predict that approximately 322 million (WHO, 2017 ) of the world's population suffer from a clinical depression. This disorder can occur from infancy to old age, with women being affected more often than men (WHO, 2017 ). Thus, depression is one of the most common chronic diseases. Depressive suffering is associated with psychological, physical, emotional, and social impairments. This can influence the whole human being in a fundamental way. Without clinical treatment, it has the tendency to recur or to take a chronic course that can lead to loneliness (Alpass and Neville, 2003 ) and an increasing social isolation (Teo, 2012 ). Depression can have many causes that range from genetic, over psychological factors (negative self-concept, pessimism, anxiety and compulsive states, etc.) to psychological trauma. In addition, substance abuse (Neighbors et al., 1992 ) or chronic diseases (Moussavi et al., 2007 ) can also trigger depression. The colloquial use of the term “depressed” has nothing to do with the depression in the clinical sense. The ICD-10 (WHO, 1992 ) and the DSM-V (APA, 2013 ) provide a classification based on symptoms, considering the patient's history and its severity, duration, course and frequency. Within the last two decades, research on the use of music medicine or music therapy to treat depression, showed a growing popularity and several publications have appeared that documented this movement (e.g., Lee, 2000 ; Loewy, 2004 ; Esfandiari and Mansouri, 2014 ; Verrusio et al., 2014 ; Chen et al., 2016 ; Fancourt et al., 2016 ). However, most researchers used a very specific experimental setup (Hillecke et al., 2005 ) and thus, for example, focused only on one music genre (i.e., classical, modern; instrumental, vocal), used a predefined experimental setup (group or individual) (e.g., Kim et al., 2006 ; Chen et al., 2016 ), or specified precisely the age range (i.e., adolescents, elderly) of participants (e.g., Koelsch et al., 2010 ; Verrusio et al., 2014 ). A recent meta-analysis (Hole et al., 2015 ) reviewed 72 randomized controlled trials and concluded that music was a notable aid for reducing postoperative symptoms of anxiety and pain.

Dementia patients showed significant cognitive and emotional benefits when they sang, or listened to familiar songs (Särkämö et al., 2008 , 2014 ). Beneficial effects were also described for CNMP (Chronic Non-Malignant Pain) patients with depression (Siedliecki and Good, 2006 ) 1 . Cardiology is an area where music interventions are commonly used for intervention purposes. Various explanations were postulated and the broad range of effects on the cardiovascular system was investigated (Trappe, 2010 ; Hanser, 2014 ). Music as a therapeutic approach was evaluated (Gold et al., 2004 ), and found to have positive effects before heart surgery (Twiss et al., 2006 ), used to increase relaxation during angiography (Bally et al., 2003 ), or decrease anxiety (Doğan and Senturan, 2012 ; Yinger and Gooding, 2015 ). A systematic review (Jespersen et al., 2015 ) concluded that music improved subjective sleep quality in adults with insomnia, verbal memory in children (Chan et al., 1998 ; Ho et al., 2003 ), and episodic long-term memory (Eschrich et al., 2008 ). Music conveyed a certain mood or atmosphere (Husain et al., 2002 ), allowed composers to trigger emotions (Bodner et al., 2007 ; Droit-Volet et al., 2013 ), based on the cultural background (Balkwill and Thompson, 1999 ), or ethnic group (Werner et al., 2009 ) someone belonged to. In contrast, the emotional state itself plays a role (Al'tman et al., 2004 ) on how music is interpreted (Al'tman et al., 2000 ), and durations are evaluated (Schäfer et al., 2013 ). Subjective impressions embedded in a composition caused physiological body reactions (Grewe et al., 2007 ; Jäncke, 2008 ) and even strengthened the immune system (McCraty et al., 1996 ; Bittman et al., 2001 ). The pace of (background) music (Oakes, 2003 ), has also been used as an essential element of many marketing concepts (North and Hargreaves, 1999 ), to create a relaxed atmosphere. An in-depth, detailed illustration described the wide variety of conscious, as well as subconscious influences music can have (Panksepp and Bernatzky, 2002 ), and endorsed future research on this subject.

Distinction between the terms “Music Therapy [MT]” and “Music Medicine [MM]”

Most of us know what kind of music or song “can cheer us up.” To treat someone else is something completely different though. Therefore, evidence-based procedures were created for a more pragmatic approach. It is important to differentiate between music therapy and the therapeutic use of music. Music used for patient treatment can be divided into two major categories, namely [MT] and [MM], although the distinction is not always that clear.

Music therapy [MT]

Term used primarily for a setting, where sessions are provided by a board-certified music therapist. Music therapy [MT] (Maratos et al., 2008 ; Bradt et al., 2015 ) stands for the “… clinical and evidence-based use of music interventions to accomplish individualized goals within a therapeutic relationship by a credentialed professional who has completed an approved music therapy program ” (AMTA) 2 . Many different fields of practice, mostly in the health care system, show an increasing amount of interest in [MT]. Mandatory is a systematic constructed therapy process that was created by a board-certified music therapist and requires an individual-specific music selection that is developed uniquely for and together with the patient in one or more sessions. Therapy settings are not limited to listening, but may also include playing, composing, or interacting with music. Presentations can be pre-recorded or live. In other cases (basic) instruments are built together. The process to create these tailor-made selections requires specific knowledge on how to select, then construct and combine the most suitable stimuli or hardware. It must also be noted that music therapy is offered as a profession-qualifying course of study.

Music medicine [MM] (i.e., functional music, music in medicine)

Carried out independently by professionals, who are not qualified music therapists, like relaxation therapists, physicians or (natural) scientists. A previous consultation, or collaboration, with a certified music therapist can be helpful (Register, 2002 ). In recent years, significant progress has been made in both the research and clinical application of music as a form of treatment. It has valuable therapeutic properties, suitable for the treatment of several diseases. The term “music medicine” is used as a term for the therapeutic use of music in medicine (Bradt et al., 2015 , 2016 ), to be able to differentiate it from “music therapy.” [MM] stands for a medical, physiological and physical evaluation of the use of music. If someone listens to his or her favorite music, this is sometimes also considered as a form of music medicine. [MM] deliberately differs from music therapy as part of psychiatric care or psychotherapy. It is important to stress out that the term “Music Therapy [MT]” should not be used for any kind of treatment involving music, although there is without doubt a relationship between [MT] and [MM]. What all of them have in common is the focus on a scientifically, artistically or clinically based approach to music.

“Seamless Transitions” between music therapy [MT] and music medicine [MM]

Activity used for treatment is ambiguous or not clearly labeled as “Music Therapy” or “Music Medicine.” It should not be forgotten that the definition of “Music Therapy” is not always clearly distinguishable from “Music Medicine.” One possible scenario would be a physician (i.e., “non-professional”), who is not officially certified by the AMTA (or comparable institutions), but still acts according to the mandatory rules. In addition, depending on one's home country, uniform standards or eligibility requirements might be substantially different. We think that every effort should be recognized and therefore postulate one definition that can describe the main principle of [MT], [MM], and everything in between, in one sentence: “ Implementation of acoustic stimuli (“music”) as a medium for the purpose of improving symptoms in a defined group of participants (patients) suffering from depression.”

Materials and methods

Literature search.

Search strategy and selection process was performed according to the recommended guidelines of the Cochrane Centre on systematic literature search (Higgins and Green, 2008 ). Our approach ( Figure 2 ) was according to their scientific relevance, supplemented by the analysis of relevant journals, conferences and workshops of recent years. We obtained 60,795 articles from various search engines as initial result. Retrieved data was collected and processed on an existing personal computer with the latest Windows operating system.

Search, collection, selection, and review strategies

We used a combination of words defining three search-categories (Music-, Treatment-, and Depression associated) as well as several words (e.g., Sound, Unhappy, and Treatment) assigned to each category as described in the collection process.below. If synonyms of those keywords were identified, they were added as well. Theme-categories 3 were created next, then related keywords identified and added into a table. “Boolean Operators 4 ” were used as logical connectives to broaden and/or narrow our search results within many databases (mostly search engines as described below).

This way the systematic variation of keyword-based queries and search terms could be performed with much more efficiency. To find the most relevant literature on the subject, keywords were entered into various scientific search engines, namely PubMed, MEDLINE, and Google Scholar. After the collection process, several different steps were used to reduce the number of retrieved results. Selection out of the collected material included to narrow down search results to a limited period of time. We decided to choose a period between 1990 and 2016 (i.e., not exceeding 26 years), because within these years several very interesting works of research were published, but often not mentioned explicitly, discussed in detail, or the main target of a comparative review. After several papers were excluded, a systematic key phrases search was conducted once more to retrieve results, limited to original research articles 5 . We also removed search results that quoted book chapters, as well as reports from international congresses and conferences. Research papers that remained were distinguished from duplicates (or miss-matches not dismissed yet). Based on our predefined criteria for in- and ex-clusion, relevant publications were then selected for an intensified review process. Our plan was to apply the following inclusion criteria: Original research article, published at time of selection, music and/or instruments were used intentionally to improve the emotional status of participants (i.e., intended or officially confirmed as music therapy). The following exclusion criteria were used: No original research, article was not published (e.g., project phase, in review), unverified data or literature was used, participants did neither receive nor interact with music. Not relevant for in- or exclusion was the kind of questionnaire used to measure depression, additional diagnostic measures for pathologies other than depression, spatial and temporal implementation of treatment, demographics (i.e., number, age, and gender) participants had, or distinctive features (like setting, duration, speakers, live version, and recorded) of stimuli. After the initial number of results, the remaining articles were manually checked for completeness and accuracy of information. Our final selection of articles included 28 research papers.

General information — (Figure ​ (Figure1 1 )

“Road-Map” Outline of the following results section (idea, concept and creation of this Figure by Leubner).

Evaluating the methodological quality of our meta-review

During the review process, we used a very strict self-monitoring procedure to ensure that the quality of scientific research was met to the best of our knowledge and stood in accordance with the standards of good scientific practice. Every effort has been made to provide the accuracy of contents as well as completeness of data published within our meta-review. Inspired by another author's meta-review (Kamioka et al., 2014 ), we evaluated our work by the AMSTAR checklist (Shea et al., 2007 ) 6 and found no reasons for objection regarding our selection of reviews. AMSTAR (acronym for “Assessment of Multiple SysTemAtic Reviews”), a questionnaire for assessing systemic reviews, is based on a rating scale with 11 items (i.e., questions). AMSTAR allows authors to determine and graduate the methodological quality of their systematic review.

Effect size

We investigated a wide variety of scholarly papers within our review. There were many different approaches and several procedures. As far as intervention approaches and procedures were concerned, we found (very) similar trends in several papers. To ensure that those different tendencies were not only based on our pure assumption as well as biased interpretation, we also calculated the effect-size correlation by using the mean scores as well as standard deviations for each of the treatment and control groups, if this setup was used by the respective researcher. Most trials showed a small difference in between the experimental and control group at baseline, what almost always turned into a large effect size regarding post-measurement.

Depression score improvement (DSI) — approach to compare questionnaires

As mentioned above, we selected 28 scholarly articles that used different questionnaires to measure symptoms of depression for experimental and control groups. According to common statistical standards we used a formula to evaluate and compare the relative standing of each mean to every other mean. To avoid confusion, we decided to refer to it as “Depression Score Improvement (DSI).” Mathematically speaking it stands for the mean difference between the pre-test and post-test results (i.e., score changes) in percent. (DSI Ind ) stands for an individual and (DSI{ Gr ) for a group setting. Please refer to the Supplementary Materials (Table: “Complete Display of Statistical Data”) 7 for additional information.

The results will review the works in terms of demographics, treatment implementation, and diagnostic measures.

Literature search results — (Figure ​ (Figure2 2 )

Overview of our Collection, Selection, and Review Process (idea, concept and creation of this Figure by Leubner). Initially, the total number of retrieved results was 118,000 as far as google-scholar was concerned. Analysis was complicated by the disproportionately high number of results from google-scholar. Therefore, we decided to narrow down this initial search query to a period from 1990 up to 2016, and reduced the results from google-scholar to 60,000 this way. Compared to the other two search engines, this process was done two steps ahead. At google-scholar we excluded patents as well as citations in the initial window for our search results. Unfortunately search options are very limited, and though we retrieved at first this overwhelming number of 118,000 results!. Some keywords (e.g., anxiety, pain, fear, violence) were deliberately excluded right from the beginning. This was done right at the start of our selection/search process, to prevent a systematic distortion of retrieved results.

Collection process – results

A large list of keywords, based on several questions we had, was created initially. They were combined into search-terms and finally put into search-categories as category-dependent keywords. In addition, we discussed several parameters and agreed on three categories (associated to music, treatment, and depression). By querying scientific databases, using the above-mentioned category-dependent keywords as input criteria, we retrieved a very large number of results. We then searched for a combination of the following words and/or phrases (e.g., “ music AND therapy AND depression”; “acoustic AND intervention AND unhappy” ), narrowed down the retrieved results according to a combination of several keywords (e.g., “ music therapy”; “acoustic intervention” ), and sorted this data according to relevance.

Selection process – results

In step two we applied the above-mentioned approach and narrowed down our search query to a limited period of time, then systematically searched for key phrases, and excluded duplicates as well as previously overlooked miss-matches. Our inclusion criteria can be summarized as follows: Original research article, already published at time of selection, music and/or instruments were used intentionally to improve the emotional status of participants. Our exclusion criteria were: No original research, article was not published (e.g., project phase, in review), unverified data or literature was used, participants did neither receive nor interact with music.

Review process – results

Based on our predefined criteria for inclusion and exclusion, relevant publications were then selected and used for our intensified review process. After reducing the initial number of results, we obtained the remaining articles, conducted a hand-search in selected scientific journals, and manually checked for completeness as well as accuracy of the contained information. The final selection of articles, according to our selection criteria, included 28 papers.

Demographics 8

To begin with, the number of participants as well as age and gender related basic demographics were analyzed.

Participants – results

Our final selection of 28 studies included 1,810 participants, with group sizes between five and 236 persons (n av = 64.64; SD = 56.13). For experimental groups, we counted 954 individuals ( n min = 5; n max = 116; n av = 34.07; SD = 27.78), and 856 ( n min = 10; n max = 120; n av = 30.57; SD = 29.10) for the control respectively. Although three authors (Ashida, 2000 ; Guétin et al., 2009b ; Schwantes and Mckinney, 2010 ) did not use a control sample, those articles were nevertheless considered for calculating accurate and up-to-date data. Depending on each review, sample groups differed profoundly in number of participants. The smallest one had five participants (Schwantes and Mckinney, 2010 ), followed by three authors (Hendricks et al., 1999 ; Ashida, 2000 ; Guétin et al., 2009b ) who used between 10 and 20 individuals in their clinical trials. Medium sized groups of up to 100 participants were found in six articles (Gupta and Gupta, 2005 ; Castillo-Pérez et al., 2010 ; Erkkilä et al., 2011 ; Wang et al., 2011 ; Lu et al., 2013 ). Large groups with more than 100 (Koelsch et al., 2010 ; Silverman, 2011 ), or 200 (Chen et al., 2016 ) participants were the exception, and 236 participants (Chang et al., 2008 ) presented the upper end in our selection.

Age groups – results

Within our selected articles, the youngest participant was 14 (Hendricks et al., 1999 ), and the oldest 95 years of age (Guétin et al., 2009a ). We then separated relevant groups, according to their age, into three categories, namely “young,” “medium,” and “elderly.”

Participants were defined as “young,” if their mean age was below or equal to 30 years (≤30). Young individuals did show minimal better (i.e., higher) depression score improvements (DSI) (mean difference between the pre-test and post-test results was calculated in percent), if they attended group (mean DSI Gr = 53.83%) 9 , rather than individual (DSI Ind = 40.47%) music intervention sessions. These results may be due to the beneficial consequences of social interactions within groups, and thus confirm previous study results (Garber et al., 2009 ; Tartakovsky, 2015 ).

We used the term “medium” for groups of participants, whose mean ages ranged between 31 (>30) and 59 years (<60). Medium-aged participants presented much better results (i.e., higher depression score improvements), if they attended a group (mean DSI Gr = 48.37%), rather than an individual (mean DSI Ind = 24.79%) intervention setting. However, it should be stressed that our findings only show a positive trend and thus should not be evidence.

The third and final group was defined by us as “elderly” and included participants with a mean age of 60 years or above (≥60). Noticeable results were found for the age group we defined as elderly, as participants showed slightly better (i.e., higher) score improvements (mean DSI Ind = 48.96%), if they attended an individual setting. Considering the music selection that had been used for elderly participants, a strong tendency toward classical compositions was found (e.g., Chan et al., 2010 ; Han et al., 2011 ). Because a relevant number of participants came from Asian countries (e.g., China, Korea), elderly people from those research articles received, in addition to classical music, quite often Asian oriented compositions as well. Despite our extensive investigations, the influence this combination had on results, remained uncertain. Positive tendencies within those groups might be due to “traditional” and/or “culture related” factors. It is, however, also conceivable that combining Western classical with traditional Asian music is notably suited to produce better results. Concerning this matter, future research on western depression patients treated with a combination of classical Western, and traditional Asian music might be a promising concept to be further explored.

Gender – results

As far as gender was concerned, we subdivided each sample group in its female and male participants. Women and men were found in 20 study designs. This was the most frequently used constellation. Within this selection, we did not find any significant differences, and so no further analysis was done. Only women took part in two studies (Chang et al., 2008 ; Esfandiari and Mansouri, 2014 ) 10 . Interestingly the same stimuli setup was used in both cases. It consisted of instrumental music without vocals, stored on a digital record, and was presented via loudspeakers from a CD (Chang et al., 2008 ) or MP3 player (Esfandiari and Mansouri, 2014 ). Only men were seen in four research papers (Gupta and Gupta, 2005 ; Schwantes and Mckinney, 2010 ; Albornoz, 2011 ; Chen et al., 2016 ). A significant improvement of depression scores was reported for every experimental group, and once (Albornoz, 2011 ) for a corresponding control setting (received only standard and no alternative treatment). Three articles (Schwantes and Mckinney, 2010 ; Albornoz, 2011 ; Chen et al., 2016 ) shared several similarities, as percussion instruments (e.g., drums, tambourines) were part of each genre selection, all participants received music interventions in a group setting, and stimuli were actively produced within a live performance. In addition, the BDI questionnaire has also been used in three cases (Gupta and Gupta, 2005 ; Albornoz, 2011 ; Chen et al., 2016 ), and thus we were able to perform a search for similarities or tendencies. The average duration for one music intervention was 80 ( SD = 45) min and the total number of sessions was 17 ( SD = 5) in average. Two publications (Hsu and Lai, 2004 ; Wang et al., 2011 ) did not offer any information about gender related distribution of participants.

Music therapy [MT] vs. music medicine [MM] — study results

Music-therapy [mt].

Within our selection of 28 articles, six explicitly mentioned a certified music therapist (Hanser and Thompson, 1994 ; Choi et al., 2008 ; Schwantes and Mckinney, 2010 ; Erkkilä et al., 2011 ; Han et al., 2011 ; Silverman, 2011 ) 11 . For five articles with available data, a combined average depression score improvement (DSI) of 40.87% ( SD = 7.70%) was calculated for the experimental groups. As far as the relevant control groups were concerned, only twice depression scores decreased at all (Choi et al., 2008 ; Erkkilä et al., 2011 ; Table ​ Table1 1 ).

Music-Therapy interventions—music types and results.

Regarding the kind of music provided by a board-certified music therapist, we found some similarities that stood out and appeared more frequently, when compared to music medicine interventions. Percussion music (mainly drumming) was used by four researchers (Choi et al., 2008 ; Schwantes and Mckinney, 2010 ; Erkkilä et al., 2011 ; Han et al., 2011 ). One author (Choi et al., 2008 ) used music based on instruments that were selected according to participant's preferences. Included were, for example, egg shakes, base-, ocean-, and paddle-drums. Participants actively played and passively listened to instruments or sounds, complemented by singing together. Another researcher (Erkkilä et al., 2011 ) preferred the African Djembe 12 drum as well as a selection of several percussion sounds created digitally by an external MIDI ( Musical Instrument Digital Interface ) synthesizer. Percussion-oriented improvisation that included rhythmic drumming and vocal patterns was another approach one scholar (Han et al., 2011 ) used for his stimuli selection. Congas, Cabassas, Ago-Gos, and Claves was the percussion-based selection (in addition a guitar and a Piano was also available) in the fourth music-therapy article (Schwantes and Mckinney, 2010 ). Twice, music without the use of percussion instruments or drums in general, was selected for the intervention. Once (Hanser and Thompson, 1994 ) relaxing, slow and rhythmic harp-samples, played from a cassette-player, were used. In addition, each of the participants was invited to bring some samples of her or his favored music titles. The second one (Silverman, 2011 ) decided to play a “12-bar Blues” (i.e., “blues changes”) 13 progression as an introduction, followed by a Blues songwriting session. The last-mentioned music-therapy project was the only article out of six, where participants within their respective music intervention group did not present a significant reduction of depression. A very interesting “fund” was that none of the music-therapy articles neither concentrated their main music selection on classical, nor on Jazz music. When we looked for other distinctive features it turned out that stimuli were actively produced within a live performance in five articles. There was only one exception (Hanser and Thompson, 1994 ), where a passive presentation of recorded stimuli was preferred by the scholar.

Music-medicine [MM]

The remaining 22 research articles did not explicitly mention a certified music therapist. In those cases, some variant of music medicine was used for intervention. Often the expression music therapy was used, although a more detailed description or specific information was neither published nor available upon our request. With one exception (Castillo-Pérez et al., 2010 ), we could calculate the (DSI) 9 for 25 articles that used some variant of music-medicine [MM].

When we investigated the kind of music that was used, a broader selection of genres was found. Percussion based tracks and drumming appeared in five scholarly papers (Ashida, 2000 ; Albornoz, 2011 ; Lu et al., 2013 ; Chen et al., 2016 ; Fancourt et al., 2016 ). Researchers that used drums reported a significant depression score improvement for every experimental group and we calculated an average of 53.71% for those five articles. Regarding the kind of genre used in our selection of music-medicine articles, a wider range of genres was found. One of the biggest differences was that only music-medicine articles used, in addition to percussion stimuli, also classical and Jazz music for their intervention. Please note that for reasons of confusion, we do not mention the Seamless Transitions between Music Therapy [MT] and Music Medicine [MM] from the “Materials and Methods Section.”

Music genres (selection of music titles) – results

Regarding the kind of music used in our selection of research articles, a wide range of genres was found. Mainly three styles, classical 14 (9x), percussion 15 (9x), and Jazz (5x) music were used more frequently for music intervention. The evaluation took place when specific compositions showed significantly greater improvements in depression compared to other research attempts. Utilizing our comprehensive data analysis, music titles were categorized according to genre or style (e.g., classical music, Jazz), narrowed down (e.g., Jazz), sorted by magnitude of depression score improvements (DSI) 9 , and finally examined for distinctive features (like setting, duration, speakers, live version, recorded). Similarities that stood out and appeared more frequently among one selected music genre were compared with the 28 scholarly articles we selected for our meta-review.

Classical music – results

In nine articles, classical music (Classical or Baroque period) 22 was used. Several well-known composers such as W.A. Mozart (Castillo-Pérez et al., 2010 ), L. v. Beethoven (Chang et al., 2008 ; Chan et al., 2009 ) and J. S. Bach (Castillo-Pérez et al., 2010 ; Koelsch et al., 2010 ) have been among the selected samples. If classical music was used as intervention, our calculations revealed that four studies out of eight 16 were among those with depression score improvements (DSI) 31 that were above the average 17 of 39.98% ( SD = 12). When we looked for similarities between these, three of the four studies (Harmat et al., 2008 ; Chan et al., 2009 ; Guétin et al., 2009a ) used individual sessions, rather than a group setting (Koelsch et al., 2010 ). For all four articles mentioned above, we calculated an average of 11 ( SD = 10) for the total number of sessions that included classical music. The remaining five articles on the other hand, presenting results not as good as the aforementioned, showed an average of 30 ( SD = 21) music interventions. One plausible hypothesis might be “saturation effect” caused by too many interventions in total. Too little variety within the selection of music titles has probably played an important role as well. A general tendency that less intervention sessions in total would lead to better results for every case where classical compositions were included could not be confirmed for our selection.

Percussion (drumming-based) music – results

Percussion music (mainly drumming) was used by nine 18 researchers, and among those, two ways of integration were found. On the one hand, rhythmic percussion compositions were included as part of the music title selection used for intervention. On the other hand, and this was the case in nine articles, various forms of drums had been offered to those who joined the experimental groups, allowing them to “produce their own” music. Sometimes participants were accompanied by a music therapist (e.g., Albornoz, 2011 ) or professional artist (Fancourt et al., 2016 ), who gave instructions on how to use and play these instruments. When we looked for trends or distinctive features percussion music (in particular drumming) had, it turned out that, except one article (Erkkilä et al., 2011 ), all were carried out within a group, rather than an individual setting. A further search for additional similarities, leading to better outcome scores, did not deliver any new findings as far as improvement of depression was concerned. Participants in altogether 7 out of 9 percussion groups were medium aged, two authors (Ashida, 2000 ; Han et al., 2011 ) described elderly participants, whereas none of the percussion groups included young participants.

A wide and even distribution of reduced depression scores across all outcome levels became apparent, when participants received percussion (or drumming) interventions. We calculated an average depression score improvement (DSI) of 47.80% ( SD = 14). Above-average results regarding depression score improvement (DSI), were achieved in four experiments that had an average percussion session duration of 63 ( SD = 19) min. In comparison, we calculated for the remaining five articles an average of 93 ( SD = 26) min. Although a difference of 30 min showed a clear tendency, it was not enough of a difference to draw any definitive conclusions.

Jazz music – results

Finally, five 19 researchers used primarily Jazz 20 as music genre for their intervention. Featured performers (artists) were Vernon Duke (“April in Paris”) (Chan et al., 2009 ), M. Greger (“Up to Date”), and Louis Armstrong (“St. Louis Blues”) (Koelsch et al., 2010 ). Unfortunately, available data was quite limited, mainly since most authors did not disclose relevant information and a detailed description was rarely seen. Some interesting points were also found for research articles that used Jazz as a treatment option. All five of them were among those with good outcome scores, as far as depression reduction was concerned. Test scores ranged between a significance level of p < 0.01 (Guétin et al., 2009a ; Verrusio et al., 2014 ; Chen et al., 2016 ) and sometimes even better than p < 0.001 (e.g., Koelsch et al., 2010 ; Fancourt et al., 2016 ). Depression score improvement (DSI) had an average of 43.41% ( SD = 6). However, there was no clear trend leading toward Jazz as a more effective intervention option, when compared to other music genres. This was assumed because the two studies that showed the best 21 reduction in depression [Chan et al., 2010 (DSI = 48.78%); Koelsch et al., 2010 (DSI = 4 6.58%)] used both classical music in addition to Jazz as an intervention. Experimental groups received two types of intervention (i.e., classical music and Jazz) which eventually blurred outcome scores or prevented more accurate results. Since it was not possible to differentiate to what extent either classical music or Jazz was responsible for the positive trend in reducing symptoms of depression, further research in this field is needed.

Additional music genres – results

Numerous other music styles were used in the experiments, ranging from Indian ragas 22 played on a flute (Gupta and Gupta, 2005 ; Deshmukh et al., 2009 ), nature sound compositions (Ashida, 2000 ; Chang et al., 2008 ), meditative (Chan et al., 2010 ), or slow rhythm music (Chan et al., 2012 ), to lullabies (Chang et al., 2008 ), pop or rock (Kim et al., 2006 ; Erkkilä et al., 2011 ), Irish folk, Salsa, and Reggae (Koelsch et al., 2010 ), only to name a few. As far as we were concerned all those genres mentioned above would present interesting approaches for future research. Due to a relatively small number and simultaneously wide-ranging variety, more thorough investigations are needed, though. These should be examined independently. As far as the above-mentioned music genres, other than classical, percussion, or Jazz were concerned, no indication for a preferable combination was observed.

Experimental vs. control groups – results

Non-significant results for experimental groups ( p > 0.05).

In two (Deshmukh et al., 2009 ; Silverman, 2011 ) out of 28 studies within our selection of research papers, no significant reduction in depression scores was reported, after participants participated in music interventions. Within those two cases all relevant statistical observations differed without any obvious similarities indicating reasons for non-significant results. Although the results did not meet statistical significance for symptom improvement, both authors explicitly pointed out that positive changes in the severity of depression became obvious for the respective experimental groups. We declared one article (Guétin et al., 2009b ) as significant, although it was marked as non-significant in our complete table. This was due to the overall results of this specific research paper, with significant [HADS-D] test scores for weeks 5, 10, and 15. Only week 20 did not follow this positive trend of improvement. It is also important to mention that after music treatment every one of the additional tests [HADS-A for Anxiety; Face(-Scale) to measure mood] showed significant improvements for the experimental group.

Alternative treatment for corresponding control groups

Control groups, who received an alternative (i.e., non-music) intervention, were found in nine research articles (e.g., Guétin et al., 2009a ; Castillo-Pérez et al., 2010 ) 23 . We investigated whether there were particularly noticeable differences in outcome scores, when relevant control groups, who received an alternative treatment, were compared to those who received no additional intervention at all (or only the usual treatment) 24 . As far as these nine articles were concerned, a significant reduction ( p < 0.05) in depression scores was found in every experimental but only one control setting (Hendricks et al., 1999 ). In this case, an entirely different result became apparent, when control participants received a Cognitive-Behavioral Therapy [CBT] and a significant reduction ( p < 0.05) in depression scores was measured compared to the respective baseline score, although music still lead to better results. Another scholar (Chan et al., 2012 ) 25 , instructed participants in the control group to take a resting period, while simultaneously the experimental attendees joined their music intervention session. This alternative approach did not reduce the [GDS-15] depression score, but even increased it. Interestingly, the same author previously published (Chan et al., 2009 ) a significant ( p = 0.007) increase (i.e., worsening of depression) for the relevant control setting. To be complete, a resting period was also conducted in another case (Hsu and Lai, 2004 ), but results showed also no significant reduction in depression scores. Other attempts to provide an alternative intervention for the control group have been monomorphic tones (Koelsch et al., 2010 ) that corresponded to the experimental music samples (in pitch-, BPM-, and duration), verbal treatment sessions (Silverman, 2011 ), antidepressant drugs (Verrusio et al., 2014 ) 26 , reading sessions (Guétin et al., 2009a ) or a “conductive-behavioral” psychotherapy (Castillo-Pérez et al., 2010 ).

Significant results for control groups ( p < 0.05):

Significant reduction of depression ( p < 0.05) in corresponding control (“non-music treatment”) groups was reported twice (Hendricks et al., 1999 ; Albornoz, 2011 ) within our selection of scholarly articles. In one instance (Albornoz, 2011 ) the relevant participants received only standard care, but in the other case (Hendricks et al., 1999 ) an already above mentioned alternative treatment (i.e., “Cognitive-Behavioral Activities”) was reported.

Spatial and temporal implementation of treatment

Individual vs. group intervention – results.

As postulated by previous literature (Wheeler et al., 2003 ; Maratos et al., 2008 ), we differentiated mainly two scenarios based on the number of participants who attended music intervention sessions and referred to them as “group” or “individual.” Group sessions can awaken participants' social interactions and individual sessions often provides motivation (Wheeler et al., 2003 ). Here, a “group” scenario was specified, if two or more persons ( n ≥ 2) were treated simultaneously, whereas “individual” determined experimental settings where only one single person received music interventions individually ( n = 1). Among our article selection we could find a well-balanced distribution of 15 trials with participants who received music interventions in a group, while 13 researchers used an individual setting. First, the impact of individual compared to group treatment was evaluated. Here an almost equivalent outcome (for the significance-level of results) across all 13 individual, compared to 15 group settings was found, without any advantage to one over the other. Non-significant improvements were seen once for a group (Silverman, 2011 ) and once 27 for an individual (Deshmukh et al., 2009 ) intervention.

Single-session duration – results

The question whether groups showed different (i.e., more or less) improvements, if the duration of one single session was altered, we decided to use the intervention length as a key metric (Figure ​ (Figure3). 3 ). Except for two instances (Hendricks et al., 1999 ; Wang et al., 2011 ), 26 research papers reported the duration one single treatment had. Among those 20 min (Guétin et al., 2009a ) was the shortest, and 120 min (Albornoz, 2011 ; Han et al., 2011 ) the longest duration for one session. The average for all 26 articles was 55 min, 70 min for 13 28 group settings, and 40 min as far as the 13 individual intervention setups were concerned.

Session- and research duration–vs.–[DSI] results in dependence of treatment setting.

Entire research (=) intervention program duration – results

Continuing our review process, some interesting diversity was found for the scheduled (i.e., total) treatment duration (Figure ​ (Figure3). 3 ). It ranged from 1 day in two cases (Koelsch et al., 2010 ; Silverman, 2011 ) up to 20 (Guétin et al., 2009b ), or even 24 weeks (Verrusio et al., 2014 ). Out of 26 trials an average duration of 7 weeks was found. In two cases, the data was missing (Wang et al., 2011 ; Esfandiari and Mansouri, 2014 ). The scheduled (i.e., total) treatment duration was determined by a variety of factors. Our investigation, whether there was any relationship between the entire duration of experimental projects and relevant outcome scores, delivered the following results. For an individual (Ind) therapy setting, we isolated eight 29 research papers with above average 30 results in depression score improvement (DSI Ind > 36.50%). We then calculated for the entire project an average duration of almost 7 weeks. For the remaining five 31 articles that also used an individual approach, but had below average depression score improvements, an average duration of 6 weeks was found. A different picture became apparent when we selected those four 32 articles that presented better than average (DSI Gr > 49.09%) results in depression score improvement, after participants received music intervention in a group (Gr). Percussion music (mainly drumming) was used by three researchers (Ashida, 2000 ; Lu et al., 2013 ; Chen et al., 2016 ). In comparison, the fourth author (Hendricks et al., 1999 ) used a selection of relaxing music for treatment. For this setup, a combined duration of six ( SD = 4) weeks was calculated for the entire project length. On the other hand, a mean close to 10 ( SD = 7) weeks was found for the remaining 7 33 group intervention projects that were less successful (i.e., below average), as far as depression score reduction was concerned. Based on these results, we concluded that the length for the entire music intervention procedure might be a crucial element for successful results, and seems to be associated with the intervention type. These findings were not enough to draw further conclusions for every project though, but as far as our selection was concerned, a slightly longer intervention duration of 7 weeks led to better results if participants were treated individually. In comparison, for a group setting our calculations revealed a different picture, when we calculated the average entire duration for all relevant research projects. Here it was 6 weeks that produced the most beneficial results within groups. Drums were used for three out of the four projects that presented above average results. Once (Ashida, 2000 ) a small African drum was used for “drumming activity” at the start of every session. Each time a different participant was asked to perform with this instrument, although nobody in the experimental group was neither a professional drummer nor a musician. African drums were also used by another researcher (Chen et al., 2016 ). In addition, equipment also included one stereo, one electronic piano, two guitars, one set of hand glockenspiel, and other percussion instruments such as cymbals, tambourines, and xylophones. Finally, percussion instruments used in the third study (Lu et al., 2013 ) included hand bells, snare drums, a castanet, a tambourine, some claves, a triangle and wood blocks.

Total number of sessions – results

Continuing the analysis, we evaluated the total number of music intervention sessions. Apparently, this metric was dependent on the duration as well as frequency (“session frequency”) each intervention had. With one exception (Wang et al., 2011 ), where relevant data was missing, the number of sessions varied considerably. Only a single treatment session was used by three authors (Chan et al., 2010 ; Koelsch et al., 2010 ; Silverman, 2011 ), whereas 56 sessions (Castillo-Pérez et al., 2010 ) marked the opposite end of the scale. For 27 articles with available data, a combined average of 15 sessions was found. As far as the total number of sessions in an individual type of setting was concerned, above average results had a combined number of 13 ( SD = 5) sessions, whereas the remaining six research works had 18 ( SD = 8) interventions. The best results in a group setting showed an average of 17 sessions ( SD = 15) and they were found in 7 scholarly publications. In comparison, we calculated 14 sessions in total for the remaining 7 articles.

Session frequency (i.e., sessions per week) – results

As described previously (Wheeler et al., 2003 ), the number of sessions can produce different results. Researchers, within our selection of 28 articles, used various approaches for their experiment, as far as the “session frequency” (i.e., number of sessions within a defined duration) was concerned. Pre-defined intervals ranged from once a week up to one time a day. Once (Choi et al., 2008 ), the article did mention the total number of sessions ( n = 15) with a “frequency” of one to two times a week and a total intervention duration of 12 weeks. To be able to present an appropriate comparison of statistical data, a mean of 1.25 sessions per week was calculated. Besides two cases (Wang et al., 2011 ; Esfandiari and Mansouri, 2014 ) where no information was provided, the combined average session frequency for the remaining 26 articles was 2.89 ( SD = 2.50) interventions per week. Usually sessions were held once a week.

Session- and research duration – vs. – [DSI] results in dependence of treatment setting

We further investigated if there was an association between therapy setting (individual or group), the length of a single session, and trial duration with regard to symptom improvement. Groups (Figure ​ (Figure3) 3 ) showed better (i.e., above average) improvements in depression, if each session had an average duration of 60 min, and the mean length of treatment was 4–8 weeks.

In comparison, the two variables, session length and trial duration, had different effects for individual treatment approaches (Figure ​ (Figure3). 3 ). Above average results were found for sessions lasting 30 min combined with a treatment duration between 4 and 8 weeks.

Diagnostic measures – results of selected questionnaires

We discovered some distinctive features as well as certain similarities in our selection of 28 articles. They might be a guidance for future research projects and as such are presented in more detail in the subsections below.

Beck depression inventory [BDI]

There are three versions of the BDI. The original [BDI] (Beck et al., 1961 ), followed by its first [BDI-I/-1A] (Beck et al., 1988 ) and second [BDI-II] revision (Beck et al., 1996 ). Beck used a novel approach to develop his inventory by writing down the verbal symptom description of his patients with depression and later sorted his notes according to intensity or severity.

Beck depression inventory [BDI] – results

The BDI 34 (Beck et al., 1961 , 1996 ) was the most widely used screening tool in our scholarly selection. It was used in eight trials, but we only selected 7 35 studies for evaluating pre-post BDI scores. Once (Harmat et al., 2008 ), results were only provided for the experimental group, although an experimental control setting was described by the author. Twice (Harmat et al., 2008 ; Esfandiari and Mansouri, 2014 ) two experimental groups and one control group were reported. In one case (Esfandiari and Mansouri, 2014 ) two different music genres were used (“Light Pop & Heavy Rock”), and in another incident (Harmat et al., 2008 ) the second experimental group listened to an audiobook (“Music & Audiobook”). BDI baseline scores, that indicated a minimal 36 to mild 37 depression, were found in two articles (Gupta and Gupta, 2005 ; Harmat et al., 2008 ). Both authors reported for their experimental group a significant improvement of (BDI) depression scores. We calculated an overall average reduction of 2.72 ( SD = 0.03). Moderate 38 signs of depression, with BDI baseline scores that ranged from 18.66 (Albornoz, 2011 ) to 24.72 (Chen et al., 2016 ), were found twice. Music intervention improved BDI scores significantly, with an overall average reduction of 10.65 ( SD = 3.63) for both articles mentioned above. For the respective control groups one author (Chen et al., 2016 ) reported non-significant pre-post changes, whereas the other researcher (Albornoz, 2011 ) described a significant 39 reduction in the standard treatment group as well. The remaining three scholarly papers (Hendricks et al., 1999 ; Choi et al., 2008 ; Esfandiari and Mansouri, 2014 ) described participants with a severe 40 depression, as confirmed by the initial (baseline) BDI results. One article (Esfandiari and Mansouri, 2014 ), of the three mentioned above, used one control and two experimental groups, who were treated with either “light” or “heavy” music. To be able to compare this work with the other studies one single baseline (31.75), post treatment (12.50), and pre-post difference score of 19.25 ( SD = 2.47) 41 was calculated (according to common statistical standards) for both experimental settings. Interestingly, the corresponding control sample showed a three-point increased BDI score ( p > 0.05) and no decrease at any time. Continuing with the remaining articles, even bigger initial baseline BDI scores of 39.00 ( SD = n/a) (Hendricks et al., 1999 ) and 49.30 ( SD = 3.10) (Choi et al., 2008 ) were found. In addition, both authors reported a significant pre-post BDI score reduction 42 for their experimental groups. Based on the published data it became evident that BDI scores improved significantly in each of the cases and this time an overall average reduction of 26.90 ( SD = 9.59) was calculated. Once (Hendricks et al., 1999 ) a significantly reduced BDI pre-post score was also reported for the control setting, where participants received a cognitive-behavioral activities program as an alternative (non-music) intervention.

We compared all research projects that used the BDI questionnaire (Table ​ (Table2). 2 ). Higher baseline scores almost always led to comparatively bigger score reductions in those experimental groups, who received music intervention. Except for two articles (Hendricks et al., 1999 ; Albornoz, 2011 ), no significant improvements were found for control samples. For one of the above-mentioned exceptions (Hendricks et al., 1999 ) an alternative treatment (“ Cognitive-Behavioral” activities ) was provided, which might be a plausible explanation why those relatively young participants (all 14 or 15 years old) showed such reductions in BDI values. Nevertheless, it is also important to mention that the relevant experimental group improved to a greater extent (BDI PRE − BDI POST = 37.66) after treatment. As far as the other case (Albornoz, 2011 ) was concerned, no alternatives (i.e., other than basic or usual care) were offered, and thus no explanation had been established as to how the results could be explained.

Comparison of BDI results.

Geriatric depression scale [GDS-15/-30]

The original Geriatric Depression Scale [GDS-30] (Yesavage et al., 1983 ) includes 30 questions (Hanser and Thompson, 1994 ; Chan et al., 2009 ; Guétin et al., 2009a ) and its shorter equivalent [GDS-15] (Yesavage and Sheikh, 1986 ) contains 15 items (Chan et al., 2010 , 2012 ; Verrusio et al., 2014 ).

Geriatric depression scale [GDS-15/-30] – results

A more precise analysis of results was also done for the Geriatric Depression Scale (GDS-15/-30) scores. As already suggested by its name, all 223 participants were elderly. Because both GDS versions are based on the same questionnaire, we combined scores of the long (i.e., GDS-30) with the short (i.e., GDS-15) test version and found a total of 223 participants in six articles (e.g., Chan et al., 2009 ; Verrusio et al., 2014 ). A possible bias could be prevented because tests were evenly distributed in number, and with respect to higher GDS-30 as well as lower GDS-15 scores, calculations were adapted accordingly. Taking a closer look at the GDS-15/-30 results (Table ​ (Table3), 3 ), some similarities could be found for the most successful (all p ≤ 0.01) four research articles (Chan et al., 2009 , 2010 ; Guétin et al., 2009a ; Verrusio et al., 2014 ). All of them used and mainly focused on classical compositions as far as their music title selection was concerned. The average reduction in depression as measured by the GDS-15/-30 depression scores was 43% (−42.62%; SD = 6.24%). In comparison, every one of the remaining four research projects (Hanser and Thompson, 1994 ; Ashida, 2000 ; Han et al., 2011 ; Chan et al., 2012 ) also presented significant results, albeit not as good as the above-mentioned (all p ≤ 0.05). Interestingly, as far as music genres were concerned, the focus of these less successful projects was rhythmic drumming in two cases (Ashida, 2000 ; Han et al., 2011 ). For the remaining two (Hanser and Thompson, 1994 ; Chan et al., 2012 ) primarily relaxing, slow paced titles 43 were selected as intervention.

Comparison of GDS-15/-30 Results ( * )GDS-15, ( ** )GDS-30.

Other diagnostic measures for depression 44 – results 45

Several times, additional questionnaires were used to measure changes in the severity of depression.

Researchers performed those surveys (Table ​ (Table4) 4 ) in addition to their “main” depression questionnaire. Please refer to our Supplementary Material for a more comprehensive test description.

Additional tests, conducted by researchers within our article selection for investigating changes in depression.

Diagnostic measures for pathologies other than depression – results

In many instances, additional questionnaires were used (Table ​ (Table5 5 ) 49 to measure symptoms other than depression (e.g., Anxiety is known to be one of the most common depression comorbidities, Sartorius et al., 1996 ; Bradt et al., 2013 ; Tiller, 2013 ). Eight 46 researchers concentrated their investigation entirely on depression, and thus only performed questionnaires related to this pathology. In comparison, most of the remaining studies measured additional pathologies, with some of them known to be often associated comorbidities with depressive symptoms. However, because these topics were not the focus of this review, we won't discuss them here in detail. A much more detailed representation is available in the Supplementary Table. Please refer to the original studies for a more comprehensive test description.

Additional tests, conducted by researchers within our selection for investigating changes in other pathologies.

Discussion, conclusion and further thoughts

Depression often reduces participation in social activities. It also has an impact on reliability or stamina at daily work and may even result in a greater susceptibility to diseases. Music can be considered an emerging treatment option for mood disorders that has not yet been explored to its full potential. To the best of our knowledge, there were only very few meta-analyses, or systematic reviews of randomized controlled trials available that generated the amount of statistical data, which we presented here.

Certain individual-specific attributes of music are recognizable, when the medium of music is decomposed (Durkin, 2014 ) 47 into its components. Numerous researchers reported the beneficial effects of music, such as strengthening awareness and sensitiveness for positive emotions (Croom, 2012 ), or improvement of psychiatric symptoms (Nizamie and Tikka, 2014 ). Group drumming, for example, helped soldiers to deal with their traumatic experiences, while they were in the process of recovery (Bensimon et al., 2008 ). However, we have concentrated our focus of interest on patients diagnosed with clinical depression, one of the most serious and frequent mental disorders worldwide.

In this review we examined whether, and to what extent, music intervention could significantly affect the emotional state of people living with depression. Our primary objective was to accurately identify, select, and analyze up-to-date research literature, which utilized music as intervention to treat participants with depressive symptoms. After a multi-stage review process, a total of 1.810 participants in 28 scholarly papers met our inclusion criteria and were finally selected for further investigations about the effectiveness music had to treat their depression. Both, quantitative as well as qualitative empirical approaches were performed to interpret the data obtained from those original research papers. To consider the different methods researchers used, we presented a detailed illustration of approaches and evaluated them during our investigation process.

Interventions included, for example, various instrumental or vocal versions of classical compositions, Jazz, world music, and meditative songs to name just a few genres. Classical music (Classical or Baroque period) for treatment was used in nine articles. Notable composers were W.A. Mozart, L. v. Beethoven and J. S. Bach. Jazz was used five times for intervention. Vernon Duke (Title: “April in Paris”), M. Greger (Title: “Up to Date”), or Louis Armstrong (Title: “St. Louis Blues”) are some of the featured artists. The third major genre researchers used for their experimental groups was percussion and drumming-based music.

Significant criteria were complete trial duration, amount of intervention sessions, age distribution within participants, and individual or group setting. We compared passive listening to recorded music (e.g., CD), with active experiencing of live music (e.g., singing, improvising with instruments). Furthermore, the analysis of similar studies has enhanced and complemented our work. Previous studies indicated positive effects of music on emotions and anxiety, what we tried to confirm in more detail. The length of an entire music treatment procedure was suspected to be an important element for reducing symptoms of depression. A longer treatment duration of 7 weeks for an individual, compared to nearly 6 weeks in a group setting led to better (i.e., above average) outcomes. Although a difference was discovered, 1 week was not enough to draw further conclusions for each and every project. As far as intervals between sessions were concerned, we found no differences between those research articles that were among the best, compared to the remaining experimental designs. Consequently no trend was becoming apparent, favoring one over the others. We further investigated if there was any association between an individual or a group setting, if the length of a single session and trial duration were compared with regard to symptom improvement. Groups showed better improvements in depression, if each session had an average duration of 60 min, and a treatment between 4 and 8 weeks long. In comparison, the two variables, session length and trial duration, had different effects for individual treatment approaches. Above average results were found for sessions lasting 30 min combined with a treatment duration between 4 and 8 weeks. Furthermore, results were compared according to age groups (“young,” “medium,” and “elderly”). Overall, elderly people benefitted in particular from this kind of non-invasive treatment. During, but mainly after completion of music-driven interventions, positive effects became apparent. Those included primarily social aspects of life (e.g., an increased motivation to participate in life again), as well as concerned participants' psychological status (e.g., a strengthened self-confidence, an improved resilience to withstand stress).

We described similarities, the integration of different music intervention approaches had on participants in experimental vs. control groups, who received an alternative, or no additional treatment at all. Additional questionnaires confirmed further improvements regarding confidence, self-esteem and motivation. Trends in the improvement of frequently occurring comorbidities (e.g., anxiety, sleeping disorders, confidence and self-esteem) 48 , associated with depression, were also discussed briefly, and showed promising outcomes after intervention as well. Particularly anxiety (Sartorius et al., 1996 ; Tiller, 2013 ) is known to be a common burden, many patients with mood disorders are additionally affected with. Interpreted as manifestation of fear, anxiety is a basic feeling in situations that are regarded as threatening. Triggers can be expected threats such as physical integrity, self-esteem or self-image. Unfortunately, researchers merely distinguished between “anxiety disorder” (i.e., mildly exceeded anxiety) and the physiological reaction. Also, the question should be raised if the response to music differs if patients are suffering from both, depression and anxiety. Sleep quality in combination with symptoms of depression (Mayers and Baldwin, 2006 ) raised the question, whether sleep disturbances lead to depression or, vice versa, depression was responsible for a reduced quantity of sleep instead. Most studies used questionnaires that were based on self-assessment. However, it is unclear whether this approach is sufficiently valid and reliable enough to diagnose changes regarding to symptom improvement. Future approaches should not solely rely on questionnaires, but rather add measurements of physiological body reactions (e.g., skin conductance, heart and respiratory rate, or AEP's via an EEG) for more objectivity.

The way auditory stimuli were presented, also raised some additional questions. We found that for individual intervention most of the times headphones were used. For a group setting speakers were the number one choice instead. For elderly participants, a different sensitivity for music perception was a concern, when music was presented directly through headphones. Headphones add at least some isolation from background noises (i.e., able to reduce noise disturbances and surround-soundings). Another concern was that most of the time a certified hearing test was not used. Although, a tendency toward a reduction in the ability to hear higher frequencies is quite common with an increased age, there might still be substantial differences between participants.

Two authors (Deshmukh et al., 2009 ; Silverman, 2011 ) reported that participants within their respective music intervention group, did not present a significant reduction of depression. Those two had almost nothing in common 49 and were not investigated further.

Control groups, who received an alternative (“non-music”) intervention, were found in nine research articles. Significant reduction of depression in corresponding control (“non-music intervention”) groups was reported by two authors (Hendricks et al., 1999 ; Albornoz, 2011 ). In one instance (Albornoz, 2011 ) the relevant participants received only standard care, but in the other case (Hendricks et al., 1999 ) an alternative treatment (Cognitive-Behavioral activities) was reported. Medical conceptions are in a constant state of change. To achieve improvements in areas of disease prevention and treatment, psychology is increasingly associated with clinical medicine and general practitioners. Under the guidance of an experienced music therapist, the patient receives a multimodal and very structured treatment approach. That is the reason why we can find specialists for music therapy in fields other than psychosomatics or psychiatry today. Examples are internal medicine departments and almost all rehabilitation centers. The acoustic and musical environment literally opens a portal to our unconscious mind. Music therapy often comes into play when other forms of treatment are not effective enough or fail completely.

Music connects us to the time when we only had preverbal communication skills (Hwang and Hughes, 2000 ; Graham, 2004 ; i.e., communication before a fully functioning language is developed; e.g., infants or children with autism spectrum disorder), without being dependent on language. Although board-certified music therapy is undeniable the most regulated, developed and professional variant, this should not hinder health professionals and researchers from other areas in the execution of their own projects using music-based interventions. The only thing they should be very precise about, is the way they define their work. Within our selection of articles the expression music therapy was used sometimes, although a more detailed description or specific information was neither published nor available upon our request. In those cases, the term “music therapy” should not be used, but instead music medicine or some of the alternatives mentioned in this manuscript (e.g., therapy with music, music for treatment). This way many obstacles as well as misunderstandings can be prevented in the first place, but high-quality research is still produced. Also, it is very important that researchers contemplate and report the details of the music intervention that they use. For example, they should report whether the music is researcher-selected or participant-selected, the specific tracks they used, the delivery method (speakers, headphones), and any other relevant details.

Encouraged by the promising potential of music as an intervention (Kemper and Danhauer, 2005 ), we pursued our ambitious goal to contribute knowledge that provides help for the affected individuals, both the patients themselves as well as their nearest relatives. Furthermore, we wanted to provide detailed information about each randomized controlled study, and therefore made all our data available, so others may benefit for their potential upcoming research project. The overall outcome of our analysis, with all significant effects considered, produced highly convincing results that music is a potential treatment option, to improve depression symptoms and quality of life across many age groups. We hope that our results provide some support for future concepts.

Author contributions

DL (Substantial contributor who meets all four authorship criteria): (1) Project idea, article concept and design, as well as planning the timeline, substantially involved in the data, material, and article acquisition, (2) mainly responsible for drafting, writing, and revising the review article, (3) responsible for selecting and final approving of the scholarly publication, (4) agreed and is accountable for all aspects connected to the work. TH (Substantial contributor who meets all four authorship criteria): (1) Substantial help with the concept and design, substantially contributed to the article and material acquisition, (2) substantially contributed to the project by drafting and revising the review article, (3) responsible for final approval of the scholarly publication, (4) agreed and is accountable for all aspects connected to the work.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

1 Participants in the two music groups (standard or patterning music) showed an increased belief in their personal power as well as a reduction in pain, depression and disability, compared to the relevant control group. The two experimental groups listened to 1 h of music each day for 7 days in a row.

2 Official definition of the American Music Therapy Association [AMTA] http://www.musictherapy.org/about/quotes/

3 Clinical speciality areas; Diagnostic, Treatment, and Therapeutic procedures, approaches, tools; Disorders; Age groups; Scientific; Country-specific; Musical Aspects; Recording hardware and equipment; Literature Genre; Publication type or medium; Year of publication; Number of authors.

4 Boolean Operators for searching databases: Concept explained by the Massachusetts Institute of Technology [MIT] .

5 Our preference was an experimental-control setting, but unfortunately three authors (Ashida, 2000 ; Guétin et al., 2009b ; Schwantes and Mckinney, 2010 ) did not use a control sample.

6 AMSTAR (Shea et al., 2007 )–Further Info & AMSTAR online calculator: https://amstar.ca/Amstar_Checklist.php ; National Collaborating Centre for Methods and Tools (NCCMT): http://www.nccmt.ca/resources/search/97 Questions included in the AMSTAR-Checklist (Shea et al., 2007 ) are: (I) Was an “a priori” design provided? (II) Was there duplicate study selection and data extraction? (III) Was a comprehensive literature search performed? (IV) Was the status of publication (i.e. gray literature) used as an inclusion criterion? (V) Was a list of studies (included and excluded) provided? (VI) Were the characteristics of the included studies provided? (VII) Was the scientific quality of the included studies assessed and documented? (VIII) Was the scientific quality of the included studies used appropriately in formulating conclusions? (IX) Were the methods used to combine the findings of studies appropriate? (X) Was the likelihood of publication bias assessed? (XI) Was any conflict of interest included?

7 In our Supplementary Table (“Complete Display of Statistical Data”), DSI was referred to as “Change [%].”

8 A much More Detailed Representation of Demographics is Available in the Supplementary Table ( Appendix-B ).

9 9DSI: Depression Score Improvement stands for the mean difference between the pre-test and post-test results (i.e., score changes) in percent. Please refer to the supplementary materials for additional information.

10 Music interventions: Individual setting (Chang et al., 2008 ); Group setting (Esfandiari and Mansouri, 2014 ).

11 One [MT] music-therapy article (Silverman, 2011 ) was not used for comparison and calculations because the relevant data was unavailable.

12 Djembe is based on the expression “anke djé, anke bé” which roughly translates as “everyone should come together in peace and harmony.”

13 12-bar Blues: Traditional Blues pattern that is 12 measures long. This chord progression is also used for many other music genres and quite popular in pop-music.

14 Ambiguity of the term “classical” music: In our review, this term refers to “Western Art Music” and thus includes, but is not limited to the “Classical” music period. Most of the time we used this term for music from the Baroque (1600–1750), Classical (1750–1820), and Romantic (1804–1910) period.

15 Within percussion groups various types of drums presented the instrument of choice most of the time.

16 Eight out of nine articles because in on case (Castillo-Pérez et al., 2010 ) scores were missing. The remaining were: Hsu and Lai, 2004 ; Chang et al., 2008 ; Harmat et al., 2008 ; Chan et al., 2009 , 2010 ; Guétin et al., 2009a ; Koelsch et al., 2010 ; Verrusio et al., 2014 .

17 Average: Arithmetic mean of all score-changes in [%] for a defined selection (e.g., classical music). Example: We calculated the score-change in [%] for each of the eight experimental groups that received classical music as intervention. In this case the arithmetic mean (DSI Clas ) was 39.98% (i.e., average). Then every individual score can be compared to this average. If it was above, we called it “above average”.

18 Percussion music (drumming): Ashida, 2000 ; Choi et al., 2008 ; Schwantes and Mckinney, 2010 ; Albornoz, 2011 ; Erkkilä et al., 2011 ; Han et al., 2011 ; Lu et al., 2013 ; Chen et al., 2016 ; Fancourt et al., 2016 .

19 Jazz: Chan et al., 2009 , 2010 ; Guétin et al., 2009a ; Koelsch et al., 2010 ; Verrusio et al., 2014 .

20 In most cases there was no further categorization between different musical sub-genres of Jazz.

21 Greatest: Best in terms of depression score improvement (DSI) (i.e., pre-post score reduction in percent) with Jazz as intervention.

22 Raga: Classification system for music that originated during the eleventh century in Asia (mainly India).

23 Setting was always: Experimental group received music as intervention, and the corresponding control group received an (non-music) alternative.

24 For example, if elderly people lived in a retirement home, a standard daily routine or common everyday activities were seen as usual or regular treatment. If, on the other hand, a resting period (e.g., Chan et al., 2012 ) was carried out simultaneously, this was interpreted as an (“non-music”) alternative.

25 In all three of his articles within our selection (Chan et al., 2009 , 2010 , 2012 ) participants were instructed to rest.

26 Pharmacotherapy treatment included SSRI (Paroxetine 20mg/die), NaSSA (Mirtazapine 30 mg/die), and Benzodiazepine (Alprazolam).

27 As already described above, the other individual setting (Guétin Soua, et al., 2009) with pre-post results of p > 0.05 was still counted as significant.

28 Information regarding the duration for one group session was unavailable in two articles (Hendricks et al., 1999 ; Wang et al., 2011 ).

29 Hanser and Thompson, 1994 ; Hsu and Lai, 2004 ; Harmat et al., 2008 ; Chan et al., 2009 , 2010 , 2012 ; Guétin et al., 2009a ; Erkkilä et al., 2011 .

30 Average DSI for all 13 articles that used an individual ( * Ind) treatment as intervention was 36.50%.

31 Gupta and Gupta, 2005 ; Kim et al., 2006 ; Chang et al., 2008 ; Deshmukh et al., 2009 ; Guétin et al., 2009b .

32 Once (Esfandiari and Mansouri, 2014 ) the relevant score was unavailable.

33 Once (Wang et al., 2011 ) the relevant score was unavailable.

34 BDI: Original BDI from1961; (1st) Revision (=) BDI-I or BDI-1A from 1978; (2nd) Revision (=) BDI-II from 1996.

35 BDI-scores were measured only once (Silverman, 2011 ), either at the end (experimental group), or at the beginning (control group) and thus was excluded for this calculation.

36 Minimal depression: BDI-I (= BDI-1A) score (=) 00–09; BDI-II score (=) 00–13.

37 Mild depression: BDI-I (= BDI-1A) score (=) 10–18; BDI-II score (=) 14–19.

38 Moderate depression: BDI-I (= BDI-1A) score (=) 19–29; BDI-II score (=) 20–28.

39 Albornoz ( 2011 ) found in both groups a significant reduction for BDI scores albeit to a significantly greater extent in the experimental (−8.08; p < 0.01) than in the control (−2.25; p < 0.05) setting.

40 Severe depression: BDI-I (=BDI-1A) score (=) 30–63; BDI-II score (=) 29–63.

41 Pre-post difference: experimental (1) “light” music (=) 17.50; experimental (2) “heavy” music (=) 21.00 (both p < 0.05 within groups) (Esfandiari and Mansouri, 2014 ).

42 Average pre-post BDI reduction of −30.73 ( SD = 9.80) combined (Hendricks et al., 1999 ; Choi et al., 2008 ).

43 One author (Chan et al., 2012 ) limited his selection to slow music (60–80 beats per minute). The other researcher (Hanser and Thompson, 1994 ) also used some “energetic” or “empowering” titles, but mainly concentrated on relaxing compositions.

44 for a reference “intervention review” about music therapy for depression see: maratos et al. ( 2008 ).

45 Every available test-result (Pre-/Post-Scores for experimental/control) can be found in our Supplementary Table 12.

46 Hendricks et al., 1999 ; Ashida, 2000 ; Hsu and Lai, 2004 ; Kim et al., 2006 ; Chan et al., 2009 , 2012 ; Castillo-Pérez et al., 2010 ; Albornoz, 2011 .

47 We used the metaphor “decomposed” based on the inspiring book by Andrew Durkin (“Decomposition: A Music Manifesto”), who refers to it “as a way…to demythologize music without demeaning it” (Review by Madison Heying).

48 A complete list, with all results we could extract, can be found in the Supplementary Table.

49 Music Therapy; Duration 90min./session; Session Frequency 7x/week; Raagas Music (Deshmukh et al., 2009 ).

Supplementary material

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fpsyg.2017.01109/full#supplementary-material

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ORIGINAL RESEARCH article

Impact of background music on reading comprehension: influence of lyrics language and study habits.

• 1 Department of Applied Psychology, College of Sports and Health, Shandong Sport University, Jinan, China
• 2 School of Physical Education, Shandong University, Jinan, China
• 3 Department of Insurance, Shandong University of Finance and Economics, Jinan, China
• 4 School of Psychology, Qufu Normal University, Qufu, China
• 5 Zizhong Middle School, Linqing, China
• 6 College of Physical Education and Health, Guangxi Normal University, Guilin, China

Numerous studies have explored the effects of background music on reading comprehension, however, little is known about how native language (L1) lyrics and second language (L2) lyrics in background music influence reading comprehension performance for college students. The present study used a mixed experimental design to examine the effects of listening habits (between-participants variable: non-listeners or listeners), music type (between-participants variable: L1 (Mandarin) pop music, L2 (English) pop music or no music) and text language (within-participants variable: L1 or L2) on reading comprehension of college students in East China. A total of 90 participants (50 females) were screened into non- listeners ( n  = 45) and listeners ( n  = 45), and then were randomly assigned to one of three groups: Mandarin pop music group ( n = 30), English pop music group ( n  = 30) and no music group ( n  = 30). The results showed that reading comprehension performance was negatively affected by music with lyrics compared to the no music condition. Furthermore, Chinese/English reading comprehension was reduced more by pop music in the same language as the written texts. As expected, non-listeners were more negatively affected by music with lyrics than listeners. For both listeners and non-listeners, average reading comprehension accuracy rates were the lowest in the condition of music with native language lyrics. Overall, our research findings indicate that listening to pop music with lyrics reduces reading comprehension performance. However, listening to background music cause much less distraction if the students commonly listen to music while reading. The current study supports the duplex-mechanism account of auditory distraction.

1 Introduction

Listening to music while studying is a common and popular trend for college students. Calderwood et al. (2014) found that 59% of the college students chose to listen to music during a 3-h study session, with 21% listening for more than 90% of the time. Although several studies have demonstrated positive effects of background instrumental music on reading comprehension ( Carlson et al., 2004 ; Khaghaninejad et al., 2016 ) and second language learning ( Kang and Williamson, 2012 ), irrelevant sound from vocal music may cause auditory distraction from the task at hand ( Martin et al., 1988 ; Furnham and Strbac, 2002 ; Perham and Currie, 2014 ; Zhang et al., 2018 ; Du et al., 2020 ). Efficient learning is extremely important for college students. However, high levels of auditory distraction will not only affect efficient learning, but also impair mental and physical function and cause irritation and headaches in schools ( Astolfi et al., 2019 ). Thus, it is important to explore the mechanisms that produce auditory distraction. According to the duplex-mechanism account of auditory distraction, the disruptive effect can be induced by interference-by-process or attentional capture ( Marsh et al., 2008 , 2009 ). To date, previous studies investigating the impact of music on reading comprehension have primarily focused on differences between instrumental and lyrical music (e.g., Erten et al., 2015 ), as well as the influence of differences in musical volume and speed (e.g., Thompson et al., 2012 ). Notably, these studies have not taken into consideration differences in participant preferences for listening to music while reading. In contrast, the present study investigated how differences in the lyrical language of the same song differentially influence reading comprehension based on reported music-listening habits. With the aim of testing the duplex-mechanism account of auditory distraction, our study explored the interactive effects of native language (L1) lyrics and second language (L2) lyrics in music on reading comprehension performance in L1 and L2 for listeners and non-listeners by using a 3-factor mixed experimental design.

1.1 A duplex-mechanism account of auditory distraction

According to the duplex-mechanism account of auditory distraction, there are two functionally different types of auditory distraction. Interference-by-process occurs when a similar process used consciously to complete a focal task competes with the involuntary processing of sound. On the other hand, regardless of the task processes involved, attentional capture occurs when the sound triggers a disengagement of attention from the dominant task ( Hughes, 2014 ). For example, semantic speech (e.g., “orange, banana, strawberry”) can cause distraction effects on semantic-based cognitive tasks (e.g., free recall of visually presented words “apple, mango, pear”) ( Marsh et al., 2008 ). According to the interference-by-process theory, semantically similar speech automatically spreads activation through a long-term semantic network, interfering with the similar process of navigating such networks to retrieve information for the focal task ( Marsh and Jones, 2010 ; Hughes, 2014 ). Interference-by-process explains the semantic distraction effects. Attentional capture falls into two categories: When a sound’s unique content (such as one’s name or one’s native language) gives it the ability to deflect attention, a specific attentional capture takes place. In contrast, when an occurrence draws attention despite having nothing inherently attention-grabbing about it, but rather because of the context in which it takes place, nonspecific attentional capture is created ( Eimer et al., 1996 ). For example, a sound “B” in “CCCCCBCC” or a sound “C” in “BBBBBCBB.” Our study focused on interference-by-process and a specific attentional capture.

1.2 The impact of background music on reading comprehension

Reading comprehension, an important and necessary skill for effective academic learning in college, refers to the active process by which individuals understand and construct the meaning of texts based on prior knowledge and experience ( Perfetti et al., 2005 ). Kämpfe et al. (2010) claimed that reading might be more disturbed by vocal music than by instrumental music ( Kämpfe et al., 2010 ). The duplex-mechanism account of auditory distraction has been supported by research evidence demonstrating the disruptive effects of background speech on various memory tasks such as serial short-term memory tasks. However, little is known about supporting evidence from the distraction effects of L1/L2 lyrics on L1/L2 reading comprehension among listeners and non-listeners. According to the simple view of reading model, reading comprehension consists of only two parts, word recognition and language comprehension, and both parts are necessary for reading success ( Hoover and Gough, 1990 ). For college students, mature readers whose word recognition has attained to a level of automation, language comprehension plays the more important role in reading comprehension. Lyrics in music contain semantic information, which will interfere with language comprehension ( Martin et al., 1988 ; Oswald et al., 2000 ). Thus, we expect that lyrics will induce semantic distraction effects on reading comprehension performance. Our first hypothesis was that the accuracy rates in music conditions would be significantly lower than the accuracy rates with no music for college students (H1).

The impact of background music on reading comprehension is generally contingent on multiple factors such as music types (instrumental or lyric music with various tempos, intensity, familiarity) ( Banbury et al., 2001 ; Hallam and Mac Donald, 2009 ). In addition to music types, previous studies have confirmed that the effects of music on reading comprehension can be significantly different in various levels of individual diversity (e.g., personality and music preferences) or difficulty of the reading comprehension task ( Kiger, 1989 ; Kallinen, 2002 ; Anderson and Fuller, 2010 ). For example, Anderson and Fuller (2010) suggested that disruptive effects of background lyrical music on reading comprehension was more pronounced for 7th- and 8th-grade students exhibiting a stronger preference for the lyrical music, compared with their performance in a quiet environment. Our experimental work focused on identifying interactive effects of music (pop music with L1/L2 lyrics), individual habits (e.g., listening to music in daily study) and tasks (L1/L2 written texts), which helps test whether interference-by-process and a specific attentional capture occurs.

First, pop music is the preferred music genre for most college students ( Etaugh and Michals, 1975 ; Wang and Wang, 2015 ). For example, Wang and Wang (2015) surveyed 3,688 Chinese college students in Beijing, Inner Mongolia, Shanghai, Henan and Jiangxi regions of Mainland China, and found that: (1) the proportion of college students who liked pop music was as high as 65.05%; (2) 35.23% college students chose “love” as their favorite pop music theme comparing with themes “nostalgic” 33.21%, “witty/humorous” 14.27%, “alternative” 9.49%,“other” 15.73%; (3) 47.85% college student’ favorite singers are from “Hong Kong and Taiwan.” Thus, we choose a masterpiece of classic Mandarin pop music “The Goodbye Kiss” (sung by Jacky Cheung) as the music. Although the song was released in 1993, from its release to 2020, there have been covers of the song by well-known singers almost every year. Specially, this song was covered by Michael Learns to Rock (MLTR) in 2004, and the English version of this song “Take me to your heart” became a classic of international music. Comparing the lyrics of the two songs, the Mandarin lyrics of “The Goodbye Kiss” have a total of 52 sentences, and the whole song is divided into two subsections. The shortest sentence in Mandarin lyrics has a total of five Chinese words, and the longest sentence has 19 words; the English lyrics reproduce the characteristics of the original Chinese sentence well in terms of sentence length and neatness, the shortest sentence consists of four words, and the longest is only 10 words ( Wei, 2012 ). Thus, we chose the pop music with lyrics “The Goodbye Kiss” as our vocal music.

Second, Mandarin Chinese (L1) and English (L2) are the top 2 most spoken languages in the world, and belong to two different language families ( Ethnologue, n.d. ). Additionally, all Chinese students begin their English study in their third year of primary school or even earlier, and studying English is a key subject for the Chinese college entrance examination required for admission to the university. They will continue to study English to pass College English Test Band 4/6 (CET- 4/6, essential English exams for Chinese college students) in college, and have considerable exposure to English music. English is the most important and widely studied second language for most Chinese college students. Hence, we chose Chinse college students from Mainland China who learn English as a second language for the experiment. Based on the duplex-mechanism account of auditory distraction, when a similar process is used purposefully to accomplish a focal cognitive task and the involuntary processing of sound competes with it, interference-by-process occurs ( Hughes, 2014 ). In our experiment, interference-by-process is produced when lyrics are presented to college students who are deliberately completing a focal reading comprehension task, especially when the lyrics language is the same as the text language in the reading comprehension tasks. That is, the semantic activation of lyrics competes with the semantic access of reading comprehension tasks with the same language as lyrics. Thus, our hypothesis is that Chinese/English reading comprehension accuracy rates when listening to music in the same language would be significantly lower than that in different languages or no music (H2). To be specific, we hypothesized that Chinese reading comprehension accuracy rates when listening to music with Mandarin lyrics would be significantly lower than when listening to music with English lyrics, and English reading comprehension accuracy rates when listening to English music would be significantly lower than when listening to Mandarin music.

Third, students frequently report that listening to music while studying is useful ( Etaugh and Ptasnik, 1982 ), and these students are more likely to form the habit of listening to music in daily study. However, students without the habit instinctively think that music listening can provide a distraction that might affect reading comprehension. Individual differences in inhibitory control may exist between two groups. Inhibitory control refers to the ability to suppress an inappropriate reaction or disregard distracting or irrelevant information, and increased inhibitory control in students probably makes it easier for them to ignore distractions in their surroundings and concentrate on tasks inside and outside of the classroom ( Privitera et al., 2022b ). However, non-listeners do not develop the habit of listening to music while studying, probably because they have a low level of inhibitory control to concentrate on the focal tasks. Thus, we hypothesized that college students who typically did not report listening to music during study (non-listeners) would have lower reading comprehension accuracy rates than listeners when music was present (H3).

Based on the duplex-mechanism account of auditory distraction, regardless of the quality of target tasks (e.g., Chinese/English comprehension), auditory attentional capture happens whenever a sound produces a disengagement from tasks. Numerous sound varieties (e.g., one’s own name, or her own infant’s screams for a mother) have abilities to specifically captivate attention ( Hughes, 2014 ). Native language (Mandarin Chinese) is familiar and highly dominant, and may cause a specific attentional capture. We expect that both non-listeners and listeners may be more susceptible to auditory distraction when Mandarin music is present rather than English music. That is, in general, people’s ability to understand what they read was worse when they listened to music with native language compared to music in a second language or no music at all. Thus, for both non-listeners and listeners, we hypothesized that average reading comprehension accuracy rates (without distinction between Chinese and English) would be the lowest in the condition of Mandarin music compared with the English/no music condition (H4).

1.3 Research questions

In sum, it is worth examining the effects of different habits of listening to music on reading comprehension performance, which can help clarify whether cultivating habits of listening to music while studying is valuable or not. In addition, few studies used both lyrics languages and music-listening habits while study to explore distractive effects of music on reading comprehension. To solve this problem, in this paper, we designed an experiment to explore the effects of music type, written text language and listening habits on reading comprehension among Chinese college students. Our research questions are: (1) would the accuracy rates in music conditions be significantly lower than the accuracy rates with no music for college students? (2) would Chinese/English reading comprehension accuracy rates when listening to music in the same language be significantly lower than that in different languages or no music? (3) would non-listeners have lower L1 and L2 reading comprehension accuracy rates than listeners when music was present? (4) would average reading comprehension accuracy rates (without distinction between Chinese and English) be the lowest in the condition of Mandarin music compared with the English/no music condition?

2.1 Participants

Before the experiment, we calculated the minimum sample size of each group of participants using G*Power 3.1.9.7 software ( Faul et al., 2007 ) to reach the statistical power. For observing a similar effect to relevant studies ( Peng et al., 2017 ), we use Effect size f  = 0.22, ɑ = 0.05, 1-β = 0.8 as parameters, number of groups = 6, number of measurements = 2, non-sphericity correction = 1; under the F test of ANOVA: repeated measures, within-between interaction ( Faul et al., 2021 ). Hence the total minimum number of participants should be 72, and the minimum number of participants in each large group should be 36.

The participants were screened by filling out a researcher-designed questionnaire of background music listening habits. All participants were recruited randomly from Shandong Sport University in Shandong Province of Mainland China. A total of 90 participants (50 females) between 18 to 21 years of age (Mean = 19.14, SD = 0.92) were selected. Our experiment divided the participants into 2 large groups first: listeners (45 participants) and non-listeners (45 participants). Participants in each large group were randomly assigned to one of three groups: 15 Mandarin pop music group, 15 English pop music group and 15 no music group. All six groups of participants were assigned Chinese and English texts.

Participants were native Mandarin Chinese speakers who started learning English in the third grade of primary school. None of the participants were music majors and English majors, and none of the participants had any formal musical training. They were all right-handed with normal or corrected-to-normal vision. The experimental protocol was approved by the Research Ethics Committee of Shandong Sport University in China, and conducted in compliance with institutional guidelines and regulations. All participants signed an informed consent form prior to the experiment.

2.2 Experimental design

This study used a mixed factorial experimental design. There were two between-participants independent variables and a within-participants independent variable. The between-participants variables were listening habits (with two levels: listeners or non-listeners) and music type (with three levels: Mandarin pop music, English pop music or no music). The within-participants variable was text language (with two levels: Chinese or English). The dependent variable was accuracy rates for the reading comprehension tasks. Accuracy rates were defined as the mean percentage of the number of Chinese (English) reading comprehension items answered correctly in the total number of Chinese (English) reading comprehension items.

2.3 Materials and apparatus

Materials consisted of a questionnaire, pop music stimuli and written texts. The questionnaire was Researcher-designed Background Music Listening Habits Questionnaire, a self-report survey that was developed to assess participants’ habits of listening to music during study. This scale contained 15 items, each item rated on a Likert 5-point scale ranging from 1 to 5 (1 = Do not agree at all, 2 = Hardly agree, 3 = not sure, 4 = Mostly agree, 5 = Completely agree), and was scored as a continuous variable from 15 (minimum score) to 75 (maximum score). The Cronbach’s ɑ of the scale was 0.87. We used the questionnaire to screen listeners (a total score higher than 60, 60 is the average score of selecting option 4) and non-listeners (a total score lower than 30, 30 is the average score of selecting option 2) to examine distinct effects of listening habits on reading comprehension performance in the formal experiment.

Mandarin song “The Goodbye Kiss” (Mandarin name “Wen3 Bie2,” sung by Jacky Cheung) and English song “Take Me to Your Heart” (sung by Michael Learns to Rock) were used as background music stimuli, as these two songs have the same rhythm and tempo. The two songs were once popular music that are familiar to most Chinese college students. We used a music editor software Adobe Audition CS6 (Adobe Systems Inc., San Jose, CA, United States) to delete the blank space of “The Goodbye Kiss,” and the part with lyrics was kept to ensure that the participants could always be in a music environment with lyrics while carrying out reading comprehension tasks.

Chinese texts (300 character for each text) were selected from simulated tests of the College Entrance Examination; these texts are all about science and technology. English texts (150 words for each text) about education were selected from Public English Test System 3 (PET-3) tests. Preliminary tests were conducted on 120 college students, and finally 7 Chinese texts (coefficient of difficulty between 0.81 and 0.87) and 7 English texts (coefficient of difficulty between 0.85 and 0.90) were selected. There are no significant differences in difficulty coefficient of the 14 written texts. The difficulty coefficient of each text was estimated by the mean number of correct answers/4 (total number of questions). The coefficient of difficulty 0.81 indicates that, on average, three questions were correctly answered by college students. Participants read passages that were two paragraphs long, and then answered four true or false items following each passage. The items include both literal and inferential comprehension questions. Answers to literal questions involve facts such as who, when, where and what, and they can always be found in the texts. For example, “As early as 1909, Max Mow confirmed that there are some cells in the blood that can make blood, True or False.” For inferential questions, participants are required to determine a text’s meaning indirectly by using the information provided in the text. For example, “By the time most students graduate from high school, they spend less time watching TV than they do in class, True or False.” 3 Chinese texts and 3 English texts were used for assessing the levels of reading comprehension of all three groups (L1 pop music, L2 pop music and no music) of participants before the formal experiments. This was done to make sure that there were no significant differences of Chinese and English reading comprehension levels among the three groups. A different set of 3 Chinese texts and 3 English texts were used for the formal experiments. A Chinese text (difficulty coefficient 0.84) and an English text (difficulty coefficient 0.90) were selected for use in the practice phase.

The apparatus consisted of Lenovo laptops (Yoga 14 s, Lenovo Group Ltd., Beijing, China), noise-canceling headphones (SONY WH-1000XM3, Sony Corp., Tokyo, Japan) and E-prime 2.0. The music stimuli, instructions, texts and questions were all presented on Lenovo laptops using programs written in E-prime 2.0 (Psychology Software Tools, Pittsburgh, PA, United States) ( Schneider et al., 2012a , b ).

2.4 Procedure

Participants filled out the informed consent for participating in the study, then were screened by filling out the Questionnaire of Background Music Listening Habits online. Based on the questionnaire total score, the participants were divided into two large group: listeners and non-listeners. Participants in each large group were randomly assigned to one of three groups (Mandarin music, English music and no music). All three groups of participants completed Chinese and English reading comprehension tasks without music before formal experiments, and no significant differences of Chinese and English reading comprehension performance were observed among the three groups.

In the formal experiment phase, all participants were asked to complete experiment tasks in a quiet lab, with 10 participants in each group seated at individual tables with Lenovo laptops and headphones. First, participants were told to put on headphones and conduct the experiment on Lenovo laptops individually. All music was played between 60 dB ~ 65 dB(A), each participant first put on the headphones and checked to see whether the playback function of the headphones was normal. Then, Participants completed Chinese and English reading comprehension test items under each condition of music type. For each condition, half of the participants read the Chinese text first and the other half read the English text first. The 3 Chinese texts and 3 English texts were presented to participants randomly. After reading each passage, participants pressed the spacebar to end the reading (The maximum reading time for each text is 5 min), and proceeded to answer comprehension questions by pressing “T” (indicating truth) or “F” (indicating false) on keyboards. The flow chart of the experimental procedure presented using E-prime 2.0 was shown in Figure 1 .

Figure 1 . The flow chart of the experimental procedure presented using E-prime 2.0.

Participants were asked to answer questions as accurately as possible after reading the passages and to ignore the music. The accuracy rate of each participant was calculated by the total number of Chinese/English items answered correctly/12 (the total number of Chinese/English reading comprehension items). Every participant completed both Chinese texts and English texts in one of three conditions (Mandarin Chinese pop music, English pop music and no music). We tested the effects of listening to music in the same language conditions (L1 music + L1 texts, L2 music + L2 texts) or different language conditions (L1 music + L2 texts, L2 music + L1 texts). For example, participants listening to L1 (Mandarin Chinese) pop music completed L1 (Chinese) texts (the same as lyrics language) and L2 (English) texts (different from lyrics language). Music was played until all participants finished reading comprehension test items.

2.5 Statistical analyses

The Statistical Package for the Social Sciences (IBM SPSS, version 23.0; IBM SPSS, Armonk, NY, United States) was used for analysis of the data. The assumptions of ANOVA (homogeneity of variances and normal distribution) were tested. Then the reading comprehension accuracy rates were analyzed using a three-way mixed ANOVA with a within-participants factor (two types of written text language) and two between-participant variables (listening habits and music type). The alpha criterion was set to 0.05. Bonferroni correction was carried out for all post hoc analyses.

One-way ANOVA revealed that baseline reading comprehension performances of three groups (Mandarin music group, English music group and no music group) have no significant difference [Chinese: F (2, 87) = 0.226, p  = 0.718; English: F (2, 87) = 0.217, p  = 0.806].

3.1 Descriptive statistics

Means and standard deviations of the reading comprehension accuracy rates are shown in Table 1 . A three-way mixed ANOVA for reading comprehension accuracy rates, including two between-participants factors (2 listening habit, 3 music type) and one within-participants factor (2 written text language) was performed ( Table 2 ).

Table 1 . Reading comprehension accuracy rates [mean (standard deviations)] by group and condition.

Table 2 . A three-way analysis of variance (ANOVA) of reading comprehension accuracy rates.

3.2 Main effect analysis and interactive effect analyses

3.2.1 main effects of music type.

We tested our hypothesis (H1) that the accuracy rates in music conditions would be significantly lower than the accuracy rates with no music for college students. We performed a three-way mixed ANOVA for reading comprehension accuracy rates to obtain the main effects and interactive effects. Significant main effects of music type [ F (2, 87) = 232.791, p < 0.001, η 2 p = 0.847] were observed as shown in Table 2 . Post hoc analyses revealed the accuracy rates in Mandarin and English music conditions are significantly lower than the accuracy rates with no music ( ps < 0.01). A mean difference of accuracy rates was −0.081 between Mandarin music and English music condition (95% CI: [−0.110, −0.051]), and was −0.175 between English music and no music condition (95% CI: [−0.205, −0.145]). Thus, the results confirmed our hypothesis H1. The result reveals that music with lyrics decreased reading comprehension performance as compared to no music.

3.2.2 Interactive effects of music type and text language

Our second hypothesis (H2) was confirmed by using a three-way mixed ANOVA. H2 was that Chinese/English reading comprehension accuracy rates when listening to music in the same language would be significantly lower than those with different languages. We observed a significant interaction between music type and text language [ F (2, 87) = 113.829, p < 0.001, η 2 p = 0.730] as shown in Table 2 . For Chinese reading comprehension, as shown in Figure 2 , post hoc analyses showed that the accuracy rates in Mandarin music group were significantly lower than English music group [ t (58) = −5.526, p < 0.001] and no music group [ t (58) = −8.420, p < 0.001]. A mean difference of Chinese reading accuracy rates was −0.286 between Mandarin music and English music condition (95% CI: [−0.392, −0.180]), and was −0.378 between Mandarin music and no music condition (95% CI: [−0.484, −0.272]). For English reading comprehension, the accuracy rates in the English music group were significantly lower than the Mandarin music group [ t (58) = −2.385, p = 0.023 < 0.05; Figure 2 ] and the no music group [ t (58) = −7.041, p < 0.001; Figure 2 ]. A mean difference of English reading accuracy rates was −0.125 between English music and Mandarin music condition (95% CI: [−0.234, −0.016]), and was −0.258 between English music and no music condition (95% CI: [−0.367, −0.150]). These results confirmed our hypothesis H2, and suggested that college students were more distracted by music in the same language as the written texts.

Figure 2 . Accuracy rates of Chinese reading comprehension and English reading comprehension for different music types. ** p  < 0.01; *** p  < 0.001.

3.2.3 Main effects of listening habits and interactive effects of listening habits and music type

Three-way mixed ANOVA was also used to test our third hypothesis (H3) that non-listeners would have lower reading comprehension accuracy rates than listeners when music was present. The results in Table 2 showed that a significant main effect of listening habits [ F (1, 88) = 634.331, p < 0.001, η 2 p = 0.883]. Post hoc analyses revealed that reading comprehension accuracy rates were lower in non-listeners than listeners ( p < 0.001). The Table 2 also showed that the interactive effects of listening habits and music type were significant [ F (2, 87) = 160.672, p < 0.001, η 2 p = 0.793]. Post hoc analyses showed significantly lower reading comprehension accuracy rates in the non-listeners compared to listeners, in conditions of music as shown in Figure 3 [Mandarin music: t (58) = −138.782, p < 0.001; English music: t (58) = −99.729, p < 0.001]. A mean difference of accuracy rates between non-listeners and listeners was −0.430 in the Mandarin music condition (95% CI: [−0.464, −0.396]), and was −0.309 in the English music condition (95% CI: [−0.343, −0.274]). These results suggest that reading comprehension performance was more negatively affected by music in the non-listeners than in the listeners, confirming our third hypothesis (H3).

Figure 3 . Reading comprehension accuracy rates in different music type groups for different listening habits. *** p  < 0.001.

Significant interaction effects between listening habits and music type [ F (2, 87) = 160.672, p < 0.001, η 2 p = 0.793] were observed as shown in Table 2 . For the non-listeners, as shown in Figure 4 , post hoc analyses revealed the accuracy rates while listening to Mandarin music are significantly lower than with English music [ t (58) = −45.508, p  < 0.001] and significantly lower than accuracy rates with no music [ t (58) = −150.401, p < 0.001]. A mean difference of reading accuracy rates was −0.142 between Mandarin music and English music condition (95% CI: [−0.183, −0.100]), and was −0.467 between Mandarin music and no music condition (95% CI: [−0.508, −0.425]); For the listener, post hoc analyses revealed the accuracy rates while listening to Mandarin music are significantly lower than accuracy rates with no music [ t (58) = −14.524, p < 0.001]. A mean difference of reading accuracy rates was −0.045 between Mandarin music and no music condition (95% CI: [−0.086, −0.003]). Thus, the results also supported our hypothesis H4 that average reading comprehension accuracy rates (without distinction between Chinese and English) would be the lowest in the condition of Mandarin music compared with the English/no music condition for both non-listeners and listeners. These results suggested that music with native language lyrics negatively affected the reading comprehension performance of college students.

Figure 4 . Reading comprehension accuracy rates in different listening habits groups for different music types. * p  < 0.05; *** p  < 0.001.

4 Discussion

The main purpose of this study was to explore the disruptive effects of background music lyrics on first language (L1) and second language (L2) reading comprehension performance among Chinese college students. We also included the influence of music-listening habits by using a 3-factor mixed factorial experimental design. First, our results showed that reading comprehension accuracy rates in music conditions are significantly lower than the accuracy rates with no music. Second, L1/L2 reading comprehension accuracy rates when listening to music in the same language are significantly lower than when listening to a different language. Third, the results showed that significantly lower accuracy rates in non-listeners than listeners when music was played. Finally, for both the non-listeners and listeners, average reading comprehension accuracy rates are the lowest in the condition of Mandarin music compared with English/no music condition. Our results provide experimental evidence in support of distraction effects of L1 or L2 music on L1 and L2 reading comprehension performance among Chinese college students. In addition, our findings also offer additional evidence in favor of the duplex-mechanism account of auditory distraction. Overall, the results support our hypotheses.

4.1 The effect of music type

Compared to the no music condition, reading comprehension performance were reduced by music with lyrics. This result is consistent with previous studies which found disruptive effects of vocal music on reading comprehension ( Anderson and Fuller, 2010 ; Perham and Currie, 2014 ; Ren and Xu, 2019 ; Dong et al., 2022 ). Thompson et al. (2012) showed that fast and loud instrumental music disrupts reading comprehension more than slow-tempo music ( Thompson et al., 2012 ). However, though the music in our study is slow-tempo, disruptive effects on reading comprehension were still observed. Lyrics had a significantly detrimental effect on reading comprehension. The finding of the current study supports the interference-by-process in the duplex-mechanism account of auditory distraction. According to the interference-by-process, music with lyrics in both L1 and L2 detracted from the performance because semantically processing of the lyrics in these two languages conflicts with semantic processing and access that reading demands ( Quan and Kuo, 2023 ). For comparison, some researchers used musical excerpts in combination with meaningless words as music stimuli. The musical excerpts with meaningless lyrics were unknown to the participants to avoid any associations between the music and semantic or episodic memory. Their results showed neither an enhancing nor a detrimental effect on verbal learning when different styles of background music were played ( Jäncke and Sandmann, 2010 ). However, the present study indicated that music with meaningful lyrics interferes with reading comprehension performance. Language comprehension plays an important role in reading comprehension performance ( Hoover and Gough, 1990 ), and both lyrics and written texts contained semantic information. According to the duplex-mechanism account, from the perspective of the interference-by-process, the semantic interference effects can be explained by assuming that semantic speech triggers automatic spreading of semantic activation over a long-term semantic network that interferes with the analogous process of steering such networks for the purpose of retrieval in the reading comprehension tasks ( Marsh and Jones, 2010 ; Hughes, 2014 ). Therefore, the lyrics act as competing stimuli with written texts and impair their access to word meaning.

4.2 The interaction between music type and text language

Regardless of whether the music and texts were in their L1 or L2 language, Chinese college students were more distracted by music in the same language as the texts. This result indicates that a more detrimental effect on reading comprehension occurred when the auditory input (music lyrics) is the same as the written text language. Based on interference-by-process, the irrelevant semantic information from the speech creates competition for the primary tasks’ dynamic semantic encoding and retrieval processes. As they both vie for semantic access, impairment can therefore be explained in terms of a relative difficulty in choosing the appropriate source of semantic information ( Marsh et al., 2009 ). When lyrics language is the same as the text, the competition process becomes stronger and thus the selection process is more difficult, which causes a more disruptive effect on reading performance. We used music lyrics with L1/L2 as different potential sources of auditory distraction, and the finding provides a further strand of support for interference-by-process.

4.3 The effect of listening habits

Our results revealed that reading comprehension performance by the non-listeners were more negatively affected by music than the listeners. These findings are in line with the results of previous studies which showed that people who seldom studied in the presence of background music performed better on reading comprehension tasks in silence ( Etaugh and Michals, 1975 ; Etaugh and Ptasnik, 1982 ). These results indicate that background music caused detrimental effects for individuals who normally study without music. In contrast, college students who regularly listen to music while studying have much experience of listening to music, and the top-down features (e.g., high working memory and high inhibitory control) can lessen the interference to cognitive activities caused by shared processing of irrelevant information ( Quan and Kuo, 2023 ; Privitera et al., 2023b ). Specifically, differences in working memory/inhibitory control between non-listeners and listeners may lead the differential effects of music on reading comprehension, because working memory may generally have an impact on individual ability to carry out cognitive tasks while listening to music ( König et al., 2005 ; Christopher and Shelton, 2017 ), and it is generally observed that those with high working memory capacity are less easily distracted by irrelevant stimuli ( Hughes, 2014 ). Recent studies also revealed that differences in inhibitory and/or attentional control could predict academic performance including reading (e.g., Privitera et al., 2023b ), thus, the relatively low working memory/inhibitory control may make non-listeners were more disrupted by music compared with listeners. In other words, though listeners are negatively affected by music, they are accustomed to reading in the presence of music, thus background music sounds are less distracting for them.

4.4 The interaction between listening habits and music type

Our results indicated that for both non-listeners and listeners, music with native language lyrics negatively affected the average reading comprehension performance. The results provide support for the duplex-mechanism account of auditory distraction: in addition to interference-by-process, sound can also produce unnecessary distraction by attentional capture. Music lyrics with the same language as the written texts distract college students by interfering specifically with the similar semantic access processes involved in the reading comprehension task. In contrast, music with native language lyrics disengages students from reading comprehension tasks. Compared to L2 lyrics, native language lyrics are high dominant and more familiar, which may make students rely too much on music rather than keeping them from reading due to music. Thus, a specific attentional capture also caused the auditory distraction. This finding of auditory distraction in different lyrics language conditions provides additional evidence in favor of the duplex-mechanism account.

4.5 Limitations and further research

Several limitations should be noted. First, the participants’ English language proficiency, cognitive control and working memory were not assessed. In future study the L2 proficiency can be balanced to explore unique music lyrics effects on reading comprehension, because recent studies have shown that L2 proficiency are correlated to inhibition and attentional control ( Privitera et al., 2022a , 2023a ), and cognitive control has been found to have a significant impact on academic performance including reading ( Privitera et al., 2023b ). Working memory/cognitive control can be included as a key variable to explore its effect on reading comprehension while listening to music among non-listeners/listeners. Second, sound without lyrics (e.g., pop music without lyrics or white noise) was not included as one level of music type. Future study can compare reading comprehension performance differences between sound without lyrics group and music with lyrics/no music group to explore the various effects of sound. Third, questions about what music genres participants listen to and their relative frequencies were not included in the researcher-designed questionnaire of background music listening habits. The questionnaire needs to be modified, and should include questions on music genres in future study. Fourth, music type should be manipulated as a within-subject factor instead of a between-subject factor in future study. Finally, this is a behavioral experiment examining music lyrics effects on reading comprehension. With the aim of obtaining the brain and neuroscience evidence to support the duplex-mechanism account of auditory distraction, future studies could explore differences in brain and neural activities when students complete reading comprehension while listening to L1/L2 music, and identify the precise locus of the interference-by-process and attentional capture. These differences may indicate that interference-by-process and attentional capture obtain the functional support of different brain regions which further supports duplex-mechanism account of auditory distraction.

4.6 Implications

The current study benefits from several strengths. It is the first study to explore effects of L1 or L2 music lyrics on L1/L2 reading comprehension performance among Chinese college students with different listening habits. For reading comprehension with L1/L2, L1/L2 reading comprehension performance reduced more when the music lyrics language was the same as the written texts. For example, L2 reading performance decreased more when both lyrics and written texts language is L2. In general, for average reading comprehension performance, music with native language lyrics affected it negatively more than L2 music/no music. The current study provided experimental evidence to support the duplex-mechanism account of auditory distraction, and revealed that the duplex-mechanism account can also be applied to auditory distraction of reading comprehension tasks other than serial short-term tasks. The novelty of our study is to distinguish effects of lyrics with native language/s language on L1/L2 reading comprehension. Reading performance difference in lyrics with L1/L2 conditions suggests that auditory distraction has two functionally distinct forms: interference-by-process and attentional capture. The contribution of our research is that choosing music and written texts with L1/L2 helps methodically separate the potential individual contributions of interference-by-process and attentional capture to the overall disruption of task performance.

Our other findings were that reading comprehension performance was reduced by pop music lyrics. In addition, non-listeners were more distracted by lyrics than listeners. These findings have practical implications. Though most college students love pop music, and they usually report that listening to music while studying is beneficial, for college students and educators, it is better not to play pop music with lyrics while students, especially students without music-listening habits, are reading articles whether in their native languages or a second language.

5 Conclusion

The present study is an important first step in examining the effects of music with L1 or L2 lyrics on L1/L2 reading comprehension performance among Chinese college students with different listening habits. By using a 3-factor mixed factorial experimental design, we showed that the results verified our hypotheses. Specifically, the key findings are: (1) reading comprehension performance was negatively affected by music with lyrics compared to the no music condition; (2) L1/L2 reading comprehension was more affected by music in the same language as the texts; (3) Non-listeners were more negatively affected by music with lyrics than listeners; (4) For both non-listener and listeners, average reading comprehension accuracy rates are the lowest in the condition of music with native language lyrics. These findings support the claim that college students’ reading performance suffers when they listen to pop music with lyrics compared to no music, and provide experimental evidence support for the duplex-mechanism account of auditory distraction.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by Research Ethics Committee of Shandong Sport University. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

YS: Conceptualization, Data curation, Methodology, Supervision, Writing – original draft, Writing – review & editing. CS: Funding acquisition, Writing – original draft, Writing – review & editing. CL: Methodology, Writing – review & editing. XS: Investigation, Writing – review & editing. QL: Investigation, Writing – review & editing. HL: Methodology, Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by Shandong University undergraduate teaching reform (grant numbers: 2023Y251; 2023YJJGND07) and undergraduate teaching reform in Shandong province (grant number: Z2022096).

Acknowledgments

We would like to thank Pamela Holt for useful discussions and critically reading the manuscript.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: reading comprehension, study habits, pop music with lyrics, native language lyrics, second language lyrics, written text language, Chinese college students

Citation: Sun Y, Sun C, Li C, Shao X, Liu Q and Liu H (2024) Impact of background music on reading comprehension: influence of lyrics language and study habits. Front. Psychol . 15:1363562. doi: 10.3389/fpsyg.2024.1363562

Received: 13 January 2024; Accepted: 25 March 2024; Published: 05 April 2024.

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*Correspondence: Chuanning Sun, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.